Reviewerly/exHarmony huggingface dataset:
New avenues and challenges in semantic map research (with a case study in the semantic field of emotions)

Abstract In this paper, we present an overview of the methods associated with semantic maps, focusing on current challenges and new avenues for research in this area, which are at the core of the contributions to this special issue. Among the fundamental questions are: (1) the validity of the basic assumption, namely, to what extent does coexpression reflect semantic similarity; (2) the central problem of identifying analytical primitives in the domain of semantics; (3) the methods of inference used for creating coexpression maps and the representation techniques (graph structure vs. Euclidean space) as well as their respective merits (including the goodness of fit of the models); and (4) the use of semantic maps to support diachronic and synchronic descriptions of individual languages. In order to illustrate and discuss key aspects, we conduct an experiment in the semantic field of emotions, for which we construct a classical semantic map based on the dataset of CLICS 3 .
Attitudes and intentions towards the adoption of mobile learning during COVID-19: building an exciting career through vocational education

Purpose Mobile learning has emerged as one of the main methods for training and academic activities in the present era. It is, also, highly relevant in the wake of the COVID-19 pandemic whereupon digitization of mobile learning has made it possible for many students to continue with their education. This study investigated attitudes and intentions towards the adoption of mobile learning in vocational education. Design/methodology/approach This is a quantitative study based on cross-sectional empirical data. In targeting vocational students throughout Pakistan, the study used a survey questionnaire with a convenience sampling method. From the responses to the questionnaire, 320 samples were used to obtain the study outcomes. Findings The structural equation modeling’s (SEM) findings reveal that learning autonomy (LA), mobile device self-efficacy (MDSE), task-technology fit (TTF), perceived ease of use (PEOU), perceived usefulness (PUS) and perceived enjoyment (PE) have a positive and significant effect on mobile usage attitudes (MUA) and intentions to adopt mobile learning (ITAML). Moreover, this study’s findings confirm, also, MUA’s predictive power on ITAML. Practical implications Further, this study’s findings encourage individuals to use mobile devices to properly promote knowledge in society. In addition, this study’s findings support vocational institutions’ operators’ and policymakers’ development of online education and training strategies to resist the complications arising from the transmission of COVID-19. Moreover, this study’s findings open new doors when conducting similar research studies on students’ perceptions and learning behaviors. Originality/value The empirical investigation of attitudes and intentions to adopt mobile learning in the context of COVID-19 helps potential adopters to test the likely behaviors.
Frequency Throttling Side-Channel Attack

Modern processors dynamically control their operating frequency to optimize resource utilization, maximize energy savings, and conform to system-defined constraints. If, during the execution of a software workload, the running average of any electrical or thermal parameter exceeds its corresponding predefined threshold value, the power management architecture will reactively adjust CPU frequency to ensure safe operating conditions. In this paper, we demonstrate how such power management-based frequency throttling activity forms a source of timing side-channel information leakage, which can be exploited by an attacker to infer secret data even from a constant-cycle victim workload. The proposed frequency throttling side-channel attack can be launched by both kernel-space and user-space attackers, thus compromising security guarantees provided by isolation boundaries. We validate our attack methodology across different systems and threat models by performing experiments on a constant-cycle implementation of AES algorithm based on AES-NI instructions. The results of our experimental evaluations demonstrate that the attacker can successfully recover all bytes of an AES key by measuring encryption execution times. Finally, we discuss different options to mitigate the threat posed by frequency throttling side-channel attacks, as well as their advantages and disadvantages.
Learning from Temporal Spatial Cubism for Cross-Dataset Skeleton-based Action Recognition

Rapid progress and superior performance have been achieved for skeleton-based action recognition recently. In this article, we investigate this problem under a cross-dataset setting, which is a new, pragmatic, and challenging task in real-world scenarios. Following the unsupervised domain adaptation (UDA) paradigm, the action labels are only available on a source dataset, but unavailable on a target dataset in the training stage. Different from the conventional adversarial learning-based approaches for UDA, we utilize a self-supervision scheme to reduce the domain shift between two skeleton-based action datasets. Our inspiration is drawn from Cubism, an art genre from the early 20th century, which breaks and reassembles the objects to convey a greater context. By segmenting and permuting temporal segments or human body parts, we design two self-supervised learning classification tasks to explore the temporal and spatial dependency of a skeleton-based action and improve the generalization ability of the model. We conduct experiments on six datasets for skeleton-based action recognition, including three large-scale datasets (NTU RGB+D, PKU-MMD, and Kinetics) where new cross-dataset settings and benchmarks are established. Extensive results demonstrate that our method outperforms state-of-the-art approaches. The source codes of our model and all the compared methods are available at https://github.com/shanice-l/st-cubism.
Information Geometry under Hierarchical Quantum Measurement

In most quantum technologies, measurements need to be performed on the parametrized quantum states to transform the quantum information to classical information. The measurements, however, inevitably distort the information. The characterization of the discrepancy is an important subject in quantum information science, which plays a key role in understanding the difference between the structures of quantum and classical informations. Here we analyze the difference in terms of the Fisher information metric and present a framework that can provide analytical bounds on the discrepancy under hierarchical quantum measurements. Specifically, we present a set of analytical bounds on the difference between the quantum and classical Fisher information metric under hierarchical p-local quantum measurements, which are measurements that can be performed collectively on at most p copies of quantum states. The results can be directly transformed to the precision limit in multiparameter quantum metrology, which leads to characterizations of the trade-off among the precision of different parameters. The framework also provides a coherent picture for various existing results by including them as special cases.
Pump Up Password Security! Evaluating and Enhancing Risk-Based Authentication on a Real-World Large-Scale Online Service

Risk-based authentication (RBA) aims to protect users against attacks involving stolen passwords. RBA monitors features during login, and requests re-authentication when feature values widely differ from previously observed ones. It is recommended by various national security organizations, and users perceive it more usable and equally secure than equivalent two-factor authentication. Despite that, RBA is still only used by very few online services. Reasons for this include a lack of validated open resources on RBA properties, implementation, and configuration. This effectively hinders the RBA research, development, and adoption progress. To close this gap, we provide the first long-term RBA analysis on a real-world large-scale online service. We collected feature data of 3.3 million users and 31.3 million login attempts over more than one year. Based on the data, we provide (i) studies on RBA's real-world characteristics, and its configurations and enhancements to balance usability, security, and privacy, (ii) a machine learning based RBA parameter optimization method to support administrators finding an optimal configuration for their own use case scenario, (iii) an evaluation of the round-trip time feature's potential to replace the IP address for enhanced user privacy, and (iv) a synthesized RBA data set to reproduce this research and to foster future RBA research. Our results provide insights on selecting an optimized RBA configuration so that users profit from RBA after just a few logins. The open data set enables researchers to study, test, and improve RBA for widespread deployment in the wild.
Residual Similarity Based Conditional Independence Test and Its Application in Causal Discovery

Recently, many regression based conditional independence (CI) test methods have been proposed to solve the problem of causal discovery. These methods provide alternatives to test CI by first removing the information of the controlling set from the two target variables, and then testing the independence between the corresponding residuals Res1 and Res2. When the residuals are linearly uncorrelated, the independence test between them is nontrivial. With the ability to calculate inner product in high-dimensional space, kernel-based methods are usually used to achieve this goal, but still consume considerable time. In this paper, we investigate the independence between two linear combinations under linear non-Gaussian structural equation model. We show that the dependence between the two residuals can be captured by the difference between the similarity of (Res1, Res2) and that of (Res1, Res3) (Res3 is generated by random permutation) in high-dimensional space. With this result, we design a new method called SCIT for CI test, where permutation test is performed to control Type I error rate. The proposed method is simpler yet more efficient and effective than the existing ones. When applied to causal discovery, the proposed method outperforms the counterparts in terms of both speed and Type II error rate, especially in the case of small sample size, which is validated by our extensive experiments on various datasets.
Anonymous and Linkable Location-Based Services

In the location-based services (LBS) scheme, the vehicle user sends his query to the LBS server and gets the data corresponding to the requested query from it. Therefore, vehicle user’s query privacy and LBS server’s data privacy are serious issues in LBS. Several LBS schemes, like <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -anonymity, private-information retrieval, and homomorphic encryption-based, have been proposed to solve these issues. These schemes either provide vehicle user’s query privacy or LBS server’s data privacy. Some of the oblivious transfer (OT) protocol-based LBS schemes preserve vehicle users’ query privacy and LBS server’s data privacy. However, the OT-based LBS schemes do not preserve vehicle user’s identity privacy. Moreover, the efficiency (communication and computation costs) of the OT protocol-based LBS schemes depends on the LBS server’s database size, which is very high. In this work, we propose an anonymous linkable location-based services (AL2BS) scheme using linkable ring signcryption technique. The scheme provides vehicle user’s query privacy and LBS server’s data privacy without revealing the vehicle user’s identity. The efficiency of the AL2BS scheme is independent of the size of the LBS server’s database. The AL2BS scheme requires only one pairing operation on both ends and a few point multiplications. The theoretical and experimental results show that the performance of the AL2BS scheme in terms of communication and computation costs is better than the existing LBS schemes.
Facial expression recognition using adapted residual based deep neural network

Emotion on our face can determine our feelings, mental state and can directly impact our decisions. Humans are subjected to undergo an emotional change in relation to their living environment and or at a present circumstance. These emotions can be anger, disgust, fear, sadness, happiness, surprise or neutral. Due to the intricacy and nuance of facial expressions and their relationship to emotions, accurate facial expression identification remains a difficult undertaking. As a result, we provide an end-to-end system that uses residual blocks to identify emotions and improve accuracy in this research field. After receiving a facial image, the framework returns its emotional state. The accuracy obtained on the test set of FERGIT dataset (an extension of the FER2013 dataset with 49300 images) was 75%. This proves the efficiency of the model in classifying facial emotions as this database poses a bunch of challenges such as imbalanced data, intraclass variance, and occlusion. To ensure the performance of our model, we also tested it on the CK+ database and its output accuracy was 97% on the test set.
Modeling Hierarchical Syntax Structure with Triplet Position for Source Code Summarization

Automatic code summarization, which aims to describe the source code in natural language, has become an essential task in software maintenance.Our fellow researchers have attempted to achieve such a purpose through various machine learning-based approaches.One key challenge keeping these approaches from being practical lies in the lacking of retaining the semantic structure of source code, which has unfortunately been overlooked by the stateof-the-art methods.Existing approaches resort to representing the syntax structure of code by modeling the Abstract Syntax Trees (ASTs).However, the hierarchical structures of ASTs have not been well explored.In this paper, we propose CODESCRIBE to model the hierarchical syntax structure of code by introducing a novel triplet position for code summarization.Specifically, CODESCRIBE leverages the graph neural network and Transformer to preserve the structural and sequential information of code, respectively.In addition, we propose a pointer-generator network that pays attention to both the structure and sequential tokens of code for a better summary generation.Experiments on two real-world datasets in Java and Python demonstrate the effectiveness of our proposed approach when compared with several state-of-the-art baselines 1 .
Global path planning of unmanned vehicle based on fusion of A<sup>*</sup> algorithm and Voronoi field

Purpose Since many global path planning algorithms cannot achieve the planned path with both safety and economy, this study aims to propose a path planning method for unmanned vehicles with a controllable distance from obstacles. Design/methodology/approach First, combining satellite image and the Voronoi field algorithm (VFA) generates rasterized environmental information and establishes navigation area boundary. Second, establishing a hazard function associated with navigation area boundary improves the evaluation function of the A * algorithm and uses the improved A * algorithm for global path planning. Finally, to reduce the number of redundant nodes in the planned path and smooth the path, node optimization and gradient descent method (GDM) are used. Then, a continuous smooth path that meets the actual navigation requirements of unmanned vehicle is obtained. Findings The simulation experiment proved that the proposed global path planning method can realize the control of the distance between the planned path and the obstacle by setting different navigation area boundaries. The node reduction rate is between 33.52% and 73.15%, and the smoothness meets the navigation requirements. This method is reasonable and effective in the global path planning process of unmanned vehicle and can provide reference to unmanned vehicles’ autonomous obstacle avoidance decision-making. Originality/value This study establishes navigation area boundary for the environment based on the VFA and uses the improved A* algorithm to generate a navigation path that takes into account both safety and economy. This study also proposes a method to solve the redundancy of grid environment path nodes and large-angle steering and to smooth the path to improve the applicability of the proposed global path planning method. The proposed global path planning method solves the requirements of path safety and smoothness.
Cost Ensemble with Gradient Selecting for GANs

Generative Adversarial Networks(GANs) are powerful generative models on numerous tasks and datasets but are also known for their training instability and mode collapse. The latter is because the optimal transportation map is discontinuous, but DNNs can only approximate continuous ones. One way to solve the problem is to introduce multiple discriminators or generators. However, their impacts are limited because the cost function of each component is the same. That is, they are homogeneous. In contrast, multiple discriminators with different cost functions can yield various gradients for the generator, which indicates we can use them to search for more transportation maps in the latent space. Inspired by this, we have proposed a framework to combat the mode collapse problem, containing multiple discriminators with different cost functions, named CES-GAN. Unfortunately, it may also lead to the generator being hard to train because the performance between discriminators is unbalanced, according to the Cannikin Law. Thus, a gradient selecting mechanism is also proposed to pick up proper gradients. We provide mathematical statements to prove our assumptions and conduct extensive experiments to verify the performance. The results show that CES-GAN is lightweight and more effective for fighting against the mode collapse problem than similar works.
Spatiality-guided Transformer for 3D Dense Captioning on Point Clouds

Dense captioning in 3D point clouds is an emerging vision-and-language task involving object-level 3D scene understanding. Apart from coarse semantic class prediction and bounding box regression as in traditional 3D object detection, 3D dense captioning aims at producing a further and finer instance-level label of natural language description on visual appearance and spatial relations for each scene object of interest. To detect and describe objects in a scene, following the spirit of neural machine translation, we propose a transformer-based encoder-decoder architecture, namely SpaCap3D, to transform objects into descriptions, where we especially investigate the relative spatiality of objects in 3D scenes and design a spatiality-guided encoder via a token-to-token spatial relation learning objective and an object-centric decoder for precise and spatiality-enhanced object caption generation. Evaluated on two benchmark datasets, ScanRefer and ReferIt3D, our proposed SpaCap3D outperforms the baseline method Scan2Cap by 4.94% and 9.61% in CIDEr@0.5IoU, respectively. Our project page with source code and supplementary files is available at https://SpaCap3D.github.io/.
Selecting tools to enhance scholarly communication through the life cycle of scientific research

Education and science digitalization belongs to the priority areas of information society development. However, we can observe that while Ukraine enters the European educational and scientific space, Ukrainian scientists cannot yet efficiently compete in the international labor market. One of the reasons behind this is that Ukrainian scientists and researchers are not fully integrated into the world system of digital scholarly communication. To help researchers use digital tools supporting scholarly communication, many companies carry out various educational events to support open science and initiate international research and projects. Under modern conditions the digitalization of scientific communication went to the front-burner due to the COVID-19 pandemic. At the same time, the forced transition to digital scholarly communications through the COVID-19 pandemic can help integrate young scientists into the international scientific space. This article provides an analysis of tools to support scholarly communication developed within the "101 Innovations in Scholarly Communication" project. The international survey demonstrates the tools that scientists prefer to use at each stage of the research. This paper characterizes the advantages of particular tools on different stages of scientific research process induced by the current tendency for scholarly communication digitalization. During the work, we outlined the scope for future research and found it necessary to conduct an additional survey for the scholarly community.
Research on Medical Text Classification Based on Improved Capsule Network

In the medical field, text classification based on natural language process (NLP) has shown good results and has great practical application prospects such as clinical medical value, but most existing research focuses on English electronic medical record data, and there is less research on the natural language processing task for Chinese electronic medical records. Most of the current Chinese electronic medical records are non-institutionalized texts, which generally have low utilization rates and inconsistent terminology, often mingling patients’ symptoms, medications, diagnoses, and other essential information. In this paper, we propose a Capsule network model for electronic medical record classification, which combines LSTM and GRU models and relies on a unique routing structure to extract complex Chinese medical text features. The experimental results show that this model outperforms several other baseline models and achieves excellent results with an F1 value of 73.51% on the Chinese electronic medical record dataset, at least 4.1% better than other baseline models.
Measuring Decentralization in Emerging Public Blockchains

Bitcoin and Ethereum have always been the two major heavyweight infrastructures in the blockchain space. However, Low throughput and high cost hinder their further development. Recently, some emerging public blockchains have become popular. They all have efficient transaction confirmation mechanism and cheap interaction costs. However, the advantages are actually a sacrifice of decentralization. As we all know, decentralization is an essential feature of blockchain. Therefore, it requires the conceiving of up-to-date metrics of decentralization measurement. However, there is little research on the degree of decentralization of these emerging public blockchains in the past. This paper studies nine popular public chains such as Binance Smart Chain, Cardano, and Avalanche. Since these public chains mostly use the consensus mechanism of POS variants, the distribution of governance token balances on the chain can reflect the decentralization of the blockchain. Hence, We evaluate the distribution of the token balance of those public blockchains to indicate their decentralization degree. Two kinds of indicators are adopted and redesigned: information entropy and Gini coefficients. Among the nine public blockchains we selected, Cardano, Tron and Polkadot have a higher degree of decentralization, while Elrond and Binance Smart Chain have a lower degree of decentralization. We think our work will be helpful for future research on the degree of blockchain decentralization.