Reviewerly/exHarmony huggingface dataset:
Digital ethnography of home use of digital personal assistants

Commercialised voice user interface devices for the home, like Amazon Echo, Google Home, and Apple HomePod, with integrated digital personal assistants have rapidly grown in popularity. These devices embody intelligent software agents that support users in their everyday life through easy and intuitive conversational interactions. While their use in everyday activities is largely unexplored, the proliferation in home use presents a valuable opportunity to add to understanding around the use of in-home digital personal assistants. In this paper, we investigate their home use in a broad context to learn more about people’s experiences, attitudes, interactions and expectations with these devices contributing new insights to current knowledge around this use. Applying the digital ethnography method, we collected 3542 reviews and comments about Amazon Echo, Google Home, and Apple HomePod on Amazon, eBay, and Reddit. Six main themes and 29 categories were derived through filtering, thematic analysis and affinity diagramming. These findings constitute a conceptual framework characterising the current landscape of home use of digital personal assistants. Additionally, we identify and discuss unique issues discovered around the invisible interface, interactive freedom, and creative appropriation. We use our findings to propose implications for interaction design of DPAs for home use.
Affect detection from arabic tweets using ensemble and deep learning techniques

Affect detection from text has captured the attention of researchers recently. This is due to the rapid use of social media sites (e.g. Twitter, Facebook), which allows users to express their feelings, emotions, and thoughts in textual format. Analyzing emotion-rich textual data of social networks has many real-life applications. The context of an emotional text can be measured by analyzing certain features of this rich source of emotional information. Classifying text into emotional labels/intensities is considered a difficult problem. This paper resolves one of the state-of-the-art NLP research emotion and intensity detection tasks using Deep Learning and ensemble implementations. In this paper, we developed several innovative approaches; (a) bidirectional GRU_CNN (BiGRU_CNN), (b) conventional neural networks (CNN), and (c) XGBoost regressor (XGB). The ensemble of BiGRU_CNN, CNN, and XGB is used to solve an emotion intensity (EI-reg) task of the SemEval-2018 Task1 (Affect in Tweets). Our proposed ensemble approach was evaluated using a reference dataset of the SemEval-2018 Task1. Results show that our approach is well above the baseline for this task. It also achieved a Pearson of (69.2%), with an enhancement of 0.7% in comparison with previous best performing models.
Power Control Framework for Green Data Centers

In recent years, renewable energy, such as wind and photovoltaic electric power has been increasingly integrated into data center power provisioning systems to address high energy consumption of data centers. However, in reality, the intermittency and randomness of renewable energy (power supply fluctuation) is detrimental to the reliable operation of sophisticated IT equipment in those so-called green data centers. In this article, we address the problem of data center power regulation explicitly taking into account the unreliability and instability of renewable energy sources. To this extent, we design a novel data center power control framework that smoothens the power fluctuation and instability of renewable energy sources. The core of our framework is two power regulation optimization algorithms. In particular, a server workload scheduling algorithm deals with high frequency fluctuations while an Uninterruptable Power Supply (UPS) power regulation algorithm handles low frequency and large extent power fluctuations. These algorithms are also designed to satisfy service level agreement (SLA) and standby power supply capacity. We have conducted an extensive evaluation study using trace data of a real data center of 30000-node cluster with 50 x 250 UPS battery groups and 24-hour power generation data from real wind farm and photovoltaic power station. The experimental results show our framework effectively smoothens fluctuations of data center power supply, more effective use of renewable energy, and extend the UPS batteries’ lives to reduce the skyrocketed data center operating expenses.
Multi-modal tuning of vibrating bars with simplified undercuts using an evolutionary optimization algorithm

We present a method to tune the natural frequencies of a generic bar of mallet percussion instruments (e.g. marimba, vibraphone), for a set of predefined target frequencies, using a global optimization algorithm. The bar is modelled as a one-dimensional Timoshenko beam and its natural frequencies are calculated via 1-D finite elements. The undercut is made up of a series of rectangular cuts, which aside from reducing the dimension of the optimization problem to a few pairs of variables (height and length of each cut) also generates shapes that are easy to manufacture. Moreover, two penalty terms are added to the objective function, in a weighted manner, to (1) minimize the amount of extracted material and (2) minimize abrupt changes in profile height, aimed to alleviate the complexity and accelerate the manufacturing process. An evolutionary optimization algorithm is developed and extensive computational experiments are carried out to assess the algorithm’s performance and find appropriate parameter values for a faster convergence. The results illustrate the effect of the different penalties on the solutions obtained. Additionally, optimized shapes for various unorthodox and demanding tuning targets are presented and compared to previously published results, illustrating the benefits of the simplified undercut model.
Object Detection with Convolutional Neural Networks

During the last years, a noticeable growth is observed in the field of computer vision research. In computer vision, object detection is a task of classifying and localizing the objects in order to detect the same. The widely used object detection applications are human–computer interaction, video surveillance, satellite imagery, transport system, and activity recognition. In the wider family of deep learning architectures, convolutional neural network (CNN) made up with set of neural network layers is used for visual imagery. Deep CNN architectures exhibit impressive results for detection of objects in digital image. This paper represents a comprehensive review of the recent development in object detection using convolutional neural networks. It explains the types of object detection models, benchmark datasets available, and research work carried out of applying object detection models for various applications.
Movable Surveillance Camera using IoT and Raspberry Pi

Surveillance is the one major thing that anyone wants and required to protect their things or monitor their places. In this paper, we proposed a movable surveillance camera using Raspberry Pi and Webcam. The movement of the proposed surveillance camera can be controlled by using the designed web browser and python scripting language. It can cover a wide area. The proposed architecture is portable and movable. It is extensively used in high-risk prone areas to humans and also used in the industries for continuous monitoring for seamless working machinery. The movement of the camera can be done by using a Robot, which is driven by the motor driver L298N module integrated with the circuit. The data collected by the proposed movable surveillance camera is more superior than the conventional CCTV Cameras, Drone Cameras, and Camera Traps. The problem with the drone camera is very less coverage area. In forest areas, the camera traps are replaced by the proposed surveillance camera. It can be used for capturing images in dark areas as it uses LED's. SSH Protocol is used for sending the captured images.
Go with the Flow

Game flow represents a state where the player is neither frustrated nor bored. In turn-based battle video games it can be achieved by Dynamic Difficulty Adjustment (DDA), whose research has begun rising over the last decade. This paper introduces an idea for incorporating DDA through the use of Reinforcement Learning (RL) to agents of turn-based battle video games. We design and implement an RL agent that shows, in a simple environment, the idea of how a game could achieve balance through adequate choices in actions depending on the player's level of skill.
Humanoid gesture control ARM with manifold actuation by embedded system

The design and implementation of humanoid gesture control robotic arm with multiple actuations is proposed. Utilization of gesture based design is made here. The design is made such a way that five separate movable fingers, flexion and extension installed wrist with tilt feature. Human palm movements are imitated in robotic arm using the motion glove. To develop the processing of arms with additional movement, to improve the aesthetics both for the safety and to look as less artificial. Already in this field metallic and plastic variants are used. The dimensions are made with the help of human palm dimensions. A human design is not sufficient. It also need the movements that is done by actual human [1]. In this paper we propose the number of movements done by the arm is made to 120 [2].
Human Activity Recognition Using Recurrent Neural Network Classifiers on Raw Signals from Insole Piezoresistors

Human Activity recognition has many potential applications in telemedicine and rehabilitation. The advent and recent improvements in deep learning have encouraged the development of more accurate classifiers that were previously unprecedented. The present work focuses on classifying human activity using small, raw datasets collected with instrumented and customized footwear. Multi-channel time series data were recorded and transmitted wirelessly to a recurrent neural network classifier, which was able to classify 6 activities with an accuracy of up to 87.0 ± 8.9%. Lower level features were detected by the use of convolution. The present work shows that it is possible to use artificial neural network based software techniques to accurately classify data, even with small datasets and low-cost electronic hardware.
Neural Network Based Bengali News Headline Multi Classification System: Selection of Features describes Comparative Performance

The modern era is gradually developing in all sectors. Moreover, development is necessary to research. Accordingly natural language processing is the expanding area of process text. At present year, memorizing the data is very tough due to the rapidly growing volume of data. Newspapers are a great habit to the whole world of all ages. To acquire a variety of knowledge from different segments is entertaining by themselves. According to this work using bengali news headline may be more specific to others by defining its news type. Therefore the machine can smartly review the sequence of sentences within reach output to find newstype. With experiment, we connect our approach by Neural Network of adoption with 90% accuracy performance. Coming with a momentous outcome we've done Multi Classification reached at SVM, NB, Logistic Regression, Neural Network, Random forest applying bengali dataset.
Multi-granularity feature learning network for deep hashing

With the ever-increasing growth of massive high-dimensional data, deep learning to hash technology has been widely used for approximate nearest neighbor search on large-scale datasets, due to its remarkable efficiency and retrieval performance. In this paper, we propose a novel supervised deep hashing method, named Multi-granularity Feature Learning Hashing (MFLH), to learn compact binary descriptors. Specifically, we design an end-to-end trainable network to jointly learn feature representations and hash codes, in which a global stream and a local stream are responsible for learning feature representations with different granularities, and a hashing stream is devoted to encoding multi-granularity features into binary codes. In the local stream, a Cyclic Shift Mechanism (CSM) strategy is developed to assist mining more discriminative local fine information. In the meantime, an approximate sign activation function, which can be used for continuous optimization, is introduced to reduce quantization error. Furthermore, an improved variant of the triplet loss is presented to enhance the representation of pair-wise similarity for hash codes. Extensive experiments demonstrate that our proposed method significantly outperforms state-of-the-art hashing methods on the benchmark datasets, thereby verifying the effectiveness of our approach. Source code is provided for reproducibility.
Development of ICT Models in Area of Safety Education

The paper addresses the challenges of developing models that are important for increasing the level of IT security culture in the field of safety-related applications. Models of resource-based approach and peculiarities of their integration into natural world in accordance with vector and levels of resource development are considered. Safety-related systems are analyzed from the position of checkability of digital components and accumulation of hidden faults, which reduce fault tolerance of circuits in emergency mode. The problem of hidden faults was identified as a growth challenge with appropriate ways to address it, including virus epidemics and Covid-19. Case studies of the development of models within the framework of European educational projects aimed at improving the level of culture in IT security are shown.
An Interactive Data Visualization Framework for Exploring Geospatial Environmental Datasets and Model Predictions

With the rise of large-scale environmental models comes new challenges for how we best utilize this information in research, management and decision making. Interactive data visualizations can make large and complex datasets easier to access and explore, which can lead to knowledge discovery, hypothesis formation and improved understanding. Here, we present a web-based interactive data visualization framework, the Interactive Catchment Explorer (ICE), for exploring environmental datasets and model outputs. Using a client-based architecture, the ICE framework provides a highly interactive user experience for discovering spatial patterns, evaluating relationships between variables and identifying specific locations using multivariate criteria. Through a series of case studies, we demonstrate the application of the ICE framework to datasets and models associated with three separate research projects covering different regions in North America. From these case studies, we provide specific examples of the broader impacts that tools like these can have, including fostering discussion and collaboration among stakeholders and playing a central role in the iterative process of data collection, analysis and decision making. Overall, the ICE framework demonstrates the potential benefits and impacts of using web-based interactive data visualization tools to place environmental datasets and model outputs directly into the hands of stakeholders, managers, decision makers and other researchers.
<scp>Real‐time</scp> adaptive speed control of vector‐controlled induction motor drive based on <scp>online‐trained</scp> Type‐2 Fuzzy Neural Network Controller

Background The speed control of induction motor having superior features such as high efficiency, robust construction, low maintenance and low cost is carried out more effectively with the developments in control theory. With innovations in power electronics and microprocessor technology, it has been made possible to use the vector control method for applications requiring high performance in induction motor drives. Aim and Objective In this study, Type-2 Fuzzy Neural Network (T2FNN) controller which is durable, adaptable and has fast dynamic response capabilities against parameter changes, is proposed to obtain a robust speed response from induction motor. Materials and Methods Matlab/RTI model is developed through DS1103 controller card to experimentally test the speed performance of the proposed controller based induction motor. The proposed controller is trained on-line to improve the robustness of the induction motor against disturbances. After that, experimental studies are built to investigate the speed control behavior and effectiveness of the induction motor. Results and Discussion The performance of T2FNN controller is compared with PI and Type-1 Fuzzy Neural Network (T1FNN) controllers. The experimental results clearly indicate that the proposed controller has a faster and more stable dynamic response capability. Conclusion The proposed controller is significantly improved the dynamic response of the induction motor compared to T1FNN and PI controllers. In addition, the settling time, overshoot and recovery time promoting percentages of T1FNN and PI controllers by T2FNN controller are in satisfactory levels during all steady-state and the transient conditions.
Journal of the Association for Information Science and Technology: Analysis of two decades of published research

Abstract The Journal of the Association for Information Science &amp; Technology (JASIST) is a leading information science journal, recognized by many ranking metrics. Understanding patterns in articles published would be helpful to new researchers seeking to publish with the journal. However, the last comprehensive bibliometric analysis on JASIST is more than a decade old. The objective of this paper is to analyze the bibliographic information of full‐length research articles published in JASIST during the last two decades. This includes metrics such as article count, authorship, international collaboration, citations, and topical areas. Data was collected from SCOPUS, JASIST website, and Scimago. The findings show that JASIST published 3,052 articles during 2000–2020, which got cited 180,608 times (59.18 times per article) to date. Joint authorship and international collaboration has been increasing. Authors from institutions in 70 countries have published, with most articles from USA, with authorship from China steadily increasing in recent years. The detailed findings in the paper would help information science researchers and practitioners to assess the areas of focus, and various patterns in a top journal of the field. Such historical understanding is critical in charting agendas and directions for future research.
SchemaBoard: Supporting Correct Assembly of Schematic Circuits using Dynamic In-Situ Visualization

Assembling circuits on breadboards using reference designs is a common activity among makers. While tools like Fritzing offer a simplified visualization of how components and wires are connected, such pictorial depictions of circuits are rare in formal educational materials and the vast bulk of online technical documentation. Electronic schematics are more common but are perceived as challenging and confusing by novice makers. To improve access to schematics, we propose SchemaBoard, a system for assisting makers in assembling and inspecting circuits on breadboards from schematic source materials. SchemaBoard uses an LED matrix integrated underneath a working breadboard to visualize via light patterns where and how components should be placed, or to highlight elements of circuit topology such as electrical nets and connected pins. This paper presents a formative study with 16 makers, the SchemaBoard system, and a summative evaluation with an additional 16 users. Results indicate that SchemaBoard is effective in reducing both the time and the number of errors associated with building a circuit based on a reference schematic, and for inspecting the circuit for correctness after its assembly.
Capturing Students’ Attention Through Visible Behavior: A Prediction Utilizing YOLOv3 Approach

One way to determine whether or not the student is conscientious in the classroom is by facial expressions. Facial expressions are facial changes in response to a person's internal mental states, thoughts, or social contact. The application of machine learning and computer vision methods have made very useful in area of automated assessment. In this paper, an experimental setup was installed for data collection. The researchers aim to present a new approach of predicting student behavior (attentive or not attentive) based from face recognition during class session. This demonstrate a real-time detection of student behavior. Using deep learning approach, the acquired data utilized the YOLO (you only look once) v3 algorithm in predicting student behavior inside the classroom. The evaluation was created right after the live feed review. Generated models were tested using mAP to decide which model is appropriate for object detection. The mAP (mean average accuracy) is a common measure used to determine the precision of the artifacts being observed. This measure was focused on the following class: high = Attentive and low = Not Attentive. The experimental testing shows that model accuracy is 88.606%. Tests indicate that this method offers reasonable pace of identification and positive outcomes for the measurement of student interest dependent on observable student actions in classroom instruction.
Reinforcement learning enhanced quantum-inspired algorithm for combinatorial optimization

Quantum hardware and quantum-inspired algorithms are becoming increasingly popular for combinatorial optimization. However, these algorithms may require careful hyperparameter tuning for each problem instance. We use a reinforcement learning agent in conjunction with a quantum-inspired algorithm to solve the Ising energy minimization problem, which is equivalent to the Maximum Cut problem. The agent controls the algorithm by tuning one of its parameters with the goal of improving recently seen solutions. We propose a new Rescaled Ranked Reward (R3) method that enables a stable single-player version of self-play training and helps the agent escape local optima. The training on any problem instance can be accelerated by applying transfer learning from an agent trained on randomly generated problems. Our approach allows sampling high quality solutions to the Ising problem with high probability and outperforms both baseline heuristics and a black-box hyperparameter optimization approach.
PTVis: Visual Narrative and Auxiliary Decision to Assist in Comprehending the Penetration Testing Process

Due to the complexity of network penetration and the diversity of penetration methods, traditional analysis approaches analyse only a single penetration method or part of the network penetration process.Moreover, the lack of customized exploration makes it difficult to discover and analyse network penetration behaviors.Characterizing and summarizing the penetration testing process based on an interpretive visual analysis approach can enhance researchers' comprehension of penetration testing and further promote the development of network security technologies.To assist with this process, we design PTVis, a visual approach for the penetration testing process summarization based on visual narrative and auxiliary decision.PTVis consists of two primary components: (1) a visual interface that displays customized penetration testing paths, and (2) a component that effectively displays the results of penetration testing.To design PTVis, penetration testing paths that combine penetration testing methods and tools are built via cooperative multi-view and customized exploration, which facilitates the exploration of penetration testing.For evaluation, a qualitative user study is performed on two groups.The feedback from the study demonstrates that PTVis can enhance the user's knowledge of the penetration testing process.