Reviewerly/exHarmony huggingface dataset:
Fine-Grained Activity of Daily Living (ADL) Recognition Through Heterogeneous Sensing Systems With Complementary Spatiotemporal Characteristics

Non-intrusive monitoring of fine-grained activities of daily living (ADL) enables various smart healthcare applications. For example, ADL pattern analysis for older adults at risk can be used to assess their loss of safety or independence. Prior work in the area of ADL recognition has focused on coarse-grained ADL recognition at the activity level (e.g., cooking, cleaning, sleeping), and/or course-grained (hourly or minutely) activity duration segmentation. It also typically relies on a high-density deployment of a variety of sensors. Finer-grained (sub-second-level and event-based) ADL recognition, could provide more detailed ADL information, which is crucial for enabling the assessment of patients' activity patterns and potential changes in behavior. To achieve this fine-grained ADL monitoring, we present a heterogeneous multi-modal cyber-physical system, where we use 1) distributed vibration sensors to capture the action-induced structural vibrations and their spatial characteristics for information aggregation, and 2) single point electrical sensor to capture appliance usage with high temporal resolution. To evaluate our system, we conducted real-world experiments with multiple human subjects to demonstrate the complementary information from these two sensing modalities. Our system achieved an average 90% accuracy in recognizing activities, which is up to 2.6x higher than baseline systems considering each state-of-the-art sensing modality separately.
Human activity recognition using improved dynamic image

In action recognition, the dynamic image (DI) approach is recently proposed to code a video signal to a still image. Since DI descriptor is strongly dependent on first frames, it cannot extract dynamics that do not occur in the first frames or even long dynamics. On the other hand, most of the video frames are not informative for the task of action recognition. Therefore, the authors' intuition is that the process of representing a video using all frames is inefficient. Thus, in this study, they proposed to remove the existing redundancy inside the frames and extract some processed informative images based on the information theory which are called key frames. The proposed method is capable enough to extract sufficient frames regardless of the duration and the position of frames in the entire video. Motivated by this method and DI, they proposed a novel key frames dynamic image (KFDI) approach. Experimental results on popular UCF11, Olympic Sports, and J-HMDB datasets show the superiority of the proposed KFDI approach compared to the DI in capturing long dynamics of videos for action recognition. Their experiments show KFDI improves the accuracy 2–6%compared to DI.
Standardised Questionnaires in Usability Evaluation. Applying Standardised Usability Questionnaires in Digital Products Evaluation

The usability evaluation plays a crucial role in the human-computer interaction. It is one of the basic elements used to verify the user interface quality and also, the quality of the system, as a whole. The goals of usability testing can vary by study, but usually they include: identifying problems in the design of a product or service, uncovering opportunities to improve, learning about the target user's behaviour and preferences. In this paper, we will present an analysis over the most commonly used standardised questionnaires. Also, based on a comparison between them, we will present the results of an analysis done by a group of students, who were asked to compare and decide which standardised questionnaire would be appropriate for their usability evaluation over a certain project. The students were split in teams, each team having a different project to analyse. Their activity is part of the "Interactivity and Usability" subject of Multimedia Technologies Master Degree Program of Politehnica University of Timișoara. We will also present a score of choosing some of the surveys and emphasise the pros and cons of the preferred questionnaires.
FPGA-assisted high-precision, high-speed 3D shape measurement

In order to achieve three dimensional (3D) shape measurement, improve measurement accuracy, a high-speed and high-precision FPGA architecture for phase measurement profilometry (PMP) algorithm is proposed. The system uses 12 sinusoidal fringes to achieve 3D shape measurement and corrects the inverse-phase of the projector to improve measurement accuracy. In addition, the coordinate rotation digital computer (CORDIC) algorithm is used to realize the arctangent function calculation, which not only improves the calculation speed, but also improves the phase calculation accuracy. The system utilizes the parallelism of FPGA and the superior performance of pipeline processing. It also redesigns the modules to greatly improve the speed of 3D reconstruction such as data buffer, phase wrapping, phase unwrapping, and point cloud. The results of experiments show that the method we proposed is faster than conventional methods.
Exploring Architectures for Cryptographic Access Control Enforcement in the Cloud for Fun and Optimization

To facilitate the adoption of cloud by organizations, Cryptographic Access Control (CAC) is the obvious solution to control data sharing among users while preventing partially trusted Cloud Service Providers (CSP) from accessing sensitive data. Indeed, several CAC schemes have been proposed in the literature. Despite their differences, available solutions are based on a common set of entities---e.g., a data storage service or a proxy mediating the access of users to encrypted data---that operate in different (security) domains---e.g., on-premise or the CSP. However, the majority of the CAC schemes assume a fixed assignment of entities to domains; this has security and usability implications that are not made explicit and can make inappropriate the use of a CAC scheme in certain scenarios with specific requirements. For instance, assuming that the proxy runs at the premises of the organization avoids the vendor lock-in effect but may substantially undermine scalability.
Bebop 2 Quadrotor as a Platform for Research and Education in Robotics and Control Engineering

In conducting research and teaching in fields related to unmanned aerial vehicles (UAVs), it is particularly important to select a universal, safe, open research platform and tools for rapid prototyping. Ready-to-use, low-cost micro-class UAVs such as Bebop 2 are successfully used in that regard. This article presents how to use the potential of this flying robot with Robot Operating System (ROS). The most important software solutions for the developed experimental testbed FlyBebop are characterized here. Their capabilities in research and education are exemplified using three distinct cases: 1) research results on the method of optimal, in-flight, iterative self-tuning of UAV position controller parameters (based only on current measurements), 2) the use of the reinforcement learning method in the autonomous landing of a single drone on a moving vehicle, 3) planning the movement of UAVs for autonomous video recording along the planned path in the arrangement: "cameraman drone" and "lighting technician drones".
Fault Diagnosis of Power Equipment Based on Infrared Image Analysis

As the need to discover abnormal temperature rise and overheating breakdown of power equipment in substations is urgent, infrared inspection is important for its quick and accurate characteristics. To improve the accuracy of infrared inspection diagnosis, this paper proposes an operational diagnosis system based on comprehensive analysis of infrared images of power equipment. Firstly, the image is preprocessed by histogram equalization, Sobel operator, Canny operator, median filtering, etc. Then, the feature points of the infrared image are extracted by SIFT algorithm, and the extracted feature points are K-means clustered. Find the type of feature points that belong to the capacitive device after clustering, and then perform SIFT feature extraction on the processed image, and eliminate the feature points that are not electrical devices according to the found feature points, thereby completing the target —— Identification of the electrical device. Finally, the target recognition results of the 3 pretreatment methods and the original without pretreatment are compared to find the best effect, that is, the pre-processing algorithm with the highest recognition accuracy.
A Self-Adaptive Selection of Subset Size Method in Digital Image Correlation Based on Shannon Entropy

Digital image correlation (DIC) is a typical non-contact full-field deformation parameters measurement technique based on image processing technology and numerical computation methods. To obtain the displacements of each point of interrogation in DIC, subsets surrounding the point must be chosen in the reference image and deformed image before correlating. In the existing DIC techniques, the size of subset is always pre-defined by users manually according to their experiences. However, the subset size has proven to be a critical parameter for the accuracy of computed displacements. In the present paper, a self-adaptive selection of subset size method based on Shannon entropy is proposed to overcome the deficiency of existing DIC methods. To verify the effectiveness and accuracy of the proposed algorithm, a numerical translated test is performed on four actual speckle patterns with different entropies, and then another test is performed on four computer-generated speckle patterns with non-uniform displacement field. All the results successfully demonstrate that the proposed algorithm can significantly improve displacement measurement accuracy without reducing too much computational efficiency. Finally, a practical application of the proposed algorithm to micro-tensile of Q235 steel is conducted.
Fast True Random Number Generator Based on Chaotic Oscillation in Self-Feedback Weakly Coupled Superlattices

Random numbers play a vital role in communications and cryptography. However, most existing true random number generators have difficulty in satisfying the requirements of high-speed communications due to their complexity and bulkiness, or low speed limitations due to equipment bandwidth. Then, the all-electron true random number generator was presented based on GaAs/Al <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0.45</sub> Ga <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0.55</sub> As superlattices conducted under direct current bias at room temperature, which not only possesses the characteristics of miniaturization but also generates random numbers at rates up to the Gbit/s. However, the bit rate of random number generators based on superlattices is still much slower compared to chaotic laser random number generators. In order to generate higher-rate random numbers, we modified a DC-excited superlattice and then redirected the signal generated by the superlattice back into itself, thus introducing a self-feedback, and achieved a self-feedback superlattice true random number generator. This improvement makes the superlattice have a more detailed signal shape, a more effective signal amplitude, and with lower power consumption. Therefore, these advances made the self-feedback superlattice more suitable for generating random numbers. Moreover, we propose a new post-processing method, called the adjacent bits reversal exclusive-or. This method can reduce the sequence bias and correlation without discarding any random bit. The random number obtained by the self-feedback superlattice at a sampling rate of 10 GS/s passed the triple standard deviation test and the random number standard test (NIST SP 800-22), indicating that it possessed good statistical properties as a miniaturized random number generator.
A Foot in Both Worlds: Current Roles and Challenges of Academic Online Learning Librarians

This qualitative study explores how online learning is defined at academic libraries in the U.S. and examines the current roles and challenges of online learning librarians. Findings from in-depth interviews reveal that approaches to online learning in academic libraries vary in terms of audience and instructional strategies, and that online learning librarians perform several roles within the institution and often face challenges related to those roles. These findings suggest that while academic libraries have defined their approaches to online learning, the functional role of the online learning librarian should be clearly delineated and leveraged to support sustainable online learning initiatives at the institution. Based on these findings, the authors make recommendations for librarians and library administrators to support this evolving area of librarianship.
Factors influencing the importance of criteria for judging answer quality on academic social Q&amp;A platforms

Purpose With the growth in popularity of academic social networking sites, evaluating the quality of the academic information they contain has become increasingly important. Users' evaluations of this are based on predefined criteria, with external factors affecting how important these are seen to be. As few studies on these influences exist, this research explores the factors affecting the importance of criteria used for judging high-quality answers on academic social Q&amp;A sites. Design/methodology/approach Scholars who had recommended answers on ResearchGate Q&amp;A were asked to complete a questionnaire survey to rate the importance of various criteria for evaluating the quality of these answers. Statistical analysis methods were used to analyze the data from 215 questionnaires to establish the influence of scholars' demographic characteristics, the question types, the discipline and the combination of these factors on the importance of each evaluation criterion. Findings Particular disciplines and academic positions had a significant impact on the importance ratings of the criteria of relevance, completeness and credibility. Also, some combinations of factors had a significant impact: for example, older scholars tended to view verifiability as more important to the quality of answers to information-seeking questions than to discussion-seeking questions within the LIS and Art disciplines. Originality/value This research can help academic social Q&amp;A platforms recommend high-quality answers based on different influencing factors, in order to meet the needs of scholars more effectively.
heSRPT: Parallel scheduling to minimize mean slowdown

Modern data centers serve workloads which are capable of exploiting parallelism. When a job parallelizes across multiple servers it will complete more quickly, but jobs receive diminishing returns from being allocated additional servers. Because allocating multiple servers to a single job is inefficient, it is unclear how best to allocate a fixed number of servers between many parallelizable jobs. This paper provides the first optimal allocation policy for minimizing the mean slowdown of parallelizable jobs of known size when all jobs are present at time 0. Our policy provides a simple closed form formula for the optimal allocations at every moment in time. Minimizing mean slowdown usually requires favoring short jobs over long ones (as in the SRPT policy). However, because parallelizable jobs have sublinear speedup functions, system efficiency is also an issue. System efficiency is maximized by giving equal allocations to all jobs and thus competes with the goal of prioritizing small jobs. Our optimal policy, high-efficiency SRPT (heSRPT), balances these competing goals. heSRPT completes jobs according to their size order, but maintains overall system efficiency by allocating some servers to each job at every moment in time. Our results generalize to also provide the optimal allocation policy with respect to mean flow time. Finally, we consider the online case where jobs arrive to the system over time. While optimizing mean slowdown in the online setting is even more difficult, we find that heSRPT provides an excellent heuristic policy for the online setting. In fact, our simulations show that heSRPT significantly outperforms state-of-the-art allocation policies for parallelizable jobs.
A Study of Variable Structure and Sliding Mode Filters for Robust Estimation of Mechatronic Systems

In this paper, a study of estimation strategies based on variable structure and sliding mode theory is performed. The smooth variable structure filter (SVSF) and the new sliding innovation filter (SIF) are based on similar sliding mode concepts but with some notable differences. The relevant literature and background are explored and the SVSF and SIF estimation algorithms are presented. For comparison purposes, the two estimation strategies are applied on a mechatronic system. The results indicate that although both the SVSF and SIF provide robust estimates to faults, the SIF formulation provides slightly more accurate estimates while maintaining robustness, and is less computationally complex.
The Taint Rabbit: Optimizing Generic Taint Analysis with Dynamic Fast Path Generation

Generic taint analysis is a pivotal technique in software security. However, it suffers from staggeringly high overhead. In this paper, we explore the hypothesis whether just-in-time (JIT) generation of fast paths for tracking taint can enhance the performance. To this end, we present the Taint Rabbit, which supports highly customizable user-defined taint policies and combines a JIT with fast context switching. Our experimental results suggest that this combination outperforms notable existing implementations of generic taint analysis and bridges the performance gap to specialized trackers. For instance, Dytan incurs an average overhead of 237x, while the Taint Rabbit achieves 1.7x on the same set of benchmarks. This compares favorably to the 1.5x overhead delivered by the bitwise, non-generic, taint engine LibDFT.
Design and synthesis of reversible data selectors for low power application

Reversible logic design has paved way for a new area of research and is gaining a lot of research attention as it is very useful in various areas such as low power VLSI design, quantum computing, and nanotechnology. Combinational logic dissipates the significant amount of energy due to bit loss. So, if circuits are designed using reversible logic, where information bits are not destroyed and reduced the significant amount of power dissipation. Reversible circuits are designed using parity preserving gates, which has the property of detecting not more than a single error in the circuit's primary output. If a circuit is both reversible and fault tolerant, it can recover bit-loss at the input as well as bit-error at output. This paper presents the design and FPGA synthesis of optimized data selectors. This paper also highlights the low switching power consumption characteristics of data selectors, which were typically around 1 to 3μW that finds its use in low power applications that are targeting ultra-speed.
Integrity monitoring for Kalman filter-based localization

The problem of quantifying robot localization safety in the presence of undetected sensor faults is critical when preparing for future applications where robots may interact with humans in life-critical situations; however, the topic is only sparsely addressed in the robotics literature. In response, this work leverages prior work in aviation integrity monitoring to tackle the more challenging case of evaluating localization safety in Global Navigation Satellite System (GNSS)-denied environments. Localization integrity risk is the probability that a robot’s pose estimate lies outside pre-defined acceptable limits while no alarm is triggered. In this article, the integrity risk (i.e., localization safety) is rigorously upper bounded by accounting for both nominal sensor noise and other non-nominal sensor faults. An extended Kalman filter is employed to estimate the robot state, and a sequence of innovations is used for fault detection. The novelty of the work includes (1) the use of a time window to limit the number of monitored fault hypotheses while still guaranteeing safety with respect to previously occurring faults and (2) a new method to account for faults in the data association process.
Boosting gender identification using author preference

Predicting the gender of a text document’s author, also known as gender identification, is a well-studied authorship categorization task in the literature. A common theme in gender identification studies is that gender is considered a binary task. However, digital communications provide users with the ability to select virtual genders leveraging physical anonymity. In this study, the additional duality on gender due to author preferences is examined along with the biological gender. Formally, the objective of this paper is to investigate whether the gender preference of an author contains any additional linguistic information. Furthermore, we explore whether this information can be exploited to improve the author characterization task. In particular, the self-assigned gender, i.e., virtual gender, of the users in text-based real-time online messaging services, along with the biological sex, is evaluated quantitatively via comparing/assessing the gender prediction performance under various settings. Experiment results show that by integrating the virtual gender into the binary classification problem of predicting an author’s gender, it is possible to further improve the prediction performance by 2.6%, up to 85.4%.
Integrating TAM/TRI/TPB frameworks and expanding their characteristic constructs for DLT adoption by Service and Manufacturing Industries-Pakistan Context

The advent of distributed ledger technology in developed nations is emerging as a new instrument to improve the traditional industry system in the developing states. The adoption of disruptive technology is indeed a necessary condition for the coming future to bring transparency and remove third party trust related issues in the industries. The aim of the current study is to understand the distributed ledger technology (DLT) adoption by service & manufacturing industries-Pakistan context. The conceptual model is based on the traditional adoption theories-namely theory of planned behavior (TPB), technology acceptance model (TAM) and technology readiness index (TRI). In order to analyze the data a partial least square structural equation modeling (PLS-SEM) is used and empirical data is collected from a sample of 211 practitioners working in the Finance, Supply chain and IT Departments. The study result shows that TRI construct-Optimism (OPT) and Innovativeness (INN) have significant impact on Perceived ease of use (PEOU) and insignificant impact on Perceived usefulness (PU). TAM construct- Perceived ease of use (PEOU), Perceived usefulness (PU), attitude (ATT) and TPB-construct perceived behavioral control (BPC) affect the Behavioral Intention (BI). TPB construct-Subjective Norms (SN) have negligible impact on the Behavioral Intention (BI). It is one of the initial studies on Blockchain Technology implementation for Pakistan industries and the result suggest that practitioners would perceive Blockchain Technology adoption free of efforts to arise the maximum benefits for improving the productions efficiency.