Logical Partitioning Research Articles

Reinforcement learning in sentiment analysis: a review and future directions

Sentiment analysis in natural language processing (NLP) is used to understand the polarity of human emotions (e.g., positive and negative) and preferences (e.g., price and quality). Reinforcement learning (RL) enables a decision maker (or agent) to observe the operating environment (or the current state) and select the optimal action to receive feedback signals (or reward) from the operating environment. Deep reinforcement learning (DRL) extends RL with deep neural networks (i.e., main and target networks) to capture the state information of inputs and address the curse of dimensionality issue of RL. In sentiment analysis, RL and DRL reduce the need for a large labeled dataset and linguistic resources, increasing scalability and preserving the context and order of logical partitions. Through enhancement, the RL and DRL algorithms identify negations, enhance the quality of the generated responses, predict the logical partitions, remove the irrelevant aspects, and ultimately capture the correct sentiment polarity. This paper presents a review of RL and DRL models and algorithms with their objectives, applications, datasets, performance, and open issues in sentiment analysis.

Structured segment rescaling with Gaussian processes for parameter efficient ConvNets

We introduce a novel mechanism for structured pruning on ConvNet blocks and channels. Our mechanism, Structured Segment Rescaling (SSR) down-samples a ConvNet’s dimensions using depth and width modifiers that respectively remove whole blocks and channels. SSR is a systematic approach for constructing ConvNets that can replace arbitrary design heuristics. The SSR modifiers rescale logical partitions (segments) of a ConvNet with grouped layers. Different modifiers on segments yield many different architectures with unique rescales for their blocks. This diversity of architectures is then systemically explored using a Gaussian Process (GP) that optimizes for modifiers that maintain accuracy and reduce parameters. We analyze SSR in the context of resource constrained environments using ResNets trained on the CIFAR datasets. An initial set of depth and width modifiers explore extreme rescales of ResNet segments, where we find up to 70% parameter reduction. The GP then generalizes on these initial rescales by being trained on them and then predicts the accuracy of other rescaled ConvNet given their segment modifiers. SSR produces over 105 ConvNets that can be trained selectively based on their GP predicted accuracy. The GP enabled SSR pushes compression to over 80% with minimal accuracy impact. While both depth and width modifiers can reduce parameters, we show reducing blocks is better for reducing latency with up to 80% faster ConvNets. Using our mechanism, we can efficiently customize ConvNets using their parameter-accuracy trade-offs. SSR only requires 101 GPU hours and modest engineering to yield efficient new ConvNets that can facilitate edge inference.

DEX: Scalable Range Indexing on Disaggregated Memory

Memory disaggregation can potentially allow memory-optimized range indexes such as B+-trees to scale beyond one machine while attaining high hardware utilization and low cost. Designing scalable indexes on disaggregated memory, however, is challenging due to rudimentary caching, unprincipled offloading and excessive inconsistency among servers. This paper proposes DEX, a new scalable B+-tree for memory disaggregation. DEX includes a set of techniques to reduce remote accesses, including logical partitioning, lightweight caching and cost-aware offloading. Our evaluation shows that DEX can outperform the state-of-the-art by 1.7--56.3×, and the advantage remains under various setups, such as cache size and skewness.

A Fundamental Duality in the Exact Sciences: The Application to Quantum Mechanics

There is a fundamental subsets–partitions duality that runs through the exact sciences. In more concrete terms, it is the duality between elements of a subset and the distinctions of a partition. In more abstract terms, it is the reverse-the-arrows of category theory that provides a major architectonic of mathematics. The paper first develops the duality between the Boolean logic of subsets and the logic of partitions. Then, probability theory and information theory (as based on logical entropy) are shown to start with the quantitative versions of subsets and partitions. Some basic universal mapping properties in the category of Sets are developed that precede the abstract duality of category theory. But by far the main application is to the clarification and interpretation of quantum mechanics. Since classical mechanics illustrates the Boolean worldview of full distinctness, it is natural that quantum mechanics would be based on the indefiniteness of its characteristic superposition states, which is modeled at the set level by partitions (or equivalence relations). This approach to interpreting quantum mechanics is not a jury-rigged or ad hoc attempt at the interpretation of quantum mechanics but is a natural application of the fundamental duality running throughout the exact sciences.

A New Logic, a New Information Measure, and a New Information-Based Approach to Interpreting Quantum Mechanics.

The new logic of partitions is dual to the usual Boolean logic of subsets (usually presented only in the special case of the logic of propositions) in the sense that partitions and subsets are category-theoretic duals. The new information measure of logical entropy is the normalized quantitative version of partitions. The new approach to interpreting quantum mechanics (QM) is showing that the mathematics (not the physics) of QM is the linearized Hilbert space version of the mathematics of partitions. Or, putting it the other way around, the math of partitions is a skeletal version of the math of QM. The key concepts throughout this progression from logic, to logical information, to quantum theory are distinctions versus indistinctions, definiteness versus indefiniteness, or distinguishability versus indistinguishability. The distinctions of a partition are the ordered pairs of elements from the underlying set that are in different blocks of the partition and logical entropy is defined (initially) as the normalized number of distinctions. The cognate notions of definiteness and distinguishability run throughout the math of QM, e.g., in the key non-classical notion of superposition (=ontic indefiniteness) and in the Feynman rules for adding amplitudes (indistinguishable alternatives) versus adding probabilities (distinguishable alternatives).

A Comprehensive Microservice Extraction Approach Integrating Business Functions and Database Entities

Cloud application practitioners are building large-scale enterprise applications as microservices, to leverage scalability, performance, and availability. Microservices architecture allows a large monolithic application to be split into small, loosely coupled services. A service communicates with other services using lightweight protocols such as RESTful APIs. Extracting microservices from the monolith is a challenging task and is mostly performed manually by system architects based on their skills. This extraction involves both: 1) Partitioning of business logic, 2) Partitioning of database. For partitioning of business logic, the existing research studies focus on decomposition by considering the dependencies in the application at the class-level. However, with the passage of time, monolith application classes outgrow their size defying the Single Responsibility Principle (SRP). So, there is a need to consider the code within the classes when identifying microservices. Current studies also lack the partitioning of database and ignore the mapping of Database Entities (DE) to the microservices. In this paper, we present a Comprehensive Microservice Extraction Approach (CMEA) that considers: 1) Both classes and their methods to define and refine microservices, 2) Associate the DE to microservices using newly devised eight guiding rules handling ownership conflicts. This approach has been applied to three benchmark web applications implemented in Java and one in-house application implemented in both Java and Python. Our results demonstrate better or similar software quality attributes in comparison to the existing related studies. CMEA improves software quality attributes by 22%. System architects can easily identify microservices along with their DE using our approach. The CMEA is generic and language-independent so it can be used for any application

Pando: Enhanced Data Skipping with Logical Data Partitioning

With enormous volumes of data, quickly retrieving data that is relevant to a query is essential for achieving high performance. Modern cloud-based database systems often partition the data into blocks and employ various techniques to skip irrelevant blocks during query execution. Several algorithms, often based on historical properties of a workload of queries run over the data, have been proposed to tune the physical layout of data to reduce the number of blocks accessed. The effectiveness of these methods at skipping blocks depends on what metadata is stored and how well the physical data layout aligns with the queries. Existing work on automatic physical database design misses significant opportunities in skipping blocks because it ignores logical predicates in the workload that exhibit strongly correlated results. In this paper, we present Pando which enables significantly better block skipping than past methods by informing physical layout decisions with correlation-aware logical partitioning. Across a range of benchmark and real-world workloads, Pando attains up to 2.8X reduction in the number of blocks scanned and up to 2.3X speedup in end-to-end query execution time over the state-of-the-art techniques.

Multi-scale latent feature-aware network for logical partition based 3D voxel reconstruction

Although prior methods have achieved promising performance for recovering the 3D geometry from a single depth image, they tend to produce incomplete 3D shapes with noise. To this end, we propose Multi-Scale Latent Feature-Aware Network (MLANet) to recover the full 3D voxel grid from a single depth view of an object. MLANet logically represents a 3D voxel grid as visible voxels, occluded voxels and non-object voxels, and aims to the reconstruction of the latter two. Thus MLANet first introduces Multi-Scale Latent Feature-Aware (MLFA) based AutoEncoder (MLFA-AE) and a logical partition module to predict an occluded voxel grid (OccVoxGd) and a non-object voxel grid (NonVoxGd) from the visible voxel grid (VisVoxGd) corresponding to the input. MLANet then introduces MLFA based Generative Adversarial Network (MLFA-GAN) to refine the OccVoxGd and the NonVoxGd, and combines them with the VisVoxGd to generate a target 3D occupancy grid. MLFA shows a strong ability of learning multi-scale features of an object effectively and can be considered as a plug-and-play component to promote existing networks. The logical partition helps suppress NonVoxGd noise and improve OccVoxGd accuracy under adversarial constraints. Experimental studies on both synthetic and real-world data show that MLANet outperforms the state-of-the-art methods, and especially reconstructs unseen object categories with a higher accuracy.

Land‒atmosphere coupling effects of soil temperature and moisture on extreme precipitation in the arid regions of Northwest China

With global warming and more frequent extreme precipitation events in recent years, the phenomenon of warming and humidification in the arid regions of Northwest China (ANWC) has attracted increasing attention. We assessed the coupling effects of soil temperature and moisture on extreme precipitation in the ANWC by using daily precipitation data from CN05.1 and monthly data on soil temperature, soil moisture, and energy from ERA5-land, 1961–2018. After logical partitioning by K-means clustering, the primary influencing routes in each partition were investigated using two indices of extreme precipitation, indicated by precipitation on very wet days (R95P) and the number of extreme precipitation days (R10day). We found that 1) Local extreme precipitation has had a steadily growing impact on overall precipitation. In summer, this impact is primarily driven by an increase in the quantity of extreme precipitation, but in winter, it is primarily driven by an increase in the intensity of single precipitation. 2) The Tianshan Mountains (TM) and Qilian Mountains (QM) are the key locations for the coupling of soil temperature and moisture with the extreme precipitation index. Both locations exhibit a positive coupling state for soil temperature with extreme precipitation with positive coupling in the TM but negative coupling in the QM for soil moisture with extreme precipitation. 3) In the coupling of soil temperature and moisture with energy, the relevant significant regions are almost all over the ANWC throughout the year and all seasons, and the coupling high-value areas are concentrated around the basin. 4) In the TM–Hami Basin (HB)–QM, the coupling between energy and the extreme precipitation index is also stronger. The specific coupling paths have been changing with seasonal and regional changes.

Distributed-Memory FastFlow Building Blocks

We present the new distributed-memory run-time system (RTS) of the C++-based open-source structured parallel programming library FastFlow. The new RTS enables the execution of FastFlow shared-memory applications written using its Building Blocks (BBs) on distributed systems with minimal changes to the original program. The changes required are all high-level and deal with introducing distributed groups (dgroup), i.e., logical partitions of the BBs composing the application streaming graph. A dgroup, which in turn is implemented using FastFlow’s BBs, can be deployed and executed on a remote machine and communicate with other dgroups according to the original shared-memory FastFlow streaming programming model. We present how to define the distributed groups and how we faced the problem of data serialization and communication performance tuning through transparent messages’ batching and their scheduling. Finally, we present a study of the overhead introduced by dgroups considering some benchmarks on a sixteen-node cluster.

Причинно-следственный анализ проблем геометро-графической подготовки обучающихся технического вуза

This article discusses the prospects for applying cause-and-effect analysis - the Ishikawa diagram for visualizing the problems of geometric-graphic training of students of a technical university. The structure of the study using the diagram included the implementation of successive stages. At the first stage, an exhaustive list of problems affecting the level of geometric and graphic training at the university was compiled. At the second stage of the cause-and-effect analysis, problems (factors) were systematized according to semantic sections (logical blocks): employees and the student (person, human factor), material and technical base and educational environment (equipment and other means to ensure the educational process); educational technologies and organization of the educational process, means and technologies for assessing learning outcomes. The next stage included systematization and ranking of problems within logical partitions. This made it possible to identify in a simple and accessible form the most significant factors in the emergence of the problem under study at three levels. The implementation of the last three stages made it possible to complete the study with obtaining the final structure of the factors of the problem of a low level of geometric and graphic training of students. The result of the study was a graphical interpretation of the results of the cause-and-effect analysis. Thus, the possibility of using the diagram to identify the most significant cause-factors that affect the level of mastering the disciplines of the graphic cycle by students, as well as a means of visualizing the cause-and-effect analysis, is shown.

Logical entropy and aggregation of fuzzy orthopartitions

Fuzzy orthopartitions are generalizations of standard fuzzy partitions useful to model situations where both vagueness and uncertainty are involved. This work deepens the study of fuzzy orthopartitions lately started in [1], focusing on their lower and upper entropies. The latter subsume the concept of entropy in partition logic and measures the quantity of information contained in a fuzzy orthopartition. We mainly discover basic properties that characterize lower and upper entropies and highlight the connections between such entropy measures and novel operations and orderings on fuzzy orthopartitions introduced in this work.

A Structure-Aware Storage Optimization for Out-of-Core Concurrent Graph Processing

With the huge demand for graph analytics in many real-world applications, massive iterative graph processing jobs are concurrently performed on the same graphs and suffer from significant high data access cost. To lower the data access cost toward high performance, several out-of-core concurrent graph processing solutions are recently designed to handle concurrent jobs by enabling these jobs to share the accesses of the same graph data. However, the set of active vertices in each partition are usually different for various concurrent jobs and also evolve with time, where some high-degree ones (or called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">hub-vertices</i> ) of these active vertices require more iterations to converge due to the power-law property of real-world graphs. In consequence, existing solutions still suffer from much unnecessary I/O traffic, because they have to entirely load each partition into the memory for concurrent jobs even if most vertices in this partition are inactive and may be shared by a few jobs. In this paper, we propose an efficient structure-aware storage system, called GraphSO, for higher throughput of the execution of concurrent graph processing jobs. It can be integrated into existing out-of-core graph processing systems to promote the execution efficiency of concurrent jobs with lower I/O overhead. The key design of GraphSO is a fine-grained storage management scheme. Specifically, it logically divides the partitions of existing graph processing systems into a series of small same-sized chunks. At runtime, these small chunks with active vertices are judiciously loaded by GraphSO to construct new logical partitions (i.e., each logical partition is a subset of active chunks) for existing graph processing systems to handle, where the most-frequently-used chunks are preferentially loaded to construct the logical partitions and the other ones are delayed to wait to be required by more jobs. In this way, it can effectively spare the cost of loading the graph data associated with the inactive vertices with low repartitioning overhead and can also enable the loaded graph data to be fully shared by concurrent jobs. Moreover, GraphSO also designs a buffering strategy to efficiently cache the most-frequently-used chunks in the main memory to further minimize the I/O traffic by avoiding repeated load of them. Experimental results show that GraphSO improves the throughput of GridGraph, GraphChi, X-Stream, DynamicShards, LUMOS, Graphene, and Wonderland by 1.4-3.5 times, 2.1-4.3 times, 1.9-4.1 times, 1.9-2.9 times, 1.5-3.1 times, 1.3-1.5 times, and 1.3-2.7 times after integrating with them, respectively.

Multilayer protection-at-lightpath for reliable slicing with isolation in optical metro-aggregation networks

The high reliability required by many future-generation network services can be enforced by proper resource assignments by means of logical partitions, i.e., network slices, applied in optical metro-aggregation networks. Different strategies can be applied to deploy the virtual network functions (VNFs) composing the slices over physical nodes, while providing different levels of resource isolation (among slices) and protection against failures, based on several available techniques. Considering that, in optical metro-aggregation networks, protection can be ensured at different layers, and the slice protection with traffic grooming calls for evolved multilayer protection approaches. In this paper, we investigate the problem of reliable slicing with protection at the lightpath layer for different levels of slice isolation and different VNF deployment strategies. We model the problem through an integer linear program (ILP), and we devise a heuristic for joint optimization of VNF placement and ligthpath selection. The heuristic maps nodes and links over the physical network in a coordinated manner and provides an effective placement of radio access network functions and the routing and wavelength assignment for the optical layer. The effectiveness of the proposed heuristic is validated by comparison with the optimal solution provided by the ILP. Our illustrative numerical results compare the impact of different levels of isolation, showing that higher levels of network and VNF isolation are characterized by higher costs in terms of optical and computation resources.

Exploring Partitioning Attacks on the Bitcoin Network

Bitcoin is the leading example of a blockchain application that facilitates peer-to-peer transactions without the need for a trusted third party. This paper considers possible attacks related to the decentralized network architecture of Bitcoin. We perform a data driven study of Bitcoin and present possible attacks based on spatial and temporal characteristics of its network. Towards that, we revisit the prior work, dedicated to the study of centralization of Bitcoin nodes over the Internet, through a fine-grained analysis of network distribution, and highlight the increasing centralization of the Bitcoin network over time. As a result, we show that Bitcoin is vulnerable to spatial, temporal, spatio-temporal, and logical partitioning attacks with an increased attack feasibility due to the network dynamics. We verify our observations through data-driven analyses and simulations, and discuss the implications of each attack on the Bitcoin network. We conclude with suggested countermeasures.

Introduction to logical entropy and its relationship to Shannon entropy

We live in the information age. Claude Shannon, as the father of the information age, gave us a theory of communications that quantified an “amount of information,” but, as he pointed out, “no concept of information itself was defined.” Logical entropy provides that definition. Logical entropy is the natural measure of the notion of information based on distinctions, differences, distinguishability, and diversity. It is the (normalized) quantitative measure of the distinctions of a partition on a set-just as the Boole–Laplace logical probability is the normalized quantitative measure of the elements of a subset of a set. And partitions and subsets are mathematically dual concepts – so the logic of partitions is dual in that sense to the usual Boolean logic of subsets, and hence the name “logical entropy.” The logical entropy of a partition has a simple interpretation as the probability that a distinction or dit (elements in different blocks) is obtained in two independent draws from the underlying set. The Shannon entropy is shown to also be based on this notion of information-as-distinctions; it is the average minimum number of binary partitions (bits) that need to be joined to make all the same distinctions of the given partition. Hence all the concepts of simple, joint, conditional, and mutual logical entropy can be transformed into the corresponding concepts of Shannon entropy by a uniform non-linear dit-bit transform. And finally logical entropy linearizes naturally to the corresponding quantum concept. The quantum logical entropy of an observable applied to a state is the probability that two different eigenvalues are obtained in two independent projective measurements of that observable on that state.

Productization as a link to combining product portfolio management and product family development

In R&D many disciplines focus on different aspects of managing the product life cycle. Generally, the focus can be divided into technical aspects and business aspects. Combining these two views, and research areas can be beneficial in the context of productization. Productization links to the concept of product structure and relates to modelling of the offering according to a consistent product structure, commercially and technically. The goal of this research is to identify how a business-specific product structure can be formed and should be managed to maximize the value of the offering. This study uses a literature review for defining the current state of product portfolio management (PPM) and product family development (PFD) concepts with linkages to productization. As a result, this study presents similarities and differences between PPM and PFD through the productization lens. A suggestion of combining the best practices of both approaches is demonstrated through an illustrative case study. Also, proposals for future research are presented. The main contribution involves indicating that effective PPM requires a business-specific physical product structure and partitioning logic. The benefits of PPM can only be truly captured through applicable product structure. Therefore, PFD is recommended to be used in designing product portfolio related technical aspects. PPM is applied for management purposes with the support of a defined product structure.

NO SUCH THING AS A FREE APP: A TAXONOMY OF FREEMIUM BUSINESS MODELS AND USER ARCHETYPES IN THE MOBILE GAME MARKET

Manuscript type: Research Paper Research Aims: The Freemium business model is becoming increasingly prominent in the current digital economy. In the mobile game market, total revenue from free to download apps is overtaking those from paid apps. The purpose of this article is to propose a taxonomy that examines and categorises the various types of Freemium business models. Design/methodology/approach: This study uses the qualitative approach of logical partitioning to generate a Freemium business model's taxonomy through systematic observation of the existing business models. Research Findings: The findings identify advertisement and microtransaction as two primary revenue sources for Freemium business models. The taxonomy also reveals different types of in-app purchases, the most dominant Freemium business model in the mobile game market. Further discussion connects the taxonomy to related issues such as user archetypes and 'user flows'.&nbsp; Theoretical Contribution/Originality: This paper conceptualises a novel taxonomy of business model integrating various research streams and differentiating types of freemium business model. This paper also extends the existing archetypes of Freemium users by adding new categories, namely Remoras and Barnacles, to differentiate free users. Finally, this paper proposes a framework of user flows between the free and the paying state, arguing for a nonlinear flow. Research limitation/Implications: The taxonomy addresses freemium business models in the mobile game market. Application of the taxonomy in a broader context will require further study.

Defining and demonstrating a smart technology configuration to improve energy performance and occupant comfort in existing buildings: a conceptual framework

PurposeTo achieve the building and property by 2050, decarbonisation goals will now require a significant increase in the rate of improvement in the energy performance of buildings. Occupant behaviour is crucial. This study seeks to guide the application of smart building technology in existing building stock to support improved building energy performance and occupant comfort.Design/methodology/approachThis study follows a logical partitioning approach to the development of a schema for building energy performance and occupant comfort. A review of the literature is presented to identify the characteristics that label and structure the problem elements. A smart building technology framework is overlaid on the schema. The framework is then applied to configure and demonstrate an actual technology implementation for existing building stock.FindingsThe developed schema represents the key components and relationships of building energy performance when combined with occupant comfort. This schema provides a basis for the definition of a smart building technologies framework for existing building stock. The study demonstrates a viable configuration of available smart building technologies that couple building energy performance with occupant comfort in the existing building stock. Technical limitations (such as relatively simple building management control regimes) and pragmatic limitations (such as change management issues) are noted for consideration.Originality/valueThis is the first development of a schema to represent how building energy performance can be coupled with occupant comfort in existing building stock using smart building technologies. The demonstration study applies one of many possible technology configurations currently available, and promotes the use of open source applications with push-pull functionality. The schema provides a common basis and guide for future studies.

MapReduce-Based Dynamic Partition Join with Shannon Entropy for Data Skewness

Join operations of data sets play a crucial role in obtaining the relations of massive data in real life. Joining two data sets with MapReduce requires a proper design of the Map and Reduce stages for different scenarios. The factors affecting MapReduce join efficiency include the density of the data sets and data transmission over clusters like Hadoop. This study aims to improve the efficiency of MapReduce join algorithms on Hadoop by leveraging Shannon entropy to measure the information changes of data sets being joined in different MapReduce stages. To reduce the uncertainty of data sets in joins through the network, a novel MapReduce join algorithm with dynamic partition strategies called dynamic partition join (DPJ) is proposed. Leveraging the changes of entropy in the partitions of data sets during the Map and Reduce stages revises the logical partitions by changing the original input of the reduce tasks in the MapReduce jobs. Experimental results indicate that the entropy-based measures can measure entropy changes of join operations. Moreover, the DPJ variant methods achieved lower entropy compared with the existing joins, thereby increasing the feasibility of MapReduce join operations for different scenarios on Hadoop.