Reviewerly/exHarmony huggingface dataset:
Learning With Label Proportions by Incorporating Unmarked Data

Learning with label proportions (LLP) deals with the problem that the training data are provided as bags, where the label proportions of training bags rather than the labels of individual training instances are accessible. Existing LLP studies assume that the label proportions of all training bags are accessible. However, in many applications, it is time-consuming to mark all training bags with label proportions, which leads to the problem of learning with both marked and unmarked bags, namely, semisupervised LLP (SLLP). In this work, we propose semisupervised proportional support vector machine (SS- ∝ SVM), which extends the proportional SVM ( ∝ SVM) model to its semisupervised version. To the best of our knowledge, SS- ∝ SVM is the first attempt to cope with the SLLP problem. Two realizations, alter-SS- ∝ SVM and conv-SS- ∝ SVM, which are based on alternating optimization and convex relaxation, respectively, are developed to solve the proposed SS- ∝ SVM model. Moreover, we design a cutting plane (CP) method to optimize conv-SS- ∝ SVM with a guaranteed convergence rate and present a fast accelerated proximal gradient method to solve the multiple kernel learning subproblem in conv-SS- ∝ SVM efficiently. Empirical experiments not only justify the superiority of SS- ∝ SVM over its supervised counterpart in classification accuracy but also demonstrate the high competitive computational efficiency of the CP optimization of conv-SS- ∝ SVM.
Real Time Emotion Support System in Text Mining [RTESTM]

Mining opinions from online reviews is an essential step in obtaining the overall sentiment of a product. Deep learning procedure is applied over various fields. User ratings are huge for recommender structures since they consolidate various kinds of energetic information that may influence the exactness of the suggestion. In this work, a deep learning model is utilized to process the user remarks and to create a potential user rating for user comments is proposed. To start with, the system uses sentiments to create a feature vector as the input nodes. Further, the framework tools reduce the noise in the dataset to recover the classification of information mining. To finish, Deep Belief Network (DBN) and sentiment analysis reaches data learning for the approvals.
Innovation work Behaviors as a mediator of the relationship between Reliable leadership and Knowledge Sharing Exploratory study at the Baghdad's Ministry of Labor and Social Affairs

This research aims to measure the impact of reliable leadership in knowledge sharing through the application of Innovation work behaviors at the Ministry of Labor and Social Affairs in Baghdad. Based on the leader-member exchange theory(LMX) so that to test the hypotheses of the study. The Non-probabilistic sample was used on (84%) employees - out of the total participants number of (275) of the senior and middle administration (directors, heads) of the Ministry of Social Affairs operating in Baghdad, The data collected along with the AMOS and SPSS V.23 statistical analyses software. The results showed a positive relation between the reliable leadership and the knowledge sharing. The results also showed that the Innovation work behavior played a partial mediation between reliable leadership and knowledge sharing, so based on these results, theoretical and applicable contents were presented in order to have the results of the study future.
ПРОТИВОДЕЙСТВИЕ ЭКСТРЕМИЗМУ В СЕТИ ИНТЕРНЕТ В РЕСПУБЛИКЕ УЗБЕКИСТАН

This article analyzes the issues of countering extremist crimes, taking into account the development of modern information and communication technologies, and the use of the Internet by extremist organizations. In conclusion, proposals and recommendations were developed to improve measures to counter extremism on the Internet in the Republic of Uzbekistan.
Techniques and Research in Text Summarization - A Survey

With the propulsion of technological innovation and advances, data today is being compared to oil of the previous century. Today, our world is overwhelmed by the gathering and dissemination of huge amounts of data. Thus, Text Summarization has become a valuable tool which would help us save time and efforts. Text summarization can be defined as the process of presenting the most important information from a source (or a set of sources) to produce a concise version for a particular user and tasks, without causing any significant loss of information. In this paper, various text summarization techniques- both extractive and abstractive have been surveyed and presented.
Bayesian Source Localization Using Stochastic Computation

Bayesian models are challenging to implement on hardware with the conventional design methodologies due to their high computational complexity. Conventional digital architectures are designed for deterministic computation and are not optimal for implementing probabilistic algorithms on hardware. In this work, we propose an alternative method to implement the probabilistic algorithms such as Bayesian models on hardware using a stochastic computation (SC) framework. This framework leverages on the probabilistic nature of the Bayesian models and facilitates the implementation of complex probabilistic models using simple logic gates. From an application standpoint, we propose a novel Bayesian source localization model (BSLM) that estimates a source's position in a noisy environment by solving the Bayesian recursive equation implemented on Field Programmable Gate Array (FPGA) with low resource utilization. The proposed SC design framework will pave the way to build complex probabilistic algorithms for real-time edge computing applications.
Intelligent Epidemiological Surveillance in the Brazilian Semiarid

Right after the Chinese example in conducting COVID-19 epidemic originated in Wuhan, the readiness to detect and respond by health authorities to local (sometimes global) epidemics has become central lately. Within the idea of health 4.0, information about the individual is essential in supporting public community health policies. This paper presents a proposal for an epidemiological surveillance system applied to arboviruses. Data mining techniques and Machine Learning (ML) are used to design mathematical models for detecting epidemics enhanced by Aedes Aegypti (vector for dengue, chikungunaya, yellow fever and zica). Based on data, it is proposed an adaptive manner to reach better stability on results. A Prove of Concept (PoC) is presented for dengue epidemics detection, a common endemic disease in the semiarid region of Brazil.
User Behaviour-Based Accurate Detection of Credit Card Frauds

The arrival of communication methods, along with online imbursement dealings, is growing every day. Together with this, fiscal scams linked with these dealings are also escalating. Amid several fiscal scams, credit card scam is the utmost common and hazardous one owing to its extensive practice. To perceive these illegal bases of communications, a credit card scam recognition structure is obligatory. In this paper, we propose to design a user behaviour-based accurate detection for credit card frauds. In this technique, user behaviours over a banking website are collected and analysed. The main features related to user behaviour are frequently visited pages, time spent on each page, clicking, etc. From these details, the page reachability and page utility are determined. From this, the normal and abnormal behaviours of users are classified using web Markov skeleton process (WMSP) model.
Game-Based Literacy for Blind People

This prototype was designed to teach the braille alphabet to blind people using the game-based teaching approach. The software (App) connects via Bluetooth the Braille module. In the first module, the teacher trains the blind student; then in the second module, the student plays and learns. The system generates a set of letters or numbers (sequential, random or personalized), then, the system emits a sound of the letter through the speakers of the mobile device (with the Google API or with the recorded voice of the teacher), and the Braille system shows a letter. The blind student must analyze if the letter heard by the audio is the same as represented in the Braille module; if they match then he or she must mark as correct; otherwise, he or she must mark incorrect. The game has been evaluated by two teachers, the first one, teaches students with special abilities, and another one, is a psychologist dedicated to education, both mention that the game-based system would help to teach the braille system to blind people.
On a complete and sufficient statistic for the correlated Bernoulli random graph model

Inference on vertex-aligned graphs is of wide theoretical and practical importance. There are, however, few flexible and tractable statistical models for correlated graphs, and even fewer comprehensive approaches to parametric inference on data arising from such graphs. In this paper, we consider the correlated Bernoulli random graph model (allowing different Bernoulli coefficients and edge correlations for different pairs of vertices), and we introduce a new variance-reducing technique—called balancing—that can refine estimators for model parameters. Specifically, we construct a disagreement statistic and show that it is complete and sufficient; balancing can be interpreted as Rao-Blackwellization with this disagreement statistic. We show that for unbiased estimators of functions of model parameters, balancing generates uniformly minimum variance unbiased estimators (UMVUEs). However, even when unbiased estimators for model parameters do not exist—which, as we prove, is the case with both the heterogeneity correlation and the total correlation parameters—balancing is still useful, and lowers mean squared error. In particular, we demonstrate how balancing can improve the efficiency of the alignment strength estimator for the total correlation, a parameter that plays a critical role in graph matchability and graph matching runtime complexity.
A Short Text Classification Method Based on Combining Label Information and Self-attention Graph Convolutional Neural Network

Deep learning classification model has achieved good results in many text classification tasks. However, these methods generally consider single text feature, and it is difficult to represent short text. To this end, this paper proposes a text classification method based on combines label information and self-attention graph convolutional neural network. This method introduces an attention mechanism based on label information to obtain more accurate text representation, and it extracts the global features of multiple texts by self-attention graph convolution network for text classification. The experimental results based on R8 and MR show that compared with the latest model, our model increases the F1 values and accuracy by 2.58% and by 2.02% on the MR data; and increases the F1 values and accuracy by 3.52% and by 2.52% on R8 dataset.
Analysing Business Process Anomalies Using Discrete-time Markov chains

Within a business context, anomalies can be viewed as indicators for inefficiencies or fraud, which impact upon product quality and customer satisfaction. The development of approaches to monitor, detect and predict anomalous business processes remains an important research topic. In this paper, we propose a method, combining Discrete-time Markov chains (DTMCs) and hitting probabilities (HP), for detecting anomalies occurring in the execution of business processes. Our method extends standard DTMCs to be able to estimate the probability of occurring for a process instance even though it is partially recorded (i.e., the initial executions are missing). The proposed method, denoted as HPDTMC, does not rely on prior knowledge about anomalies and the business process and can be trained on datasets already consisting of anomalies. A řidák correction is applied to balance the probability of instances of varying length since naturally, process instances with more executions have lower sequence probability and more likely to be detected as anomalies by using DTMCs. We demonstrate the effectiveness of the method by evaluating it on two artificial datasets and one real-life dataset against seven classic anomaly detection methods. In the experiments, our approach reached an F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> score of 0.904 on average. Moreover, the proposed method outperforms competitors under noisy conditions. The main contribution of this paper is the proposed noise-robust method which is able to detect fully or partially recorded process instances of varying lengths.
Algorithms for Securing Medical Web Pages and Files

It is a challenge for online active pharmacies and medical clinics to protect themselves from a wide range of security threats, from a technical point of view and also from the risks involved when working with personal information, such as patients' electronic records. In general, the security of digital information ensures the integrity, confidentiality and availability of data. In particular, the security of digital medical information must be ensured through the implementation of appropriate control measures. In addition, networking and connecting to the Internet entails additional risks, of unauthorized access to medical data or even fraud. In order to avoid the loss, partial or total destruction of medical data, safety and protection, measures are required. Worldwide security solutions have been developed by equipment and application manufacturers, including increasingly effective technical protection methods. The protection measures are designed according to the degree of sensitivity and the significance of the concerned resources. Our paper presents a software solution developed and implemented by using simple, but efficient algorithms compared to the solutions currently available in the field, created mainly for securing platforms/web pages and medical data files. The algorithms are obtained either by modifying the operators or the procedures of the fundamental algorithms and have been implemented in Java or C. Moreover, the parameters of the functions become constant references, in order to not copy them to objects.
Image Recognition System of Pointer Meter in Substation

Instrument monitoring and automatic analysis of instrument reading in substations place priority in the substation digitization process. In this paper, an image recognition system of the pointer instrument in the substation is designed. The embedded technology is used in the image acquisition module of this system to realize image collection and ensure the quality of acquired images. The template matching method is applied in the image analysis model to correct the position of the instrument dial, and the center projection method is designed to automatically recognize the indicator pointer scale. The experimental results show that the system can meet the image quality requirements in field operations. The absolute error of the analytical result is less than 0.004 in the pointer meter reading recognition algorithm, which meets the technical standards of the powering industry.
Logo Based on Improved Generative Countermeasure Network Image Super Resolution Reconstruction Method

Abstract In order to solve the problem that the existing learning based super-resolution reconstruction methods rely too much on the degenerate model while the degradation model is unknown, which leads to noise and edge jagged in the reconstructed image, an image super-resolution reconstruction method based on improved generative countermeasure network is proposed. The generator of the network consists of up sampling module, denoising module and anti-aliasing module Firstly, the input image is sampled by the up sampling module to generate the initial super-resolution image; then, the denoising module and anti-aliasing module are used to reconstruct the clear super-resolution image; in order to reconstruct a better logo image, a joint training loss is introduced, including content loss and resistance loss, and the content loss includes perception loss and edge loss Lost. The experimental results show that, compared with the current super-resolution reconstruction method based on generative countermeasure network (SRGAN), the peak signal-to-noise ratio (PSNR) of the reconstructed image is improved to 0.2675dB and the structural similarity is improved to 0.035, which can effectively improve the quality of logo image reconstruction.
On Compare-and-Swap Optimization for Fully Homomorphic Encrypted Data

Fully homomorphic encryption (FHE) is a powerful scheme that can be applied to perform computations directly on encrypted data for ensuring the security of cloud computing. Using the concept of aggregate plaintext, this paper explores how to optimize the compare-and-swap operation, commonly used for sorting and searching in cloud computing, for FHE data. The resulting performance is optimized by properly arranging the operand locations, corresponding to the desired plaintext slots, and then scheduling a sequence of fundamental homomorphic operations designed to accomplish the targeted operation. Moreover, the hypercube plaintext slot structure is considered to efficiently manipulate the required shift operation and merge multiple operands in an aggregate plaintext. Analytical results show that the number of homomorphic multiplication and shift operations needed in the proposed compare-and-swap operation are logn + 4 and 2logn + 1, respectively, for n-bit data, which is at least 16 times faster than related works for n = 64. Applying the proposed scheme can not only reduce the size of required FHE data, but also improve the total computation time of the chosen operation.
Speaker Change Detection for Conversational Speech using Synthesized Voice Embedding

Speaker change detection is a prominent area of research in voice tasks for many years but used in very limited areas. Speech segmentation, feature extraction, and classification techniques are used as pipeline modules to detect speaker change in speech signals like conversations. This paper provides an approach to detect speaker changes in a conversational speech. Along with that, this paper demonstrates experiments on various sub modules and hyper parameters in the proposed pipeline.
Multi interactive and mixed teaching system of law distance education based on P2P technology

In order to improve the transmission performance and security performance of the multi interactive hybrid teaching system of law distance education, this paper proposes and designs a multi interactive hybrid teaching system of law distance education based on P2P technology. This paper analyzes the network structure of the system, designs the P2P network mode structure, the remote operation principle of the multi interactive teaching mode system, and realizes the design of the multi interactive hybrid hardware. In the software part, node scheduling is carried out under the P2P network of the system to improve the resource transmission performance and security of the multi interactive hybrid teaching system of law distance education. The experimental results show that the designed system has high efficiency of legal resources transmission and low intrusion rate, and can ensure the high quality operation of the system.
Optimization strategy of crossing the desert game based on heuristic algorithm

The game of crossing the desert is a novel mathematical model problem that requires players to keep as much funds as possible when they arrive at the end under the constraints of time, weather, materials and other conditions. Based on Matlab software, this paper uses dynamic programming, heuristic algorithms, Monte Carlo and other methods to study the optimal strategy that players should choose in a situation, and establishes a “simulation action criterion” The final result shows the optimal Game strategy, the optimal strategy gives the result: the highest remaining fund is 10450 yuan.
Problems of CI/CD and DevOps on Security Compliance

In this chapter, the authors define the main problems when working on products in DevOps Teams and on CI/CD pipelines with regard to security and risk management. It focusses on the regulatory requirements and cyberthreats that have impact on organisations. Regulator requirements vary from industry and country. Working with multiple teams on products requires proper alignment in frameworks, controls, and architecture principles in order to be end-to-end protected throughout the connected platforms. This chapter examines the multiple compliance frameworks and architectural principles that can be applied to agile way of working and more precise to CICD pipelines. It defines the main problem statement and questions the authors wanted to answer. The authors looked with a lens of regulated industry since this industry suffers the most and therefore has the biggest benefit from this research project.
Investigating the Learning Progress of CNNs in Script Identification Using Gradient Values

Demands for an automatic translation based on Camera-based Multilingual Optical Character Recognition (CM-OCR) are increasing. In addition, CM-OCR methods usually employ a script identification step before character recognition. Recent approaches for script identification depend on a Convolutional Neural Networks (CNN) thanks to its promising performance in the image recognition task. However, researchers mentioned the importance to understand the decision criteria in CNNs as a warning to employ them for actual tasks as black-box classifiers. Thus, the purpose of this research is to investigate the hyperparameter dependence of CNNs and to visualize the region focused by CNNs in the task of script identification. In this research, we applied Grad-CAM to the script identification task of image classification and used the SIW-13 dataset. We investigated the learning progress of CNNs by defining the value used in Grad-CAM as the "reaction" and visualized the region focused by CNNs in script identification. As a result, the learning process was stabilized in the case that the number of hyperparameters was sufficient for the given training samples even though the hyperparameters which should be tuned were increased. This result demonstrated that the capacity to stably learn training samples depends on the number of hyperparameters. In the insufficient capacity case, the learning process was destabilized and it caused scripts with relatively low accuracy. Analyzing one of the low accuracy scripts of the model using Grad-CAM, we found that some failures progress greatly changes by the difference in hyperparameters of CNNs. Scatter plots of the reaction and the probability clarified the capacity of CNNs in each script.
CONFISCA: An SIMD-Based Concurrent FI and SCA Countermeasure with Switchable Performance and Security Modes

CONFISCA is the first generic SIMD-based software countermeasure that can concurrently resist against Side-Channel Attack (SCA) and Fault Injection (FI). Its promising strength is presented in a PRESENT cipher case study and compared to software-based Dual-rail with Pre-charge Logic concurrent countermeasure. It has lower overhead, wider usability, and higher protection. Its protection has been compared using Correlation Power Analysis, Welch’s T-Test, Signal-to-Noise Ratio and Normalized Inter-Class Variance testing methods. CONFISCA can on-the-fly switch between its two modes of operation: The High-Performance and High-Security by having only one instance of the cipher. This gives us the flexibility to trade performance/energy with security, based on the actual critical needs.