Reviewerly/exHarmony huggingface dataset:
Usability Testing of Twitter App with Indian Users

This research paper presents a usability testing of the Twitter app with Indian Twitter users for identifying usability problems, especially for the new users or beginners. This usability test mainly focuses on a normal usage context and subject of observations, which significantly affects the user experience with the Twitter app. Various Twitter functionalities/features have been explored during usability testing, and ten frequently used tasks were selected. The selected users have been ten Indian college students in the age group of 21-23 years but are not using Twitter now or have not used Twitter before. It is directly affected by usability problems related to the app like Twitter. Therefore, several usability problems have been identified during usability testing. These problems include the ambiguous design of icons, faulty design of buttons, hidden options inside the menu, information overload, and inefficiency with functions. Few remedies are also proposed for dealing with identified usability problems with the Twitter app. It is believed that the design updates based on identified usability problems and remedies will improve the user experience of Indian Twitter users, boosting the popularity of the Twitter app among Indian youths.
The importance of disaster mitigation literacy in social studies learning

Abstract Education is one of the conscious and planned efforts in shaping the character of students for the cultivation of knowledge, attitudes, and skills. One of them is that social studies learning contributes to providing students’ knowledge, attitudes, and skills in dealing with disasters that are objective and subjective. In sustainable development efforts, social studies learning has a crucial role in reducing disaster risk. This paper aims to reveal that Indonesia is a disaster-prone country, especially in the community, one of which is the school environment. There are approximately 126,681 education units located in moderate and high disaster risk areas. Disaster literacy seeks to minimize disasters by providing knowledge about disasters, attitudes, and skills about disaster risk reduction. This research approach uses a literature review. The research results that have been carried out on disaster literacy show that there is still a lack of knowledge and understanding of social studies teachers and students related to disasters, lack of learning tool guidelines on disaster and disaster vulnerability: physical condition, facilities, and infrastructure of schools against catastrophe.
A trial to construct the Cultural-Image-Frame-Network based on Big-data framework

This research is to construct the multi-lungual Cultural Image Network Element(CIFN) based on big-data framework. Our research team has already made a desktop application version Korean-Japanese CEMS at LEE & Han(2016) with a purpose of enhancing language education efficiency. Since then, we could produce several achievements which extract cultural elements from the corpus of each language. But we could find that the CEMS has several limitations i.e. 1. basically, CEMS is a desktop version application with lack of openness, 2. CEMS handles limited languages, Korean and Japanese, 3. The corpuses which CEMS depends on have only the fixed data. In this paper, we try to find out the way out to overcome the limitations which CEMS has by constructing the Cultural-Image-Frame-Network.
Enhanced feature pyramidal network for object detection

Powerful features, which contain more representative information, have become increasingly important in object detection. We exploit the attention mechanism and dilated convolution to strengthen the features used to construct the original feature pyramid network (FPN) and introduce a network that combines the dilated convolution and attention mechanism based on FPN (DAFPN). Specifically, motivated by the attention mechanism, a level-independent attention module (LIAM) is proposed to make high-level feature maps focus on semantic information and low-level feature maps concentrate on spatial information. Meanwhile, we present a pyramidal dilated convolution module (PDCM) that replaces standard convolution with dilated convolution. Instead of previous works that use the same dilation rate for all scales of feature maps, the PDCM applies dilation convolution with various dilation rates to enlarge the effective receptive field of each level’s feature maps suitably. Extensive experiments show that our DAFPN achieves extraordinary performance compared to the state-of-the-art FPN-based detectors on MS COCO benchmark.
Lightweight Hardware Architecture for Object Detection in Driver Assistance Systems

Object detection on hardware platforms plays a very significant role in developing driver assistance systems (DASs) with limited computational resources. Object detection for DAS is a multiclass detection problem that involves detecting various objects like cars, auto, traffic lights, bicycles, pedestrians, etc. DAS also requires accuracy, speed, and sensitivity for detecting these objects in various challenging conditions. The lighting and weather conditions pose a serious challenge for accurate object detection for DAS. This paper proposes a speed-efficient and lightweight fully convolutional neural network (CNN) architecture for object detection in adverse rainy conditions. The proposed architecture uses a CNN-based deraining network with a custom SSIM loss function in the object detection pipeline, which can give an accurate performance using limited computational and memory resources. The object detection architecture contains some architectural modifications to the existing single shot multibox detector (SSD) architecture to make it more hardware efficient and improve accuracy on small objects. It uses a trainable color transformation module using [Formula: see text] convolutions for handling the adverse lighting conditions encountered in DAS. The architecture uses feature fusion and the dilated convolution approach to enhance the accuracy of the proposed architecture on small objects. The datasets available for object detection in DAS are very imbalanced with cars as a predominant object. The class weight penalization technique is used to improve the performance of the architecture on scarcely present objects. The performance of the architecture is evaluated on well-known datasets like Kitti, Udacity, Indian Driving Dataset (IDD), and DAWN. The architecture achieves satisfactory performance in terms of mean average precision (mAP) and detection time on all these datasets. It requires three times fewer hardware resources compared to existing architectures. The lightweight nature of the proposed architecture and modification of CNN architecture with TensorRT allow the efficient implementation on the jetson nanohardware platform for prototyping, which can be integrated with other intelligent transportation systems.
Blockchain Technology and Cryptocurrency

The aim of this study is to examine cryptocurrencies that use blockchain technology, reveal the current situation and discuss the future. In the study, cryptocurrencies are examined in detail with their definition, history, legal status, factors affecting their value, features and functions, advantages and disadvantages, and usage areas. As a result of the discussions in the study, five main conclusions were reached. (1) Cryptocurrencies cannot be defined as money, since they cannot fully fulfil the features and functions of money with their current status. (2) Although the values of cryptocurrencies are shaped depending on the supply and demand conditions, they are significantly affected by factors such as security, legal status, volume, and speculative and manipulative initiatives. (3) Cryptocurrencies do not yet have a widespread legal status worldwide. (4) Among the many advantages and disadvantages of cryptocurrencies, the most prominent advantage is security, and the most striking disadvantage is volatility. (5) Cryptocurrencies, with what they have demonstrated to date and promise for the future, will have the ability to be used wherever money is used, and even more.
Realization of R Programming and Data Fusion for Dynamic Prediction of Construction Management Time Limit

Aiming at the problems in the dynamic prediction of construction management schedules, a method based on data fusion theory is proposed, which uses R programming and adaptive weighting for information processing and data fusion, and uses Dempster-Shafer evidence theory to solve the problems in the construction schedule prediction process. The problem of certainty and inaccuracy, and a new method that comprehensively considers the relevant parameters such as the concentration of the underground coal mine construction period, realizes the online integration of the construction period prediction R programming, optimizes the prediction parameters, and improves the accuracy of the construction management construction period dynamic prediction forecast. This method establishes the completeness of the same source data as a whole, and realizes the dynamic prediction of the construction management period dynamic by 6.8%.
A Survey on IoT Authentication Security Service

Internet of things is becoming the most important technology now a days and it is next era of communication. By the use of IOT, various physical things can create, send and receive the data seamlessly. Different IoT applications' main focus is to automate the maximum tasks so with minimum human intervention efficiency and productivity can be increased. "Things" are directly connected with the Open Web, so any intruder can easily become the part of the network and can launch different attacks. So, to increase the level of comfort and efficiency, high security policy in terms of the authentication is required. Existing authentication methods can not be deployed on "Things" as they require high amount of resources and because of that life span of the network will be decreased. In this paper, we have discussed different security threats present at each IoT layer, existing research done in this domain, research gap in existing identity validation methods and also we have find out the future work directions to enhance IoT Security in terms of accurate validation of device identity.
Modeling Full Information with Graph Network for Joint Entity-Relation Extraction

Fully capturing contextual information and analyzing the association between entity semantics and type is helpful for joint extraction task: 1) The context can reflect the part of speech and semantics of entity. 2) The entity type is closely related to the relation between entities. Previous research used to simply embed the contextual information into shallow layer of the model, ignoring the association between entity semantics and type. In this paper, we propose a graph network with full-information modeling to explicitly model different-level information in the text. The contextual information of entity is dynamically embedded in each span representation to improve the reasoning ability. To capture the fine-grained association between the semantics and type of entity, the graph network uses the feature of entity types to generate edge information between different nodes. Experimental results show that our model outperforms previous models on the CoNLL04 dataset and obtains competitive results on the SciERC dataset in both entity recognition and relation extraction. Extensive additional experiments further verify the effectiveness of the model.
Exploiting Evolutionary Computation Techniques for Service Industries

Well-controlled mechanical systems allow the development of useful and successful automated applications to benefit the service industries in Society 5.0. These systems use effective controllers to govern their dynamic behaviors, and all controllers need correct parameter tuning to maximize the system performance and prevent undesired effects. The adjustment of controllers typically requires an accurate system model to make output predictions and evaluate performances without damaging the actual system. Due to uncertainties and unmodeled dynamics, obtaining such an accurate model is challenging, and the use of meta-heuristics from evolutionary computation or swarm intelligence is necessary. This work proposes an evolutionary machine learning framework applied to the identification and control of mechanical systems Based on the widely-studied Cross-Industry Standard Process for Data Mining (CRISP-DM). In this framework, the actual system is excited with particular control signals to generate valuable input/output information to train and test a model. Since well-established laws from classical mechanics are available to describe mechanical systems, the system model is obtained through Lagrangian analyses. Its adjustable parameters are related to the dynamic ones of the system. Different model alternatives are obtained by meta-heuristics and then evaluated to choose the most suitable. The selected model is used in the controller tuning through meta-heuristic optimization. Finally, the optimized controller parameters can be implanted in the actual system. Numerical simulations show the proposal’s effectiveness for two study cases: the position regulation of a simple pendulum and the trajectory tracking with a fully-actuated inverted pendulum.
Your Noise, My Signal

Attacks based on power analysis have been long existing and studied, with some recentworks focused on data exfiltration from victim systems without using conventional communications (e.g., WiFi). Nonetheless, prior works typically rely on intrusive direct power measurement, either by implanting meters in the power outlet or tapping into the power cable, thus jeopardizing the stealthiness of attacks. In this paper, we propose NoDE (Noise for Data Exfiltration), a new system for stealthy data exfiltration from enterprise desktop computers. Specifically, NoDE achieves data exfiltration over a building's power network by exploiting high-frequency voltage ripples (i.e., switching noises) generated by power factor correction circuits built into today's computers. Located at a distance and even from a different room, the receiver can non-intrusively measure the voltage of a power outlet to capture the high-frequency switching noises for online information decoding without supervised training/learning. To evaluate NoDE, we run experiments on seven different computers from top vendors and using top-brand power supply units. Our results show that for a single transmitter, NoDE achieves a rate of up to 28.48 bits/second with a distance of 90 feet (27.4 meters) without the line of sight, demonstrating a practically stealthy threat. Based on the orthogonality of switching noise frequencies of different computers, we also demonstrate simultaneous data exfiltration from four computers using only one receiver. Finally, we present a few possible defenses, such as installing noise filters, and discuss their limitations.
On the measurement and estimation of cognitive processes with electrophysiological recordings and reaction time modeling

A primary goal in cognitive psychology is to describe the latent information processingunits that operate between the onset of a stimulus and a measured behavior. Mathe-matical models of cognition aim at decomposing behavior into such processing unitsby formalizing an assumed generative model. Unfortunately, a generative model mayexplain the behavioral data while not necessarily reflecting the underlying processes.Obtaining measurements between the stimulus and the responses could provideadditional information that fruitfully constrains the processing assumptions.The present thesis explores this issue by focusing on models of perceptual deci-sion making, a field with a long tradition of cognitive modeling. These models areconstructed to account for decision choices and their durations (reaction time inthe range of a second) on the basis of a decomposition into encoding, decision andresponse execution stages. We used electrophysiological measures (electromyographyand electroencephalography) to decompose each reaction time into different intervals,presumed to contain these stages. Simultaneously, we manipulated time-honoredexperimental factors to compare the cognitive locus of experimental effects inferredfrom both electrophysiological recordings and from model fitting procedures.Throughout four empirical chapters, we show that the inferences drawn from cogni-tive models conflict with the electrophysiological decomposition when: 1) the model’score assumption of independence between decision and non-decision processes isproven to be false; 2) standard modeling strategies are inadequate to capture thelocus of an experimental effect revealed by the electrophysiological decomposition;3) opposite experimental effects are revealed in decision vs. encoding and responseexecution processes.This thorough assessment of a generative model of decision making delineates itsvalidity, merits and limitations to account for the latent cognitive processes. Newinsights are thus provided on the information processes that allow humans to decidebetween alternatives.
Results and Guidelines From a Repeated-Measures Design Experiment Comparing Standing and Seated Full-Body Gesture-Based Immersive Virtual Reality Exergames: Within-Subjects Evaluation (Preprint)

<sec> <title>BACKGROUND</title> Although full-body seated exercises have been studied in a wide range of settings (ie, homes, hospitals, and daycare centers), they have rarely been converted to seated exergames. In addition, there is an increasing number of studies on immersive virtual reality (iVR) full-body gesture-based standing exergames, but the suitability and usefulness of seated exergames remain largely unexplored. </sec> <sec> <title>OBJECTIVE</title> This study aimed to evaluate the difference between playing a full-body gesture-based iVR standing exergame and seated exergame in terms of gameplay performance, intrinsic motivation, and motion sickness. </sec> <sec> <title>METHODS</title> A total of 52 participants completed the experiment. The order of the game mode (standing and sitting) was counterbalanced. Gameplay performance was evaluated by action or gesture completion time and the number of missed gestures. Exertion was measured by the average heart rate (HR) percentage (AvgHR%), increased HR%, calories burned, and the Borg 6-20 questionnaire. Intrinsic motivation was assessed with the Intrinsic Motivation Inventory (IMI), whereas motion sickness was assessed via the Motion Sickness Assessment Questionnaire (MSAQ). In addition, we measured the fear of falling using a 10-point Likert scale questionnaire. </sec> <sec> <title>RESULTS</title> Players missed more gestures in the seated exergame than in the standing exergame, but the overall miss rate was low (2.3/120, 1.9%). The analysis yielded significantly higher AvgHR%, increased HR%, calories burned, and Borg 6-20 rating of perceived exertion values for the seated exergame (all &lt;i&gt;P&lt;/i&gt;&amp;lt;.001). The seated exergame was rated significantly higher on peripheral sickness (&lt;i&gt;P&lt;/i&gt;=.02) and sopite-related sickness (MSAQ) (&lt;i&gt;P&lt;/i&gt;=.004) than the standing exergame. The score of the subscale “value/usefulness” from IMI was reported to be higher for the seated exergame than the standing exergame. There was no significant difference between the seated exergame and standing exergame in terms of intrinsic motivation (interest/enjoyment, &lt;i&gt;P&lt;/i&gt;=.96; perceived competence, &lt;i&gt;P&lt;/i&gt;=.26; pressure/tension, &lt;i&gt;P&lt;/i&gt;=.42) and the fear of falling (&lt;i&gt;P&lt;/i&gt;=.25). </sec> <sec> <title>CONCLUSIONS</title> Seated iVR full-body gesture-based exergames can be valuable complements to standing exergames. Seated exergames have the potential to lead to higher exertion, provide higher value to players, and be more applicable in small spaces compared with standing exergames. However, gestures for seated exergames need to be designed carefully to minimize motion sickness, and more time should be given to users to perform gestures in seated exergames compared with standing exergames. </sec>
Staleness Control for Edge Data Analytics

A new generation of cyber-physical systems has emerged with a large number of devices that continuously generate and consume massive amounts of data in a distributed and mobile manner. Accurate and near real-time decisions based on such streaming data are in high demand in many areas of optimization for such systems. Edge data analytics bring processing power in the proximity of data sources, reduce the network delay for data transmission, allowlargescale distributed training, and consequently help meeting real-time requirements. Nevertheless, the multiplicity of data sources leads to multiple distributed machine learning models that may suffer from sub-optimal performance due to the inconsistency in their states. In this work, we tackle the insularity, concept drift, and connectivity issues in edge data analytics to minimize its accuracy handicap without losing its timeliness benefits. Thus, we propose an efficient model synchronization mechanism for distributed and stateful data analytics. Staleness Control for Edge Data Analytics (SCEDA) ensures the high adaptability of synchronization frequency in the face of an unpredictable environment by addressing the trade-off between the generality and timeliness of the model.
Bases of Riemann–Roch spaces from Kummer extensions and algebraic geometry codes

We construct explicit bases of Riemann–Roch spaces from Kummer extensions and algebraic geometry codes with good parameters. This correspondence is a generalization of a work of Maharaj, Matthews, and Pirsic.
Semi-supervised Multilevel Symmetric Image Registration Method for Magnetic Resonance Whole Brain Images

This paper describes a contribution to the second edition of the Learn2Reg challenge organized jointly with the MICCAI 2021 conference, more specifically, to the OASIS MRI task that is related to the registration of whole brain magnetic resonance images. The proposed algorithm is a multi-level, learning-based, and semi-supervised procedure. The algorithm consists of a multi-level input/output U-Net-like architecture trained with additional symmetry constraints. The method was ranked as the third-best for the brain registration task in terms of the combined challenge evaluation criteria.
Named Entity Recognition Using Stanford Classes and NLTK

The Information Extraction is a branch of natural language processing that focuses on extracting actual information from unstructured text. The goal of Information Extraction (IE) is to find a predetermined set of ideas, or a machine-understandable representation of the data. The traditional IE duties include the following: The challenge of identifying and classifying predefined categories of named entities is addressed by Named Entity Recognition (NER). The idea of Named Entity Recognition is to identify and categorized hard designators like Names, Locations, Time, medical terms, and temporal expressions from text. We conduct experiment using NLTK-NER, Stanford 3-Class NER, Stanford 4-Class NER and Stanford 7-Class NER model to extract Named Entities from text corpora and proved that Stanford class-3 model higher accuracy than other algorithms. Also proved that Stanford 7-Class more effective than others.
ViK: practical mitigation of temporal memory safety violations through object ID inspection

Temporal memory safety violations, such as use-after-free (UAF) vulnerabilities, are a critical security issue for software written in memory-unsafe languages such as C and C++.