Reviewerly/exHarmony huggingface dataset:
Similarities and Proximity Symmetries for Decisions of Complex Valuation of Mining Resources in Anthropically Affected Areas

After 1990, when the economic system changed in Romania, the mining industry was the most controversial field from a productive-economic point of view and subject to reforms and transformations for efficiency. Currently (2022), there are nine main mining perimeters in which the production of useful, energetic, and nonenergetic mineral substances is operational, and in others it has decreased or stopped. Still active mining areas need economic and ecological assessments to identify similarities and proximity symmetries for informed exploitation decisions and feasible complex resource utilization. The main objective of our study is to define a framework for the theoretical and practical contribution to the substantiation of decisions and expressions of interest regarding future investments in mining projects for useful and energetic and non-energetic mineral substances in Romania. Investments in the mining industry are expensive, with major risks and subunit success rates for specific geotechnological conditions. The purpose of the research is to provide the methodology for using some variables of similarities from proximity mining deposits in the stage of exploitation or post-exploitation affected by anthropogenic activity in the national geological territory through a case study of Romania. With the help of statistical scales, the research results highlight that in the exploitation and post-mining perimeters in Romania, the states of “affect” and “post-affect” anthropic, respectively, of eco-economic damage are in a maximum proportion of approximately 36% in relation to the ideal situations of no affect. For a mining investment project, knowing similar or symmetrical exploitation and post-exploitation properties and situations, and from the geological deposits in the vicinity, premises are created for optimized strategic and tactical decisions, based on reality and, above all, for the provision of expressions of interest for new investments that have a programmed, expected success rate.
Local Cloud Computing Service Adoption in Nigeria: Challenges and Solutions

Cloud momentum seems unstoppable in Nigeria, as businesses and organizations in the country see less and less advantage in the slog of maintaining their infrastructure. The shift to the cloud in today's COVID-19 driven world has created an opportunity for investments to improve local cloud computing services. However, there are key challenges that must be addressed by the local cloud service providers in the country in order not to lose out to the foreign cloud service providers. This paper assessed the challenges to local cloud computing services adoption among sixty-seven (67) businesses and organizations in Nigeria. The research employed a non-probability purposive sampling approach. The surveyed data were obtained through an online form which was distributed via Linkedln. Descriptive and inferential analysis was used in analyzing the collected data via IBM SPSS software. Findings from the research showed the key challenges to include inadequate awareness of local cloud service vendors, poor innovation and local content, inadequate cloud infrastructure, local cloud vendor interoperability issue, national insecurity, shortages in skilled personnel, Service Level Agreement (SLA), security strategies, privacy, compliance terms, and requirements issues. Thus, adequate local cloud service offerings, skilled personnel, and the IT infrastructural backbone of the country have to be well established to increase the trust in local cloud computing, open up Nigeria to offshore markets while driving economic competitiveness and growth.
Trusted Media Challenge Dataset and User Study

The emergence of fake media that can be easily created by technology has the potential to generate potent misinformation causing harm to both society and individuals. To tackle the issue, we have organized the Trusted Media Challenge (TMC) to explore how Artificial Intelligence (AI) technologies could be leveraged to combat fake media. To enable further research, we are releasing the dataset from the TMC, consists of 4,380 fake and 2,563 real videos, with various video and audio manipulation methods employed to produce different types of fake media. We have also carried out a user study to demonstrate the quality of the TMC dataset and to compare the performance of humans and AI models. The results show that the TMC dataset can fool human participants in many cases. The TMC dataset is available for research purposes upon request via [email protected]
Coherent information of a quantum channel or its complement is generically positive

The task of determining whether a given quantum channel has positive capacity to transmit quantum information is a fundamental open problem in quantum information theory. In general, the coherent information needs to be computed for an unbounded number of copies of a channel in order to detect a positive value of its quantum capacity. However, in this paper, we show that the coherent information of a <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow class="MJX-TeXAtom-ORD"><mml:mtext class="MJX-tex-mathit" mathvariant="italic">single copy</mml:mtext></mml:mrow></mml:math> of a <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow class="MJX-TeXAtom-ORD"><mml:mtext class="MJX-tex-mathit" mathvariant="italic">randomly selected channel</mml:mtext></mml:mrow></mml:math> is positive almost surely if the channel's output space is larger than its environment. Hence, in this case, a single copy of the channel typically suffices to determine positivity of its quantum capacity. Put differently, channels with zero coherent information have measure zero in the subset of channels for which the output space is larger than the environment. On the other hand, if the environment is larger than the channel's output space, identical results hold for the channel's complement.
The effective BRKGA algorithm for the <i>k</i>-medoids clustering problem

This paper presents a biased random-key genetic algorithm for k -medoids clustering problem. A novel heuristic operator was implemented and combined with a parallelized local search procedure. Experiments were carried out with fifty literature data sets with small, medium, and large sizes, considering several numbers of clusters, showed that the proposed algorithm outperformed eight other algorithms, for example, the classics PAM and CLARA algorithms. Furthermore, with the results of a linear integer programming formulation, we found that our algorithm obtained the global optimal solutions for most cases and, despite its stochastic nature, presented stability in terms of quality of the solutions obtained and the number of generations required to produce such solutions. In addition, considering the solutions (clusterings) produced by the algorithms, a relative validation index (average silhouette) was applied, where, again, was observed that our method performed well, producing cluster with a good structure.
Active Directory Attacks—Steps, Types, and Signatures

Active Directory Domain is a Microsoft service that allows and facilitates the centralized administration of all workstations and servers in any environment. Due to the wide use and adoption of this service, it has become a target for many attackers. Active Directory attacks have evolved through years. The attacks target different functions and features provided by Active Directory. In this paper, we provide insights on the criticality, impact, and detection of Active Directory attacks. We review the different Active Directory attacks. We introduce the steps of the Active Directory attack and the Kerberos authentication workflow, which is abused in most attacks to compromise the Active Directory environment. Further, we conduct experiments on two attacks that are based on privilege escalation in order to examine the attack signatures on Windows event logs. The content designed in this paper may serve as a baseline for organizations implementing detection mechanisms for their Active Directory environments.
Polarization-driven camouflaged object segmentation via gated fusion

Recently, polarization-based models for camouflaged object segmentation have attracted research attention. However, to construct this camouflaged object segmentation model, the main challenge is to effectively fuse polarization and light intensity features. Therefore, we propose a multi-modal camouflaged object segmentation method via gated fusion. First, the spatial positioning module is designed to perform channel calibration and global spatial attention alignment between polarization mode and light intensity mode from high-level feature representation to locate object positioning accurately. Then, the gated fusion module (GFM) is designed to selectively fuse the object information contained in the polarization and light intensity features. Among them, semantic information of location features is introduced in the GFM to guide each mode to aggregate dominant features. Finally, the features of each layer are aggregated to obtain an accurate segmentation result map. At the same time, considering the lack of public evaluation and training data on light intensity-polarization (I-P) camouflaged detection, we build the light I-P camouflaged detection dataset. Experimental results demonstrate that our proposed method outperforms other typical multi-modal segmentation methods in this dataset.
Analysis of the Problems of Training Bachelors in the Direction of “Technosphere Security” Taking Into Account the Cognitive Characteristics of New Generations of Students

The research aims to analyze possible ways to improve undergraduate student performance in current conditions. The use of new directions in the methods of training and conducting training in undergraduate agricultural higher education institutions in the “Technosphere safety” training program. When developing teaching methods and technologies, teachers should consider changes in the cognitive abilities of the Generation Z students, which differ significantly from previous generations in terms of attention, information perception, and communication abilities. The author argues a need for the digital transformation of education in the wake of the trend towards the digital transformation of the economy and agriculture. This is primarily an opportunity to activate the combination of the group educational process in the classroom using the capabilities of current computer technologies with the individualization (personification) of education when each student gets the opportunity for independent creative work with remote control and correction by the teacher.
From Easy to Hard

Contextual information in search sessions is important for capturing users' search intents. Various approaches have been proposed to model user behavior sequences to improve document ranking in a session. Typically, training samples of (search context, document) pairs are sampled randomly in each training epoch. In reality, the difficulty to understand user's search intent and to judge document's relevance varies greatly from one search context to another. Mixing up training samples of different difficulties may confuse the model's optimization process. In this work, we propose a curriculum learning framework for context-aware document ranking, in which the ranking model learns matching signals between the search context and the candidate document in an easy-to-hard manner. In so doing, we aim to guide the model gradually toward a global optimum. To leverage both positive and negative examples, two curricula are designed. Experiments on two real query log datasets show that our proposed framework can improve the performance of several existing methods significantly, demonstrating the effectiveness of curriculum learning for context-aware document ranking.
A Hybrid approach combining blocklists, machine learning and deep learning for detection of malicious URLs

Malicious URLs are one of the most commonly used methods of distributing malware and ransomware, launching phishing attacks, sending spam, and defacement of websites. Attackers send malicious URLS via email, advertisement, or embed them in legitimate websites. Unsuspecting users click malicious URLs and become targets of attackers. Detecting malicious URLs and alerting the user can prevent some of the attacks and hence detection of malicious URLs is an important part of cyber security. In this paper, a hybrid approach, combining traditional, machine learning, and deep learning methods for detecting malicious URLs is proposed. The traditional approach is a signature or a blocklist based approach that can detect existing malicious URLs. The signature-based approach is augmented with a shallow and a deep learning based approach that can detect new malicious URLs for which no signatures exist. The machine learning and deep learning based approach is trained and tested on a large dataset consisting of 2.5 million malicious and benign URLs. The combined system of traditional blocklist, shallow learning and deep learning approach achieves a precision, recall and f1- score of greater than 0.97.
New Results on Attacking AES with a Secret S-box

AES has been an research priority and a difficulty in cryptography since it was proposed. Replacing AES S-box with a random S-box can highly increase the secret information of the cipher, which can be a significant method to improve AES, therefore research on AES with a secret S-box has great theoretical significance. Subspace trail cryptanalysis can be regarded as an expansion of invariant subspace, which was proposed recently to attack cryptographic cipher. Subspace trail cryptanalysis has the advantages that it works independently of S-box and can work together with other cryptographic methods, which can be used to attack AES with a secret S-box. In this paper, we present a new distinguisher of 5 rounds AES-128 that exploits subspace trail cryptanalysis and impossible differential cryptanalysis, then we show an attack on 6 rounds AES with data complexity of 2<sup>108</sup> and computational cost of 2<sup>106.35</sup>round-reduced AES encryption. Compared to the past attacking result, our attack has promotion in several respects.
Toad in the Hole or Mapo Tofu? Comparative Analysis of English and Chinese Darknet Markets

The popularity of online shopping and cryptocurrency has contributed to drive the economy of darknet markets in recent years. These are often perceived to be conducive to (or may even facilitate) cybercrime related activities. It is, therefore, worthwhile to have a deeper understanding of how various darknet markets operate, so that researchers and law enforcement agencies can test and deploy appropriate countermeasures to fight against online crime. Currently, there is a knowledge gap regarding the similarities and differences among darknet markets in different languages. This study aims to compare between darknet markets operating in English and Chinese. Data from three English and two Chinese darknet markets was collected. The gathered data is described, compared, and analysed in six main aspects: operation model and structures, product categories, market policies, payment methods, security mechanisms, and vendors&#x2019; characteristics. Our datasets were collected during a seven-week period between 17 July and 30 August 2021, and they contain data from 384 vendors in the English darknet markets and 4,429 in the Chinese ones. The Chinese darknet markets generally seem to have more liberal policies than their English counterparts, as demonstrated by the variety and types of goods and services offered, many of which would have been banned in the English speaking ones. All darknet markets suffer from reputation issues. Cross-market actors are active, but they represent only a small proportion of the vendors observed in our study. In summary, our findings reveal key characteristics of darknet markets in two widely used languages. This information can provide useful insights for security researchers and law enforcement agencies in combating cybercrime.
Bayesian dropout

In the past decade, Dropout has emerged as a powerful and simple method for training neural networks preventing co-adaptation by stochastically omitting neurons. Dropout is currently not grounded in explicit modelling assumptions which so far has precluded its adoption in Bayesian modeling. Using Bayesian entropic reasoning we show that dropout can be interpreted as optimal inference under constraints. We demonstrate this on an analytically tractable regression model providing a Bayesian interpretation of its mechanism for regularizing and preventing co-adaptation as well as its connection to other Bayesian techniques, and in our experiments we find that dropout can provide robustness under model misspecification. Our framework roots dropout as a theoretically justified and practical tool for statistical modeling allowing Bayesian practitioners to tap into the benefits of dropout training.
A Comprehensive Comparison of Accuracy and Practicality of Different Types of Algorithms for Pre-Impact Fall Detection Using Both Young and Old Adults

This study aims to comprehensively compare the accuracy and practicality of three different types of algorithms for pre-impact fall detection using both young and old subjects. Threshold-based, conventional machine learning (SVM) and deep learning (ConvLSTM) algorithms were compared. Results showed that ConvLSTM had an accuracy of 99.16% (sensitivity: 99.32%, specificity: 99.01%) and an averaged lead time of 403 ms on young subjects, which outperformed SVM (97.16%, 385 ms) and much superior to the threshold-based algorithm (89.06%, 333 ms). In addition, latency tests on an embedded device showed that the Lite model of ConvLSTM had a low latency of 2.1 ms, which was comparable to the threshold-based algorithm (<1 ms) but much lower than SVM (86.9 ms). The feasibility and effectiveness of applying algorithms trained on young subjects to old subjects were also validated. These findings suggested that ConvLSTM has great potential for detecting pre-impact falls and preventing fall-related injuries.
Entropy-Based Discovery of Summary Causal Graphs in Time Series

This study addresses the problem of learning a summary causal graph on time series with potentially different sampling rates. To do so, we first propose a new causal temporal mutual information measure for time series. We then show how this measure relates to an entropy reduction principle that can be seen as a special case of the probability raising principle. We finally combine these two ingredients in PC-like and FCI-like algorithms to construct the summary causal graph. There algorithm are evaluated on several datasets, which shows both their efficacy and efficiency.
Deep Video Hashing Using 3DCNN with BERT

Deep video hashing (DVH) is a very appealing way to decrease storage costs and query times.In this work we propose a hashing model using two separated modules.A 3DCNN is proposed with a bidirectional encoder representations from transformers (BERT) layer.And a hashing neural network (NN) module will learn to encode those features into hash codes.The proposed model that separates feature extraction from hash generation process results in better performance with respect to training time consumption and accuracy.We achieve a significant improvement in video retrieval performance on two benchmark datasets compared to state-of-the-art deep learning models for video retrieval that use convolutional neural networks (CNN)s or 3DCNNs along with other temporal feature extraction techniques and supervised hashing methods.For UCF101, HMDB51 datasets, more than 2 % mAP and 24 % improvement is achieved respectively for tested bit sizes.
Software Fault Detection using Multi-Distinguished-Features Sampling with Ensemble Random Forest Classifier

Finding faults in software modules is an emerging issue in software reliability systems, and the assessment of the fault is performed by software fault prediction systems (SFPS).The identification process of fault-prone software modules is one of the prioritized aspects before initiating the testing process of the same modules.The SFPS helps improve software quality within the specified time and cost values.Early fault prediction in SFPS for the different software components showed significant results concerning the cost and time parameters.According to the state-ofthe-art SFPS, ensemble-based classifiers were performed best and most cost-effective compared to other classifier methods.Recently, a random ensemble forest with adaptive synthetic sampling (E-RF-ADASYN) has been developed, is tested on a sample of PROMISE datasets, and shown the cost-effective classifier results.In the logistic regression to software quality models, and the other knowledge of account for prior probability and costs of misclassification.Probabilities and costs of misclassification in a logistic regression-based classification algorithm for software quality modeling.The decision tree algorithm is an ensemble learning approach for prediction.The algorithm works based on developing several decision trees and later decides the output class based on the most popular one.The proposed work focuses on developing an alternative sampling method called ensemble-random forest with multi-distinguishedfeatures sampling (E-RF-MDFS), for obtaining the best sample illustration for representing the entire dataset.Batinduced butterfly optimization (BBO) has been used for the feature extraction process.The experiments are conducted on 8 datasets of the PROMISE database.The proposed E-RF-MDFS has improved performance than E-RF-ADASYN in fault detection accuracy, real positive rate, and Pearson's correlation coefficient.On comparing the performance of E-RF-based classifiers, the performance of the proposed MDFS is the best, with an FDA of 99.3 % (Xalan v2.6) than the ADASYN classifier.
Implementation of Ethereum Accounts and Transactions on Embedded IoT Devices

The growing interest in Internet of Things (IoT) and Industrial IoT (IIoT) poses the challenge of finding robust solutions for the certification and notarization of data produced and collected by embedded devices. The blockchain and distributed ledger technologies represent a promising solution to address these issues, but rise other questions, for example regarding their practical feasibility. In fact, IoT devices have limited resources and, consequently, may not be able to easily perform all the operations required to participate in a blockchain. In this paper we propose a minimal architecture to allow IoT devices performing data certification and notarization on the Ethereum blockchain. We develop a hardware-software platform through which a lightweight device (e.g., an IoT sensor), holding a secret key and the associated public address, produces signed transactions, which are then submitted to the blockchain network. This guarantees data integrity and authenticity and, on the other hand, minimizes the computational burden on the lightweight device. To show the practicality of the proposed approach, we report and discuss the results of benchmarks performed on ARM Cortex-M4 hardware architectures, sending transactions over the Ropsten testnet. Our results show that all the necessary operations can be performed with small latency, thus proving that an IoT device can directly interact with the blockchain, without apparent bottlenecks.
Dimensional Data KNN-Based Imputation

Data Warehouses (DWs) are core components of Business Intelligence (BI). Missing data in DWs have a great impact on data analyses. Therefore, missing data need to be completed. Unlike other existing data imputation methods mainly adapted for facts, we propose a new imputation method for dimensions. This method contains two steps: 1) a hierarchical imputation and 2) a k-nearest neighbors (KNN) based imputation. Our solution has the advantage of taking into account the DW structure and dependency constraints. Experimental assessments validate our method in terms of effectiveness and efficiency.