Reviewerly/exHarmony huggingface dataset:
An Unsupervised Way to Understand Artifact Generating Internal Units in Generative Neural Networks

Despite significant improvements on the image generation performance of Generative Adversarial Networks (GANs), generations with low visual fidelity still have been observed. As widely used metrics for GANs focus more on the overall performance of the model, evaluation on the quality of individual generations or detection of defective generations is challenging. While recent studies try to detect featuremap units that cause artifacts and evaluate individual samples, these approaches require additional resources such as external networks or a number of training data to approximate the real data manifold. In this work, we propose the concept of local activation, and devise a metric on the local activation to detect artifact generations without additional supervision. We empirically verify that our approach can detect and correct artifact generations from GANs with various datasets. Finally, we discuss a geometrical analysis to partially reveal the relation between the proposed concept and low visual fidelity.
Swot Analysis of Cloud Computing Problems in Higher Education

The article analyzes the net income from electricity production in the energy system of the Republic of Uzbekistan for 2005-2020 and developed proposals for its improvement. The mathematical model of this process is analyzed, and on the basis of the goal to be achieved in 2030, the net income from the supply of electricity to the country until 2030 is determined.
StreamDFP: A General Stream Mining Framework for Adaptive Disk Failure Prediction

We explore machine learning for accurately predicting imminent disk failures and hence providing proactive fault tolerance for modern large-scale storage systems. Current disk failure prediction approaches are mostly offline and assume that the disk logs required for training learning models are available a priori. However, disk logs are often continuously generated as an evolving data stream, in which the statistical patterns vary over time (also known as concept drift). Such a challenge motivates the need of online techniques that perform training and prediction on the incoming stream of disk logs in real time, while being adaptive to concept drift. We first measure and demonstrate the existence of concept drift on various disk models in production. Motivated by our study, we design <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">StreamDFP</small> , a general stream mining framework for disk failure prediction with concept-drift adaptation based on three key techniques, namely online labeling, concept-drift-aware training, and general prediction, with a primary objective of supporting various machine learning algorithms. We extend <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">StreamDFP</small> to support online transfer learning for minority disk models with concept-drift adaptation. Our evaluation shows that <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">StreamDFP</small> improves the prediction accuracy significantly compared to without concept-drift adaptation under various settings, and achieves reasonably high stream processing performance.
Research on the Construction of Unmanned Combat System under the Background of “Network Cloud Enabling”

&#x201C;Unmanned combat, as the key direction of army transformation, has put forward high requirements for equipment information networking, and &#x201C;network cloud enabling&#x201D; is a collection of network communication technologies under the new situation, covering distributed computing technology, cloud technology and load balancing technology, etc. It can be effectively applied in the macro battlefield to provide effective, flexible, dynamic and highly reliable data services and information support for each unmanned combat unit. This paper analyzes the essence of &#x201C;network cloud enabling&#x201D; technology from the perspective of its connotation, and analyzes the system construction of &#x201C;network cloud enabling&#x201D; in unmanned combat battlefield application according to the framework of resource layer, capability layer, platform layer, application layer and management layer.
A Novel Reversible Data Hiding in Encrypted Images by Controlled Swapping of Adjacent Pixels

Reversible data hiding (RDH) got much attention in the past few years due to its applications in various areas. In this research, we have considered the challenging task of performing RDH in encrypted images. The security of the image encryption process is mainly evaluated using two types of analysis: one is the entropy measure and the other is histogram analysis. The RDH schemes in encrypted images always result in some alterations in the entropy measure and some changes in the histogram of the encrypted images. In this paper, we propose a novel RDH scheme that will be capable to generate an encrypted image with hidden data without any change in the entropy and in the histogram. The new algorithm is a block-wise RDH process in which the data hider can hide one single bit of additional data in an encrypted image block having size $$B\times B$$ pixels. The pixels of a selected block will be divided into two groups: white pixels and black pixels. The classification of pixels is done based on the checkerboard pattern. The block will remain unchanged to hide a bit value 0, and all the adjacent white pixels and black pixels will be swapped to hide a bit value 1. To extract the hidden bits at the time of image restoration, the closeness between the pixel intensity value is analyzed. The experimental results show that the new RDH schemes provide a good embedding rate without affecting the bit error rate.
Deperturbation of Online Social Networks via Bayesian Label Transition

Online social networks (OSNs) classify users into different categories based on their online activities and interests, a task which is referred as a node classification task. Such a task can be solved effectively using Graph Convolutional Networks (GCNs). However, a small number of users, so-called perturbators, may perform random activities on an OSN, which significantly deteriorate the performance of a GCN-based node classification task. Existing works in this direction defend GCNs either by adversarial training or by identifying the attacker nodes followed by their removal. However, both of these approaches require that the attack patterns or attacker nodes be identified first, which is difficult in the scenario when the number of perturbator nodes is very small. In this work, we develop a GCN defense model, namely GraphLT, which uses the concept of label transition. GraphLT assumes that perturbators' random activities deteriorate GCN's performance. To overcome this issue, GraphLT subsequently uses a novel Bayesian label transition model, which takes GCN's predicted labels and applies label transitions by Gibbs-sampling-based inference and thus repairs GCN's prediction to achieve better node classification. Extensive experiments on seven benchmark datasets show that GraphLT considerably enhances the performance of the node classifier in an unperturbed environment; furthermore, it validates that GraphLT can successfully repair a GCN-based node classifier with superior performance than several competing methods.
Constrained Reachability Problems for a Planar Manipulator

We address an optimal reachability problem for a planar manipulator in a constrained environment. After introducing the optmization problem in full generality, we practically embed the geometry of the workspace in the problem, by considering some classes of obstacles. To this end, we present an analytical approximation of the distance function from the ellipse. We then apply our method to particular models of hyper-redundant and soft manipulators, by also presenting some numerical experiments.
E-Commerce Knowledge Extraction via Multi-modal Machine Reading Comprehension

Extracting commodity attributes from unstructured data is an essential information extraction task in the e-commerce domain. It plays an important role in tasks such as commodity recommendation and commodity knowledge base extension. Traditional models for these tasks often only used text modal information, which is not enough to describe the commodity. In recent years, we increasingly see using multi-modal data to describe commodities such as text, images, and videos, which provides the possibility for better product attribute extraction. To this end, we propose a novel model named E-commerce Knowledge Extraction via Multi-modal Machine Reading Comprehension (EKE-MMRC). Specifically, it finds the missing attributes from the existing knowledge base and generates problems. Then packages them with multi-modal descriptions and encode as fusion vector. And then, decode the correlation of description with the attribute and generate an answer from the fusion vector. Finally, use the correlation as the weight of voting for the answer. At the same time, we contributed a dataset for this task based on public e-commerce data: E-commence Multi-modal Commodity Attributes Extraction Dataset (E-MCAE). We also conducted experiments on a public dataset. The experimental results show that our proposed method is effective, and it achieves more than 15% improvement compared with the SOTA single-modal extraction method.
A Label Similarity Attention Mechanism for Multi-label Emotion Recognition

In recent years, multi-label emotion recognition has attracted more and more attention, and its purpose is to identify the emotional state of a text, such as happiness and like. At present, most of the methods to solve the problem of multi-label emotion recognition focus on analyzing the semantics of the text, ignoring the semantic information brought by emotion tags, and not modeling the semantic relationship between emotion labels and text. In this paper, a method of multi-label emotion recognition based on label similarity attention mechanism is proposed. In this method, bidirectional LSTM (Long Short-Term Memory) is used to obtain the context knowledge of the text, so as to achieve the purpose of learning the global information of the text. The label features are learned by the label encoder. The label similarity attention mechanism is used to obtain the correlation matrix between the label and the word representation, and further strengthen the semantic information. At the same time, CNN (Convolutional Neural Networks) is used to obtain local semantic information. The semantic representation of sentences is enriched. Finally, emotion recognition is carried out through the classifier. In the classifier, this paper proposes a joint loss function, which enables the model to learn the emotional characteristics of texts and labels at the same time. The model combines the global and local information of the text to show the semantic relationship between the text and the emotional label. The experimental results on the NLPCC2014Task1 dataset show that the proposed model has a certain performance improvement compared with the benchmark method.
WDM-Conscious Synaptic Receptor Assisted by SOA+EAM

We experimentally demonstrate the simultaneous weighing and summation of two 23-nm spaced, frequency-coded spike trains with 100-ps spike width. Operation of the synaptic receptor at low BER is confirmed at 10 Gb/s information rate.
Hierarchical Reinforcement Learning Under Mixed Observability

The framework of mixed observable Markov decision processes (MOMDP) models many robotic domains in which some state variables are fully observable while others are not. In this work, we identify a significant subclass of MOMDPs defined by how actions influence the fully observable components of the state and how those, in turn, influence the partially observable components and the rewards. This unique property allows for a two-level hierarchical approach we call HIerarchical Reinforcement Learning under Mixed Observability (HILMO), which restricts partial observability to the top level while the bottom level remains fully observable, enabling higher learning efficiency. The top level produces desired goals to be reached by the bottom level until the task is solved. We further develop theoretical guarantees to show that our approach can achieve optimal and quasi-optimal behavior under mild assumptions. Empirical results on long-horizon continuous control tasks demonstrate the efficacy and efficiency of our approach in terms of improved success rate, sample efficiency, and wall-clock training time. We also deploy policies learned in simulation on a real robot.
Evolving Trends in Scholarly Publishing and Impact on the Monotony of Predators

Open access (OA) journals have emerged in the past two decades and have influenced scholarly publications to a great extent, contributing to the benefits of OA publishing. The research focuses on the evolving trends of OA and the measures required to address the issue of exponentially growing predatory publishing. The study concludes the research by proposing measures for the scholarly world to address predatory publishing globally. The scope of the research is qualitative, discussing the fair publishing practices and implications of Plan S and open peer review. In the methodology, the study reviews the wealth of published literature in the discipline to decide the variables and impact on the dependent variable.
Sentiment Analysis of German Texts in Finance: Improving and Testing the BPW Dictionary

Using the dictionary-based approach to measure the sentiment of finance-related texts is primarily focused on English-speaking content. This is due to the need for domain-specific dictionaries and the primary availability of those in English. Through the contribution of Bannier et al. (2019b), the first finance-related dictionary is available for the German language. Because of the novelty of this dictionary, this paper proposes several reforms and extensions of the original word lists. Additionally, I tested multiple measurements of sentiment. I show that using the edited and extended dictionary to calculate a relative measurement of sentiment, central assumptions regarding textual analysis can be fulfilled and more significant relations between the sentiment of a speech by a CEO at the Annual General Meeting and subsequent abnormal stock returns can be calculated.
RST-DE: Rough Sets-Based New Differential Evolution Algorithm for Scalable Big Data Feature Selection in Distributed Computing Platforms

In data analysis, data scientists usually focus on the size of data instead of features selection. Owing to the extreme growth of internet resources data are growing exponentially with more features, which leads to big data dimensionality problems. The high volume of features contains much of redundant data, which may affect the feature classification in terms of accuracy. In the current scenario, feature selection attracts the research community to identify and to remove irrelevant features with more scalability and accuracy. To accommodate this, in this research study, we present a novel feature selection framework that is implemented on Hadoop and Apache Spark platform. In contrast, the proposed model also includes rough sets and differential evolution (DE) algorithm, where rough sets are used to find the minimum features, but rough sets do not consider the degree of overlying in the data. Therefore, DE algorithm is used to find the most optimal features. The proposed model is studied with Random Forest and Naive Bayes classifiers on five well-known data sets and compared with existing feature selection models presented in the literature. The results show that the proposed model performs well in terms of scalability and accuracy.
Alerting effects occur in simple—But not in compound—Visual search tasks.

Alerting (e.g., a brief brightening of the screen just before a target display) is known to facilitate visual search in simple tasks that involve the single step of detecting a pop-out item within a stimulus array. What is not known is whether alerting facilitates performance also in compound search tasks which involve two steps: First, locate the pop-out item, then identify a detail of that item. In a series of five experiments, we show that alerting facilitates performance of each component of a compound task when tested separately, (Experiments 2a and 2b) but not when the components are combined in a compound task (Experiment 1). Yet, alerting does facilitate performance in a compound task when the pop-out item is displayed in the same location on successive trials (Experiment 3). We hypothesized that such spatial repetition allows attention to linger at that location, thus allowing the first component (locate the pop-out item) to be bypassed. In practice, this turns the compound task into a simple task. That hypothesis was confirmed in Experiment 4 using a reorienting cue to shift the focus of attention to another location. An overall account of the absence of alerting effects in compound search tasks is proposed in terms of the temporal relationship between a period of enhancement rendered as an ex-Gaussian function and the hypothesized sequence of processing stages in visual search. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
A federated machine learning approach to detect international revenue share fraud on the 5G edge

The fifth-generation (5G) of broadband cellular networks is giving rise to new paradigms of distributed computing, such as Edge Computing and Multi-access Edge Computing (MEC). The possibility of hosting Machine Learning (ML) applications close to the end-users presents advantages, such as better privacy (e.g., sensitive data is not shared to other systems), the reduction of communication latency, improvement of application performance, and more efficient energy consumption. However, the Edge Computing and MEC paradigms also pose challenges to ML. For instance, the data can be distributed among distinct edges and might not be shared (e.g., due to privacy issues). Also, the ML models might be trained on edge devices with limited computational resources. In this paper, we propose a Federated ML architecture to train ML models on the 5G Edge, using decentralized data and light ML training algorithms. Our architecture includes edge nodes to train models with local data and a centralized node to aggregate the resulting models. As a case study, we address an International Revenue Share Fraud (IRSF) task, assuming a real-world dataset collected from a leading provider of analytics solutions for the Telecom industry. We evaluate our architecture during two iterations of a Federated ML procedure and then we compare it with a centralized baseline ML model that is currently adopted by the software company. Overall, the experimental results show that the proposed Federated ML approach outperforms the baseline ML model, thus supporting its potential usage to detect IRSF on the 5G mobile network edge.
Education Industry 4.0: Big Data Security

This paper is to focus on how computer security can protect big--data in education systems. Solutions need to be sought so that the available data is secured in terms of privacy, authenticity, and integrity. Education trends have traditionally needed to be changed to a pattern of education shifting to industry 4.0. The push towards the 4.0 education era has resulted in a lot of data being exposed. To prevent this problem from occurring, studies on how to keep storage data from being deleted without permission, protect users ’privacy rights, and how users can use the right rules to obtain information need to be carefully studied. Big-data security techniques and models need to be applied in the field of education so that the data is preserved. In addition, elements in big-data will also be explained to allow a clearer understanding of the meaning of big-data. As such, several suggestions have been expressed to protect big-data architecture by introducing Hadoop technology. Furthermore, data privacy is also protected, such as cryptography and access control. Integrity security and data management are also suggested to be taken into account so that big-data in the education system is maintained as a whole. The methodology used is according to Kofod-Petersen where the information is searched according to the selected keywords and titles. It is hoped that the study and recommendations that have been given, can solve the main problems in the security dimension. In addition, the amount of big-data that exists can be protected by privacy and integrity. Finally, it is hoped that research on big-data security will continue to be done to keep the data safe.
Encryption and Decryption of Text

One of the most effective ways to protect the privacy and security of data is by implementing encryption. This method uses a key known as a decryption process to hide the original content of the data. The objective of this method is to prevent unauthorized access to the data. This method is very simple and can be easily implemented. It takes 8 bit code value of the alphabet and performs some simple calculation such as logical not and simple binary division.
Redefining prior feature space via finetuning a triplet network for few‐shot learning

Few-shot learning is to distinguish novel concepts with few annotated data, which has attracted much attention due to its requirement of limited training data for target classes. Recent few-shot learning methods usually pretrain a feature extractor with images from the base set to boost the performance of few-shot tasks and classify novel categories in this prior feature space. However, it is difficult for the pretrained feature extractor to extract accurate representations for novel categories, resulting in large amounts of overlapping areas between new classes. To address these issues, the prior feature space with a triplet network to learn a more discriminative space is refined, where features belonging to same class are pulled together and that from different classes are pushed apart. Specifically, the authors first follow recent paradigm of pretraining to obtain a prior feature space. Then, a triplet network with contrastive learning is trained to project the features from this space into a low-dimensional one. The main difference lies in that the authors’ model is based on Maximum A Posteriori (MAP) and the triplet network with hallucinated features is finetuned from it to make them generalise well to novel categories. Finally, the authors conduct classification tasks in the finetuned space. The authors’ intuition is that the overlapping areas in novel categories can be separated by finetuning the triplet network pretrained on base set with contrastive learning. Experimental results on four few-shot benchmarks show that it significantly outperforms the baseline methods, improves around 1.09% ∼ 13.09% than the best results in each dataset on both 1- and 5-shot tasks.
Digital Regulation of Online Communication: Privacy and Oblivion versus Publicity and Trust

Cyber-space creates transparency similar to small groups’ collective action. Digital regulative tools (code and behavior patterns) replace the openness of human life in a traditional community. Online communicative activity is regulated by State laws, cultural habits to perceive and react, program code (limited ways of acting), ethical norms. Thus, courtesy rules differ for interaction by email, social media (channels, blogs) or messengers’ chats. Survey results show the balance of regulation tools.