Reviewerly/exHarmony huggingface dataset:
Sentiment Analysis of Public Health Concerns of Tokyo 2020 Olympics Using LSTM

In recent years, in this age of social media, the Tokyo Olympic Games 2020 has received massive online sentiments and feedback from individuals, despite the Tokyo Olympic Games 2020’s motto of “united by emotions.” In this paper, a Recurrent Neural Network model was trained and implemented for the Tokyo 2020 Olympics public health sentiment analysis. A total of ten thousand (10,000) tweets were collected and trained, with tweets classified as positive, negative, or neutral. The RNN model implemented was the LSTM model. The model's performance was evaluated using performance metrics such as accuracy, precision, recall, and F1-score. According to the results, the LSTM model performed effectively, with a final validation accuracy of 88.2% and precision values of 0.86, 0.90, and 0.89 for negative, neutral, and positive predictions, respectively. The results of the LSTM model's high accuracy indicate that the model successfully determined the public health sentiments of the Tokyo 2020 Olympics, which were found to be positive at 40.39%.
Enhanced Parallelized DNA-Coded Stream Cipher Based on Multiplayer Prisoners’ Dilemma

Data encryption is essential in securing exchanged data between connected parties. Encryption is the process of transforming readable text into scrambled, unreadable text using secure keys. Stream ciphers are one type of an encryption algorithm that relies on only one key for decryption and as well as encryption. Many existing encryption algorithms are developed based on either a mathematical foundation or on other biological, social or physical behaviours. One technique is to utilise the behavioural aspects of game theory in a stream cipher. In this paper, we introduce an enhanced Deoxyribonucleic acid (DNA)-coded stream cipher based on an iterated <i>n</i>-player prisoner’s dilemma paradigm. Our main goal is to contribute to adding more layers of randomness to the behaviour of the keystream generation process; these layers are inspired by the behaviour of multiple players playing a prisoner’s dilemma game. We implement parallelism to compensate for the additional processing time that may result from adding these extra layers of randomness. The results show that our enhanced design passes the statistical tests and achieves an encryption throughput of about 1,877 Mbit/s, which makes it a feasible secure stream cipher.
Demystifying Semantic Intelligence for Enabling Intelligent Applications

An intelligent agent is required to fuse heterogeneous sources of information together for which it should be equipped with both the data-driven (statistical) and knowledge-driven (symbolic) AI disciplines. Semantic Technologies make it possible by creating links between disparate and heterogeneous data. When the data is linked as well as open, it is termed as Linked Open Data (LOD). The Symbolic AI and sub-symbolic AI have to go together. The symbolist approach nowadays is manifested as a knowledge graph that advanced statistics and machine learning can run on top of.
NoR-VDPNet++: Real-Time No-Reference Image Quality Metrics

Efficiency and efficacy are desirable properties for any evaluation metric having to do with Standard Dynamic Range (SDR) imaging or with High Dynamic Range (HDR) imaging.However, it is a daunting task to satisfy both properties simultaneously.On the one side, existing evaluation metrics like HDR-VDP 2.2 can accurately mimic the Human Visual System (HVS), but this typically comes at a very high computational cost.On the other side, computationally cheaper alternatives (e.g., PSNR, MSE, etc.) fail to capture many crucial aspects of the HVS.In this work, we present NoR-VDPNet++, a deep learning architecture for converting full-reference accurate metrics into no-reference metrics thus reducing the computational burden.We show NoR-VDPNet++ can be successfully employed in different application scenarios.
Reducing VR Sickness by Directing User Gaze to Motion Singularity Point/Region as Effective Rest Frame

Simulation sickness is one major obstacle in proliferation of virtual reality.The sensory mismatch between the visual and vestibular senses about user motion is attributed as the main cause.One effective method has been the use of the rest frame, which refers to the reference object that remains fixed in position with respect to the user.A popular choice of the rest frame is the virtual nose, but it can direct one's attention away from the main part of the navigation content.We propose to instead use the area within the screen space that represents the least amount of motion in the navigation content, called the Motion Singularity Point/Region (MSP/R) as such a rest frame.Viewing such a region is thus expected to reduce the sensory conflict and ensuing sickness.Such a region can be found by analyzing the content with respect to motion and estimating the region(s) of the image space with the least relative amount of the total optical flow.We experimentally validated the VR sickness reduction effect of looking at the MSP/R compared to that of the virtual nose.In addition, we confirmed that the content agnostic MSP/R to be much coinciding with the user's natural viewing direction, making it less distracting from the main content.This makes the MSP/R a more practical and viable rest frame object for sickness reduction than e.g. the virtual nose.
Security of wireless keyboards: Threats, vulnerabilities and countermeasures

Introduction/purpose: This paper provides an overview of research on computer system vulnerabilities caused by compromised electromagnetic radiation by wireless keyboards. Wireless devices that use event-triggered communication have been shown to have critical privacy issues due to the inherent leakage associated with radio frequency emissions. Wireless connectivity technology is a source of signal emanation that must be protected in terms of performance and security. Methods: Wireless device vulnerabilities and side-channel attacks are observed, along with electromagnetic emission of radio waves. Results: The findings highlight a specific wireless keyboard's security and encryption flaws. The results of penetration testing reveal vulnerabilities of targeted wireless keyboards in terms of outdated firmware, encryption, wireless reliability, and connection strength. Conclusion: Wireless keyboards have security flaws that disrupt radio communication, giving a malicious user complete access to the computer to which the keyboard is connected. An attacker can steal sensitive data by observing how the system works using compromised electromagnetic emissions.
Baseline Model of High-Sensitivity Data Transfer in Power Grid Business

In the process of implementing data leakage prevention project, power grid enterprises encounter unclear data life cycle security, it is difficult to implement the data classification and classification system, and the formulation of DLP strategy relies too much on individual subjective decisions, aiming at these problems, the author proposes a set of methodology which conforms to the life cycle security of grid data, the design of data classification and classification standards in line with power grid enterprises can objectively guide the data classification and classification work of power grid enterprises and the formulation of DLP strategy, improve the efficiency of data leakage prevention work, and make the data leakage prevention work more standardized and standardized.
An Attention-Based Architecture for Hierarchical Classification With CNNs

Branch Convolutional Neural Nets have become a popular approach for hierarchical classification in computer vision and other areas.Unfortunately, these models often led to hierarchical inconsistency: predictions for the different hierarchy levels do not necessarily respect the class-subclass constraints imposed by the hierarchy.Several architectures to connect the branches have arisen to overcome this limitation.In this paper, we propose a more straightforward and flexible method: let the neural net decide how these branches must be connected.We achieve this by formulating an attention mechanism that dynamically determines how branches influence each other during training and inference.Experiments on image classification benchmarks show that the proposed method can outperform stateof-the-art models in terms of hierarchical performance metrics and consistency.Furthermore, although sometimes we found a slightly lower performance at the deeper level of the hierarchy, the model predicts much more accurately the ground-truth path between a concept and its ancestors in the hierarchy.This result suggests that the model does learn not only local class memberships but also hierarchical dependencies between concepts.
Research on Leakage Recognition Method of Photovoltaic Connected to Distribution Network Based on NCA and SSA-KELM

When leakage fault occurs in photovoltaic connected to the distribution network,there is a security risk in conventional leakage fault devices since they cannot distinguish between fault leakage current and normal leakage current. To resolve these issues, a leakage identification method for photovoltaic connected to distribution network based on NCA and SSA-KELM is proposed in this paper. First, a 9-dimensional original fault feature set is constructed, and a 4-dimensional high-correlation feature subset is selected from the 9-dimensional feature set by NCA. Then, the optimized feature subset is used as input to establish a leakage recognition model based on KELM, and SSA optimizes KELM model parameters. Finally, the proposed SSA-KELM method is applied to leakage recognition, and is combined with the standard KELM, SVM, BPNN. Comparative results indicate that the proposed method is more accurate and generalizable.
Meta-Learning Multi-Scale Radiology Medical Image Super-Resolution

High-resolution medical images have important medical value, but are difficult to obtain directly. Limited by hardware equipment and patient’s physical condition, the resolution of directly acquired medical images is often not high. Therefore, many researchers have thought of using super-resolution algorithms for secondary processing to obtain high-resolution medical images. However, current super-resolution algorithms only work on a single scale, and multiple networks need to be trained when super-resolution images of different scales are needed. This definitely raises the cost of acquiring high-resolution medical images. Thus, we propose a multi-scale super-resolution algorithm using meta-learning. The algorithm combines a meta-learning approach with an enhanced depth of residual super-resolution network to design a meta-upscale module. The meta-upscale module utilizes the weight prediction property of meta-learning and is able to perform the super-resolution task of medical images at any scale. Meanwhile, we design a non-integer mapping relation for super-resolution, which allows the network to be trained under non-integer magnification requirements. Compared to the state-of-the-art single-image super-resolution algorithm on computed tomography images of the pelvic region. The meta-learning multiscale super-resolution algorithm obtained a surpassing of about 2% at a smaller model volume. Testing on different parts proves the high generalizability of our algorithm. Multi-scale super-resolution algorithms using meta-learning can compensate for hardware device defects and reduce secondary harm to patients while obtaining high-resolution medical images. It can be of great use in imaging related fields.
Swarm-like ground-state searching based on a path-integral quantum Monte Carlo method for automatically fluctuation-controlled simulated annealing

The path-integral quantum Monte Carlo (PIQMC) method is widely used as a classical simulation algorithm for quantum annealing. Replicas represent different points in imaginary time. We propose and demonstrate a swarm-like ground-state searching algorithm based on the PIQMC method for automatically fluctuation-controlled simulated annealing. Replicas help one another to search for the ground state of an Ising system as if forming a swarm and working cooperatively. Their interactions are local, simple and fluctuate to a certain degree. Such fluctuations are necessary to escape local minima, but the fluctuations do not need to be explicitly controlled. The size of fluctuations is automatically adjusted as annealing proceeds. We solve max-cut problems for algorithm evaluation, each of which corresponds to a graph of 100 vertices. We also solve the same problems using a conventional method based on the Metropolis algorithm for comparison.
Application of Cube IQ software and multicriteria optimization models for the selection of vehicles for the transport of goods in the Serbian Armed Forces

Introduction/purpose: An adequate selection of vehicles used for the transport of goods is a very important factor that affects the economical and rational use of vehicle fleets, as well as the quality and efficiency of carrying out transport activities in the Serbian Armed Forces. The goal of this work is to design a model that should be of help to the traffic service authorities to select the vehicle that is best for the performance of the assigned transport task based on the defined criteria. Methods: This paper therefore proposes a model for the selection of vehicles for the transport of goods using a fuzzy logic system, as a type of artificial intelligence system. In order to solve the problem of choosing a vehicle for the transport of goods, five criteria are defined in the work based on a survey of the commanders of the transport lines, which represent the input values in the fuzzy logic system.The vehicle is selected based on five criteria. The input variables are represented by three membership functions, while the output variable is defined by five membership functions. All the rules in the fuzzy logic system are determined using the rule premise weight aggregation method (ATPP), which enables the formation of a rule base based on experience. By applying this method and based on the number of input variables and the number of their membership functions, a base of 243 rules was defined. The values of the weighting coefficients of the membership functions were determined using the LMAW method. A user "interface" program was created for the developed fuzzy logic system, which enables the practical application of this model. Results: The model was tested on the example of choosing the optimal vehicle for goods transported to the IVP "Pasuljanske livade" in 2020. The selection of the optimal means of transport was made among the transport motor vehicles that are most used in the Serbian Army, namely: TAM 150 T11, FAP 2026 and FAP 1118. After packing all three vehicles with these goods in Cube IQ and after performing calculation and evaluation of individual vehicles in the user "interface" program, the values of the output variable for each vehicle were obtained. The obtained values for each vehicle were ranked and the optimal vehicle for the transport of defined goods was shown to be the FAP 1118. Conclusion: The significance of this study is that it is among the first ones to demonstrate the application of a model based on artificial intelligence that solves the problem of vehicle selection for the transportation of movable assets. The study provides considerable opportunity for further research.
Case Analysis: BeefLedger

This chapter provides the findings of the case analysis of BeefLedger. It introduces the background of the company, the issues that the company wishes to address, different stages of blockchain implementation, the benefits of blockchain, and how the company overcame the barriers.
Reinforcing Artificial Neural Networks through Traditional Machine Learning Algorithms for Robust Classification of Cancer

Machine Learning (ML)-based prediction and classification systems employ data and learning algorithms to forecast target values. However, improving predictive accuracy is a crucial step for informed decision-making. In the healthcare domain, data are available in the form of genetic profiles and clinical characteristics to build prediction models for complex tasks like cancer detection or diagnosis. Among ML algorithms, Artificial Neural Networks (ANNs) are considered the most suitable framework for many classification tasks. The network weights and the activation functions are the two crucial elements in the learning process of an ANN. These weights affect the prediction ability and the convergence efficiency of the network. In traditional settings, ANNs assign random weights to the inputs. This research aims to develop a learning system for reliable cancer prediction by initializing more realistic weights computed using a supervised setting instead of random weights. The proposed learning system uses hybrid and traditional machine learning techniques such as Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), Random Forest (RF), k-Nearest Neighbour (kNN), and ANN to achieve better accuracy in colon and breast cancer classification. This system computes the confusion matrix-based metrics for traditional and proposed frameworks. The proposed framework attains the highest accuracy of 89.24 percent using the colon cancer dataset and 72.20 percent using the breast cancer dataset, which outperforms the other models. The results show that the proposed learning system has higher predictive accuracies than conventional classifiers for each dataset, overcoming previous research limitations. Moreover, the proposed framework is of use to predict and classify cancer patients accurately. Consequently, this will facilitate the effective management of cancer patients.
ROCKAD: Transferring ROCKET to Whole Time Series Anomaly Detection

The analysis of time series data is of high relevance in fields like manufacturing, health, automotive, or science. In this paper, we propose ROCKAD, a kernel-based approach for semi-supervised whole time series anomaly detection, i.e. the assignment of a single anomaly score to an entire time series. Our key idea is to use ROCKET as an unsupervised feature extractor and to train a single as well as an ensemble of k-nearest neighbors anomaly detectors to deduce an anomaly score. To the best of our knowledge, this is the first approach to transfer the ideas of ROCKET to the task of anomaly detection. We systematically evaluate ROCKAD for univariate time series and show it is statistically significantly better compared to baseline methods. Additionally, we show in a case study that ROCKAD is also applicable to multivariate time series.
Gated Memory Unit: A Novel Recurrent Neural Network Architecture for Sequential Analysis

The predominant models used to analyze sequential data today are recurrent neural networks, specifically Long-Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models, which utilize a temporal value known as the hidden state. These recurrent neural networks process sequential data by storing and modifying a hidden state through the use of mathematical functions known as gates. However, these networks hold many flaws such as limited temporal vision, insufficient memory capacity, and ineffective training times. In response, we propose a simple architecture, the Gated Memory Unit, which utilizes a new element, the hidden stack, a data stack implementation of the hidden state, as well as novel gates. This, along with a parameterized bounded activation function (PBA), allows the Gated Memory Unit (GMU) to outperform existing recurrent models effectively and efficiently. Trials on three datasets were used to display the new architecture’s superior performance and reduced training time as well as the utility of the novel hidden stack compared to existing recurrent networks. On data which measures the daily death rate of SARS-Cov-2, the GMU was able to reduce losses to half that of comparable models and did so in nearly half the training time. Additionally, through the use of a generated spiking dataset, the GMU depicted its ability to use its hidden stack to store information past directly observable time steps. We prove that the Gated Memory Unit performs well on a variety of tasks and can outperform existing recurrent architectures.
T2-fuzzy multi-fused facial image fusion (T2FMFImgF): An efficient face recognition

This paper presents a novel decision-making method for face recognition where the features were extracted from the original image fused with its corresponding true and partial diagonal images. To extract features, we adopted the generalized two-dimensional FLD (G2DFLD) feature extraction technique. The feature vectors from a test image are given as input to neural network-based classifier. It is trained with the feature vectors of original image and diagonally fused images and thereby the merit weights with respect to different classes were generated. To address the factors that affect the face recognition accuracy and uncertainty related to raw biometric data, a fuzzy score for each of the classes is generated by treating a type-2 fuzzy set. This type-2 fuzzy set is formed by the feature vectors of both the diagonally fused training samples and the test image of the respective classes. A concluding score for each of the classes under consideration is computed by fusing complemented merit weight with the complemented fuzzy score. These class-wise concluding scores are considered in the face recognition process. In this study, the well-known face databases (AT&amp;T, UMIST and CMU-PIE) are used to evaluate the performance of the proposed method. The experimental results illustrate the fact that the proposed method has exhibited superior classification precision as compared with other state-of-art methods. Our T2FMFImgF method achieves highest face recognition accuracies of 99.41%, 98.36% and 89.80% in case of AT&amp;T, UMIST and CMU-PIE (with expression), respectively while for CMU-PIE (with Light) the highest recognition accuracy is 97.957%. In addition to it, the presented method is quite successful in fusing and classifying textural information from the original and partial diagonal images by integrating them with type-2 fuzzy set-based treatment.
On Compositionality in Data Embedding

Representing data items as vectors in a space is a common practice in machine learning, where it often goes under the name of "data embedding". This representation is typically learnt from known relations that exist in the original data, such as co-occurrence of words, or connections in graphs. A property of these embeddings is known as compositionality, whereby the vector representation of an item can be decomposed into different parts, which can be understood separately. This property, first observed in the case of word embeddings, could help with various challenges of modern AI: detection of unwanted bias in the representation, explainability of AI decisions based on these representations, and the possibility of performing analogical reasoning or counterfactual question answering. One important direction of research is to understand the origins, properties and limitations of compositional data embeddings, with the idea of going beyond word embeddings. In this paper, we propose two methods to test for this property, demonstrating their use in the case of sentence embedding and knowledge graph embedding.
Research on Speech Adversarial Sample Attack Based on APA

More and more scholars have pointed out that the deep learning model is easy to deceive. This paper proposes an APA-based speech adversarial sample attack, which is a black-box untargeted attack method and is recommended for evaluating the reliability of acoustic models. To verify the effectiveness of this attack method, this paper conducts adversarial sample attacks on the SpeechCommands and LibriSpeech datasets for the Deepspeech model. The attack results show that the method proposed in this paper can generate adversarial samples with a wrong word rate of up to 50% within 50 rounds, and the difference between the adversarial samples and the original samples is very small, and the similarity of samples is as high as 90%.