Reviewerly/exHarmony huggingface dataset:
Rapid Classification of Massive Images Based on Cloud Computing Platform

With the continuously increasing requirement of data processing speed, it is more and more suitable for images to be processed on cloud computing platform with distributed parallel computing.Therefore, this paper studied the rapid classification of massive images based on cloud computing platform.The multi-mode information fusion of massive images was carried out, which gave the implementation steps of the proposed massive image fusion method based on information complementation.This paper proposed to classify the massive images based on salient interactive feedback network.The model constructed in this paper not only simplified the complicated network parameter initialization and tuning process, but also avoided the classification failure caused by the scarcity of labeled images, which is more suitable for application and practical application scenarios.The experimental results verified the effectiveness of the constructed model in rapidly classifying massive images.
Creation and stable development of a unified professional development educational environment in the Republican Medical College of the Ministry of Health of the Udmurt Republic

The article presents the experience of creation and stable development of a unified professional development educational environment (hereinafter referred to as PDEE) in the Autonomous Professional Educational Institution of the Udmurt Republic «Republican Medical College named after the Hero of the Soviet Union F. A. Pushina of the Ministry of Health of the Udmurt Republic» (hereinafter referred to as the College) since the reorganization of the educational institutions subordinate to the Ministry of Health of the Udmurt Republic by merging into single educational institution from 05.06.2018. The process of formation and development of the PDEE in the College arose from the need to ensure and implement uniform requirements for the professional training of specialists and proceeded in stages, with specific goals and objectives. Difficulties in solving a number of problematic issues were overcome by the joint efforts of the formed methodological service, including the educational and methodological department and chairpersons of the cyclic methodological commissions. The purpose of the study. Creation and development of a unified professional development educational environment in the College as a way to improve the quality of training of mid-level healthcare professionals. Results. Training of specialists according to the unified curriculum and educational and methodological support of the educational process in compliance with the principles of unity, continuity, and consistency in the content of training, teaching, and monitoring the mastering of educational programs, updating the material and technical base according to modern requirements, and using modern educational technologies and teaching methods contribute to optimization of educational process and improvement of the pedagogical competence of teachers which, based on the results of accreditation, improves the quality of specialists’ training and graduates’ competitiveness. Conclusion. The study is of practical importance for secondary vocational educational organizations that are at the initial stage of consolidation through the merger of several institutions, in order to demonstrate the effectiveness of implementing unity in content, teaching, and monitoring learning outcomes.
Session-based Recommendation with Preference Interaction

Graph neural network have achieved great success in session-based recommendation. Recently, some works have achieved improvement by incorporating income and outcome adjacent matrices to generate global and local preferences, and directly model the two preferences to build session representation. However, firstly, we observe that the income matrix and outcome matrix of a session have no strong relevance, and their concatenation may introduce noise for building two preferences. Secondly, we find the global and local preferences can benefit from each other, and collaborative information from neighborhood sessions may help to improve recommendation performance. Therefore, we propose a session-based recommendation with preference interaction from separate income adjacent matrix and outcome adjacent matrix framework, which includes two parallel modules: An Income Session Representation Encoder (ISE) and an Outcome Session Representation En-coder (OSE). A fusion gating mechanism is introduced to balance the importance of session representations resulting from ISE and OSE. The experimental results show that our model obviously outperforms other state-of-the-art methods on Yoochoose and Diginetica datasets.
Research on Financial Fraud Identification System Based on Differential Privacy

Data sharing among financial institutions is often not possible, resulting in a "data silo" situation. In this paper, we train an efficient financial fraud detection model under the framework of privacy protection from the perspective of facilitating the collaboration of multiple financial institutions to train a fraud identification system. This paper first used traditional oversampling and under sampling methods to balance the data and train models such as logistic regression, support vector machines and random forests, but did not obtain the desired results. In contrast, the optimal subsampling method based on logistic regression performs well in terms of training results and program runtime. To protect data security, differential privacy is introduced on this basis to find the classification accuracy of the model under different privacy budgets. It is concluded that a certain balance between the degree of privacy protection and model effectiveness should be achieved according to privacy requirements.
Chinese review sentiment analysis based on deep learning and attention mechanism

With the development of the Internet and the continuous growth of the number of netizens, more and more people publish their attitudes and emotions on products, services, etc. on the Internet, accumulating a large number of comments containing personal opinions. The sentiment analysis of text reviews is of great significance to explore the emotional tendencies of users and help merchants adjust product positioning and marketing strategies in a timely manner. The deep learning framework adopts CNN and LSTM models respectively to introduce the attention mechanism for sentiment classification. The effects of CNN model and LSTM model on sentiment classification in Weibo comments were analyzed. From the experimental data, after the same number of iterations, the accuracy of the CNN model reaches 97.94%, and the accuracy of the LSTM model reaches 98.18%. The experimental results show that LSTM is better than CNN in the sentiment classification of Weibo comments.
CD-BCM:Cross-Domain Batch Certificates Management Based On Blockchain

Abstract With the development of information networks, the entities from different network domains interact with each other more and more frequently. Therefore, identity management and authentication are essential in cross-domain setting. The traditional Public Key Infrastructure (PKI) architecture has some problems, including single point of failure, inefficient certificate revocation status management and also lack of privacy protection, which cannot meet the demand of cross-domain identity authentication. Blockchain is suitable for multi-participant collaboration in multi-trust domain scenarios. In this paper, a cross-domain certificate management scheme CD-BCM based on the consortium blockchain is proposed. For the issue of Certificate Authority’s single point of failure, we design a multi-signature algorithm. In addition, we propose a unified structure for batch certificates verification and conversion, which improve the efficiency of erroneous certificate identification. Finally, by comparing with current related schemes, our scheme achieves good functionality and scalability in the scenario of cross-domain certificate management.
Metrological analysis of the four-point bending test for quantifying the photoelastic behavior of materials

In this work, a method for evaluating stress-optic coefficient measurements and their uncertainty on a four-point bending specimen is presented. Other previously used methods approached this issue by considering multiple data through an ordinary least squares fit; but they only take into account the repeatability contribution to uncertainty, so they are not consistent with the ISO-Guide to the expression of Uncertainty in Measurement. The method presented here uses a general least squares fit with which it is possible to consider all possible contributions to uncertainty. The application of the method is illustrated with an example, from which it can be seen that uncertainty has been underestimated in the results of the other methods used to date. In addition to the estimate of the stress-optic coefficient and its corresponding standard uncertainty, the method also provides a test to check the consistency of the data and an outlier identification tool.
FORMATION AND DIFFUSION OF THE SUFFIX -ETTIN ITALIAN PERFECT (PASSATO REMOTO): THEORIES AND REALITY

The article deals with a subject of origins and spread of suffix -ett-. The suffix is used to form the fi rst and the third persons singular and the third person plural of the Italian perfect form (passato remoto) of some second conjugation verbs (-ere conjugation verbs). Since the mid-nineteenth century a number of European experts in Romanic languages have offered different theories of suffi x -ett- origins (ideas by Demattio, Amedeo and others). On considering them we have found the majority of the theories groundless and uncreditable. We believe the Bybee — Slobin supposition to be worth noticing among contemporary ones. Th eir idea suggests the spread of suffix -ett- as a consequence of rethinking the pattern that reads as follows: “Passato remoto can have a segment with a double sibilant”. The double sibilant is really present in such Italian perfect forms as disse, scrisse, visse, resse and in the corresponding forms of cognate verbs. Th is supposition is quite logical but still has a number of drawbacks, which prevents us from admitting it as the principle one. We suppose the theory of suffix -ett- originating from perfect forms of the verb stare offered by Friedrich Diez to be the most logical and verifiable. Documented evidence of it is contained in the works of Italian scholars F. D’Ovidio and R. Spina, yet the latter has some discrepancies between the periodization of the suffi x spread offered in the work and the actual data. Some written sources may have failed to survive till present, which makes it diffi cult to reconstruct an objective picture of the process under consideration development. Yet that doesn’t mean that we shouldn’t tackle the issues of the Italian verb historical morphology. Summarizing and analyzing the views of the experts in Romanic languages mentioned above, we have put forward our own periodization of the phenomenon under consideration basing on the documented data.
Youth slang in Jordanian Facebook communities (on the materials of humorous posts)

This paper deals with the investigation of youth language in Jordanian Facebook communities. The analysis covered nearly 500 humorous posts containing the samples of the studied age group's language creativity online. It was also explicated that the participants of such multilingual and multicultural communities as the social media feel the urge to use specific tools of self-expression and self-identification. For this purpose, a particular language and sociocultural codes are actively utilized. The subject of investigation involves linguistic and sociolinguistic peculiarities of Jordanian youth posts in online communities. This study reveals some graphic transmission peculiarities of the slang in the informal online communication since the colloquial Arabic varieties utilized in the new media are de facto not encrypted in the official register. It is pointed out that the society's democratic aspirations, the worship of western culture and the lack of strict state control over the online communication contribute to the development of unofficial Arabic varieties on Facebook. The author focuses on the Arabic / English code mixing, the integration of the English slang lexis in the Jordanian online communication, the utilization of language play in the derivation process etc. The present study has shown the high level of openness of the Jordanian youth slang for the borrowing, language and culture contacts, innovative tools for the language change. This phenomenon is caused by the long lasting impact of the English culture on the Jordanian society both in the real world and in the web. On the other hand, it was found that the informal youth communication involving the obscene lexicon and enantiosemy preserves the world tendency to the usage of low level language as a means of self-expression in the social media.
Contribution of Blockchain Technology In Energy to the Climate Change Efforts

Power systems are evolving rapidly with the integration and active use of emerging digitalization technologies, including but not limited to Artificial Intelligence, Blockchain / Distributed Ledger Technology (B/DLT), and other Information and Communication Technologies. Besides the active integration of digitalization technologies into modern power systems, decarbonization efforts are also shaping the energy transition process to be further demystified under the Digital Green Shift convention in a joint manner. B/DLT appears to be one of the most promising enabling technologies to realize the full energy transition toward next-generation power markets and systems. Standardization of such technologies is playing a critical role in the rapid adoption of emerging technologies. This article aims to reflect the industrial and academic perspectives of practical approaches of B/DLT in the Energy Domain and the potential contributions of the proposed scope in terms of climate change and sustainability aspects.
Edge-Computing Based Dynamic Anomaly Detection for Transmission Lines

Dynamic anomalies often unpredictably intrude into the transmission corridor, threatening secure operation of the grid. Due to the wide geographic spread of the transmission system, it is often difficult to monitor and respond quickly. Automated dynamic anomaly detection algorithms are needed to promptly detect and respond to these ongoing threats. Challenges include identifying warning zones, anomalies, and the anomaly's motion status. In this work, we use images taken by the surveillance camera on the tower to determine the transmission corridor and warning zones. The anomalies inside the warning zone are identified by an object detection model and tracked using an Intersection Over Union (IOU)-based tracking algorithm. In the event of a threat, an alarm will be generated. Extensive testing with real-world data demonstrates the effectiveness of the proposed framework and its potential to scale through deployment on low-cost edge devices.
Hierarchical Relational Graph Learning for Autonomous Multirobot Cooperative Navigation in Dynamic Environments

As a specific kind of cyber–physical systems (CPSs), autonomous robot clusters play an important role in various intelligent manufacturing fields. However, due to the increasing design complexity of robot clusters, it is becoming more and more challenging to guarantee the safety and efficiency for multirobot cooperative navigation in dynamic and complex environments. Although deep reinforcement learning (DRL) shows great potential in learning multirobot cooperative navigation policies, existing DRL-based approaches suffer from scalability issues and rarely consider the transferability of trained policies to new tasks. To address these problems, this article presents a novel DRL-based multirobot cooperative navigation approach named HRMR-Navi that equips each robot with both a two-layered hierarchical graph network model and an attention-based communication model. In our approach, the hierarchical graph network model can efficiently figure out hierarchical relations among all agents that either cooperate for efficiency or avoid obstacles for safety to derive more advanced strategies, and the communication model can accurately form a global view of the environment for a specific robot, thus, the multirobot cooperation efficiency can be further strengthened. Meanwhile, we propose an improved proximal policy optimization (PPO) algorithm based on the Maximum Entropy Reinforcement Learning, named MEPPO, to enhance the robot exploration ability. Comprehensive experimental results demonstrate that, compared with state-of-the-art approaches, HRMR-Navi can achieve more efficient cooperative navigation with less time cost, lower collision rate, higher scalability, and better knowledge transferability.
Probability-Based Synthetic Minority Oversampling Technique

Many real-life datasets suffer from class imbalance, where one or more classes are under-represented in the dataset, resulting in reduced classifier performance, with the expected decline in quality of procedures depending on the classification results, such as financial losses to businesses or inferior product quality. Improving classifier accuracy by handling class imbalance will positively impact classifier accuracy. In this study, we present a Probability-Based Synthetic Minority Oversampling Technique (P-SMOTE) to generate new examples for the minority class. Our proposed solution improves classifier accuracy by enhancing the oversampled examples through sampling the probability distributions present in the data. Results show improved performance over algorithms in the literature, with an average F-score of 0.821 over 13 datasets using 5 classifiers.
A Brand-New Simple, Fast, and Effective Residual-Based Method for Radial Basis Function Neural Networks Training

The radial basis function (RBF) neural network is a type of universal approximator, and has been widely used in various fields.Improving the training speed and compactness of RBF networks are critical for promoting their applications.In the present study, we propose a simple, fast, and effective RBF networks training method, which is based on the residual extreme points and their neighborhoods (thus called the REN method for short in this paper).The REN method calculates RBF centers and widths through a two-level iterative process, and realizes two main functionalities, namely 1) adding multiple centers within one pass through the whole data set, and 2) calculating RBF widths specifically for each center.The use of this algorithm does not need any parameter adjustments, and the models for approximation or classification can be obtained by only one run.The performance of the proposed REN algorithm is compared with the classic and powerful orthogonal least squares (OLS) algorithm.By reaching the same accuracies, the REN algorithm trains RBF networks 50 and 320 times faster, in the chirp (0˜50 Hz, 2 s, 1 kHz, 2001 samples) and two-dimensional peaks (2401 samples) signal approximation tasks respectively, than the OLS algorithm does, and the number of centers obtained by the REN algorithm is reduced by half.When incorporating the same number of centers, the REN algorithm achieves accuracies up to 3 orders of magnitude higher than the best results obtained by the OLS algorithm.In the classification task of a real discrete breast cancer data, both methods result in accuracies comparable to many existent methods, but the REN algorithm has the advantages of fast training speeds and no requirements for parameter adjustments.The REN algorithm proposed in this study may potentially be used for tasks with large scale of data or applications that require high model performances.
Model Predictive Path Planning of AGVs: Mixed Logical Dynamical Formulation and Distributed Coordination

Most of the existing path planning methods of automated guided vehicles (AGVs) are static. This paper proposes a new methodology for the path planning of a fleet of AGVs to improve the flexibility, robustness, and scalability of the AGV system. We mathematically describe the transport process as a dynamical system using an ad hoc mixed logical dynamical (MLD) model. Based on our MLD model, model predictive control is proposed to determine the collision paths dynamically, and the corresponding optimization problem is formulated as 0–1 integer linear programming. An alternating direction method of multipliers (ADMM)-based decomposition technique is then developed to coordinate the AGVs and reduce the computational burden, aiming for real-time decisions. The proposed methodology is tested on industrial scenarios, and results from numerical experiments show that the proposed method can obtain high transport productivity of the multi-AGV system at a low computational burden and deal with uncertainties resulting from the industrial environment.
Vizard: Passing Over Profiling-Based Detection by Manipulating Performance Counters

Cache side-channel attacks have been serious security threats to server computer systems, thus researchers have proposed software-based defense approaches that can detect the security attacks. Profiling-based detectors are lightweight detection solutions that rely on hardware performance counters to identify unique cache performance behaviors by cache side-channel attacks. The detectors typically need to set appropriate criteria to differentiate between attack processes and normal applications. In this paper, we explore the limitations of profiling-based detectors that rely on hardware performance counters.We present an attack scenario, called Vizard, that can bypass the existing profiling-based detectors by manipulating cache performance behaviors of an attack process. Our analysis discloses that cache side-channel attacks include idle periods that can be exploited as attack windows for creating cache events. Vizard generates counterbalancing cache events within the attack windows to hide particular cache performance behaviors of cache side-channel attacks. Our evaluation exhibits that Vizard can effectively bypass profiling-based detectors while maintaining high attack success rates. Our research work represents that attackers can bypass the existing detection approaches by manipulating performance counters.
Amharic Language Hate Speech Detection System from Facebook Memes Using Deep Learning System

In recent years, there has been an increase in the spread of hate speech on social media platforms such as Facebook, as well as an urgent need for governments and scholars to conduct research to address the problem. Hate speech can be spread on social media via video, text, or text-image. As a solution to these problems, we proposed an Amharic language hate speech detection system from Facebook image posts based on deep learning. The research began by manually collecting text-images posted by Facebook users, followed by text extraction, data annotation, text preprocessing, data splitting, feature extraction, model building, evaluation, and testing. Text extraction from posted images is done using Abyssinia-OCR, MetaAppz, and Amharic-OCR. The text preprocessing stage consists of removing unwanted characters, normalizing characters, removing stop words, and word stemming. The dataset is divided into three parts using StratifiedKFold: the train set (80%) to train models, the validation set (10%), to evaluate models, and the test set (10%). The embedding layer and Fasttext word embedding approaches are used to convert the preprocessed texts into vectors. The features are then fed to deep learning models built based on BiLSTM, BiGRU, and Dense Algorithms. After many experiments, the best model with the best hyperparameters is selected. The proposed system detected Amharic hateful text-image posts on Facebook with an accuracy of 81%.