Static Context Research Articles

Synthesizing a three Layer Architectural Framework for Adaptive Intelligent User Interfaces for the Disabled Community

Knowledge acquisition has been a bottleneck in the design for efficient User Interfaces for Disabled Community. The best suited interfaces have been oriented towards adaptability that is the User Interface needs to adapt and configure itself according to the needs and likes of the user. Such user interfaces can arouse interest in the mind of the disabled and motivate them to become an asset to the nation, and use their intellect to contribute to the society. This study pioneers an innovative approach, aiming to develop User Interfaces (UIs) for the Disabled Community. The research introduces a comprehensive three-layer architectural framework employing Learning, Trust, and Knowledge Models. Unlike conventional approaches, this Architectural Framework is designed to learn not only from the Disabled but also from their Assistants/Guides, employing principles of knowledge engineering. While the former offers static context, the latter contributes dynamic insights by observing behaviors, responses, and actions of the Disabled. This symbiotic relationship forms the foundational basis for developing intelligent adaptive UI’s that not only accommodate but actively engage and motivate the Disabled Community. The proposed research methodology involves a meticulous gathering of data through extensive user studies and interactions. By using the Human Computer Interface technology, the study aims to develop a robust framework capable of comprehensively understanding the preferences, needs, and nuances of disabled users. The expected outcomes include the development of a prototype adaptive UI, validated through rigorous testing and feedback from the disabled community. Additionally, the study anticipates contributing a comprehensive set of guidelines and best practices for designing intelligent adaptive UIs, ensuring comfort and ease for impaired users, boosting their confidence and encouraging greater involvement in socio-development activities.

Statically Contextualizing Large Language Models with Typed Holes

Large language models (LLMs) have reshaped the landscape of program synthesis. However, contemporary LLM-based code completion systems often hallucinate broken code because they lack appropriate code context, particularly when working with definitions that are neither in the training data nor near the cursor. This paper demonstrates that tighter integration with the type and binding structure of the programming language in use, as exposed by its language server, can help address this contextualization problem in a token-efficient manner. In short, we contend that AIs need IDEs, too! In particular, we integrate LLM code generation into the Hazel live program sketching environment. The Hazel Language Server is able to identify the type and typing context of the hole that the programmer is filling, with Hazel's total syntax and type error correction ensuring that a meaningful program sketch is available whenever the developer requests a completion. This allows the system to prompt the LLM with codebase-wide contextual information that is not lexically local to the cursor, nor necessarily in the same file, but that is likely to be semantically local to the developer's goal. Completions synthesized by the LLM are then iteratively refined via further dialog with the language server, which provides error localization and error messages. To evaluate these techniques, we introduce MVUBench, a dataset of model-view-update (MVU) web applications with accompanying unit tests that have been written from scratch to avoid data contamination, and that can easily be ported to new languages because they do not have large external library dependencies. These applications serve as challenge problems due to their extensive reliance on application-specific data structures. Through an ablation study, we examine the impact of contextualization with type definitions, function headers, and errors messages, individually and in combination. We find that contextualization with type definitions is particularly impactful. After introducing our ideas in the context of Hazel, a low-resource language, we duplicate our techniques and port MVUBench to TypeScript in order to validate the applicability of these methods to higher-resource mainstream languages. Finally, we outline ChatLSP, a conservative extension to the Language Server Protocol (LSP) that language servers can implement to expose capabilities that AI code completion systems of various designs can use to incorporate static context when generating prompts for an LLM.

Forecasting intraoperative hypotension during hepatobiliary surgery.

Prediction and avoidance of intraoperative hypotension (IOH) can lead to less postoperative morbidity. Machine learning (ML) is increasingly being applied to predict IOH. We hypothesize that incorporating demographic and physiological features in an ML model will improve the performance of IOH prediction. In addition, we added a "dial" feature to alter prediction performance. An ML prediction model was built based on a multivariate random forest (RF) trained algorithm using 13 physiologic time series and patient demographic data (age, sex, and BMI) for adult patients undergoing hepatobiliary surgery. A novel implementation was developed with an adjustable, multi-model voting (MMV) approach to improve performance in the challenging context of a dynamic, sliding window for which the propensity of data is normal (negative for IOH). The study cohort included 85% of subjects exhibiting at least one IOH event. Males constituted 70% of the cohort, median age was 55.8years, and median BMI was 27.7. The multivariate model yielded average AUC = 0.97 in the static context of a single prediction made up to 8min before a possible IOH event, and it outperformed a univariate model based on MAP-only (average AUC = 0.83). The MMV model demonstrated AUC = 0.96, PPV = 0.89, and NPV = 0.98 within the challenging context of a dynamic sliding window across 40min prior to a possible IOH event. We present a novel ML model to predict IOH with a distinctive "dial" on sensitivity and specificity to predict first IOH episode during liver resection surgeries.

A photovoltaic cell defect detection model capable of topological knowledge extraction

As the global transition towards clean energy accelerates, the demand for the widespread adoption of solar energy continues to rise. However, traditional object detection models prove inadequate for handling photovoltaic cell electroluminescence (EL) images, which are characterized by high levels of noise. To address this challenge, we developed an advanced defect detection model specifically designed for photovoltaic cells, which integrates topological knowledge extraction. Our approach begins with the introduction of a multi-scale dynamic context-based feature extraction method, capable of generating static context by thoroughly capturing the local texture and structural information of multi-scale defects. This static context is then combined with dynamic context to produce fine-grained local features. Subsequently, we developed a centralized feature pyramid structure, enhanced by spatial semantics, which models the explicit visual center. This structure effectively elucidates the relationship between local and global features in defect images, thereby improving the representation of defect characteristics. Finally, we implemented a feature enhancement strategy grounded in spatial semantic knowledge extraction. This strategy uncovers potential correlations among defect targets by constructing a spatial semantic topology of features, mapping these features to a higher-order representation, and ultimately delivering precise defect detection results.

Touristic Learning Behaviors and Outcomes in Theme Parks: The Role of Activity and Affectional Contexts

ABSTRACTExisting studies have highlighted the significance of touristic learning; however, few have explored the effectiveness of different learning contexts. This study employs a combination of spatial–temporal behavior analysis and survey research to investigate how activity context (static vs. interactive) and affectional context (positive vs. negative) influence visitors' spatial–temporal behaviors and learning outcomes in zoological theme parks. The results indicated that interactive contexts are more effective than static contexts in extending visitors' duration of stay, increasing the diversity of venues explored, and enhancing both emotion and learning outcomes of visitors. The moderating effect of affectional context is significant, with the positive context notably enhancing emotion and learning outcomes of visitors associated with the interactive context. Additionally, visitors' emotion played a mediating role in the relationship between activity contexts and learning outcomes. These empirical findings offer valuable practical implications for the design of educational experiences within theme parks.

RSSI-based attacks for identification of BLE devices

To prevent tracking, the Bluetooth Low Energy (BLE) protocol integrates privacy mechanisms such as address randomization. However, as highlighted by previous researches address randomization is not a silver bullet and can be circumvented by exploiting other types of information disclosed by the protocol such as counters or timing. In this work, we propose two novel attack to break address randomization in BLE exploiting side information in the form of Received Signal Strength Indication (RSSI). More precisely, we demonstrate how RSSI measurements, extracted from received BLE advertising packets, can be used to link together the traces emitted by the same device or directly re-identify it despite address randomization. The proposed attacks leverage the distribution of RSSI to create a fingerprint of devices with an empirical evaluation on various scenarios demonstrating their effectiveness. For instance in the static context, in which devices remain at the same position, the proposed approach yields a re-identification accuracy of up to 97%, which can even be boosted to perfect accuracy by increasing the number of receivers controlled by the adversary. We also discuss the factors influencing the success of the attacks and evaluate two possible countermeasures whose effectiveness is limited, highlighting the difficulty in mitigating this threat.

Integrating order picking and vehicle routing decisions in a dynamic e-commerce setting

Responding to e-commerce orders as quickly as possible is indispensable for companies competing in the current retailing landscape. After a customer orders online, the requested items should be picked in the warehouse and delivered to the customer’s location. Previous research showed the benefits of integrating the order picking and vehicle routing decisions in a static context. To achieve shorter customer response times, we propose multiple new algorithms in this study, able to handle the integrated problem of picking and delivery in a setting with dynamic order arrivals and considering more complex picking policies. We develop and implement four online large neighbourhood search algorithms, differing in their degree of integration between picking and routing decisions. The algorithms are tested on different operational settings. Differences between the algorithms and the impact of the operating context are analysed by use of an ANOVA. The results highlight the importance of integrated decision-making, as well as the large impact of the operating context on the operational efficiency. Furthermore, we demonstrate the environmental impact of stringent customer requests. These insights can be used by companies to optimise their operations.

Dynamic saccade context triggers more stable object-location binding.

Our visual systems rapidly perceive and integrate information about object identities and locations. There is long-standing debate about if and how we achieve world-centered (spatiotopic) object representations across eye movements, with many studies reporting persistent retinotopic (eye-centered) effects even for higher level object-location binding. But these studies are generally conducted in fairly static experimental contexts. Might spatiotopic object-location binding only emerge in more dynamic saccade contexts? In the present study, we investigated this using the spatial congruency bias paradigm in healthy adults. In the static (single-saccade) context, we found purely retinotopic binding, as before. However, robust spatiotopic binding emerged in the dynamic saccade context (multiple frequent saccades and saccades during stimulus presentation). We further isolated specific factors that modulate retinotopic and spatiotopic binding. Our results provide strong evidence that dynamic saccade context can trigger more stable object-location binding in ecologically relevant spatiotopic coordinates, perhaps via a more flexible brain state that accommodates improved visual stability in the dynamic world. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

MCARS-CC: A Salable Multicontext-Aware Recommender System

Context-aware recommendation systems (CARSs) leverage contextual information, e.g., time, location, or mood, to generate more personalized recommendations with high accuracy; however, existing CARSs fall short in: 1) handling the high sparsity of data; 2) designing scalable solutions in real time; and 3) providing more personalized solutions with the current limited static contexts. This article proposes a multi-CARS based on consensus clustering (MCARS-CC) to solve these challenges. The item-based contextual information is acquired using explicit static and inferred contexts by applying sentiment analysis to the users’ reviews. The proposed model is experimented using contextual prefiltering and postfiltering techniques applied to two benchmark datasets, Yelp and TripAdvisor. The model is evaluated using mean absolute error (MAE), root-mean-squared error (RMSE), response time, precision, recall, and F-measure. The experimental results show that the proposed MCARS-CC model outperforms other baseline techniques using the accuracy and error-based metrics. Incorporating hypergraph partitioning algorithm (HGPA) could improve the MAE and RMSE by 25.96% and 8.94% (Yelp), respectively. Also, HGPA led to an 18.47% and 15.94% improvement ratio in terms of MAE and RMSE (TripAdvisor), respectively.

Enabling Extremely Energy-Efficient End-to-End Secure Communications for Smart Metering Internet of Things Applications Using Static Context Header Compression

Smart metering IoT applications are among the most energy-critical in the current panorama. Metering sensors are battery-powered and are expected to have a lifetime exceeding ten years. In order to achieve such long operation duration, a generic header compression mechanism named Static Context Header Compression (SCHC) has been introduced and accepted as a standard by the Internet Engineering Task Force (IETF). This paper aims to demonstrate the energy savings enabled by the use of SCHC on a cellular IoT network by the means of real-life implementation and measurements. Experiments are conducted in a controlled environment for different scenarios and considering multiple parameters such as message size and radio conditions. Measurements show the high impact of this header compression mechanism, particularly when the radio conditions are bad and repetitions are used to improve the reliability of the transmission: a reduction of up to 40% in energy consumption is observed. Using SCHC over the non-IP transport mode (NIDD) of NB-IoT compared to the legacy IP mode also enables significant energy savings and allows the latency to be reduced while maintaining the interoperability provided by the IP layer.

Innovative security and compression for constrained IoT networks

Low-Power Wide-Area Networks (LPWAN) are a type of wireless communication technologies that enable efficient data transfer between low-power devices. This article explores the challenges of ensuring security in LPWANs and proposes a solution that combines two key mechanisms: Object Security for Constrained RESTful Environments (OSCORE) and Static Context Header Compression (SCHC). LPWANs are increasingly being used in Internet of Things (IoT) applications, but the features that endow these technologies with low power consumption and long range also make it necessary to adapt protocols and innovate in the approaches to achieve security while maintaining communications viable in technologies like these, in some cases with severe limitations. OSCORE provides end-to-end security for constrained devices, while SCHC enables efficient IP-based transmission over LPWANs. By combining these two protocols, the proposed solution aims to address the security and efficiency challenges of LPWANs, ensuring that IoT devices and data are protected from unauthorized access and manipulation. The article also presents experimental results demonstrating the feasibility and effectiveness of the proposed approach using a real-life scenario, with industrial-grade hardware. The study concludes that the use of OSCORE and SCHC in LPWAN networks can significantly enhance the security and privacy of IoT devices.

DCMF-AFNet: An anchor-free photovoltaic hot-spot fault detection network based on deformable context transformer and bi-branch multi-level feature fusion

In the process of photovoltaic hot-spot detection by thermal infrared sensors, the fault features cannot be effectively represented due to low pixel ratios and complex environmental interference, which makes it difficult for the detection network to accurately detect hot-spot faults. Therefore, an anchor-free photovoltaic hot-spot fault detection algorithm based on deformable context Transformer and bi-branch multi-level feature fusion is proposed. First, to improve the feature extraction ability of the backbone network for small-scale hot-spot faults, a deformable context Transformer module is constructed. By building an offset network in a multi-headed self-attention mechanism, dynamic and static context information with small-scale fault features can be explored from shallow feature maps. Second, to solve the problem of low target saliency due to the complex background interference, a bi-branch multi-level feature fusion module is designed to aggregate global and local multi-level features in a parallel fusion manner, enabling the detection network to rapidly focus on the fault target region in complex environments. Then, an anchor-free mechanism is introduced, and a dynamic task alignment prediction module is proposed to avoid feature misalignment in the classification and localization tasks and further improve the algorithm detection accuracy. Finally, to verify the superiority of the proposed network, seven detection algorithms are selected for comparison experiments. The experimental results show that the DCMF-AFNet network can accurately detect multi-scale hot-spot targets under various harsh conditions, and the detection accuracy can reach 87.3%.

Embedding Cognition: Judgment and Choice in an Interdependent and Dynamic World

Society is facing pressing interrelated, multilevel, and systemic challenges. Human consumption patterns are driving biodiversity loss and climate change, with unevenly distributed impacts that exacerbate preexisting inequalities. Structural or systems-level solutions to these challenges depend on group- and individual-level change, and vice versa. Although cognitive psychology has advanced our understanding of individual and group-level decision-making, it rarely links these microlevel processes and behaviors to network- and systems-level structures, resulting in a fragmented theory of how individual and collective action can drive broader social change. For example, psychology has established that physical, social, and historical contexts shape perception, reasoning, and decision-making but has largely ignored the dynamic and interdependent relationship between behavior and contexts. In this review, we urge researchers to move beyond a focus on static contexts and individual and group-level cognition to incorporate deeper theorizing about the temporal dynamics and feedbacks between individuals and the broader contexts in which they are embedded. We review literature emphasizing the role of context in shaping psychological processes as well as emerging research that considers individuals as embedded in complex adaptive systems. Complex adaptive systems frameworks are well suited to study the dynamic and interdependent relationship among individuals and the broader systems and structures they inhabit. The integration of research in psychology with these perspectives may thus offer traction in identifying solution pathways to complex, multilevel challenges by pointing to theories and methods that integrate across levels of analysis and account for coupled nature-society systems.

IPv6 over Cross-Technology Communication with Wake-up Radio

A variety of wireless (and wired) technologies are being used to enable Internet of Things (IoT) device connectivity. Two examples of popular technologies in some crucial IoT domains (e.g., smart home, smart factories and smart cities, among others) are IEEE 802.15.4 and IEEE 802.11. However, IoT devices supporting different wireless technologies are not interoperable without a gateway. One solution to this problem is exploiting bidirectional Cross-Technology Communication with Wake-up Radio (WuR-CTC). Nevertheless, existing WuR-CTC approaches do not support IPv6, and therefore cannot offer full Internet protocol stack interoperability. For the first time to our knowledge, in this paper, we present the design, implementation and evaluation of an adaptation layer to provide IPv6 support over WuR-CTC, by leveraging the IETF Static Context Header Compression and fragmentation (SCHC) framework. Among others, experimental results show that our solution allows transferring a 127-byte IPv6 packet from an IEEE 802.15.4 device to an IEEE 802.11 device, without a gateway, in 69 ms (in average). Therefore, the designed solution supports latency-stringent applications in smart environments, where a human in the loop expects real-time interaction between devices.

Multi-domain Encoding of Spatiotemporal Dynamics in EEG for Emotion Recognition.

The common goal of the studies is to map any emotional states encoded from electroencephalogram (EEG) into 2-dimensional arousal-valance scores. It is still challenging due to each emotion having its specific spatial structure and dynamic dependence over the distinct time segments among EEG signals. This paper aims to model human dynamic emotional behavior by considering the location connectivity and context dependency of brain electrodes. Thus, we designed a hybrid EEG modeling method that mainly adopts the attention mechanism, combining a multi-domain spatial transformer (MST) module and a dynamic temporal transformer (DTT) module, named MSDTTs. Specifically, the MST module extracts single-domain and cross-domain features from different brain regions and fuses them into multi-domain spatial features. Meanwhile, the temporal dynamic excitation (TDE) is inserted into the multi-head convolutional transformer to form the DTT module. These two blocks work together to activate and extract the emotion-related dynamic temporal features within the DTT module. Furthermore, we place the convolutional mapping into the transformer structure to mine the static context features among the keyframes. Overall results show that high classification accuracy of 98.91%/0.14% was obtained by the β frequency band of the DEAP dataset, and 97.52%/0.12% and 96.70%/0.26% were obtained by the γ frequency band of SEED and SEED-IV datasets. Empirical experiments indicate that our proposed method can achieve remarkable results in comparison with state-of-the-art algorithms.

Assessing a Methodology for Evaluating the Latency of IPv6 with SCHC Compression in LoRaWAN Deployments.

The Internet of Things (IoT) approach relies on the use of the Internet Protocol (IP) as a pervasive network protocol. IP acts as a "glue" for interconnecting end devices (on the field side) and end users, leveraging on very diverse lower-level and upper-level protocols. The need for scalability would suggest the adoption of IPv6, but the large overhead and payloads do not match with the constraints dictated by common wireless solutions. For this reason, compression strategies have been proposed to avoid redundant information in the IPv6 header and to provide fragmentation and reassembly of long messages. For example, the Static Context Header Compression (SCHC) protocol has been recently referenced by the LoRa Alliance as a standard IPv6 compression scheme for LoRaWAN-based applications. In this way, IoT end points can seamlessly share an end-to-end IP link. However, implementation details are out of the specifications' scope. For this reason, formal test procedures for comparing solutions from different providers are important. In this paper, a test method for assessing architectural delays of real-world deployments of SCHC-over-LoRaWAN implementations is presented. The original proposal includes a mapping phase, for identifying information flows, and a subsequent evaluation phase, in which flows are timestamped and time-related metrics are computed. The proposed strategy has been tested in different use cases involving LoRaWAN backends deployed all around the world. The feasibility of the proposed approach has been tested by measuring the end-to-end latency of IPv6 data in sample use cases, obtaining a delay of less than 1 s. However, the main result is the demonstration that the suggested methodology permits a comparison of the behavior of IPv6 with SCHC-over-LoRaWAN, allowing the optimization of choices and parameters during deployment and commissioning of both infrastructure components and software.

General solution scheme for the static link transmission model

Most static traffic assignment models, both in the literature and in practice, are neither capacity constrained nor storage constrained. They allow flows to exceed the link capacity and/or queues to exceed the link length. Recent studies in this area have resulted in novel approaches that do consider capacity constraints, resulting in residual queues, and sometimes even storage constraints, resulting in possible queue spillback. We build upon the results of these works and in particular on the model formulated in Bliemer and Raadsen (2020) that introduced a static network loading model formulation that is both capacity constrained as well as storage constrained. Their static network loading model formulation is derived from – and consistent with - the link transmission model, a well-established dynamic network loading model. It is referred to as the static link transmission model (sLTM) in this paper. This model considers a general concave fundamental diagram for each link and a general first order node model. It is well known that ignoring queue spillback can result in significant underestimation of path travel times. This is especially true for paths that do not traverse any of the bottleneck(s) directly, but that are affected by space occupied by queues that are spilling back. The prospect of being able to capture queuing and spillback effects in static assignment provides new opportunities for improving the modelling capabilities of this paradigm. In this paper, we propose a solution scheme to sLTM capable of finding a solution on large scale networks. This is the first time that an algorithm is proposed for solving an analytical static model with queue spillback. The inclusion of a node model in a static context - while enhancing the model's capabilities – generally results in the absence of a guaranteed convergent algorithm, and introducing spillback exacerbates the issue. Given the challenges of finding a stable solution, we discuss a base solution scheme and three (configurable) extensions. Further, we investigate algorithmic settings with respect to convergence and its impact on computational cost. A large-scale case study demonstrates the feasibility of the proposed scheme by finding solutions under the most challenging of conditions in a real-world setting. We show that improving convergence capabilities negatively affects computational efficiency. To this end, several potential improvements based on our findings are discussed. Lastly, we discuss the potential of this line of research in more general terms, highlighting strengths and weaknesses following our experiences.

DG_summ: A schema-driven approach for personalized summarizing heterogeneous data graphs

Advances in computing resources have enabled the processing of vast amounts of data. However, identifying trends in such data remains challenging for humans, especially in fields like medicine and social networks. These challenges make it difficult to process, analyze, and visualize the data. In this context, graph summarization has emerged as an effective framework aiming to facilitate the identification of structure and meaning in data. The problem of graph summarization has been studied in the literature and many approaches for static contexts are proposed to summarize the graph. These approaches provide a compressed version of the graph that removes many details while retaining its essential structure. However, they are computationally prohibitive and do not scale to large graphs in terms of both structure and content. Additionally, there is no framework providing summarization of mixed sources with the goal of creating a dynamic, syntactic, and semantic data summary. In this paper, our key contribution is focused on modeling data graphs, summarizing data from multiple sources using a schema-driven approach, and visualizing the graph summary version according to the needs of each user. We demonstrate this approach through a case study on the use of the E-health domain.

MSTS-Net: malignancy evolution prediction of pulmonary nodules from longitudinal CT images via multi-task spatial-temporal self-attention network.

Longitudinal CT images contain the law of lesion growth and evolution over time. Therefore, our purpose is to explore the growth and evolution law of pulmonary lesions in the time dimension to improve the performance of predicting the malignant evolution of pulmonary nodules. In this paper, we propose a Multi-task Spatial-Temporal Self-attention network (MSTS-Net) to predict the malignancy growth trend of pulmonary nodules from different periods. More specifically, the model achieves lesion segmentation task and lesion prediction task by sharing the same encoder. Segmentation task boosts the performance of the prediction task. In addition, a Static Context Spatial Self-attention Module and a Dynamic Adaptive Temporal Self-Attention Module are introduced to capture both static spatial coherence patterns between consecutive slices of lesions in the same period and temporal dynamics across different time points. We repeatedly evaluated the proposed method on the National Lung Screening Trial dataset and the Shanxi Cancer Hospital dataset. The final experimental results show that our MSTS-Net has an area under the ROC curve score of 0.919. In the computer-aided prediction of the malignant evolution of pulmonary nodules, combining the characteristics of the temporal dimension of pulmonary nodules with CT data can effectively improve the accuracy of prediction. The MSTS-Net we developed has high predictive value and broad prospects for clinical application.

A Comprehensive Dynamic Life Cycle Assessment Model: Considering Temporally and Spatially Dependent Variations.

Life cycle assessment (LCA) is a widely-used international environmental evaluation and management method. However, the conventional LCA is in a static context without temporal and spatial variations considered, which fails to bring accurate evaluation values and hinders practical applications. Dynamic LCA research has developed vigorously in the past decade and become a hot topic. However, systematical analysis of spatiotemporal dynamic variations and comprehensive operable dynamic models are still lacking. This study follows LCA paradigm and incorporates time- and space-dependent variations to establish a spatiotemporal dynamic LCA model. The dynamic changes are classified into four types: dynamic foreground elementary flows, dynamic background system, dynamic characterization factors, and dynamic weighting factors. Their potential dynamics and possible quantification methods are analyzed. The dynamic LCA model is applied to a residential building, and significant differences can be observed between dynamic and static assessment results from both temporal and spatial perspectives. This study makes a theoretical contribution by establishing a comprehensive dynamic model with both temporal and spatial variations involved. It is expected to provide practical values for LCA practitioners and help with decision-making and environmental management.