Reviewerly/exHarmony huggingface dataset:
Quantum Information Science: From foundations to new technologies

The recent race to investigate and produce quantum-enhanced technologies shows how important were the long years of investigation on the foundations of quantum theory. From the first discussions on the completeness of quantum mechanics by Einstein, Podolsky, and Rosen to the quantum algorithms running on quantum computers much effort was done by generations of scientists dedicated their careers. The Nobel prize of 2022 celebrates such effort and congratulates three leading scientists in the field. Here we are going to discuss a bit of the history and the recent impacts in novel technologies.
LightPoW: A trust based time-constrained PoW for blockchain in internet of things

Blockchain has been widely used in the Internet of Things (IoT) to address security problems such as DDoS and Sybil. However, how to design a lightweight Proof-of-Work (PoW) for the IoT without compromising security is still a problem. To solve this problem, we propose a lightweight PoW for blockchain in IoT. Firstly, we present Heterogeneity Considered Trust Mechanism (HCTM) for IoT nodes, which evaluates the trust value according to capacity and quality, and makes the trust evaluation more reasonable. Then, we present Trust based Time-Constrained PoW (TBTC-PoW). We allocate reasonable time windows to the consensus nodes according to the computing resources, trust values and mining difficulty, which reduces the energy consumption and improves the security of PoW. Theoretical analysis and experimental results show that our method has a lower energy consumption and higher security.
Classification of Malicious URLs by CNN Model Based on Genetic Algorithm

Researchers have proposed many models for the identification of malicious URLs in network security, but they have not achieved good results. In order to improve this defect, the current popular machine learning algorithm is combined to train the model, thus improving the accuracy of malicious URL classification. This paper proposes a model of a convolutional neural network based on genetic algorithm optimization. Firstly, the genetic algorithm was used to reduce the data dimension of the grammatical features, structural features, and probabilistic features in the extracted malicious URL text, and then the convolutional neural network was used to establish the model and classify the malicious URL. Through experimental verification, the model has achieved good results. Compared with the traditional machine learning model, it improves the accuracy of malicious URL recognition and provides a reference for malicious URL recognition.
Low-level factors increase gaze-guidance under cognitive load: A comparison of image-salience and semantic-salience models

Growing evidence links eye movements and cognitive functioning, however there is debate concerning what image content is fixated in natural scenes. Competing approaches have argued that low-level/feedforward and high-level/feedback factors contribute to gaze-guidance. We used one low-level model (Graph Based Visual Salience, GBVS) and a novel language-based high-level model (Global Vectors for Word Representation, GloVe) to predict gaze locations in a natural image search task, and we examined how fixated locations during this task vary under increasing levels of cognitive load. Participants (N = 30) freely viewed a series of 100 natural scenes for 10 seconds each. Between scenes, subjects identified a target object from the scene a specified number of trials (N) back among three distracter objects of the same type but from alternate scenes. The N-back was adaptive: N-back increased following two correct trials and decreased following one incorrect trial. Receiver operating characteristic (ROC) analysis of gaze locations showed that as cognitive load increased, there was a significant increase in prediction power for GBVS, but not for GloVe. Similarly, there was no significant difference in the area under the ROC between the minimum and maximum N-back achieved across subjects for GloVe (t(29) = -1.062, p = 0.297), while there was a cohesive upwards trend for GBVS (t(29) = -1.975, p = .058), although not significant. A permutation analysis showed that gaze locations were correlated with GBVS indicating that salient features were more likely to be fixated. However, gaze locations were anti-correlated with GloVe, indicating that objects with low semantic consistency with the scene were more likely to be fixated. These results suggest that fixations are drawn towards salient low-level image features and this bias increases with cognitive load. Additionally, there is a bias towards fixating improbable objects that does not vary under increasing levels of cognitive load.
Russian and foreign students’ involvement in the educational process in the context of the digital distance educational technologies use in the Russian universities

In the context of global changes in the world socio-economic system, the formation of an international educational research space, in the technological platform of the education system, the widespread use of distance learning technologies it is extremely relevant to conduct a comparative analysis of the involvement of Russian and foreign students in the educational process in Russian universities. The article provides the results of an electronic questionnaire survey of 760 foreign and 2,280 Russian students at universities located in 38 regions of the Russian Federation in all federal districts. Russian and foreign students assessed their level of involvement in the educational process in distance and face-to-face learning according to the parameters of four types of involvement: emotional (affective), behavioral, cognitive and academic. Involvement in the educational process during the distance learning decreases among all students, while for foreign students it is to a greater extent than for Russian students, which is due to the linguistic, cultural, mental characteristics of this category of students. Organizational, technical, psychological and emotional barriers were identified as problems that reduce the involvement in the educational process in the distance learning format of Russian and foreign students.
Design of anchor boxes and data augmentation for transformer-based vehicle localization

Vehicle localization is an important task in the signal processing field. In recent years, context exploration has been widely studied, especially the nonlocal dependencies in an image, using, for example, attention and transformer mechanisms. However, these approaches encounter difficulties in achieving accurate localization owing to ineffective design and use of queries. Motivated by the fact that spatial information is determined by decoder embeddings and details of reference boxes, we propose a method of explicitly and dynamically modeling anchor boxes in the query generation module. Moreover, we design a geometry-aware data augmentation approach to increase the diversity of the data by employing multiple augmentation methods on an image. Experiments conducted on public datasets show that our approach can improve the average precision by approximately 1.1%.
A self-supervised temporal temperature prediction method based on dilated contrastive learning

Due to the scarcity of the labeled data, traditional supervised learning methods have a limited application scope, which caused the supervised-based model performance will greatly be decreased. In this paper, we propose a promising model based on self-supervised learning. To update the weight and the contrastive relation in the features, a new self-supervised loss, is introduced. First, the convolution neural network is used in the proposed network to extract the deep feature in the first processing. Second, the self-supervised long–short time memory (LSTM) sequential is constructed for further deal. At last, the teacher net and student net have coordinately fine-tuned the credibility of the temperature prediction. By the experimental comparison, our proposed CNN-SSDLSTM is competitive with other supervised and semi-supervised methods. The evaluation experiments achieve state-of-the-art performance in aluminum electrolysis temperature prediction applications.
Syllable-PBWT for space-efficient haplotype long-match query

The positional Burrows-Wheeler transform (PBWT) has led to tremendous strides in haplotype matching on biobank-scale data. For genetic genealogical search, PBWT-based methods have optimized the asymptotic runtime of finding long matches between a query haplotype and a predefined panel of haplotypes. However, to enable fast query searches, the full-sized panel and PBWT data structures must be kept in memory, preventing existing algorithms from scaling up to modern biobank panels consisting of millions of haplotypes. In this work, we propose a space-efficient variation of PBWT named Syllable-PBWT, which divides every haplotype into syllables, builds the PBWT positional prefix arrays on the compressed syllabic panel, and leverages the polynomial rolling hash function for positional substring comparison. With the Syllable-PBWT data structures, we then present a long match query algorithm named Syllable-Query.Compared to the most time- and space-efficient previously published solution to the long match query problem, Syllable-Query reduced the memory use by a factor of over 100 on both the UK Biobank genotype data and the 1000 Genomes Project sequence data. Surprisingly, the smaller size of our syllabic data structures allows for more efficient iteration and CPU cache usage, granting Syllable-Query even faster runtime than existing solutions.https://github.com/ZhiGroup/Syllable-PBWT.Supplementary data are available at Bioinformatics online.
EBSD-Net: Enhancing Brightness and Suppressing Degradation for Low-light Color Image using Deep Networks

Image enhancement in the RGB color space typically leads to undesirable artifacts, especially color distortion owing to insufficient consideration of the correlation between color channels. This paper presents enhancing brightness and suppressing degradation for low-light color image using deep networks (defined as EBSD-Net). To begin with, the input image is converted from RGB color space to the YCbCr color space, and then the three channels Y, Cb, and Cr are separated from the YCbCr image. Next, the Boosting-Net is proposed to enhance the luminance channel Y with minimized halo artifacts. In addition, the Chrominance-Net is introduced to reduce color distortion of two chroma channels Cb and Cr. After the enhancing brightness and suppressing degradation are completed, the luminance-enhanced channel and two chrominance-corrected channels are transformed back into RGB color space to obtain the enhanced image. The extensive experimental results show that the proposed network outperforms other competitive methods in qualitative and quantitative terms.
A framework of blockchain/IPFS-based platform for smart crop production

The paper presents a framework of platform SCPDx (Smart Crop Production Data Exchange) for facilitation of data provision and information sharing between parties involved in research and development of smart crop production. The framework integrates blockchain and distributed file system technologies and is based on a private eos.io-based blockchain and private Interplanetary File System networks (IPFS). The main concepts and capabilities of the used blockchain platform and the IPFS technology are presented. The main building stages, the general infrastructures and the elements of the platform architecture are defined. As а part of its implementation, a description of the deployed blockchain, IPFS networks, the automated blockchain installation procedure, the developed test application with embedded file upload/download algorithm and a smart contract for managing data is laid out. The source code and script files are provided on GitHub repository.
Research on Small Target Detection Technology Based on the MPH-SSD Algorithm

To address the problems of less semantic information and low measurement accuracy when the SSD (single shot multibox detector) algorithm detects small targets, an MPH-SSD (multiscale pyramid hybrid SSD) algorithm that integrates the attention mechanism and multiscale double pyramid feature enhancement is proposed in this paper. In this algorithm, firstly, the SSD algorithm is used to extract the feature map of small targets, and the shallow feature enhancement module is added to expand the receptive field of the shallow feature layer so as to enrich the semantic information in the feature layer for small targets and improve the expression ability of shallow features. The processed shallow feature layer and deep feature layer are fused at multiple scales, and the semantic information and location information are fused together to obtain a feature map with rich information. Secondly, the cascaded double pyramid structure is used to transfer from the deep layer to the shallow layer so that the context information between different feature layers can be effectively transferred and the feature information can be further strengthened. The hybrid attention mechanism can retain more context information in the network, adaptively adjust the feature map after addition and fusion, and reduce the background interference. The experimental analysis of MPH-SSD algorithm on Pascal VOC and MS COCO datasets shows that the map of this algorithm is 87.7% and 51.1%, respectively. The results show that the MPH-SSD algorithm can make better use of the feature information in the shallow feature layer in the process of small target detection and has better detection performance for small targets.
Do Neural Transformers Learn Human-Defined Concepts? An Extensive Study in Source Code Processing Domain

State-of-the-art neural networks build an internal model of the training data, tailored to a given classification task. The study of such a model is of interest, and therefore, research on explainable artificial intelligence (XAI) aims at investigating if, in the internal states of a network, it is possible to identify rules that associate data to their corresponding classification. This work moves toward XAI research on neural networks trained in the classification of source code snippets, in the specific domain of cybersecurity. In this context, typically, textual instances have firstly to be encoded with non-invertible transformation into numerical vectors to feed the models, and this limits the applicability of known XAI methods based on the differentiation of neural signals with respect to real valued instances. In this work, we start from the known TCAV method, designed to study the human understandable concepts that emerge in the internal layers of a neural network, and we adapt it to transformers architectures trained in solving source code classification problems. We first determine domain-specific concepts (e.g., the presence of given patterns in the source code), and for each concept, we train support vector classifiers to separate points in the vector activation spaces that represent input instances with the concept from those without the concept. Then, we study if the presence (or the absence) of such concepts affects the decision process of the neural network. Finally, we discuss about how our approach contributes to general XAI goals and we suggest specific applications in the source code analysis field.
NextDet: Efficient Sparse-to-Dense Object Detection with Attentive Feature Aggregation

Object detection is a computer vision task of detecting instances of objects of a certain class, identifying types of objects, determining its location, and accurately labelling them in an input image or a video. The scope of the work presented within this paper proposes a modern object detection network called NextDet to efficiently detect objects of multiple classes which utilizes CondenseNeXt, an award-winning lightweight image classification convolutional neural network algorithm with reduced number of FLOPs and parameters as the backbone, to efficiently extract and aggregate image features at different granularities in addition to other novel and modified strategies such as attentive feature aggregation in the head, to perform object detection and draw bounding boxes around the detected objects. Extensive experiments and ablation tests, as outlined in this paper, are performed on Argoverse-HD and COCO datasets, which provide numerous temporarily sparse to dense annotated images, demonstrate that the proposed object detection algorithm with CondenseNeXt as the backbone result in an increase in mean Average Precision (mAP) performance and interpretability on Argoverse-HD’s monocular ego-vehicle camera captured scenarios by up to 17.39% as well as COCO’s large set of images of everyday scenes of real-world common objects by up to 14.62%.
Source orientation, anthropomorphism, and social presence in human-chatbot communication: how to proceed with these concepts

Abstract Source orientation, anthropomorphism, and social presence are three concepts that play a pivotal role in present-day research on users’ responses to communication with chatbots. However, there are also problems regarding the three conceptualizations and the related measurements. Therefore, the present qualitative interview study analyzes users’ perceptions of their interactions with chatbots through the lens of source orientation, anthropomorphism as well as social presence, in order to unravel how these three concepts can help to understand human-chatbot communication—each in their unique ways. Interviews were conducted with a sample ( N = 24) that was varied in terms of gender, age, educational level and household composition. Findings are presented for source orientation, anthropomorphism, and social presence, and the discussion elaborates on the theoretical and methodological implications.
Multi-UAV Cooperative Path Planning with Monitoring Privacy Preservation

UAVs have shown great potential application in persistent monitoring, but still have problems such as difficulty in ensuring monitoring frequency and easy leakage of monitoring path information. Therefore, under the premise of covering all monitoring targets by UAVs, it is necessary to improve the monitoring frequency of the target and the privacy protection of the monitoring intention as much as possible. In response to the above problems, this research proposes monitoring overdue time to evaluate the monitoring frequency and monitoring period entropy in order to evaluate the ability to ensure monitoring privacy protection. It then establishes a multi-UAV cooperative persistent monitoring path planning model. In addition, the multi-group ant colony optimization algorithm, called overdue-aware multiple ant colony optimization (OMACO), is improved based on the monitoring overdue time. Finally, an optimal flight path for multi-UAV monitoring with high monitoring frequency and strong privacy preservation of monitoring intention is obtained. The simulation results show that the method proposed in this paper can effectively improve the monitoring frequency of each monitoring node and the privacy preservation of the UAV monitoring path and has great significance for enhancing security monitoring and preventing intrusion.
Digital forensic analysis of discord on google chrome

Over the past decade, social media applications have significantly increased their market share and garnered a wide user base. However, these applications have also attracted the attention of criminals desiring to exploit the apps to support illicit operations due to the low barrier to entry and ease of usage. A digital forensic investigation of these applications can reveal valuable information about criminal activity and the suspect. Discord is a Voice over Internet Protocol (VoIP) service that enables text, image, video, and audio chats. It has grown in popularity, and as a result, it is subject to increased use by cybercriminals. In this paper, we examine the remnants of the increasingly popular social media application ''Discord" when used on the Google Chrome web browser. We recovered various artifacts such as payment information, sent messages, account settings, conversations, uploaded attachments, and much more, all of which could be utilized in a forensic investigation.
A fuzzy-based mechanism for automatic personalized assessment in an e-learning system for computer programming

Testing is a significant part of the teaching and learning process. An assessment test has to include test items that are tailored to the individual learning needs of the students in order to be more accurate and support learning in a more effective way. In this paper, a fuzzy-based mechanism is presented for automatic personalized assessment in an e-learning system for computer programming. Particularly, the selection of the most appropriate test items for each individual student is based on a variety of criteria: (i) the student’s knowledge level, (ii) the student’s prior knowledge of computer programming, (iii) the type of programming errors that the student is prone to make, and (iv) the difficulty level of the test items. Linguistic values are used to determine these criteria. Additionally, 45 fuzzy rules are used over these criteria, which imitate the way of thinking of human tutors with regard to deciding about the most appropriate test items that have to be included in an adaptive test. The presented mechanism was used under real conditions and evaluated by experts and students of the Department of Informatics of the University of Piraeus, Greece with very encouraging results. Specifically, both the participating students and experts found that the presented mechanism creates non-repetitive balanced tests that meet learners’ knowledge level and needs.
Variational approach to quantum state tomography based on maximal entropy formalism

Quantum state tomography is an integral part of quantum computation and offers the starting point for the validation of various quantum devices. One of the central tasks in the field of state tomography is to reconstruct, with high fidelity, the quantum states of a quantum system. From an experiment on a real quantum device, one can obtain the mean measurement values of different operators. With such data as input, in this report we employ the maximal entropy formalism to construct the least biased mixed quantum state that is consistent with the given set of expectation values. Even though, in principle, the reported formalism is quite general and should work for an arbitrary set of observables, in practice we shall demonstrate the efficacy of the algorithm on an informationally complete (IC) set of Hermitian operators. Such a set possesses the advantage of uniquely specifying a single quantum state from which the experimental measurements have been sampled and hence renders the rare opportunity not only to construct a least-biased quantum state but even replicate the exact state prepared experimentally within a preset tolerance. The primary workhorse of the algorithm is reconstructing an energy function which we designate as the effective Hamiltonian of the system, and parameterizing it with Lagrange multipliers, according to the formalism of maximal entropy. These parameters are thereafter optimized variationally so that the reconstructed quantum state of the system converges to the true quantum state within an error threshold. To this end, we employ a parameterized quantum circuit and a hybrid quantum-classical variational algorithm to obtain such a target state, making our recipe easily implementable on a near-term quantum device.