Reviewerly/exHarmony huggingface dataset:
Daisen: A Framework for Visualizing Detailed GPU Execution

Abstract Graphics Processing Units (GPUs) have been widely used to accelerate artificial intelligence, physics simulation, medical imaging, and information visualization applications. To improve GPU performance, GPU hardware designers need to identify performance issues by inspecting a huge amount of simulator‐generated traces. Visualizing the execution traces can reduce the cognitive burden of users and facilitate making sense of behaviors of GPU hardware components. In this paper, we first formalize the process of GPU performance analysis and characterize the design requirements of visualizing execution traces based on a survey study and interviews with GPU hardware designers. We contribute data and task abstraction for GPU performance analysis. Based on our task analysis, we propose Daisen, a framework that supports data collection from GPU simulators and provides visualization of the simulator‐generated GPU execution traces. Daisen features a data abstraction and trace format that can record simulator‐generated GPU execution traces. Daisen also includes a web‐based visualization tool that helps GPU hardware designers examine GPU execution traces, identify performance bottlenecks, and verify performance improvement. Our qualitative evaluation with GPU hardware designers demonstrates that the design of Daisen reflects the typical workflow of GPU hardware designers. Using Daisen, participants were able to effectively identify potential performance bottlenecks and opportunities for performance improvement. The open‐sourced implementation of Daisen can be found at gitlab.com/akita/vis . Supplemental materials including a demo video, survey questions, evaluation study guide, and post‐study evaluation survey are available at osf.io/j5ghq .
An editable learner model for text recommendation for language learning

Abstract Extracurricular reading is important for learning foreign languages. Text recommendation systems typically classify users and documents into levels, and then match users with documents at the same level. Although this approach can be effective, it has two significant shortcomings. First, the levels assume a standard order of language acquisition and cannot be personalized to the users’ learning patterns. Second, recommendation decisions are not transparent because the leveling algorithms can be difficult for users to interpret. We propose a novel method for text recommendation that addresses these two issues. To enhance personalization, an open, editable learner model estimates user knowledge of each word in the foreign language. The documents are ranked by new-word density (NWD) – that is, the percentage of words that are new to the user in the document. The system then recommends documents according to a user-specified target NWD. This design offers complete transparency as users can scrutinize recommendations by reviewing the NWD estimation of the learner model. This article describes an implementation of this method in a mobile app for learners of Chinese as a foreign language. Evaluation results show that users were able to manipulate the learner model and NWD parameters to adjust the difficulty of the recommended documents. In a survey, users reported satisfaction with both the concept and implementation of this text recommendation method.
Improved Framework using Rider Optimization Algorithm for Precise Image Caption Generation

“Image Captioning is the process of generating a textual description of an image”. It deploys both computer vision and natural language processing for caption generation. However, the majority of the image captioning systems offer unclear depictions regarding the objects like “man”, “woman”, “group of people”, “building”, etc. Hence, this paper intends to develop an intelligent-based image captioning model. The adopted model comprises of few steps like word generation, sentence formation, and caption generation. Initially, the input image is subjected to the Deep learning classifier called Convolutional Neural Network (CNN). Since the classifier is already trained in the relevant words that are related to all images, it can easily classify the associated words of the given image. Further, a set of sentences is formed with the generated words using Long-Short Term Memory (LSTM) model. The likelihood of the formed sentences is computed using the Maximum Likelihood (ML) function, and the sentences with higher probability are taken, which is further used for generating the visual representation of the scene in terms of image caption. As a major novelty, this paper aims to enhance the performance of CNN by optimally tuning its weight and activation function. This paper introduces a new enhanced optimization algorithm Rider with Randomized Bypass and Over-taker update (RR-BOU) for this optimal selection. In the proposed RR-BOU is the enhanced version of the Rider Optimization Algorithm (ROA). Finally, the performance of the proposed captioning model is compared over other conventional models with respect to statistical analysis.
Post Quantum Blockchain with Segregation Witness

Blockchain is a very important technology and financial innovation since the birth of the Internet. It is an innovative and integrated application of many technologies, with the characteristics of open and transparent data, not easy to tamper with, easy to trace and so on. Its cryptographic security relies on asymmetric cryptography, such as ECC, RSA. However, with the surprising development of quantum technology, asymmetric cryptography schemes mentioned above would become vulnerable. Recently, some lattice-based blockchain systems have been proposed to be secure against attacks in the quantum era. Although these schemes have theoretical significance, it is unpractical in actual situation due to handling capacity. In this paper, aiming at tackling the critical issue of throughput, we proposed post quantum blockchain with segregation witness which can effectively the proportion of signatures in block size. Based on the hardness assumption of Short Integer Solution (SIS), we demonstrate that the proposed post quantum blockchain with segregation witness existential unforgeability against adaptive chosen-message attacks in the random oracle. As compared to the existing scheme, our scheme has better performance in handling capacity. As the underlying lattice problem is intractable even for quantum computers, our scheme would work well in the quantum age.
The digital platform of the future school: the digital-cognitive approach against the digital-algorithmic reduction of educational practice

The paper differentiates two approaches to digitalization of the educational practice: the digital-algorithmic approach and the digital-cognitive approach. The digital-algorithmic approach puts in correspondence pupil’s answer with described and established rules and operations to achieve “correct” responses. Application of the first approach forces students to guess right operation sequence to find the right solution. The digital-cognitive approach provides a background for modeling, visualizing and making representation of thinking acts and action for school students and teacher. Realization of the second approach aims to organize the digital educational milieu of the new generation. This the digital educational milieu supports and stimulates student work to find types of operations, the operation set and the operation sequence according to student’s understanding of the problem and grasping the situation. The model of the thinking act that is elaborated by Vasiliy Davydov as the “cell” for the interdisciplinary theory of thought processes is considered to be the core for constructing a digital educational milieu of the new generation. Described forms of thinking act with increasing complexity provides organization of digital-cognitive systems in education.
Human flow recognition using deep networks and vision methods

The paper focuses on developing the system for people flow recognition based on many video camera images. The project arose based on the real need — its application in the city malls to find the most visited places and support customers' campaigns. The potential application in other domains is possible (public buildings, airports). In contrast to the existing solutions, our approach involves on-edge image analysis to decrease the risk of data loss and the system cost. In the project, we design the whole processing pipeline composed of the component modules responsible for detecting, tracking, and reidentifying (shuffling) people. Our experimental platform enabled us to compare multiple method variants for each module. Based on extensive experimental research, the final solution uses the pretrained SSD method in the detection module. The centroid algorithm applied to displacement vectors combined with the Siamese network is the basis for the object tracking module. The best model to solve the reidentification task is the Resnet50. For the Market 1501 dataset, it achieved Rank-1 efficiency of 84.6%. The system gives a visualization of the main paths of people's movements in the form of a heat map and assigns the direction where people most often look. In the experimental study, we assessed the system's effectiveness and time efficiency, and the current results give a perspective for its commercialization in the nearest future.
A Fast-Mining Method for Target Behavior Pattern Based on Trajectory Data

With the development of location-acquisition techniques, a large amount of trajectory data has been accumulated in the early warning and surveillance system, which contains lots of information and knowledge. By using unsupervised clustering of data mining technology, a new fast mining method for target behavior pattern is proposed in this paper. First, target trajectory data is compressed by using Douglas-Peucker algorithm to reduce the amount of calculation. Then, a new Hausdorff distance with direction recognition is designed to measure the similarity of target trajectory. Finally, an improved Density Peaks Clustering (DPC) and K-medoids algorithm is proposed to mine the regular behavior. In this paper, two datasets of simulation and measurement are used to test the performance of the proposed method. Compared with the existing classical algorithms, the verification results show that the proposed method can detect the target behavior pattern more quickly and accurately in a large number of complex historical trajectory data.
Differential Residual Learning for Facial Expression Recognition

The facial expression recognition algorithm based on convolution neural network (Convolutional Neural Network) still has the problem of imperfect feature point extraction. Therefore, a facial expression recognition algorithm based on the combination of the detail feature extraction network and pre-training model is proposed. According to the emotional face and neutral face of the residual of the images, learn the residual to extract features, and make the feature information accurate. Then, the face feature markers generated by the feature extraction network are loaded into the pre-training model respectively for classification and recognition. By grouping and crossing experiments on datasets CK+ and FER2013, the average accuracy of face recognition is 95.74% and 73.11%, respectively. Compared with the state-of-the-art recognition model, this method is effective in facial expression recognition to some extent.
Grassroots information divides in China: Theorising everyday information practices in the Global South

The internet is becoming a major source of everyday information, yet existing research often focuses on specific information seeking contexts such as health, climate change, news, or hobbies. In this paper, we put forward a more holistic theoretical model of information seeking practices in everyday life that combines the social phenomenological perspective of everyday life information seeking practices and the domestication framework. We extend the cultural context to the Global South countries, where there is less research on "mobile first" societies centred on emerging mobile platforms. Based on extensive and mixed-methods fieldwork in rural and industrial China about everyday information seeking practices, the paper goes beyond previous research about digital divides in internet access and internet literacy. Instead, it focuses on divides in everyday information seeking practices. We argue that new divides are emerging between those with more restricted everyday internet uses and those with broader and more diverse ones. We compare everyday information practices and divides in China with those in India and show how a theory of digital divides in everyday information seeking practices can be applied beyond both countries to the Global South. Such a theory can also contribute to the design of informatisation policies.
A New Three-Part Phase Unwrapping Algorithm for 3-D Measurement of Dynamic Objects

For three-dimensional (3-D) measurement of dynamic objects, a new three-part phase unwrapping algorithm is proposed. First, a novel fringe modulation degree threshold estimation method is proposed. Then, the unwrapped phase can be obtained by three segments. The first-segment is calculated by 2-D spacial phase unwrapping algorithm while the fringe modulation degree is within the threshold. The second-segment is calculated between two frames when their fringe modulation degree difference is within the threshold. The third-segment is calculated by a estimation method of 2π jump order. This method only need one wrapped phase to calculate the unwrapped phase. Simulations and experiments have proved the effectiveness of the proposed method.
Robust prediction of domain compositions from uncertain data using isometric logratio transformations in a penalized multivariate Fay–Herriot model

Assessing regional population compositions is an important task in many research fields. Small area estimation with generalized linear mixed models marks a powerful tool for this purpose. However, the method has limitations in practice. When the data are subject to measurement errors, small area models produce inefficient or biased results since they cannot account for data uncertainty. This is particularly problematic for composition prediction, since generalized linear mixed models often rely on approximate likelihood inference. Obtained predictions are not reliable. We propose a robust multivariate Fay–Herriot model to solve these issues. It combines compositional data analysis with robust optimization theory. The nonlinear estimation of compositions is restated as a linear problem through isometric logratio transformations. Robust model parameter estimation is performed via penalized maximum likelihood. A robust best predictor is derived. Simulations are conducted to demonstrate the effectiveness of the approach. An application to alcohol consumption in Germany is provided.
Designing and comparing performances of image processing pipeline for enhancement of I-131-metaiodobenzylguanidine images

An image processing pipeline can have more than one image processing technique in sequence, and the output of the first technique becomes input for the next technique and so on. In this study, we have designed and compared the performances of image processing pipelines for enhancement of I-131-metaiodobenzylguanidine (mIBG) images.Five different image processing pipelines (A [Gaussian filter, normalization], B [histogram specification (image 1), Gaussian filter, normalization], C [histogram specification (image 2), Gaussian filter, normalization], D [{histogram specification (image 3), Gaussian filter, and normalization], and E [histogram specification (image 4), Gaussian filter, normalization]) were designed and their performances were evaluated on I-131-mIBG images (n = 122). The image quality was assessed objectively using Perception-based Image Quality Evaluator (PIQE) score and subjectively (on scale 1-4) by two nuclear medicine physician. Sign test was applied to find the statistically significant difference between the image quality obtained using image processing pipelines. We applied test of proportion to compute difference in proportion of image quality score assigned to images obtained using image processing pipelines.Based on PIQE score, the quality of images obtained using all the five image processing pipelines were significantly better than that of input images (P < 0.001). The highest image quality score (=4) was assigned maximum number of times (n = 90) to the images obtained using image processing pipeline D and was significantly different from that of the second best image processing pipeline E (P = 0.015).The image processing pipeline D was found to be better for enhancement of I-131-mIBG images.
From Blackboard to the Office: A Look Into How Practitioners Perceive Software Testing Education

The teaching-learning process may require specific pedagogical approaches to establish a relationship with industry practices. Recently, some studies investigated the educators’ perspectives and the undergraduate courses curriculum to identify potential weaknesses and solutions for the software testing teaching process. However, it is still unclear how the practitioners evaluate the acquisition of knowledge about software testing in undergraduate courses. This study carried out an expert survey with 68 newly graduated practitioners to determine what the industry expects from them and what they learned in academia. The yielded results indicated that those practitioners learned at a similar rate as others with a long industry experience. Also, they studied less than half of the 35 software testing topics collected in the survey and took industry-backed extracurricular courses to complement their learning. Additionally, our findings point out a set of implications for future research, as the respondents’ learning difficulties (e.g., lack of learning sources) and the gap between academic education and industry expectations (e.g., certifications).
A Preliminary Scoping Review of Immersive Virtual Soft Skills Learning and Training of Employees

Soft skills training is considered important for employees to be successful at work. Several companies are offering immersive virtual soft skills training with head-mounted displays. The main contribution of this paper is to provide an overview of the research literature within the field of using immersive virtual soft skills learning and training of employees. The results of this preliminary scoping review show that there is a lack of research literature and empirical studies within this topic.
Machine Learning for Dense Crowd Direction Prediction Using Long Short-Term Memory

The safety of a dense crowd is one of the most important matters for an event’s organizers. Therefore, management of the crowd, and noticing any serious issues in advance becomes important. Developing a crowd simulation by using a social force model simulates the behavior of crowds in reality. The prediction of individual agents’ behavior in the simulation and how the agents interact with each other can improve the safety of dense crowds. Depending on the success of Recurrent Neural Network(RNN) handling of sequential data, we propose a model that is based on Long Short Term Memory (LSTM) to predict individual agents’ next locations. Our proposed approach will be tested two different densities of crowds, structured crowds, and unstructured crowds. In structured crowds, people generally move in one direction and head to the same destination, such as at the Islamic Hajj. In unstructured crowds people move in many different directions and head for different destinations, such as in public town squares.
Smartphone Inertial Sensors-Based Human Activity Detection Using Support Vector Machine

Perceiving human activity has earned a great amount of interest and has brought noteworthy research in recognizing logical data helpful to human activity recognition. It is the most essential technology behind different applications including health care, survey systems, surveillance, and many more. This paper presents work on human activity detection based on data collected from smartphones inertial sensors. Smartphones have installed inbuilt sensors that are fit to detect logical data of its clients, having a wide range capability of network connections. Installed sensors which include gyroscope sensor, tri-axial linear accelerometer, tri-axial accelerometer, and direction sensors are utilized for movement information assortment. Dataset used includes six activities: sitting, standing, laying, walking, walking upstairs and walking downstairs. In this paper, support vector machine learning algorithm with Gaussian radial basis kernel to classify all the six activities uniquely is used which resulted in better performance rate and less error rate as compared to the previous works already done in the area of HAR. Overlapping of dynamic and non-dynamic activity is improved with the overall accuracy of 98% which is more than the previous work results.
Application of Decision Support System using Composite Performance Index Algorithm

Abstract Every village has a site of leadership. The village head is the person who is entitled to manage the village. The person is the one that is elected to build and develop the village. The village heads will try to improve the situation of the village as well as its residents. People who have this rank have obligations and responsibilities for carrying out village activities. However, democracy is the way to accomplish the best outcomes in terms of peace, justice, and security. The decision support system using the Composite Performance Index (CPI) method functions to assess village head candidates and process them in order to get good ranking results. The most important result of the election is what the village head decides. The application of the Composite Performance Index (CPI) method is very good in determining the right person who will occupy the position of the village head.
A Genetic Algorithm With Projection Operator for the Traveling Salesman Problem

The traveling salesman problem is a NP-hard combinatorial optimization problem. So far, many algorithms are proposed for the problem. However, exact algorithms are time-consuming, while heuristic approaches may not obtain a global optimum. In fact, the genetic algorithm based on edge assembly crossover shows it good performance. Nevertheless, a drawback of the algorithm is that the algorithm may not work well for instances with cities produced by lattice, such as the instances coming from VLSI. In this paper, we propose projection operator for the algorithm to overcome the drawback. Under the control of the operator, when the state of trapped into local optimum is detected, individuals are projected and leave the neighborhood of the local optimum. Experimental results show that our projection operator can improve solution of the instances coming from the field of VLSI application.
SWIPEGAN

Behavioral biometric-based smartphone user authentication schemes based on touch/swipe have shown to provide the desired usability. However, their accuracy is not yet considered up to the mark. This is primarily due to the lack of a sufficient number of training samples, e.g., swiping gestures1: users are reluctant to provide many. Consequently, the application of such authentication techniques in the real world is still limited.