Memory Space Research Articles

H3DM: A High-bandwidth High-capacity Hybrid 3D Memory Design for GPUs

Graphics Processing Units (GPUs) are widely used for modern applications with huge data sizes. However, the performance benefit of GPUs is limited by their memory capacity and bandwidth. Although GPU vendors improve memory capacity and bandwidth using 3D memory technology (HBM), many important workloads with terabytes of data still cannot fit in the provided capacity and are bound by the provided bandwidth. With a limited GPU memory capacity, programmers should handle the data movement between GPU and host memories by themselves, causing a significant programming burden. To improve programming ease, GPUs use a unified address space with the host that allows over-subscribing GPU memory, but this approach is not effective in terms of performance once GPUs encounter memory page faults. Many recent works have tried to remedy capacity and bandwidth bottlenecks using dense non-volatile memories (NVMs) and true-3D stacking. However, these works mainly focus on one bottleneck or do not provide a scalable solution that fits future requirements. In this paper, we investigate true-3D stacking of dense, low-power, and refresh-free non-volatile phase change memory (PCM) on top of state-of-the-art GPU configurations to provide higher capacity and bandwidth within the available area and power budget. The higher density and lower power consumption of PCM provide higher capacity through integrating more cells in each 3D layer and enabling stacking more layers. However, we observe that stacking more than six layers of pure-PCM memory violates the thermal constraint and severely harms the performance and power efficiency due to its higher write latency and energy. Further, it degrades the lifetime of GPU to less than one year. Utilizing a hybrid architecture that leverages the benefits of both DRAM and PCM memories has been widely studied by prior proposals; however, true-3D integration of such a hybrid memory architecture especially on top of state-of-the-art powerful GPU architecture has not been investigated yet. We experimentally demonstrate that by covering 80% of write requests in DRAM and eliminating refresh overhead, true-3D stacking of eight 32GB layers of PCM along with two 8GB layers of DRAM is possible resulting in a total of 272GB memory capacity. Based on the explored design requirements, We propose a 3D high-bandwidth high-capacity hybrid memory (H3DM) system utilizing a hybrid-3D (H3D)-aware remapping scheme to reduce expensive PCM writes to under 20% while avoiding DRAM refresh overhead. H3DM improves the performance up to 291% compared to the baseline GPU architecture while remaining within only 3% of an ideal case with DRAM-like access latency, on average. Moreover, by increasing the dataset size above the baseline GPU memory space, H3DM improves performance and power up to 648% and 87% compared to the baseline GPU architecture since it avoids expensive data transfers through off-chip communication links.

Extension VM: Interleaved Data Layout in Vector Memory

While vector architecture is widely employed in processors for neural networks, signal processing, and high-performance computing; however, its performance is limited by inefficient column-major memory access. The column-major access limitation originates from the unsuitable mapping of multidimensional data structures to two-dimensional vector memory spaces. In addition, the traditional data layout mapping method creates an irreconcilable conflict between row- and column-major accesses. Ideally, both row- and column-major accesses can take advantage of the bank parallelism of vector memory. To this end, we propose the Interleaved Data Layout (IDL) method in vector memory, which can distribute vector elements into different banks regardless of whether they are in the row- or column-major category, so that any vector memory access can benefit from bank parallelism. Additionally, we propose an Extension Vector Memory (EVM) architecture to achieve IDL in vector memory. EVM can support two data layout methods and vector memory access modes simultaneously. The key idea is to continuously distribute the data that needs to be accessed from the main memory to different banks during the loading period. Thus, EVM can provide a larger spatial locality level through careful programming and the extension ISA support. The experimental results showed a 1.43-fold improvement of state-of-the-art vector processors by the proposed architecture, with an area cost of only 1.73%. Furthermore, the energy consumption was reduced by 50.1%.

A negative emotional state impairs individuals' ability to filter distractors from working memory: an ERP study.

Capacity-limited visual working memory (VWM) requires that individuals have sufficient memory space and the ability to filter distractors. Negative emotional states are known to impact VWM storage, yet their influence on distractor filtering within VWM remains underexplored. We conducted direct neural measurement of participants (n = 56) who conducted a lateralized change detection task with distractors, while manipulating the emotional state by presenting neutral or negative images before each trial. We found a detrimental effect of distractors on memory accuracy under both neutral and negative emotional states. Usingtheevent-related potential (ERP) component, contralateral delay activity (CDA; sensitive to VWM load), to observe the VWM load in each condition, we found that in the neutral state, the participants showed significantly higher late CDA amplitudes when remembering 4 targets compared with 2 targets and 2 targets with 2 distractors but no significant difference when remembering 2 targets compared with 2 targets with 2 distractors. In the negative state, no significant CDA amplitude differences were evident when remembering 4 targets and 2 targets, but CDA was significantly higher when remembering 2 targets with 2 distractors compared with 2 targets. These results suggest that the maximum number of items participants could store in VWM was lower under negative emotional states than under neutral emotional states. Importantly, the participants could filter out distractors when in a neutral emotional state but not in a negative emotional state, indicating that negative emotional states impair their ability to filter out distractors in VWM.

The journey within: mental navigation as a novel framework for understanding psychotherapeutic transformation

BackgroundDespite the demonstrated efficacy of psychotherapy, the precise mechanisms that drive therapeutic transformations have posed a challenge and still remain unresolved. Here, we suggest a potential solution to this problem by introducing a framework based on the concept of mental navigation. It refers to our ability to navigate our cognitive space of thoughts, ideas, concepts, and memories, similar to how we navigate physical space. We start by analyzing the neural, cognitive, and experiential constituents intrinsic to mental navigation. Subsequently, we posit that the metaphoric spatial language we employ to articulate introspective experiences (e.g., “unexplored territory” or “going in circles”) serves as a robust marker of mental navigation.MethodsUsing large text corpora, we compared the utilization of spatial language between transcripts of psychotherapy sessions (≈ 12 M. words), casual everyday conversations (≈ 12 M. words), and fictional dialogues in movies (≈ 14 M. words). We also examined 110 psychotherapy transcripts qualitatively to discern patterns and dynamics associated with mental navigation.ResultsWe found a notable increase in the utilization of spatial metaphors during psychotherapy compared to casual everyday dialogues (U = 192.0, p = .001, d = 0.549) and fictional conversations (U = 211, p < .001, d = 0.792). In turn, analyzing the usage of non-spatial metaphors, we did not find significant differences between the three datasets (H = 0.682, p = 0.710). The qualitative analysis highlighted specific examples of mental navigation at play.ConclusionMental navigation might underlie the psychotherapy process and serve as a robust framework for understanding the transformative changes it brings about.

An Application of Fuzzy Symbolic Time-Series for Energy Demand Forecasting

In this paper, we present a new fuzzy symbolization technique for energy load forecasting with neural networks, FPLS-Sym. Symbolization techniques transform a numerical time series into a smaller string of symbols, providing a high-level representation of time series by combining segmentation, aggregation and discretization. The dimensional reduction obtained with symbolization can speed up substantially the time required to train neural networks, however, it can also lead to considerable information losses that could lead to a less accurate forecast. FPLS-Sym introduces the use of fuzzy logic in the discretization process, maintaining more information about each segment of the neural network at the expense of requiring more space in memory. Extensive experimentation was made to evaluate FPLS-Sym with various neural-network-based models, including different neural network architectures and activation functions. The evaluation was done with energy demand data from Spain taken from 2009 to 2019. Results show that FPLS-Sym provides better quality metrics than other symbolization techniques and outperforms the use of the standard numerical time series representation in both quality metrics and training time.

Geneviève Zubrzycki. Resurrecting the Jew: Nationalism, Philosemitism, and Poland’s Jewish Revival. Princeton, NJ: Princeton University Press, 2022

Geneviève Zubrzycki, a distinguished comparative-historical and cultural sociologist who studies national identity, religion, and collective memory, has written a fascinating and insightful book, based on a decade of participant observation and interviews in multiple Polish cities and towns, about an astonishing Jewish revival in Poland since the early 2000s. This revival takes various forms: the organizing of Jewish festivals in cities and towns throughout Poland since the mid-2000s, the “popularity of klezmer music,” the “proliferation of Judaica bookstores and Jewish-style restaurants,” the creation of “new museums, memorials, and memory spaces,” the development of “Jewish and Holocaust studies programs in universities,” the publication of books and articles on Jewish topics, and even a “modest but steady number of conversions to Judaism” (p. 8). Text in English

Rcmp: Reconstructing RDMA-Based Memory Disaggregation via CXL

Memory disaggregation is a promising architecture for modern datacenters that separates compute and memory resources into independent pools connected by ultra-fast networks, which can improve memory utilization, reduce cost, and enable elastic scaling of compute and memory resources. However, existing memory disaggregation solutions based on remote direct memory access (RDMA) suffer from high latency and additional overheads including page faults and code refactoring. Emerging cache-coherent interconnects such as CXL offer opportunities to reconstruct high-performance memory disaggregation. However, existing CXL-based approaches have physical distance limitation and cannot be deployed across racks. In this article, we propose Rcmp, a novel low-latency and highly scalable memory disaggregation system based on RDMA and CXL. The significant feature is that Rcmp improves the performance of RDMA-based systems via CXL, and leverages RDMA to overcome CXL’s distance limitation. To address the challenges of the mismatch between RDMA and CXL in terms of granularity, communication, and performance, Rcmp (1) provides a global page-based memory space management and enables fine-grained data access, (2) designs an efficient communication mechanism to avoid communication blocking issues, (3) proposes a hot-page identification and swapping strategy to reduce RDMA communications, and (4) designs an RDMA-optimized RPC framework to accelerate RDMA transfers. We implement a prototype of Rcmp and evaluate its performance by using micro-benchmarks and running a key-value store with YCSB benchmarks. The results show that Rcmp can achieve 5.2× lower latency and 3.8× higher throughput than RDMA-based systems. We also demonstrate that Rcmp can scale well with the increasing number of nodes without compromising performance.

Fine-grain Quantitative Analysis of Demand Paging in Unified Virtual Memory

The abstraction of a shared memory space over separate CPU and GPU memory domains has eased the burden of portability for many HPC codebases. However, users pay for ease of use provided by system-managed memory with a moderate-to-high performance overhead. NVIDIA Unified Virtual Memory (UVM) is currently the primary real-world implementation of such abstraction and offers a functionally equivalent testbed for in-depth performance study for both UVM and future Linux Heterogeneous Memory Management (HMM) compatible systems. The continued advocacy for UVM and HMM motivates improvement of the underlying system. We focus on UVM-based systems and investigate the root causes of UVM overhead, a non-trivial task due to complex interactions of multiple hardware and software constituents and the desired cost granularity. In our prior work, we delved deeply into UVM system architecture and showed internal behaviors of page fault servicing in batches. We provided quantitative evaluation of batch handling for various applications under different scenarios, including prefetching and oversubscription. We revealed that the driver workload depends on the interactions among application access patterns, GPU hardware constraints, and host OS components. Host OS components have significant overhead present across implementations, warranting close attention. This extension furthers our prior study in three aspects: fine-grain cost analysis and breakdown, extension to multiple GPUs, and investigation of platforms with different GPU-GPU interconnects. We take a top-down approach to quantitative batch analysis and uncover how constituent component costs accumulate and overlap, governed by synchronous and asynchronous operations. Our multi-GPU analysis shows reduced cost of GPU-GPU batch workloads compared to CPU-GPU workloads. We further demonstrate that while specialized interconnects, NVLink, can improve batch cost, their benefits are limited by host OS software overhead and GPU oversubscription. This study serves as a proxy for future shared memory systems, such as those that interface with HMM, and the development of interconnects.

Quantum image encryption algorithm via optimized quantum circuit and parity bit-plane permutation

Quantum image encryption technology employs the unique features of superposition, entanglement, and quantum state instability, offering advantages like high efficiency, parallelism, and robust resistance to decryption attempts. In this work, we propose a quantum image encryption algorithm via an optimized quantum circuit and parity bit-plane permutation named OCPBP. Our research commences with applying an optimization algorithm to BRQI image preparation. This optimization significantly reduces the number of auxiliary qubits from n+3 to 2 in the preparation of (n+5)-CNOT gate preparation. This reduction makes a significant simplification of the complexity and memory space required for BRQI image preparation. Moreover, we introduce an innovative operation based on parity pixel bit-planes for permutation, which provides a fresh perspective on quantum image encryption based on the BRQI model. Furthermore, we generate quantum key images for XOR operations using the improved logistic map, effectively tackling the challenge of non-random keys that can be a concern in conventional BRQI encryption techniques. We also perform a row-column-based permutation operation to enhance the algorithm’s resilience against potential attacks. Through extensive theoretical analysis and comparative experiments, we confirm the heightened security and reduced complexity of the proposed OCPBP algorithm compared to BRQI-based and chaotic image encryption methods. It holds significant promise for improving quantum image communication and application.

Big data clustering using fuzzy based energy efficient clustering and MobileNet V2

Big data analytics (BDA) is a systematic way to analyze and detect various patterns, relationships, and trends in vast amounts of data. Big data analysis and processing require significant effort, techniques, and equipment. The Hadoop framework software uses the MapReduce approach to do large-scale data analysis using parallel processing in order to generate results as soon as possible. Due to the traditional algorithm’s longer execution time and difficulty in processing big amounts of data, this is one of the main issues. Clusters are highly correlated inside each other but are not highly correlated with one another. The technique of effectively allocating limited resources is known as an optimization algorithm for clustering. For processing large amounts of data with several dimensions, the conventional optimization approach is insufficient. By using a fuzzy method, this can be prevented. In this paper, we proposed Fuzzy based energy efficient clustering approach to enhance the clustering mechanism. In summary, Fuzzy based energy efficient clustering introduces a function that measures the distance between the cluster center and the instance, which aids in improved clustering, and we then present the MobileNet V2 model to improve efficiency and speed up computation. To enhance the method’s performance and reduce its time complexity, the distributed database simulates the shared memory space and parallelizes on the MapReduce framework on the Hadoop cloud computing platform. The proposed approach is evaluated using performance metrics such as Accuracy, Precision, Adjusted Rand Index (ARI), Recall, F1-Score, and Normalized Mutual Information (NMI). The experimental findings indicate that the proposed approach outperforms the existing techniques in terms of clustering accuracy.

Exploring compiler optimization space for control flow obfuscation

Code obfuscation is a promising technique for securing software and protecting it from adversaries. The objective is to harden the exploitation of security vulnerabilities for the attacker as well as launching of successful attacks. Obfuscation can be classified into layout, data, and control flow obfuscation. Control flow obfuscation impedes the understanding of the application logic by making it complicated to determine the actual control flows. Although numerous control flow methods exist in the literature, the role of existing compiler optimizations has just been discovered. This paper is the first one that explores the existing optimization space of LLVM compiler for obfuscating code. Our techniques optimally explore the native compiler's optimizations to improve the original code performance and reduce memory space with no disruptive efforts, tools, or extra costs. In the CBench benchmark suite, our work is able to improve 246%, 143%, and 468% in cyclomatic complexity, program length, and implementation effort, respectively, compared to unobfuscated code. Therefore, instead of inventing new obfuscation tools, the existing compiler optimizations can easily be used to obfuscate control flows, saving the overall cost and efforts.

Memories of a Glorious or Difficult Past? Portugal, Padrão dos Descobrimentos and the (Lack of a) 21st Century Reckoning

AbstractThe purpose of this paper is to analyse a particularly influential case of memory continuity in Portugal, that of Padrão dos Descobrimentos. Spaces of collective memory (such as public monuments) raise questions about what we celebrate, remember or rescue from oblivion, providing an opportunity to rethink the trauma. As such, care for public spaces is associated with ethical and cultural values. One of the difficulties with certain monuments has to do with the fact that they recall actions that today we see as traumatic acts. Thus, it is important to reflect on a critical use of memory. On August 8, 2021, Padrão dos Descobrimentos was the subject of a graffiti. One of the sides of the monument could read: “Blindly sailing for money, humanity is drowning in a scarlet sea”. A great controversy immediately arose around the meaning of this gesture, as well as the role played by a monument that, after being temporarily built in 1940 for the Portuguese World Exhibition, was, in its current version, inaugurated in 1960, on the occasion of the fifth centenary of the death of Infante D. Henrique. This episode reignited a deeper cleavage around the uses of history and memory, the Portuguese colonial past, and the role of the Padrão dos Descobrimentos as an instrument for the reproduction of nationalism. In this sense, and with the authors proposing a new theoretical frame of reference based on the thought of Arendt and Ricoeur, its critical reading becomes relevant.

Comprehensive analysis of the perception of commemoration sites by St. Petersburg residents

The paper represents the author’s model of comprehensive analysis of perception, as well as the experience of using it to diagnose the perception of the commemoration sites by the residents of St. Petersburg and to identify the features of perception os such places by “generation Z” representatives. Perception in the work is considered as a complex phenomenon, whereas the subject of perception records the formal, meaningful and dynamic parameters of object, forms, and then represents (returns) its image. To assess the characteristics of the perception process, the author use the approach based on three types of analysis: phenomenological, structural, dynamic. Commemoration sites are considered as objects typical of the natural, psychosocial and social environment. The field study is based on the survey of residents of St. Petersburg of four generations are presented (from “generation Z” to “generation raised in the USSR”). Such stratification allows us to consider the space of memory of St. Petersburg through the prism of the transfer of social capital. The study used locations identified by respondents as commemoration sites. Based on the results of the analysis of the respondents’ answers, the authors obtained the results showing the reproduction of commemoration sites as social, historical and cultural objects of perception, noted the evolution of the semantic load on individual locations, and confirmed the hypothesis about the preservation of the basic model of perception in all generations.

Spaces of Memory: Historical Cemeteries of Ural Cities in the Context of Cultural Politics

This article examines historical burials and memorial ensembles included in the lists of cultural heritage sites in cemeteries of the Sverdlovsk, Chelyabinsk and Perm regions. The role of the cemetery is considered as an important socio-cultural urban space that preserves the memory of key historical events, significant historical figures, as well as crafts characteristic of specific territories, such as Kasli casting. It is shown that the way in which cemeteries and burials are included in the lists of cultural heritage sites varies from region to region. Thus, in the Chelyabinsk Region the most significant group of “necropolitan” cultural heritage objects is formed by many burials from the Civil War; at the same time, in the Perm Region, more often than in the Sverdlovsk and Chelyabinsk Regions, the graves of outstanding historical figures of the region were included in the lists of these objects. The article identifies the potential for the development of cemeteries as a kind of open-air museums, an important resource for the cultural economy of cities. The author substantiates the need to include these “lieux de mémoire” (using the concept proposed by P. Nora) in the cultural policy of cities, and in particular to create guides to the Ural necropolises, continuing the traditions of pre-revolutionary reference publications on the one hand, and rethinking cemeteries as platforms that participate in the formation and consolidation in the collective memory of narratives about local identity and the era as a whole, uniting the individual and the public. It is proposed that cemeteries and memorials, as objects of cultural policy, require special accounting and the development of a special system of management and conservation due to their specific qualities.

“СЛІПІ СОНЯХИ” АЛЬБЕРТО МЕНДЕСА: ГОЛОСИ ПОРАЗКИ, ЩО ПЕРЕМАГАЮТЬ ЗАБУТТЯ

Background. The article is devoted to the problem of reconstruction of collective memory in Alberto Méndez's novel "The blind sunflowers" (2004). Méndez's book is a part of contemporary Spanish narratives that, since the 1990s, have witnessed the emergence of the so-called "memory boom", a trend in literature and cinema aimed at preserving, restoring and transmitting the memory of the tragic events of Spanish Civil War and Franco repression. The purpose of the research is to reveal the ways and mechanisms of reproduction and elaboration of traumatic collective memory in "The blind sunflowers" by Méndez. Methods. The study is based on the following methods: a historical-cultural approach and "memory studies", which made it possible to determine the place of the Alberto Mendez's novel in the context of the Spanish "literature of memory" beginning 21st century; the narratological and intertextual approaches were used to investigate various techniques of the author's creation of a polyphonic narrative in four stories that make up the book, and which represent different voices of the same past, functioning as symbols of restoring the memory of defeat. Results. The artistic recreation in "The Blind Sunflowers" of the traumatic experience of the events and consequences of the Civil War in Spain was investigated in dichotomies typical of the depicted historical moment: winners-losers, victim-executioner, death-life, memory-oblivion and voice-silence. The analysis of this traumatic context in the novel proved that writing (the voice of memory) functions as one of the main ways of preserving the experience of erased generations, restoring life after physical death, and as a guarantee of resistance to oblivion. Conclusion. The reconstruction of the memory of the Civil War in Spain and its consequences during the period of Francoism as one of the leading trends in modern Spanish literature demonstrates the still openness of collective trauma and, accordingly, the need to find ways to overcome it. In view of this, the approach proposed by Méndez in "The Blind Sunflowers" to restore and transmit memory through a collective discourse of defeat becomes a significant step towards healing the unhealed wounds of a society traumatized by war and a dictatorial regime, as it emphasizes not only the importance of the grieving process in overcoming tragedies, but also attests to the creation of literature as a space of memory, in which the past is reproduced as a voice that breaks the silence and overcomes oblivion, not allowing to turn the page and close the past.

Conflict-Free Parallel Data Access Technology for Matrix Calculation in Memory System of ASIP of 5G/6G Macro Base Stations

Among the physical layer baseband algorithms in macro base stations, the matrix processing has the dom-inant computing cost, large data access overhead and com-plicated addressing mode. The existing data access methods are not the best solution for memory system of ASIP (Ap-plication Specific Instruction Set Processor) in 5G/6G macro stations. We hence proposed a parallel conflict-free data access method for matrix. Moreover, we proposed a non-redundant access method for positive definite matrix that stores only trigonometric part, and the corresponding parallel addressing method with low storage overhead. Our method solves the problem of data conflict and minimizes the memory space of ASIP, and supports ASIP to approach the performance limit of architecture to the maximum ex-tent under the constraint of architecture and data parallel-ism. We implemented the proposed method as a static memory optimizer, which provides a tool for the design and optimization of memory system of ASIP in 5G/6G macro base stations with low overhead. Experimental results show that for 64W64 positive definite matrix inversion algorithm based on Cholesky decomposition, our addressing method can save 32% of the execution time, and the cost of memory space is reduced by half.

Opportunistic Block Validation for IoT Blockchain Networks

The blockchain network architecture is a promising technology for constructing highly secure Internet of Things (IoT) networks. IoT networks typically consist of many sensors and actuators. Blockchain network technology can be applied to secure control robots in smart factories or for reliable drone delivery in smart cities. The distributed ledger and data block validation across blockchain networks guarantee the ultimate data security. However, the current blockchain technology is restricted in terms of its overall deployment across IoT networks. A general permissionless blockchain technology typically targets high-performance network nodes with sufficient computing power and memory space. A blockchain node with less computing power and memory, such as an IoT sensor or actuator, cannot employ blockchain technology as a fully functional node. A lightweight blockchain provides practical blockchain availability to IoT networks. We propose an operational advance to develop a lightweight blockchain for IoT networks. The opportunistic block validation optimizes the block validation process. It measures the network vulnerability based on the node reputation, block validation degree, number of fraudulent messages, and network stability. The reinforcement learning mechanism employed in the opportunistic block validation determines whether block validation is to be performed. Two separately developed reinforcement learning methods can increase the processing performance while maintain the transaction data integrity. In addition, the proposed blockchain technology is easily implementable because it adopts a Hyperledger development environment. Directly embedding the proposed blockchain middleware platform in small computing devices proves the practicability of the proposed block validation mechanism.

Partition of India and Museums as Cultural Memory

Partition of the Indian subcontinent generated a trauma which has not subsumed even after seventy five years. ‘Post memories’ as well as ‘prosthetic memories’ of partition continue to haunt generations born much later. It would not be an exaggeration to state that Partition qualifies as a ‘monumental traumatic event that resists understanding and integration’. Several factors contribute towards this lack of ‘integration’. A major one being, that Partition in its immediate aftermath was relegated to the realm of the “unspeakable.” No public memorial of partition existed until 2017. This paper seeks to examine how memorial spaces, and museums help in allowing for ritual mourning and enables healing in instances of corrosive cultural trauma.

LOCAL FEATURE SELECTION USING THE WRAPPER APPROACH FOR FACIAL EXPRESSION RECOGNITION

Automatic Facial Expression Recognition (FER) systems provide an important way to express and interpret the emotional and mental states of human beings. These FER systems transform the facial image into a set of features to train a classifier capable of distinguishing between different classes of emotions. However, the problem often posed is that the extracted feature vectors sometimes contain irrelevant or redundant features, which decreases the accuracy of the induced classifier and increases the computation time. To overcome this problem, dimensionality must be reduced by selecting only the most relevant features. In this paper, we study the impact of adding the "Wrapper" selection approach and using the information provided by different local regions of the face such as the mouth, eyes and eyebrows, on the performance of a traditional FER system based on a local geometric feature extraction method. The objective here is to test and analyze how this combination can improve the overall performance of the original traditional system. The obtained results, based on the Multimedia Understanding Group (MUG) database, showed that the FER system combined with the proposed feature selection strategy gives better classification results than to the original system for all four classification models, namely, K-Nearest Neighbor (KNN) classifier, Tree classifier, NB classifier and Linear Discriminant Analysis (LDA). Indeed, a considerable reduction (up to 50%) in the number of features used and an accuracy of 100%, using the LDA classifier, were observed, which represents a significant improvement in terms of computation time, efficiency and memory space. Furthermore, the majority of relevant features used are part of the "eyebrows region", which proves the importance of using information from local regions of the face in emotion recognition tasks.

ОСОБЛИВОСТІ УВІЧНЕННЯ ПАМ’ЯТІ ЖЕРТВ НАЦИСТСЬКОГО ТЕРОРУ НА ХАРКІВЩИНІ У ВОЄННИЙ ТА ПОВОЄННИЙ ЧАС

The aim of the work. The article is devoted to a comprehensive study of commemorative initiatives and building of monuments connected with the commemoration of victims of Nazi terror on the territory of the Kharkiv region. The article explains the specifics of honoring the memory of different groups of victims of Nazi terror on the territory of the Kharkiv region in wartime and in the post-war period. The methodological base consists of general scientific methods (analysis, synthesis, deduction, induction, systematic approach) along with special historical ones (historical-comparative, historical-systematic). The principles of scientificity, objectivity, and historicism are also applied. The scientific novelty of the research is defined by the fact that the author conducted for the first time a comprehensive study of commemorative initiatives of victims of Nazi terror on the territory of the Kharkiv region. Conclusions. During 1941–1945, the Kharkiv region was controlled by the German Military Administration. The German occupation regime established in the Kharkiv region led to numerous deaths among civilians and prisoners of war. The people's initiative served as the first step in commemorating the victims of war. Further, the state assumed control over the building of monuments. Works of monumental art became the instruments for the distribution of Soviet myths about «The Great Patriotic War». The official Soviet ceremonial model of commemoration was characterized by an intrusive cult of internationalism, heroism, Soviet patriotism, and self-sacrifice. The authorities stayed indifferent to people, keeping no record of victims and depersonalizing the dead. Due to the fatal defeats of the Red Army, the Kharkiv region lost about half a million citizens, but it wasn't highly ranked in the national narrative about the war. The state demonstrated discriminatory methods for commemoration of certain categories of the dead. Some of them were represented in the memorial space, and others were deprived of their rights. First of all, this concerned victims of mass terror – Jews, orphans, and prisoners of war. Only with the collapse of the communist regime and the independence of Ukraine these categories of victims of Nazi terror got the opportunity to convey their own vision of war to the general public.