Reviewerly/exHarmony huggingface dataset:
An Empirical Analysis of Cryptocurrency Trading Using Wazirex

In today's era of new technology and online job, most people choose to have a second source of income. Cryptocurrency is one of the most talked-about subjects these days. It's gaining popularity by the day. From the beginning, crypto currency was created for the safe online movement of money, but as time goes on, more and more of them are listed throughout the world. Following that, the concept of exchanging them is presented, either for profit or for loss. Binance, Wazirex, Hotbit, Coinsbit, and other platforms have been created specifically for trading. After the trading concept becomes well-known, several institutions develop strategies for profitably trading them. In this paper, we'll take a close look at some advanced crypto trading strategies. We'll go over the fundamentals of candlestick charts, trading types, moving average concepts, support and resistance levels, bitcoin price action, pattern break out, volume action, trend type and trend identification, as well as an informative section on some key oscillators used by many experienced traders. Finally, we combine all of the concepts into a trading strategy with a 77 percent accuracy and a trading strategy with a 70 percent accuracy.
An Ensemble Learning Recommender System for Interactive Platforms

In interactive platforms, we often want to predict which items could be more relevant for users, either based on their previous interactions with the system or their preferences.Such systems are called Recommender Systems.They are divided into three main groups, including content-based, collaborative and hybrid recommenders.In this paper, we focus on collaborative filtering and the improvement of the accuracy of its techniques.Then, we suggest an Ensemble Learning Recommender System model made of a probabilistic model and an efficient matrix factorization method.The interactions between users and the platform are scored by explicit and implicit scores.At each user session, implicit scores are used to train a probabilistic model to compute the maximum likelihood estimator for the probability that an item will be recommended in the next session.The explicit scores are used to know the impact of the user's vote on an item at the time of the recommendation.
Tabletop 3D Digital Map Interaction with Virtual Reality Handheld Controllers

Immersive technologies, such as virtual reality, enable users to view and evaluate three-dimensional content, e.g., geographic data. Besides navigating this data at a life-size scale, a tabletop display offers a better overview of a larger area. This paper presents six different techniques to interact with immersive digital map table displays, i.e., panning, rotating, and zooming the map and indicating a position. The implemented interaction methods were evaluated in a user study with 12 participants. The results show that using a virtual laser pointer in combination with the buttons and joystick on a controller yields the best results regarding interaction time, workload, and user preference. The user study also shows that interaction methods should be customizable so that users can adapt them to their abilities. However, the proposed virtual laser pointer technique achieves a good balance between physical and cognitive effort and yields good results for users with varying experience levels.
Tourism, ICT and Inclusive Development: Global Evidence

This study empirically examines the effect of tourism and ICT on inclusive development. Inclusive development is approached as human development adjusted for environmental sustainability; ICT is based on mobile phones subscription rate, internet penetration and fixed broadband subscription and a composite indicator of these, while tourism is approached as a the number of arrivals. The data are collected for 142 countries globally between the 2000-2019 period and the regression methodologies involve the POLS, the Driscoll and Kraay estimator, the Mean Group, the System GMM and the fixed effects Tobit regression. The results of the linear model show that, tourism enhances sustainable development and ICT has a negative significant effect. While the effect of tourism is robust across income groups, regional groupings and regression methodologies, the effect of ICT varies across these different specifications. When non-linearity is considered, the effects of both ICT and tourism are positive and robustly non-linear. The non-linear effect of tourism is not however feasible across income groups. Besides, while the effect of tourism is positively and non-lineally related to sustainable development in politically-stable economies, the effect is non-significant in unstable economies. From the results, countries should seize the opportunity offered by the tourism sector and ICT as effective policy tools towards sustainable development. In this regard, countries should invest in both ICT and tourism while observing the thresholds where complementary policies should be used. Also, politically-unstable economies should engage in peace talks such that they could join their politically-stable counterparts in benefiting from the positive economic effects offered by tourism and ICT.
Hybrid of Array-Based and Improved Playfair Cipher for Data Security

In any era, a secured communication is an essential part of human life. Data exchange is the act of transferring data from one node to another utilizing computer and communication technologies. This requires electronic or digital data to be transferred between two or more nodes, whatever the geographical location, medium or data contents are. The processing of data in a secured manner is the essential part in any transmission. Cryptography would help in this secure transmission for encoding the information or data in a format which cannot be understood easily and insensitive to brute force attack. The HABIP algorithm is proposed which does two levels of encryption. The encryption process simulates array-based encryption and improved Playfair cipher that severs the text to be transmitted into 26-bit string and encrypts it based on its position value in the root array. In next level, two successive characters are compared with two different key matrices. In this approach, high chances of repetition are reduced to 95% as each character gets encrypted as different character in every occurrence. HABIP, thus, improves the purity and security to a greater level than the contemporary encryption methods, and it is insensitive to brute force attack.
Co-evolutionary Self-adjusting Optimization Algorithm Based on Patterns of Individual and Collective Behavior of Agents

The article describes a co-evolutionary self-tuning algorithm for solving global optimization problems. The algorithm simulates the selfish behavior of herbivores attacked by a herd of predators. Search agents are controlled by a set of attractive search operators based on patterns of individual and collective behavior of agents, as well as mechanisms of population selection in the “prey-predator” system. Agents move in the space of solutions to the optimization problem using a set of operators imitating various types of behavior, including selfishness. The proposed co-evolutionary self-adjusting algorithm allows not only simulating multiple types of selfish behavior, unlike most competing algorithms. The algorithm includes computational mechanisms to maintain a balance between the rate of convergence of the algorithm and the diversification of the solution search space. The algorithm's performance is analyzed using a series of experiments for the problems of finding the global minimum in a set of 5 known test functions. The authors have compared the results with seven competing bioheuristics for indicators such as the average best-to-date solution, the median best-to-date solution, and the standard deviation of the current best solution. The accuracy of the proposed algorithm turned out to be higher than that of competing algorithms. Nonparametric proof of the statistical significance of the results obtained using the Wilcoxon signed-rank test allows us to assert that the results of the co-evolutionary self-tuning algorithm are statistically significant.
Improvement of Training Innovatively Competent Specialists Within the Framework of the School-University-Enterprise Program

Purpose Economies of the most developed countries are called innovative, since innovation is the main driver of their development. The scale of generation and diffusion of innovations largely depends on competencies and qualification of the workforce. Modern experience confirms that investments in the training system provide a leading position in the field of innovation for an economic entity of any level—from an individual entrepreneur to the national economy as a whole. Methodology Authors suggest that the training of innovatively competent specialists within the higher education system is a result of the interaction effect or the synergistic effect resulting from the integrated use of several elements in the educational process—a competency-based approach, innovative and information technologies, digital resources and interactive forms. Findings The use of modern forms of interaction within the framework of the school-university-enterprise program is justified, allowing students to form both professional and communicative competencies. It is proposed to strengthen the innovative component of vocational training through the selection of research and design types of professional activity in the implementation of educational programs. Originality/value For the real training of innovatively competent specialists, the school-university-enterprise system should be constantly enriched with new forms of interaction that allow the most effective use of the interaction effect. Proposed forms of interaction can be used by all interested parties for more effective cooperation in order to train highly qualified specialists with the necessary set of competencies.
Age Estimation and Sex Determination Using Antegonial Angle-A Retrospective Study

Objective: To assess the values of the antegonial angle on panoramic radiographs and assess their role in age and sex determination.Materials & Method: A study was carried out on 400 selected panoramic radiographs (200 males and 200 females) in the age group of 20 to 70 years. The antecedent angle was evaluated, as well as its predictability for sex determination and age assessment. Statistical analysis was done.Results: There was a significant difference in mean values of antegonial angle between males and females.Conclusion: The antegonial region values are influenced by sex and have the potential to be used as a forensic tool in sex determination but not for age estimation.
Long-Tailed Classification Based on Dual Branch Learning Network

The phenomenon of long-tailed data distribution widely exists in every corner of life. And in fact, the fundamental solution to long-tailed distribution problem is actually the combination of imbalanced classification and few-shot learning. At present, the mainstream methods under the framework of deep learning are re-sampling, re-weighting and other strategies like transfer learning. This paper proves that when dealing with long-tailed data, the conventional deep learning methods turn to show a low accuracy of few-shot samples and many-shot samples. Therefore, this paper proposes a long-tailed recognition model based on Dual Branch Learning Network (DBLN). While the imbalanced learning branch is using class-balanced loss function to modify model preference for head data, DBLN leads in a data augmentation learning branch based on improved rebalanced Mixup algorithm. These two branches update the weights by sharing dynamic parameters. DBLN model shows an excellent performance in improving the tendency of the deep learning algorithm to prefer the head classes and the recognition ability of the tail data. The experiments on two benchmark datasets (imbalanced CIFAR-10/100 dataset and rail fastener dataset which contains 5,000 rail pictures about 4 types of fasteners) show that the proposed improved dual branch long-tailed recognition model is superior to other advanced methods.
Towards Micro-architectural Leakage Simulators: Reverse Engineering Micro-architectural Leakage Features Is Practical

Leakage simulators offer the tantalising promise of easy and quick testing of software with respect to the presence of side channel leakage. The quality of their build in leakage models is therefore crucial, this includes the faithful inclusion of micro-architectural leakage. Micro-architectural leakage is a reality even on low- to mid-range commercial processors, such as the ARM Cortex M series. Dealing with it seems initially infeasible in a “grey box” setting: how should we describe it if micro-architectural elements are not publicly known? We demonstrate, for the first time, that it is feasible, using a recent leakage modelling technique, to reverse engineer significant elements of the micro-architectural leakage of a commercial processor. Our approach first recovers the micro-architectural leakage of each stage in the pipeline, and the leakage of elements that are known to produce glitches. Using the reverse engineered leakage features we build an enhanced version of the popular leakage simulator ELMO.
Chinese Sentence Similarity Calculation Based on Modifiers

To compute the similarity of Chinese sentences accurately, a revised Chinese sentence similarity approach is proposed though enhancing the importance of the modifiers of stem of sentence. After extracting the modified part of the sentence by Language Technology Platform (LTP), this part of each structure could be removed the longest common substring, to better capture the similarities of modified parts. The entire method includes three phases, which are to split the sentences into principal and predicate object structures using the syntactic analysis tool, to generate modifiers and sentence stem vectors and calculate the similarity between the vectors using the Word2Vec, and to obtain the similarity between two sentences by weighting each part. Experimental results on 200 sentences of the LCQMC dataset and corresponding analysis reveal that the proposed method can obtain more accurate similarity calculation results by effectively gaining the modified part - which affects the whole sentence meaning effectively-of the sentence structure.
Strategies for Boosting Cybersecurity

By many accounts, the worst year on record for data and system breaches was 2020; however, across numerous metrics such dubious distinction was topped in 2021. The growing epidemic of ransomware and other cyber-enabled incidents and attacks is causing policymakers and business leaders to consider how to both reward “reasonable” cybersecurity and disincentivize under-investment in cybersecurity best practices. Such steps are critical amidst a current cybersecurity landscape fraught with regulatory gaps, opaque self-regulation, and resource constraints, especially among small and medium sized businesses.Bold and coordinated actions are needed to dislodge the unsustainable trend of increasingly damaging cyber attacks and to create a more holistically secure digital future. To meet such need, this Article proposes breaking the mandate-versus-self-regulation dichotomy by leveraging a tailored sticks and carrots tax approach to spur more universal investment in secure systems. The proposal includes a Federal Cybersecurity Investment Tax Credit, tailored and mapped to select entity types, combined with a tax on cyberinsecurity. The proposed incentive structure promotes the principle that businesses have basic cybersecurity responsibilities and fundamental duties to operate securely in a digital society. In addition, this Article introduces supplementary tools as part of an enhanced cybersecurity tax policy toolkit. Given the pressing national and global cyber risks, this Article jumpstarts a long-needed conversation about the operative use of tax policy to promote good governance and effective cybersecurity risk management.
Helphed: Hybrid Ensemble Learning Phishing Email Detection

Phishing email attack is a dominant cyber-criminal strategy for decades. Despite its longevity, it has evolved during the COVID-19 pandemic, indicating that adversaries exploit critical situations to lure victims. Plenty of detectors have been proposed over the years, which mainly focus on the contents or the textual information of emails; however, to cope with the evolution of phishing emails more sophisticated approaches should be introduced that will exploit all the emails’ traits to enhance the detection capability of Machine Learning classifiers. To tackle the limitations of previous works, this paper proposes a phishing email detection methodology, named HELPHED that focuses on the detection of phishing emails by combining Ensemble Learning methods with hybrid features. The hybrid features provide an accurate representation of emails by fusing their content and textual traits. We propose two methods of HELPHED, the first one employs the Stacking Ensemble Learning method, while the second method utilizes the Soft Voting Ensemble Learning. Both methods deploy two different Machine Learning algorithms to handle the hybrid features separately, yet in parallel, minimizing the features’ complexity and improving the model’s performance. A thorough evaluation analysis is carried out considering innovative guidelines that aim to prevent partial and misleading results. Experimental tests verified that the combination of hybrid features with Ensemble Learning, overall, accomplishes better detection performance than when employing only content-based or text-based features. Numerical results on a rich imbalanced dataset (i.e., 32,051 benign and 3,460 phishing email samples) that considers the evolution of phishing emails show that Soft Voting Ensemble Learning outperforms other prominent Machine Learning algorithms and previous works yielding F1-score equal to 0.9942.
Hand Gesture Recognition for Disabled Person with Speech Using CNN

Because handicapped people account for a large percentage of our community, we should make an effort to interact with them in order to exchange knowledge, perspectives, and ideas. To that aim, we wish to establish a means of contact. Individuals who are deaf or hard of hearing can communicate with one another using sign language. A handicapped person can communicate without using acoustic noises when they use sign language. The objective of this article is to explain the design and development of a hand gesture-based sign language recognition system. To aid handicapped individuals, particularly those who are unable to communicate verbally, sign language is translated into text and subsequently into speech. The solution is based on a web camera as the major component, which is used to record a live stream video using a proprietary MATLAB algorithm. Recognition of hand movements is possible with the technology. Recognizing hand gestures is a straightforward technique of providing a meaningful, highly flexible interaction between robots and their users. There is no physical communication between the user and the devices. A deep learning system that is efficient at picture recognition is used to locate the dynamically recorded hand movements. Convolutional neural networks are used to optimize performance. A static image of a hand gesture is used to train the model. Without relying on a pre-trained model, the CNN is constructed.
Data Removal from an AUC Optimization Model

Our learned model may be required to make some dynamic adjustments owing to data removals in privacy, adversarial learning, etc. Previous studies on this issue mostly focus on the standard classification accuracy. This work takes one step on data removal for AUC optimization, where previous methods can not be applied directly since AUC is measured by a sum of losses defined over pairs of instances from different classes. We develop the Data Removal algorithm for AUC optimization (DRAUC), and the basic idea is to adjust the trained model according to the removed data, rather than retrain another model again from the scratch. Our algorithm only needs to maintain some data statistics, without storing the training data in memory. For high-dimensional data, we utilize the frequent direction algorithm to approximate the second-order statistics, and solve the numerical solution based on gradient descent so as to avoid calculating the inverse of Hessian matrix. We verify the effectiveness of the proposed DRAUC both theoretically and empirically.
Few Shot Learning with Embedding Alignment, Normalization and Shifting

Few-shot learning (FSL) refers to learning with a limited amount of training data, as opposed to the classical ways of constructing Machine Learning models with a large amount of data. FSL learns dissimilarity among instances, which allows it to generalize at new tasks. Many state-of-the-art (SoA) FSL models have been proposed in learning the dissimilarity measure in euclidean or latent space in recent years, such as Matching Net, Relational Net, and Prototypical Net, among others. Recent research has attempted to improve accuracy by increasing the depth of the embedding module (e.g., WRN, ResNet12 or higher) as well as the complexity of embedding transformation and adaptation. We propose a ”Few Shot Learning with Embedding Alignment, Normalization and Shifting” (FEANS) method which maximizes the inter-class dissimilarity by integrating semantic attributes (5 generic, objective, and ternary type) of the input instances. It learns to align and shift (the margins to) the embedding of the input instances, thereby improves generalizing the classification tasks significantly. The results demonstrate that the accuracy of the proposed model (At miniImageNet data set) is 82% at (5-way 1-shot, convNet4) and 88% at (5-way 5-shot, convNet4), in other words it outperformed Prototypical Net by 29% (approx).
Covid-19 and Online Teaching: Impact on Academic Results in the Subjects of Architectural Graphic Expression at the Polytechnic University of Cartagena

The health crisis resulting from the pandemic (COVID-19) led to a hasty adaptation of the university towards teaching methodologies based on online experiences. As a result, teaching and assessment methods were adapted according to different formulas. Teaching in Architecture, where drawing is a fundamental tool in communication, required a particular adaptation given the specificity of the competences and learning outcomes in this type of teaching. The aim of this communication is to analyse the students’ academic results after the pandemic period (academic years 2019/20 and 2020/21) with online and blended learning teaching and assessment respectively, and to contrast them with those of previous academic years, developed with traditional face-to-face teaching and assessment methodologies. The focus is on the basic courses of the Area of Architectural Graphic Expression of the Bachelor Degree in Fundamentals of Architecture at the School of Architecture and Building of the Technical University of Cartagena. The conclusions address statistical data relating to the student performance rate and provide an overview of the effect that virtual teaching and assessment have had on academic results.