Reviewerly/exHarmony huggingface dataset:
A Bayesian approach to model individual differences and to partition individuals: case studies in growth and learning curves

Abstract The first objective of the paper is to implement a two stage Bayesian hierarchical nonlinear model for growth and learning curves, particular cases of longitudinal data with an underlying nonlinear time dependence. The aim is to model simultaneously individual trajectories over time, each with specific and potentially different characteristics, and a time-dependent behavior shared among individuals, including eventual effect of covariates. At the first stage inter-individual differences are taken into account, while, at the second stage, we search for an average model. The second objective is to partition individuals into homogeneous groups, when inter individual parameters present high level of heterogeneity. A new multivariate partitioning approach is proposed to cluster individuals according to the posterior distributions of the parameters describing the individual time-dependent behaviour. To assess the proposed methods, we present simulated data and two applications to real data, one related to growth curve modeling in agriculture and one related to learning curves for motor skills. Furthermore a comparison with finite mixture analysis is shown.
Image Quality Evaluation Based on Gradient, Visual Saliency, and Color Information

This paper proposes an image quality evaluation (IQE) metric by considering gradient, visual saliency, and color information. Visual saliency and gradient information are two types of effective features for quality evaluation research. Different regions within an image are not uniformly important for IQE. Visual saliency can find the most attractive regions to the human visual system in a given image. These attractive image regions are more strongly correlated with image quality results. In addition, the degradation of gradient information is related to the structure distortion which is a very important factor for image quality. However, the two types of features cannot accurately evaluate the color distortion of images. In order to evaluate chromatic distortion, this paper proposes the color similarity which is measured in the YIQ color space. The computation of the proposed method begins with the similarity calculation of local gradient information, visual saliency, and color information. Then, the final quality score is obtained by the standard deviation on each similarity component. The experimental results on five benchmark databases (i.e., CSIQ, IVC, LIVE, TID2013, and TID2008) show that the proposed IQE method performs better than other methods in the correlation with subjective quality judgment.
Machine learning based video segmentation of moving scene by motion index using IO detector and shot segmentation

Video segmentation has a major role in several applications like autonomous vehicles, medical image analysis, video surveillance, and augmented reality. Portrait segmentation, a subset of semantic image segmentation, is the key step of preprocessing step in numerous applications and a few among them are entertainment applications, security systems, video conferences, and so on. For the given video, every object exhibiting independent motion in at least a single frame is segmented. This is formulated as a learning problem and designed as an Ensemble Soft Voting Algorithm based on Supervised Machine Learning (ESVA-SML). The motion stream requires an ensemble learning method trained on synthetic videos for segmenting independent objects at motion in the optical flow field. The spatial–temporal features are extracted from each sub-bands which efficiently resembles the human visual system characteristics concerning the feature extract using Gray Level Size Zone Matrix (GLSZM). This method linear combines the previous vectors within a stipulated time interval thereby describing each spatial–temporal feature vector. Based on the prediction errors, shot boundaries of every shot are efficiently identified from which at least a single keyframe is extracted for further analysis. The proposed method is extensively evaluated using SegTrack-v2, DAVIS, and Fusion databases by comparing eight standard methods in various parameters. By analyzing all these three databases, SegTrack-v2 is best in terms of accuracy, precision, and recall. While both DAVIS and Fusion databases produce less MAE. Finally, Fusion produces less RMSE and RAE with minimal response time.
Blockchain Consortium for Electric Vehicles to Enhance the Security

Blockchain technology is proven as one of the promising digital transformation technologies that can engender immutable records, provide end to culminate transparency, and distributed ledger. The electrical vehicle (EV) era commenced in the past few years. Many automobile manufacturers are moving towards zero-carbon vehicles. In order to achieve the sustainability goals and truncate the carbon footprint, fight against climate change, EV is considered as one of the solutions. This research fixates on the integration of blockchain with EV that can monitor the lifecycle, introduce special schemes for EV buyers like toll-free, free charging, parking free that can incentivize the people to buy the EV. Since it is integrated with blockchain, tracking becomes much more facile, and the reward points could be given through cryptocurrency. Ethereum truffle framework was used to test the EV blockchain consortium.
Towards improving user-recommender systems interactions

Recent years have witnessed the increasing deployment of recommender systems as decision-support tools, application of recommender systems is ubiquitous in our daily life.As algorithms used in recommender systems become more inscrutable, some recommender systems remain blackbox for most people, which makes it difficult for people to fully trust such systems.Therefore, improving the user-recommender systems interactions is attracting increasing attention, one of the hot topics is explainable recommendation.In this paper, we investigate the possibility of providing context-aware explanations for recommendations and discuss the potential methods of evaluating such explanations.
How Do People Rank Multiple Mutant Agents?

Faced with several AI-powered sequential decision-making systems, how might someone choose on which to rely? For example, imagine car buyer Blair shopping for a self-driving car, or developer Dillon trying to choose an appropriate ML model to use in their application. Their first choice might be infeasible (i.e., too expensive in money or execution time), so they may need to select their second or third choice. To address this question, this paper presents: 1) Explanation Resolution, a quantifiable direct measurement concept; 2) a new XAI empirical task to measure explanations: "the Ranking Task"; and 3) a new strategy for inducing controllable agent variations—Mutant Agent Generation. In support of those main contributions, it also presents 4) novel explanations for sequential decision-making agents; 5) an adaptation to the AAR/AI assessment process; and 6) a qualitative study around these devices with 10 participants to investigate how they performed the Ranking Task on our mutant agents, using our explanations, and structured by AAR/AI. From an XAI researcher perspective, just as mutation testing can be applied to any code, mutant agent generation can be applied to essentially any neural network for which one wants to evaluate an assessment process or explanation type. As to an XAI user's perspective, the participants ranked the agents well overall, but showed the importance of high explanation resolution for close differences between agents. The participants also revealed the importance of supporting a wide diversity of explanation diets and agent "test selection" strategies.
Construction of a Chinese Corpus for Multi-Type Economic Event Relation

We construct a Chinese Economic Event Treebank (CEETB) , focusing on revealing economic and finance events and their relations. Investigating economic event relations will benefit academic research and practice in not just economics but many other scientific areas. The characteristics of economic-related texts (e.g., abundant longer enterprises names and terms) and the Chinese language speciality (e.g., component ellipsis in long sentences) have resulted in challenges in the event relation extraction task. Existing Chinese corpora containing economic event relations mainly focused on finance areas (e.g., the equity market) and only covered a few event types. To support research that may involve economic text analysis in Chinese, our CEETB is constructed following a carefully designed process. First, based on practical and research requirements, we summarize nine different types of event relations and four types of component ellipses in economic texts. Then, an excellent annotation scheme is presented to hyalinize the model, strategy, and process in annotation, followed by statistical analysis and quality evaluation for the CEETB corpus. Finally, to demonstrate the strengths of the constructed corpus in practical applications, we conduct experiments on five SOTA models for event relation extraction.
SLAM 3D Digital Terrain Mapping with SqueezeNet Driven by Road Traffic Data

In order to improve the efficiency of dynamic visualization of large-scale road traffic data in a web environment, this paper proposes a SLAM 3D digital terrain map visualization method using SqueezeNet in a web environment. We propose a hierarchical organization method of the road network, taking into account road attributes and drawing roads at different view heights; a multiroad merging method based on the line segment indexing feature of WebGL (web graphics library) technology, and optimizing the scene by combining view rejection and multithreading technology. The prototype system was developed and case studies were carried out using the national road network data as an example. The experimental results show that the frame rate of large-scale road traffic data visualization in the network environment is above 40 frames/second, which is 20–30 frames/second higher than that of Baidu’s ECharts GL visualization method.
Analysis of the “Third mission of universities” in the aspect of educational risks of digital divide

The article analyses the “Third mission of universities”, which is understood as a set of socially signiﬁcant functions aimed at the individual and society development in a local (regional) focus. The relevance of the study is caused by the digital divide – a social phenomenon that manifests itself in the uneven development and digital technologies use by the population, depending on territorial and socio-demographic characteristics. Digital divide in the context of Russian reality is viewed as one of the types of educational risk, especially in relation to socially vulnerable population segments. It has been noted that universities can become “conductors” of digital transformations, they can contribute to the inclusion of people from “risk groups” into the digital society. The results of the study using the method of qualitative and quantitative documents analysis – Internet sites of Russian universities, their legal and regulatory documentation addressing the “third mission” have allowed us to conclude that Russian universities are focused on online education, massive open educational courses and training in digital professions. This circumstance is conﬁrmed by the ratings analysis “Three University Missions” (MosIUR) in 2020–2021. Educational programs aimed at mastering digital professions for socially vulnerable population groups are becoming widespread – they cover people of retirement and pre-retirement age, the unemployed, mothers with “small” children. Such programs provide for a budgetary ﬁnancing system and co-financing. However, the regional factor that affects the digitalisation level of the population, as well as its material well-being, is not taken into account. Hence, it has been concluded that it is necessary to expand the citizens list eligible for preferential training in digital professions. They should include poor people, small settlements residents in the periphery, crisis regions.
H-Container: Enabling Heterogeneous-ISA Container Migration in Edge Computing

Edge computing is a recent computing paradigm that brings cloud services closer to the client. Among other features, edge computing offers extremely low client/server latencies. To consistently provide such low latencies, services should run on edge nodes that are physically as close as possible to their clients. Thus, when the physical location of a client changes, a service should migrate between edge nodes to maintain proximity. Differently from cloud nodes, edge nodes integrate CPUs of different Instruction Set Architectures (ISAs), hence a program natively compiled for a given ISA cannot migrate to a server equipped with a CPU of a different ISA. This hinders migration to the closest node. We introduce H-Container, a system that migrates natively compiled containerized applications across compute nodes featuring CPUs of different ISAs. H-Container advances over existing heterogeneous-ISA migration systems by being (a) highly compatible – no user’s source-code nor compiler toolchain modifications are needed; (b) easily deployable – fully implemented in user space, thus without any OS or hypervisor dependency, and (c) largely Linux-compliant – it can migrate most Linux software, including server applications and dynamically linked binaries. H-Container targets Linux and its already-compiled executables, adopts LLVM, extends CRIU, and integrates with Docker. Experiments demonstrate that H-Container adds no overheads during program execution, while 10–100 ms are added during migration. Furthermore, we show the benefits of H-Container in real-world scenarios, demonstrating, for example, up to 94% increase in Redis throughput when client/server proximity is maintained through heterogeneous container migration.
(Compress and Restore) <sup>N</sup> : A Robust Defense Against Adversarial Attacks on Image Classification

Modern image classification approaches often rely on deep neural networks, which have shown pronounced weakness to adversarial examples: images corrupted with specifically designed yet imperceptible noise that causes the network to misclassify. In this article, we propose a conceptually simple yet robust solution to tackle adversarial attacks on image classification. Our defense works by first applying a JPEG compression with a random quality factor; compression artifacts are subsequently removed by means of a generative model Artifact Restoration GAN. The process can be iterated ensuring the image is not degraded and hence the classification not compromised. We train different AR-GANs for different compression factors, so that we can change its parameters dynamically at each iteration depending on the current compression, making the gradient approximation difficult. We experiment with our defense against three white-box and two black-box attacks, with a particular focus on the state-of-the-art BPDA attack. Our method does not require any adversarial training, and is independent of both the classifier and the attack. Experiments demonstrate that dynamically changing the AR-GAN parameters is of fundamental importance to obtain significant robustness.
Investigating the Effects of Task Type and Linguistic Background on Accuracy in Automated Speech Recognition Systems: Implications for Use in Language Assessment of Young Learners

As a branch of artificial intelligence, automated speech recognition (ASR) technology is increasingly used to detect speech, process it to text, and derive the meaning of natural language for various learning and assessment purposes. ASR inaccuracy may pose serious threats to valid score interpretations and fair score use for all when it is exacerbated by test takers’ characteristics, such as language background and accent, and assessment task type. The present study investigated the extent to which speech-to-text accuracy rates of three major ASR systems vary across different oral tasks and students’ language background variables. Results indicate that task types and students’ language backgrounds have statistically significant main and interaction effects on ASR accuracy. The paper discusses the implications of the study results for applying ASR to computerized assessment design and automated scoring.
VAPE-BRIDGE: Bridging OpenVAS Results for Automating Metasploit Framework

Vulnerability assessment (VA) and penetration test (PenTest) are required by many organizations to satisfy their security auditing and compliance. VA and PenTest are conducted in the different stage and they are done through the software tools. Implementing the system that is able to convert the VA scan result to be rendered in the PenTest tool is a real challenge. This paper proposes a design and development of a system called VAPE-BRIDGE that provides the automatic conversion of the scan result of Open Vulnerability assessment scanner (OpenVAS) to be the exploitable scripts that will be executed in the Metasploit which is a widely-used opensource PenTest program. Specifically, the tool is designed to automatically extract the vulnerabilities listed in Open Web Application Security Project 10 (OWASP 10) and exploit them to be tested in the Metasploit. Our VAPE-BRIDGE encompasses three main components including (1) Scan Result Extraction responsible for extracting the VA scan results related to OWASP10 (2) Target List Repository responsible for retaining lists of vulnerabilities to be used in the process of Metasploit, and (3) Automated Shell Scripts Exploitation responsible for generating the script to render the exploit module to be executed in Metasploit. For the implementation, the VAPE-Bridge protype system was tested with a number of test cases in converting the scan results into shell code and rendering results to be tested in Metasploit. The experimental results showed that the system is functionally correct for all cases.
Quantum-key-expansion protocol based on number-state-entanglement-preserving tensor network with compression

Quantum-key-distribution (QKD) protocols allow exchanging a cryptographic key between two parties. With the development of the Internet, however, all forms of communications consume a huge number of cryptographic keys. Improving the secure key rate of QKD is becoming increasingly important. Quantum compression and key expansion are two ways to increase the secure key rate. In this paper, we propose a quantum-key-expansion (QKE) protocol. A series of singlet states are compressed by number-state-entanglement-preserving tensors and disentanglers to obtain a compressed two-body-entangled state. The cryptographic key is expanded by the number-state-entanglement-preserving tensor network. The advantages of the proposed QKE protocol over the classical-key-expansion algorithm in the key-expansion method, the key length, and the security are discussed in this study. Moreover, due to the structure of the number-state-preserving compression tensor network, our protocol can resist the intercept-resend attack, entanglement-and-measurement attack, and coherent attack.
Imaging Parameters-Considered Slender Target Detection in Optical Satellite Images

The existing slender target detection methods based on optical satellite images are greatly affected by the satellite perspective and the solar perspective. Due to limited data sources, it is difficult to implement a fully data-driven approach. This work introduces the imaging parameters of optical satellite images, which greatly reduces the influence of the satellite perspectives and the solar perspectives, and reduces the demand for the amount of data. We improve the oriented bounding box (OBB) detector based on faster R-CNN (region convolutional neural networks) and propose an imaging parameters-considered detector (IPC-Det) which is more suitable for our task. Specifically, in the first stage, the umbra and the shadow are extracted by horizontal bounding box (HBB), respectively, and then the matching of the umbra and the shadow is realized according to the imaging parameters. In the second stage, the paired umbra and shadow features are used to complete the classification and regression, and the target is obtained by OBB. In experiments, after introducing imaging parameters, our detection accuracy is improved by 3.9% (up to 87.5%), proving that this work is a successful attempt to introduce imaging parameters for slender target detection.
Spamming Forensic Analysis Using Network Forensics Development Life Cycle Method

E-mail is electronic mail used to send files, pictures, and others easily and quickly. However, as time goes by, there is a lot of misuse of e-mail, causing inconvenience to the recipient. One of them is spam e-mail sent to many people without prior permission from the intended owner. Hackers can forge e-mail headers anonymously for malicious purposes. The research object is to simulate sending spamming e-mails to 1 victim with a total of 40 spamming e-mails. The research follows the flow of the Network Forensics Development Life Cycle (NFDLC) method with the stages of initiation, acquisition, implementation, operation, and disposition. Simulation of sending e-mail using easy e-mail spammer tools and testing of e-mail using Wireshark tools. The test results show that 40 e-mails were successfully received or entered into the victim's inbox, and the test was successfully carried out by getting results based on predetermined parameters. The parameter is the IP address of the sender or spammer found is 72.125.68.109, the victim's IP address is 192.168.1.12.
Changing Trends of Blockchain in IoT: Benefits and Challenges

The domain of Internet of Things which is popularly referred as IoT, is enabling the vision of data driven connected world. Conventional IoT ecosystems which stream data to fog and cloud servers, are incapable of managing the mammoth volumes of data leading to numerous concerns like compromising the security and privacy of data. With huge success of IoT technology, it becomes imperative to offer more intrepidity in future data generating services and applications. Blockchain is one such technology which is designed with the objectives of inherent security through cryptographic techniques. Thus, it provides opportunity to rule out the challenges of IoT. This paper unfolds various drawbacks of IoT which raises concern for data holder and entrepreneurs. Simultaneously robust features of Blockchain are discussed which ensures to fulfill the requirements posed by IoT. As researchers moved on to explore the fusion Blockchain with IoT, few challenges are encountered even with Blockchain. This paper gives a deep insight of benefits and challenges for fusion of blockchain with IoT.
Adjective Phrases in PNL and its Application to Reverse Dictionary

Owing to the large information content of adjectives in natural language-based data, we have dealt with the subject of adjective phrases in the paradigm of PNL. We have proposed a GCR form for the same and demonstrated its application in building a specific instance of Reverse Dictionary. A Reverse Dictionary takes a natural language description of a concept in the user’s mind as input and generates semantically appropriate word/s as output. For example, it could accept descriptions like ‘to run slowly,’ ‘to run but not very fast,’ etc., for the word ‘jog’. As Reverse Dictionary deals with natural language, handling perception-based information is important in its context for capturing the user intent. The D-RD, Reverse Dictionary for Descriptive words, designed in this paper, takes a natural language description of a human as input and generates appropriate adjectives for usage in text/speech as output. As a part of the design, we have proposed a novel CWW based similarity measure for calculating semantic similarity between adjective phrases. The D-RD is evaluated against two benchmarks, Onelook.com and WantWords Online RD, and is reported to outperform both for the test set under consideration.