Reviewerly/exHarmony huggingface dataset:
Spatial distribution of the geochemical associations in the Babyak Mo-Ag-Au-W-Bi-base metal deposit, Western Rhodopes (Bulgaria): application of factor analysis

A new approach combining geochemical and mineralogical studies with statistical methods in the Babyak Mo-Ag-Au-W-Bi-base metal deposit is presented. The data are used for 3D geological modelling of the deposit and help to better understand the zonation of the elements and the complex geological evolution of the deposit.
CHARACTERISTIC FEATURES OF SELF-EXPRESSION DURING PREPARATION FUTURE TEACHERS OF MUSICAL ART

The article discusses the issues of self-expression of future teachers of musical art in the process of their preparation for further professional activity. It was determined that self-expression is associated with cultural awareness and creativity of future music teachers. The paper analyzes different approaches to determining the characteristics of self-expression in the future musical and pedagogical activity of applicants. On the basis of scientific achievements, the structural components of self-expression of future teachers of musical art have been determined. This structure includes a motivational-axiological component (stability of motives of creative self-realization; readiness for successful creative self-expression in the process of performing and instrumental training); cognitive-search component (demonstrates the effectiveness and consistency of musical and instrumental knowledge); communicative and interpretive (students have the ability to interact with the audience and with their results of creative musical and instrumental self-expression, the ability to express themselves, to carry out creative self-disclosure and self-presentation in the process of performing and instrumental training); reflective-productive component (the ability of future teachers of musical art to realize their intentions for artistic self-expression in the process of performing and instrumental training, their creative qualities, as well as their importance for professional development, self-perception skills when performing creative musical and instrumental activities). The experience of self-expression of music teachers in different regions is analyzed. Attention is focused on the possibilities of digital means of communication in the process of self-expression in different conditions.
Blockchain Fuzzy as Innovative Tool for the Certification of Corporate Social Responsibility

A Blockchain is a new technology and with semantic layer, it can bring exceptional changes in the corporate social responsibility. This paper introduced a hybrid revolutionary approach to Semantic Blockchain and a relative fuzzy approach. Blockchain is characterized by a proof of work approach, which logic is to be solved by a miner. In depth analysis has been carried out by focusing on the potentiality of semantic proof of work, which adds a lot of complexity to the mining scope. The fuzzy hashing approach is the key to allow a better application of this approach. The authors proved that proposed fuzzy hash algorithm generated the required results in less minutes as compared to traditional hash alrorithms which increased the efficiency of the blockchain.
A Study of Post Quantum Cipher Suites for Key Exchange

Current cryptographic solutions used in information technologies today like Transport Layer Security utilize algorithms with underlying computationally difficult problems to solve. With the ongoing research and development of quantum computers, these same computationally difficult problems become solvable within reasonable (polynomial) time. The emergence of large-scale quantum computers would put the integrity and confidentiality of today’s data in jeopardy. It then becomes urgent to develop, implement, and test a new suite of cybersecurity measures against attacks from a quantum computer. This paper explores, understands, and evaluates this new category of cryptosystems as well as the many tradeoffs among them. All the algorithms submitted to the National Institute of Standards and Technology (NIST) for standardization can be categorized into three major categories, each relating to the new underlying hard problem: namely error code correcting, algebraic lattices (including ring learning with errors), and supersingular isogenies. These new mathematical hard problems have shown to be resistant to the same type of quantum attack. Utilizing hardware clock cycle registers, the work sets up the benchmarks of the four Round 3 NIST algorithms in two environments: cloud computing and embedded system. As expected, there are many tradeoffs and advantages in each algorithm for applications. Saber and Kyber are exceedingly fast but have larger ciphertext size for transmission over a wire. McEliece key size and key generation are the largest drawbacks but having the smallest ciphertext size and only slightly decreased performance allow a use case where key reuse is prioritized. NTRU finds a middle ground in these tradeoffs, being better than McEliece performance wise and better than Kyber and Saber in ciphertext size allows for a use case of highly varied environments, which need to value speed and ciphertext size equally. Going forward, the benchmarking system developed could be applied to digital signature, another vital aspect to a cryptosystem.
Handling High-Dimensionality on Software Defect Prediction with FLDA

For years, Software Defect Prediction (SDP) has been used as a way to improve software reliability. It is used as a tool to detect the defects on software module before the testing phase. The steps consist of building machine learning model by training dataset with classifier and predict on defective modules. Many datasets suffer from high dimensionality because of the high number of features and those features are mostly irrelevant to predict defect. This results the data to be unnecessarily bulky and the classification process to be time-consuming. We propose a feature extraction technique called FLDA for handling dimensionality problem of dataset and improving the classification performance. We use a total of four dataset from NASA MDP. For classifiers, we use Support Vector Machine (SVM), Random Forest (RF), Naive Bayes (NF), and Multi Layer Perceptron (MLP). Based on the study results, FLDA can significantly reduced the dimension of datasets by creating new feature that contains the most relevant information. FLDA can also shorten the processing time of the classifiers. When compared to another feature extraction technique such as PCA, FLDA can easily outperform it in terms of Recall and AUC.
A Non-interactive Multi-user Protocol for Private Authorised Query Processing on Genomic Data

This paper introduces a new non-interactive multi-user model for secure and efficient query executions on outsourced genomic data to the cloud. We instantiate this model by leveraging searchable symmetric encryption (SSE). This new construction supports various types of queries (i.e., count, Boolean, \(k'\)-out-of-k match queries) on encrypted genomic data, and we call it NIMUPrivGenDB. Most importantly, it eliminates the need for the data owner and/or trusted entity to be online and avoids per-query interaction between the data owner and/or trusted entity and users. This is achieved by introducing a new mechanism called QUAuth to enforce access control based on the types of queries (Q) each user (U) is authorised (Auth) to submit. To the best of our knowledge, this is the first paper proposing an authorisation mechanism based on queries on genomic data. Moreover, QUAuth offers user management by supporting authorisation updates. We proved that our construction achieves strong security against malicious behaviour among authorised users, where a malicious user pretends to be other users by using others' unique IDs, and colluding attacks among these users are also considered. Finally, our proposed protocol's implementation and evaluation demonstrate its practicality and efficiency in terms of search computational complexity and storage cost.
Efficient Residual Network Compression for Optimizing the Accuracy-Complexity Tradeoff

Image recognition algorithms based on deep learning techniques have played an important role in the military, medical, industrial, and many other applications. However, most existing deep neural networks consume excessive computational resources which are unaffordable for the widely used edge devices, such as mobile phones. In this paper, we propose a lightweight network CResNet based on ResNet-50 by combining efficient channel pruning with depthwise decomposition. Ablation experiments are carried out based on the Animals-10 dataset for measuring the impact of each adopted technique. Great compression performance of the model parameters can be achieved at the price of slightly lower accuracy. Eventually, CResNet results in 4.08 M parameters, which is only one-fifth of the parameter size of the original ResNet-50, sufficiently reducing resource consumption. Approximately 90.2% Top-1 classification accuracy estimated on Animals-10 can be achieved by our lightweight CResNet. Compared to ResNet-50 and many existing lightweight networks, this work achieves a better tradeoff between segmentation accuracy and computing complexity by optimizing the computational efficiency, resulting in a small model size and a decent accuracy.
Proterozoic carbonate-hosted Morro Agudo sulfide Pb-Zn district, Brazil: Mineralogical and geochemical evidence of fluid mixing during the ore stage

The Proterozoic Vazante-Paracatu mineral belt hosts distinct styles of mineralization in carbonate rocks, including world-class hypogene silicate-zinc deposits in the south and sulfide Zn-Pb deposits in the north. This study focuses on the Morro Agudo District (Morro Agudo deposit and Bento Carmelo, Sucuri, and Morro do Capão occurrences). The genesis of the Morro Agudo sulfide deposit is controversial (i.e., syn-diagenetic or syn-orogenic), and little was known about the other occurrences. Ore is stratigraphically controlled within distinct dolomitic units of the Morro do Calcário and Lapa formations and structurally controlled at the Morro Agudo deposit along a NW normal fault system. Mineralization is characterized by multiple generations of sphalerite and galena, coeval with hydrothermal Fe-rich dolomite, pyrite, and quartz, which overprint micritic and sparitic dolomite and diagenetic pyrite. The mineralized zones comprises Cd and Hg within the high-grade Zn-Pb ore and Ag, As, Cu, Fe, In, Se, Sb, and Tl extending into the hydrothermal halos surrounding the orebodies. The sphalerite compositions encompass variable proportions of Zn (58.75–66.92 wt%), Fe (0.15–5.03 wt%), and Cd (0.18–0.94 wt%) grouped in five populations based on Cd-Fe ratios and principal component analysis. Sphalerite from various generations and locations in the orebodies and occurrences do not show any specific signature, suggesting episodic contribution of multiple fluids with distinct Fe and Cd contents. The whole rock lithogeochemistry revealed similar trace element signatures for the orebodies of Morro Agudo deposit and surrounding occurrences indicating, despite the textural differences, that these orebodies formed from similar mineralizing fluids and processes, except for the Bento Carmelo occurrence. This study shows clear evidence that the mineralization at the Morro Agudo deposit, Sucuri and Morro do Capão occurrences is epigenetic, forming after the diagenesis of the host carbonate succession, and may have involved progressive mixing of multiple fluids with distinct compositions that interacted with sulfur reservoirs in the dolomitic rocks, similar to other MVT districts worldwide. The Morro Agudo district is one of the rare examples of Proterozoic MVT districts, and this study brings new insights to the exploration of this type of mineralization in similar basins.
Immersive Virtual Reality in Preserving Historical Tourism

The tourism industry in cultural heritage has been adversely affected by the spread of the virus, Covid-19. This study focuses on preserving the cultural heritage of artifacts and sustaining tourism activities for the history of Hang Tuah Village in Melaka, Malaysia, through immersive virtual reality technology. Tourists can interact within the surrounding village, interact with the artefacts, and experienced more enjoyable learning history using the immersive virtual reality. There is no traveling required, and the exploration at its places is safer due to no Covid-19 exposition and no disturbance to the heritage place. We evaluate the usefulness of immersive virtual using the User Experience Questionnaire that originally has 16 dimensions. Five dimensions are applied: attractiveness, perspicuity, reliability, utility, and novelty. A total number of 17 questionnaires were involved and tested on 35 respondents. As a result, the acceptability reaches ‘high’ with an average mean score of 4.01 or 81.2% from the overall score. With the high acceptance response, we conclude that immersive virtual reality can assist in sustaining the tourism industry to preserve the heritage of Hang Tuah Village.
Sentiment Analysis of E-commerce Review using Lexicon Sentiment Method

Customer satisfaction is a top priority for any company engaged in the e-commerce sector. Therefore, it is very important for any e-commerce, especially those that have served transactions between countries such as Amazon, eBay, and Rakuten to see how the impressions or sentiments of their customers regarding the quality of products and services provided in order to improve or improve their quality. Through the rapid development of technology, this sentiment has become easier to detect. One of them is by utilizing comments on social media such as Twitter. By analyzing Twitter user comments related to the determinants of customer satisfaction with e-commerce using the Lexicon classification method, it is found that the most dominant factor in determining customer satisfaction is the quality of information. E-commerce that wants to increase customer satisfaction, refers to these three factors, because these factors are the main focus of customers when entering an e-commerce.
Mammograms Classification with NeuroMem® Chip for Breast Cancer Detection

This paper presents an embedded AI model for breast cancer detection on mammogram images. Two different methods are defined and evaluated using the NeuroMem® neural network chip device. Both methods are tested on the CBIS-DDSM which is a public dataset of labeled mammograms and deliver a classification accuracy of 85 %.
A Robust Invariant Image-Based Paper-Currency Recognition Based on F-kNN

The innovation of currency recognition intends to look, distinguish and remove the noticeable just as imperceptible subtleties on paper-money for effective classification of currency. Many a times, currency notes are hazy or harmed; a considerable lot of them have complex structures as well. This makes the assignment of currency recognition troublesome. Currency recognition is applied in order to diminish the human influence, put resources into this procedure. So it is essential to choose the correct highlights and legitimate calculation for this reason. This work presents a framework for automated currency notes recognition utilizing supervised image processing strategies. This work is critical considering the mentioned dimensions, namely, a) They got worn-out ahead of their schedule in comparison to coins; b) The possibility of joining wear-out currency is more noteworthy than that of coin currency; c) Coin currency is restricted to lesser population. We have to actualize a calculation which should be straightforward, less mind-boggling and profoundly effective. Recognition of Paper-Currency is significant in the zone of pattern recognition. Image processing is used to acquire the final outcome, with accuracy of 51.0%, 56.8%, 65.6% for the Decision Tree, SVM, Fine-KNN classifiers respectively.
Feature Fusion with Deep Neural Network in Kernelized Correlation Filters Tracker

Moving object tracking is the most important component in many computer vision applications. Currently, the ability of computer vision is almost like human vision. Humans can see and track moving objects by looking at notable features such as color, shape, and function. The computer can track moving objects by calculating the characteristics, such as the Histogram of Oriented Gradient (HOG) and grayscale features. These features were used as input in the tracker algorithm. The correlation filter algorithm is extensively used in object tracking applications because of its accuracy and speed. Kernelized Correlation Filters (KCF) is a method that uses correlation for object tracking. The feature fusion is widely used to make tracking more robust. In this paper, the HOG and grayscale features were implemented in the KCF method. Deep Neural Network (DNN) regression was used as a decision feature fusion. With almost similar principle as Non-Maximum Suppression (NMS), where two candidates are detected from overlapping HOG and grayscale features, the region-of-interest (ROI) will be pruned by replacing one ROI to produce a more accurate object candidate. In this study, three TB dataset videos were used for testing, and two videos were used for training. The DNN Regression architecture uses six hidden layers with 512, 256,64,32,16, and 8 nodes. The training accuracy results were 95.76%, with MSE of 9.94 and a loss of 9.93. This research shows that the system can track objects more precisely and robustly with RMSE of 9.38 while achieving 32 FPS.
A Comparison of Lexicon-based and Transformer-based Sentiment Analysis on Code-mixed of Low-Resource Languages

Sentiment analysis from code-mixed texts has been gaining wide attention in the past decade from researchers and practicians from various communities motivated, among others, by the increasing popularity of social media resulted in a huge volume of code-mixed texts. Sentiment analysis is an interesting problem in Natural Language Processing with wide potential applications, among others, to understand public concerns or aspirations toward some issues. This paper presents experimentation results aim to compare performance of lexicon-based and Sentence-BERT as sentiment analysis models from code-mixed of low-resources texts as input. In this study, some code-mixed texts of Bahasa Indonesia and Javanese language are used as sample of low-resource code-mixed languages. The input dataset are first translated to English using Google Machine Translation. The Sentiwordnet and VADER are two English lexicon label datasets used in this study as basis for predicting sentiment category using lexicon-based sentiment analysis method. In addition, a pretrained Sentence-BERT model is used as classification model from the translated input text to English. In this study, the dataset is categorized into positives and negative categories. The model performance was measured using accuracy, precision, recall, and F1 score. The experimentation found that the combined Google machine translator and Sentence-BERT model achieved 83 % average accuracy, 90 % average precision, 76 % average recall, and 83 % average F1 Score.
K-means panning – Developing a new standard in automated MSNA signal recognition with a weakly supervised learning approach

Accessibility of labelled datasets is often a key limitation for the application of Machine Learning in clinical research. A novel semi-automated weak-labelling approach based on unsupervised clustering was developed to classify a large dataset of microneurography signals and subsequently used to train a Neural Network to reproduce the labelling process.Clusters of microneurography signals were created with k-means and then labelled in terms of the validity of the signals contained in each cluster. Only purely positive or negative clusters were labelled, whereas clusters with mixed content were passed on to the next iteration of the algorithm to undergo another cycle of unsupervised clustering and labelling of the clusters. After several iterations of this process, only pure labelled clusters remained which were used to train a Deep Neural Network.Overall, 334,548 individual signal peaks form the integrated data were extracted and more than 99.99% of the data was labelled in six iterations of this novel application of weak labelling with the help of a domain expert. A Deep Neural Network trained based on this dataset achieved consistent accuracies above 95%.Data extraction and the novel iterative approach of labelling unsupervised clusters enabled creation of a large, labelled dataset combining unsupervised learning and expert ratings of signal-peaks on cluster basis in a time effective manner. Further research is needed to validate the methodology and employ it on other types of physiologic data for which it may enable efficient generation of large labelled datasets.
Preliminary analysis of visual cognition estimation in VR toward effective assistance timing for iterative visual search tasks

This research aims to develop a method to assist iterative visual search tasks, and it focuses on visual cognition to achieve effective assistance. As a first step to this goal, we analyzed the participants’ gaze behaviors when they visually recognized a target in a VR environment. In the experiment, the effect of visual cognition difficulty (VCD) is considered. Analysis results show that the participants could visually recognize lower-VCD targets at an earlier timing. This suggests that VCD-based guidance may improve task performance.
The polynomial hierarchy of functions and its levels

We give recursion-theoretic characterizations of all levels of the polynomial hierarchy of time and of the hierarchy itself. We characterize both, the □nP and the ΣnP levels. In this work, only composition and recursion schemes are used. We identify the recursion scheme which added to FPtime leads to the full polynomial hierarchy.
An Empirical Study On Software Metrics and Machine Learning to Identify Untrustworthy Code

The increasingly intensive use of software systems in diverse sectors, especially in business, government, healthcare, and critical infrastructures, makes it essential to deliver code that is secure. In this work, we present two sets of experiments aiming at helping developers to improve software security from the early development stages. The first experiment is focused on using software metrics to build prediction models to distinguish vulnerable from non-vulnerable code. The second experiment studies the hypothesis of developing a consensus-based decision-making approach on top of several machine learning-based prediction models, trained using software metrics data to categorize code units with respect to their security. Such categories suggest a priority (ranking) of software code units based on the potential existence of security vulnerabilities. Results show that software metrics do not constitute sufficient evidence of security issues and cannot effectively be used to build a prediction model to distinguish vulnerable from non-vulnerable code. However, with a consensus-based decision-making approach, it is possible to classify code units from a security perspective, which allows developers to decide (considering the criticality of the system under development and the available resources) which parts of the software should be the focal point for the detection and removal of security vulnerabilities.