Reviewerly/exHarmony huggingface dataset:
ARtist: Interactive Augmented Reality for Curating Children's Artworks

In current art education, children's artwork is typically displayed on physical spaces at school. However, presenting children's artwork becomes challenging when spaces are limited. Many schools have also transitioned to virtual education because of the spread of COVID-19, which restricts hands-on art activities and physical display opportunities. Thus, seeking alternative ways to present and share children's artwork is critical to support children's creativity and communication. Recent innovations in augmented reality (AR) technology have opened new opportunities to enhance art experiences. ARtist is an AR platform that provides a virtual space to display artworks. Users can curate and visualize virtual artworks in their own physical environment using a smartphone. A formal user study with children was conducted to investigate how ARtist supports children's ability to curate, display, and share their artwork. Results show that children were highly engaged and wanted to share their curated shows with others.
Work-in-Progress–LIVE: Model for Learning in Interactive and Immersive Virtual Environments

Combining immersive environments with intelligent tutoring (ITS) provides synergies that can accelerate learning beyond what is possible individually. The integration of these and other sub-domains represents a new domain-LIVE-conceived through an affordance-based design approach and informed by embodied interaction ideals. Offered as a tutorial model for immersive virtual education, it implements adaptive pedagogical methods to provide social support for tutor-to-student and peer-to-peer instruction in inquiry-based activities (such as science labs). This work-in-progress paper communicates the basic domain definition, its place in the literature, and efforts to create and evaluate a prototype application.
Analysis to Develop Computerized Adaptive Testing with the Force Concept Inventory

Abstract As a method to shorten the test time of the Force Concept Inventory (FCI), Computerized Adaptive Testing (CAT) is suggested. CAT is a test administered on a computer, where items (i.e. questions) are selected based on the responses of examinees to prior items. As a step of the development, we conducted analyses to find an optimal way for the administration of CAT with the FCI. Specifically, since CAT is based on Item Response Theory (IRT), we examined which IRT model is the most preferable. Using 2812 responses of the FCI of Japanese students, we estimated the item parameters of the One-, Two-, Three-, and Four-parameter logistic model of IRT and then we evaluated the statistics for the goodness of fit and for model selection. Based on the analysis, we suggest that the Two-parameter logistic model is the most preferable for the administration of CAT with the FCI.
SC-DGCN: Sentiment Classification Based on Densely Connected Graph Convolutional Network

Recently, various neural network frameworks have achieved good results in sentiment classification task, such as Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN). However, these methods only take into account semantic information in local contexts and ignore the global syntactic structure information due to the network structure. To solve this problem, we propose a novel neural architecture called SC-DGCN that combines Graph Convolutional Network (GCN) and Bi-LSTM. In SC-DGCN model, we utilize a GCN over the dependency tree of a sentence to exploit syntactical information and words dependencies. In addition, we further introduce dense connection strategy into GCN blocks to aggregate more syntactic information from neighbors and multi-hops in the dependency tree, and employ attention mechanism to generate the final representation of text. Our proposed SC-DGCN model can automatically extract semantic feature in local contexts and the global syntactic structure feature. A series of experiments on MR and SST datasets also indicate that our model is effective for sentiment classification task.
A Lightweight Visual Programming tool for Machine Learning and Data Manipulation

In recent decades, visual programming tools have focused on scientific workflow tasks. However, the current focus has shifted to machine learning tasks, which are most often amenable to unification. The existing solutions in this area demonstrate many alternatives with the possibility of using models from various machine learning packages. The proposed solution is based on the concept of minimal graphical notation, where all typical operations on data and machine learning models are possible. Another distinguishing feature of the proposed solution is the explicit separation of data and models, making the proposed notation clearer for perception. In the proposed engine, computational graph traversal does not have a rigorous sequence. The call of the sequence of calculations depends on the specific model that the user currently needs.
Countermeasures and Challenges for Detection of Spoofing Attacks in Automatic Speaker Verification System

The automatic Speaker Verification (ASV) system has gained popularity due to its application in the area of biometrics. ASV system's performance deteriorates because of spoofing attacks. In this paper, types of spoofing attacks are presented. The performance of ASV system depends on the front-end processing which includes extraction of features. This paper presents the contribution of researchers to the detection of various spoofing attacks. The research community has drawn attention to that among the various spoofing attacks that affect the ASV systems, a replay attack is challenging. In this paper, recent approaches for the detection of replay attack are presented.
Identifying the Cybercrime Awareness of Undergraduate and Postgraduate Students: Example of Kazakhstan

Cybercrime is the offense related to the ethical and legal violation of the computer and IT system, a threat to the security of information systems, and abuse of the Internet platform. It is committed through the Internet or computer systems. Its direct and indirect results disrupt the flow of technological infrastructure. Gaining the knowledge or skills related to the types and content of the cybercrimes is an essential prerequisite in the digital era. Thus, it is very significant to raise the awareness of the younger generation, who will shape the future of societies. In accordance with this, our study aims to identify the cybercrime awareness of undergraduate and postgraduate students in one of the higher education institutions of Kazakhstan. Totally 133 student participants attended the survey which included questions about the awareness of cybercrime concept and its scope. The findings of the study have revealed that cybercrime awareness of the participants is in moderate-level. Their knowledge and skills need to be improved further. Based upon this, it is very important to teach cybercrime in all levels of education and include it in the educational curriculum Higher cybercrime awareness level of young people will have a positive impact upon their interaction with information systems and tools. They will have better and safer use of Internet and information systems which is quite significant in today's post-modern and post-truth world. This article was prepared within the frame of AP08052656 "Readiness assessment of Kazakhstani higher educational institutions for transformation within the context of Triple Helix project, funded by the Ministry of Education and Science of the Republic of Kazakhstan.
QPR: Quantizing PageRank with Coherent Shared Memory Accelerators

Graph algorithms often require fine-grained, random access across substantially large data structures. Previous work on FPGA-based acceleration has required significant preprocessing and restructuring to transform the memory access patterns into a streaming format that is more friendly to of fchip hardware. However, the emergence of cache-coherent shared memory interfaces, such as CAPI, allows designers to more easily work with the natural in-memory organization of the data. This paper introduces a vertex-centric shared-memory accelerator for the PageRank algorithm, optimized for high performance while effectively using coherent caching on the FPGA hardware. The proposed design achieves up to 14.9x speedups by selectively caching graph data for the accelerator while taking into account locality and reuse, compared to naively using the shared address space access and DRAM only. We also introduce PageRank Quantization, an innovative technique to represent page-ranks with 32-bit quantized fixed-point values. This approach is up to 1.5x faster than 64-bit fixed-point while keeping precision within a tolerable error margin. As a result, we maintain both the hardware scalability of fixed-point representation and the cache performance of 32-bit floating-point.
Basic physics course with MATLAB’s symbolic toolbox and live editor

Abstract The course offers a compact presentation of the basic content of classical mechanics, quantum mechanics, interaction and chaos theory. In 26 MATLAB Live scripts, the basic physical laws are presented, and examples are solved. Since it is assumed that the reader already has basic knowledge of physics and mathematics, a more modern view of physics, mathematics and calculation can be chosen. The dynamic laws, uniform throughout the course, stem from the concepts of invariance and continuity. Throughout the course, ground vectors and metric are used instead of basis vectors. The use of symbolic computer software enables more complicated problems, vivid graphics, and lots of animation. Students like it because they are play-like. Even students who major in mathematics, computer science and similar subjects have gained a good understanding of the basic physical principles.
Scalable string analysis: an experience report

In this paper we present OLSA - a tool for scalable static string analysis of Java programs. OLSA is based on intra-procedural string value flow graphs connected via call-graph edges. Formally, this uses a context-sensitive grammar to generate the set of possible strings. The analysis is focused on scalability and is thus not sound. This trade off is acceptable in the context of bug-finding in large web applications.
Analysis of the user experience in a web-based university staff’s publication management system

Research and analysis of user experience in software applications is a current area in the design and development of the user interface. Analysis of users' behavior in a specific application can help detect vulnerabilities in the interface and direct developers to places to change. There are different approaches to data collection and analysis of user experience, as well as ready-made software environments that implement this. In this paper, we describe a particular approach to collecting, processing, and analyzing user behavior data in a Web-based specific-task-oriented system. Information about the number of user sessions and clicks on various elements of the user interface is stored with the purpose of collecting enough historical data that can be further analyzed. The dataset examined in this paper consist of information about the sequences of actions of each user for five months. The dataset is unique because it contains user sessions right from the release of the application, giving the opportunity to examine the first responses of the users and to follow the development of their habits while working with it. The different groups of users (known in advance) and their behavior in the system are described. Conclusions are made about the benefits of changes to the interface and the added new features, as well as the way users perceive and use them.
MKIPS: MKI‐based protocol steganography method in SRTP

This paper presents master key identifier based protocol steganography (MKIPS), a new approach toward creating a covert channel within the Secure Real-time Transfer Protocol, also known as SRTP. This can be achieved using the ability of the sender of Voice-over-Internet Protocol packets to select a master key from a pre-shared list of available cryptographic keys. This list is handed to the SRTP sender and receiver by an external key management protocol during session initiation. In this work, by intelligent utilization of the master key identifier field in the SRTP packet creation process, a covert channel is created. The proposed covert channel can reach a relatively high transfer rate, and its capacity may vary based on the underlying SRTP channel properties. In comparison to existing data embedding methods in SRTP, MKIPS can convey a secret message without adding to the traffic overhead of the channel and packet loss in the destination. Additionally, the proposed covert channel is as robust as its underlying user datagram protocol channel.
Introducing the CCI UX Playbook

Child Computer Interaction is concerned with the research, design, and evaluation of interactive technologies for children. Whilst many aspects of general HCI can be applied into this field, there are important adaptations to be made when conducting work for and with children throughout all stages of the design cycle. This course explores the use of five evaluation methods that are presented in the recently launched CCI UX Playbook. The Fun Toolkit, Drawing Intervention, The MemoLine, RAiD and Ethics Toolkits are all described in the course and then will be applied in a hands-on activity. Included in the course will be methods for the analysis of data for research papers as well as cut price techniques for the use of the methods in industrial settings.
Multi-layer Features Fusion Strategy with GIoU Loss for Siamese Network Tracking

In recent years, trackers based on siamese networks have been widely used in the field of visual tracking. Early the backbone networks generally have shallow layers, but deep neural networks are more expressive and will have a better effect on feature extraction than shallow neural networks. But we tried to replace and observed that the actual result is different from what we expected. In this work, our experiment shows that as the number of network layers deepens and the receptive field of neurons increases, the network performance will increase firstly and then decrease. Through further experiment verifies the range of neuron receptive field which is suitable for the virtual tracking, and we fuse three output layers of the network to improve the generalization ability. In addition, we add GIoU (Generalized Intersection over Union) loss as a part of the target regression loss function to improve accuracy. Experiment shows that the tracker achieves good performance on the OTB2013 and OTB2015 datasets.
Generate And Adjust: A Novel Framework For Semi-Supervised Pedestrian Attribute Recognition

Conventional pedestrian attribute recognition methods typically rely on the assumption that enough well-labeled training samples are available. However, it is hard to be satisfied due to the unaffordable cost of human annotation, especially on big surveillance data. To address this problem, we propose a novel task-semi-supervised pedestrian attribute recognition (Semi-PAR), in which only a few limited labeled data and plenty of unlabeled data are available during training. We tackle the Semi-PAR problem by presenting a novel semi-supervised learning framework. In this framework, a PAR network is firstly designed to extract attribute-specific features, and adopts a similarity graph-based algorithm to generate pseudo-labels. Then we utilize the relationship of each attribute to modify the pseudo-labels, and feed them back to the PAR network to guide the training. Finally, a harmonic confidence loss is introduced to diminish the impact of harmful pseudo-labels. Experiments on three widely-used datasets verify the superiority of our method.
Chatbot Implementation for Enhancement of Student Understanding—A Natural Language Processing Approach

As of late, chatbots are under stupendous development and consideration from industry and different exploration networks as a chat-based interface to propel human–computer communication. In 2019, over 60% of organizations have either made a POC or had a chatbot completely working for their business. In this paper, we have portrayed how IBM Watson Natural Language Understanding can be utilized with Tone Analyzer, speech to text, and text to speech for an upgrade of understudies during a pandemic like COVID-19. The accompanying exercises have been thought of: acquiring information from a few pages of a spreadsheet (staff/foundation, bunch number, day of the week, period number, discipline name, data about the educator); introduction of information in a helpful structure for the bot (JSON); usage of the instrument of the advantageous introduction of information in the bot.
A Tangled Web of 12 Lens Laws

Bidirectional transformation has played important roles in broad areas, database management, programming language, and model-driven development after Foster et al. revisited view updating problems introduced by Bancilhon and Spyratos. They introduced the concept of (asymmetric) lens as a pair of a forward $$ get $$ and a backward $$ put $$ functions to synchronize a source data and its view consistently. For the $$ get $$ and $$ put $$ functions to be consistent, they should satisfy several lens laws such as the (StrongGetPut), (GetPut), (PutGet) and (PutPut) laws. By combining some of these lens laws, we can represent how consistent a lens satisfying the laws is. Fischer et al. has introduced nine meaningful weaker lens laws to give a “clear picture” of the laws where they show relations among lens laws, for example, that one law implies another and combination of two laws is equivalent to combination of the other three laws. This paper gives more precise relationship among 12 lens laws that have been presented in literature. The relationship makes an intertwined implication diagram like a tangled web. The results can be used for easily verifying the desirable lens laws.
Depth image restoration method based on improved FMM algorithm

Aiming at the lack of depth information obtained by Kinect sensor, the collected depth images have holes, noise that affect subsequent practical applications such as the accuracy of semantic map construction , proposed a method of improved FMM algorithm to improve the quality of depth images. Firstly, clustering color images by K-means, the result as a guide image; Secondly, according to the source of the holes, it is divided into object surface holes and occlusion holes; The holes introduced by the surface holes of the object are repaired by the improved Fast Marching Method algorithm(FMM), the occlusion holes are filled by the Directional Joint Bilateral Filter(DJBF), and finally the adaptive median filter is used to denoise. The experimental results show that the way can effectively repair holes and the edge details of the depth map are clearer, and more importantly, the restored point cloud image is more complete.
Employee Recognition Programs: Experience and New Reality

The article is devoted to the problems of using tools for employee recognition in modern practice. The main and forms of recognition, their advantages and disadvantages are considered. It summarizes research evidence supporting both the positive effects of recognition on variables such as employee productivity and retention, morale, motivation, job satisfaction, happiness and positive mood, organizational and professional commitment and engagement, and the impact of lack of recognition on employee burnout. The factors that determine the increasing importance of justifying the strategy and programs of employee recognition, as well as trends in their development, including the desire to harmonize recognition programs, alignment with business goals, talent management processes are highlighted. An analysis of the reasons for the insufficient effectiveness of employee recognition programs is presented. Recognition programs have been shown to fail due to a lack of structure for employees to recognize each other, difficulty in choosing people to express recognition, lack of clarity about the goals of recognition programs, insufficient attention to age and perceptions of recognition, and the state of organizational culture. (Leaders declare certain values, but no one in the organization gains recognition in realizing these values; competitive cultures often suppress people's desire to recognize and praise their colleagues). Recommendations for improving the effectiveness of employee recognition programs are presented.
Reevaluating the Safety Impact of Inherent Interpretability on Deep Neural Networks for Pedestrian Detection

AI-based perception is a key factor towards the automation of driving systems. A conclusive safety argumentation must provide evidence for safe functioning. Existing safety standards are not suitable to deal with non-interpretable deep neural networks (DNN) learning from unstructured data. This work provides a proof of concept for a comprehensible requirements analysis based on an interpretable DNN. Recent work on interpretability motivates to rethink software considerations of safety standards. We describe the application of established considerations to DNNs by integrating interpretability and identifying artifacts. DNN artifacts result from a meaningful decomposition of requirements and adaptions of the perception pipeline. To prove our concept, we propose an interpretable method for the center, scale and prototype prediction (CSPP) that learns an explicitly structured latent space. The interpretability-based requirements analysis of CSPP is completed by tracing artifacts and source code to decomposed requirements. Finally, qualitative post-hoc evaluations provide evidence for the fulfillment of defined requirements for the latent space.
Cheating-Tolerant and Threshold-Based Secure Information Exchange Among Propinquity of Adversaries

Secret-key negotiations are important that enable any user to achieve information security as well as data privacy. Two efficient key negotiation schemes, which provide base security for developing different cryptographic techniques, are proposed. We work with Shamir’s (t, n) threshold secret sharing scheme (SSS) and provide protection against misbehaved activities of dealer and/or dishonest participants as they exist in the scheme unfortunately. The first SSS is designed based on subset secret such that if the reconstructed secret belongs to this set, then probably, no share cheating is occurred. The other scheme has two variations―(1) a dealer-based (t, t) SS and (2) a KGC-based (t, n) SS, where shares’ cheating and both dealer and shares’ cheating are protected as key generation center (KGC) is cryptographically trusted and used in variety of security applications. As performance study, a comparison of the proposed SSSs with Shamir’s one is given.
PIGO: A Parallel Graph Input/Output Library

Graph and sparse matrix systems are highly tuned, able to run complex graph analytics in fractions of seconds on billion-edge graphs. For both developers and researchers, the focus has been on computational kernels and not end-to-end runtime. Despite the significant improvements that modern hardware and operating systems have made towards input and output, these can still become application bottlenecks. Unfortunately, on high-performance shared-memory graph systems running billion-scale graphs, reading the graph from file systems easily takes over 2000× longer than running the computational kernel. This slowdown causes both a disconnect for end users and a loss of productivity for researchers and developers.We close the gap by providing a simple to use, small, header-only, and dependency-free C++11 library that brings I/O improvements to graph and matrix systems. Using our library, we improve the end-to-end performance for state-of-the-art systems significantly-in many cases by over 40×.