Reviewerly/exHarmony huggingface dataset:
When Domain Adaptation Meets Semi-supervised Learning Through Optimal Transport

This paper deals with the problem of unsupervised domain adaptation that aims to learn a classifier with a slight target risk while labeled samples are only available in the source domain. The proposed approach, called DA-SSL (Domain Adaptation meets Semi-Supervised Learning) attempts to find a joint subspace of the source and target domains using Linear Discriminant Analysis, such that the projections of the data into this latent subspace can be both domain invariant and discriminative. This aim, however, can be rather difficult to accomplish because of the missing labeled data in the target domain. To defeat this challenge, we use an incremental semi-supervised approach based on optimal transport theory, that conducts selective pseudo-labeling for unlabeled target instances. The selected pseudo-labeled target data are then combined with the source data to incrementally learn a robust classifier in a self-training fashion after the subspace alignment. Experiments show the competitiveness of the proposed approach over contemporary state-of-the-art methods on two benchmark domain adaptation datasets. We make our code publicly available (Code is available at: https://github.com/MouradElHamri/DA-SSL ).
Generative Target Update for Adaptive Siamese Tracking

Siamese trackers perform similarity matching with templates (i.e., target models) to recursively localize objects within a search region. Several strategies have been proposed in the literature to update a template based on the tracker output, typically extracted from the target search region in the current frame, and thereby mitigate the effects of target drift. However, this may lead to corrupted templates, limiting the potential benefits of a template update strategy. This paper proposes a model adaptation method for Siamese trackers that uses a generative model to produce a synthetic template from the object search regions of several previous frames, rather than directly using the tracker output. Since the search region encompasses the target, attention from the search region is used for robust model adaptation. In particular, our approach relies on an auto-encoder trained through adversarial learning to detect changes in a target object’s appearance, and predict a future target template, using a set of target templates localized from tracker outputs at previous frames. To prevent template corruption during the update, the proposed tracker also performs change detection using the generative model to suspend updates until the tracker stabilizes, and robust matching can resume through dynamic template fusion. Extensive experiments conducted on VOT-16, VOT-17, OTB-50, and OTB-100 datasets highlight the effectiveness of our method, along with the impact of its key components. Results indicate that our proposed approach can outperform state-of-the-art trackers, and its overall robustness allows tracking for a longer time before failure. Code: https://github.com/madhukiranets/AdaptiveSiamese .
Oskar Becker and the Modal Translation of Intuitionistic Logic

We reconsider Oskar Becker’s pioneering contributions to modal logic in On the Logic of Modalities (1930), in particular Becker’s unjustly neglected anticipation of the idea of a modal interpretation of intuitionistic logic, which was realized three years later by Kurt Gödel.
Clustering Algorithms for Efficient Neighbourhood Identification in Session-Based Recommender Systems

Recommender systems are applications to support users in searching relevant information or items on the Internet. Such examples are/news, articles, products in e-stores, books in libraries, music in broadcasting media. Recently, a new approach to recommendation generation emerged that concentrates on short-term users’ activities organized in sessions. Such an approach is effective in real-world solutions due to a predominant number of one-time users and a limited time of items’ availability. Session-based recommender systems are algorithms that focus particularly on users’ ongoing sessions with the aim to predict their next actions. This article presents results of experiments with session-based recommenders which rely on an active user’s neighbourhood identification. The algorithms were used in a standard form, where the neighbourhood was calculated with k-nearest neighbours and in a form of clusters generated in advance by clustering methods. The cluster-based approach was more efficient in terms of accuracy and diversity of recommendation lists.
Your First Line of Android Code

Welcome to the Android World! Android is by now the most popular mobile operating system (OS) in the world, and you can find Android phones wherever you go. But do you know how did Android has become the world’s No. 1 mobile OS? Let us take a look at the history of Android.
Analysis and Research on the Construction of Book Retrieval Platform Based on Disconnected Apriori Algorithm

This paper introduces an improved Apriori algorithm. The algorithm can directly get the final frequent itemset by finding the maximum frequent itemset which is greater than the minimum holding count. The improved algorithm is applied to the library bibliography recommendation service. The analysis and Research on the construction of the book retrieval platform under the disconnected Apriori algorithm, The performance of the improved algorithm and Apriori algorithm is analyzed and the running time of the experimental data is compared. The experimental results show that the improved algorithm has a significant improvement in running speed and mining performance compared with the classical Apriori algorithm.
Research on Cooperative Formation Motion Control Method of Multi Ground Unmanned Platforms

Unmanned combat is the strategic frontier and development hotspot of information battlefield. Compared with single ground unmanned platform, multi ground unmanned platform has higher robustness, reliability and work efficiency. Aiming at the cooperative formation motion control method of multi ground unmanned platforms in complex land environment, this paper combines the leader-follower method and virtual structure method to control the formation of multi ground unmanned platforms. In the process of formation movement, the anti-collision of multiple unmanned platforms, obstacle avoidance and speed regulation for complex environment are studied, and the simulation verification is carried out based on ROS environment. These studies provide a technical basis for the follow-up research of formation control technology.
Cybersecurity Ethics Education: A Curriculum Proposal

Cybersecurity ethics has emerged as a new and increasingly significant area for research and education. New and complex ethical dilemmas and conflicts in values and judgments arise as new cybersecurity technologies and policies are constantly explored and implemented to defend our cyberspace for a safe and secure living and work environment. As various cyber threats, attacks, and risks pose increasing challenges to the diverse and interconnected world we live in, there is an increasing demand for quality cybersecurity education to prepare and produce qualified and ethically competent professionals to address the cybersecurity challenges. The national Centers of Academic Excellence in Cyber Defense Education (CAE-CDE) designation by the U.S. National Security Agency and Department of Homeland Security (NSA/DHS) is a high-quality program that promotes excellence in cybersecurity education for developing qualified cyber talent. Strong curriculum and courses supported by regular mentoring are essential to successful preparation of cybersecurity professionals. This research paper proposes a new credit course in cybersecurity ethics supported by an adopted comprehensive model of mentoring for an undergraduate cybersecurity education program at a CAE-CDE designated university in the United States. The curriculum proposal will present the rationale, course description, mappings of learning outcomes and topics to the CAE-CDE knowledge unit, suggested methods of assessment, and mentoring activities. The goal of this research is to contribute a new course design with ethical mentoring to enrich and enhance national and international cybersecurity curriculum and education.
Eletroterapia na Fibromialgia

The chronicity of musculoskeletal pain in the clinical picture in fibromyalgia associated with stages of exacerbation, for various temporal, emotional, physical, work and social reasons, instigates the need for adequate and effective therapies to maintain functionality and quality of life, in the face of the absence of cure. This research aimed to measure the effects of physical therapy conduct in electrotherapy aimed at the acute stage of fibromyalgia, given the complexity of impairments related to signs and symptoms. 15 electrothermophototherapy sessions were carried out, 3 times a week, with protocol parameters according to the clinical condition of the disease. The Fibromyalgia Impact Questionnaire (QIFR-Br) quantified pain intensity, functionality and quality of life before and after the intervention. Statistical analysis showed statistically significant results (p&lt;0.05) for all variables, that is, the proposed protocol is effective for restoring functionality, as a result of the analgesia and anti-inflammatory effect achieved with physical therapy, providing better prognosis and stabilization of the clinical picture.
Solar Energy Forecasting Using Fuzzy Logic and Artificial Neural Network

Forecasting solar energy is very important while designing and developing the solar photovoltaic system. Because of the variation in meteorological parameters and solar irradiance, power developed by the solar photovoltaic system varies, therefore forecasting becomes important. In this paper, a short-term solar energy forecasting is being done using fuzzy logic and artificial neural network. Both fuzzy logic and artificial neural network are widely used for forecasting purposes. Fuzzy logic is based on decision-making of a human. How it takes decision based on imprecise or vague data. Artificial neural network is based on machine learning. For the development of the model based on fuzzy logic and artificial neural network, various applications provided in the MATLAB software are used. Normalized dataset is used forecasting purposes. For the performance evaluation mean absolute percentage error for both the cases is calculated. For Fuzzy Logic, it comes out to be 4.02% and for ANN it comes out to be 1.96%.
Design of an Optimal Deep Learning-Based Self-Healing Mechanism With Failure Prediction Model for Web Services

Recently, web service composition technology becomes familiar and it raise the quality offered by the systems designed followed by the service oriented architecture (SOA) framework. Web service composition mainly functioning in dynamic environment, susceptible to the incidence of unpredictable disruption and modifications which could influence the performance of the system. Therefore, the ability to self-healing and manage the execution of web service composition can enhance the reliability and fault tolerance of the system.This study develops an Optimal Deep Learning based Self Healing Mechanism with Failure Prediction (ODL-SHMFP) model for Web Services. The proposed ODL-SHMFP technique aims to accomplish a self healing model for minimizing the failure in web services.
Secure Smart Contract Based on Elliptic Curve in Property Exchange Applications Using Blockchain

As we live in a fast and rapidly developing society with goring businesses, more and more people chasing after the dream of a better financially independent life stray from the large corporations and organizations to form startups and small businesses, increasing the demand and need putting more pressure on employees of such institutes to prepare and manage contracts, papers, and verifications, the more businesses activities, means more needed employees to manage their assets by adding more to the humanly mistakes that may made. So, to reduce time and provide more confident, organized and secure labor new strategy has been employed. In this paper, blockchain technology has been adopted for secure smart contract that implemented it in a real-estate scenario. The main purpose is to create a distributed ledger, reducing the need for centralization procedures and adding more security and integrity by implementing elliptic-curve cryptography as a signature generation/verification and encryption/decryption protocol.
High-Fidelity Synthesis with Causal Disentangled Representation

Numerous disentangled representation learning algorithms have a general problem, which is sacrificing generation performance to improve disentanglement performance. In order to solve the contradiction between disentanglement and generative performance, a high-fidelity synthetic causal disentangled representation learning framework(HCAPE) that combined Variational Auto-Encoder(VAE) and Generative Adversarial Network(GAN) was proposed. In the inference stage, the assumption that the latent factors of the variation are mutually independent was abandoned. And a structural causal model in the encoder was resorted to learn causal representations. In the generation stage, the idea of GAN was adopted, that is, the high-fidelity image with the highest score was obtained through the constant competition between the generator and the discriminator. Our experiments had excellent and extensive performance on synthetic datasets and real datasets. Experiments shown that the causal representations learned by our model were semantically interpretable, and our model was able to generate counterfactual data through “do-operation” to the causal factors. What counts is that our model successfully distilled causal disentangled generative factors learned by the inference network in VAE, while producing diverse and high-fidelity outputs using generative factors encoded in the nuisance variable.
Robust Face Recognition Under Advanced Occlusion Proposal of an Approach Based on Skin Detection and Eigenfaces

Facial occlusion is a critical problem in many face recognition applications. It complicates the process of automatic face recognition because many factors such as occluded facial region, shape occlusion, occluded region color, and occlusion position are variable. Existing face recognition approaches that deal with occlusion issues focus mainly on classic facial accessories. In this paper, we consider occlusions types well studied in the literature (sunglasses, neck warmer, beard, hair, etc.) as well as other occlusions, which are not studied extensively. We assess the Eigenface method in the presence of occlusions and we develop an original optimal approach of simple and more robust facial recognition allowing operating the Eigenfaces method even for occluded faces in more advanced conditions. For this, we have combined skin detection and the Eigenface method. We validated our method on several facial occlusions using FEI database containing several types of faces.
Intelligent Blockchain Services for Smart City Economy Using Emotion Analysis

Universally large amount of initiation was taken up by different governments to make their countries prime cities as Smart cities. The major component of Smart cities is Internet of Things (IoT) devices and sensor data. When comes to financial data transmission in those smart cities it required a high security and privacy-oriented IoT infrastructure. To support such robust infrastructure, blockchain is one of the best solutions. While processing the IoT sensor data, if an Artificial Intelligent (AI)-based processing and analysis were used, it will provide a semantic digital data analysis. In this paper, a concept has been introduced for semantic data extraction and data pattern analysis using intelligent emotion extraction. This framework provides a highly secured economic services for smart cities.
Prediction of Solar Power Using Machine Learning Algorithm

The stability and reliability of power in an integrated renewable energy system vary according to changes in environmental conditions, as the radiation, temperature, and humidity change continuously; the power generation through PV system also changes, and hence, the power scheduling and operation mainly depend on the estimation of power through renewable energy sources. The power generation in PV systems initially depends on the radiation and temperature, so prediction of weather conditions helps in predicting the power generation through PV solar plant. The stability of a power system can be improved by predicting solar energy which can tell approximately how much solar power can be generated in the future at a particular location. Solar power forecasting has several methods, one of all methods is using machine learning/neural networks. In this paper, the power generation with a solar plant is forecasted by predicting the future weather generation using machine learning algorithms. The accuracy of forecasting will be checked directly with the practical data which is generated and simulated data using MATLAB/Simulink by applying various machine learning algorithms.
Designing Information Security Culture Artifacts to Improve Security Behavior: An Evaluation in SMEs

This article examines the relationship between the information system security culture and the security behaviors of users of the information system (IS). This research follows the design science in information systems research guidelines proposed by [43] to conceptualize the IS security culture in its context, where we propose a model based on Schein’s three-level culture model (1985) [15], and evaluated at the level of our research context, which is SMEs, through a qualitative study conducted with twenty-two users belonging to eight French small and medium-sized enterprises (SMEs). The results of this study show that there is a strong relationship between IS security culture and user behaviors related to IS security, in the sense that a positive security culture is conducive to the creation of security behaviors.
Secret Information Sharing Using Probability and Bilinear Transformation

Information security is very much important in this Internet world, especially in electronic communications such as system security, smart card, mobile communications. Cryptography is based on transformation of multiple rounds of transformation of messages in the form of plain text as input into encrypted text message. Through suitable mathematical technique, secrecy of the information is maintained. This paper proposes a cryptographic technique using probability and bilinear transformation for encryption and decryption of a message. The algorithm for encryption and decryption is given. The probability concept is employed to secure the key between the communicator and recipient. The methodologies are used to get a safe communication between communicator and recipient. The bilinear transformation gives more secure for the process in key transformation. The bilinear transformation is used to encrypt the message. The inverse bilinear transformation is used to decrypt the message. The example is presented to validate the theory part.
Personality Prediction in Human-Robot-Interaction (HRI)

For an efficient and smooth human-robot interaction, communication protocols such as verbal and non-verbal communication, emotions, and personality plays an important role. Human-Robot-Interaction is an emerging field and robots are now a part of daily life where it can grasp both verbal and non-verbal cues. Personality prediction is an important research area in Human-Robot Interaction (HRI). Several important question in personality prediction includes: which personality traits will be important and which specific phycology model and robot do researchers use? Secondly, how emotions, facial expression, paralanguage, and bodily movements are related to personality traits? And finally, how will we acquire data to train a robot and what kind of questionnaires can be used? With the support of prior research studies and experiments, this paper will contribute towards developing the ground basis for personality prediction using a robot.
Video Analysis and Diagnosis System of National Traditional Sports Confrontation

At present, the analysis of national traditional sports confrontation training is mainly based on the subjective experience judgment of coaches, which is lack of objectivity and timeliness. This study builds an arm + DSP + CPLD technology framework platform, uses video image intelligent analysis and processing technology to record and diagnose athletes’ training and competition in real time, and obtains the processing results that can be used as a reference for coaches and athletes, so as to improve training efficiency and competition performance.
Development of a Roadmap for the Formation of Clusters of Professional Competencies in Spatial Zones of “Accelerated Development” of the Region

The HRM gaps are not sufficiently developed for the raw materials regions of Russia, characterized by technological multidimensional economy and high differentiation of innovative activity of industries. The solution of the problem is complicated by the lack of advanced diagnostics of gaps in labor markets, the fuzziness of definitions of clusters of professional competencies and the lack of tools for their formation. The concept of “cluster of professional competencies” was clarified. It is proved that the use of developed road map will increase the efficiency of managing the balanced development of the region. The obtained results represent the basis for the application of the developed instrumentarium by the subjects of state and municipal management of the regions of Russia based on leveling the gaps between business needs in professional competencies in the implementation of priority areas of socio-economic development.
Information and Communication Technologies Learning Methodologies for Children with ADHD

ADHD is a disorder due to attention deficit, impulsiveness and hyperactivity due to various functional disputes, these anatomical brain inconsistencies can be genetic and/or environmental that hinders the child's lifestyle and their environment. Within the educational field, teachers cannot distinguish the attitude of a child with ADHD from one without disorders, especially in the preschool stage, which motivates the search for teaching alternatives, among them we have information and communication technologies - ICT, which allow evaluating, diagnosing, enriching and strengthening the learning of children with or without ADHD through educational software on a mobile phone, a computer and/or digital whiteboard; increasing attention, concentration, creativity, security, decreasing aggressiveness, gradually developing positive behaviors, their self-esteem improves, they feel motivated and progressively create solutions to their internal conflicts in such a way that their community learning guarantees the development of new social skills such as respect, solidarity, empathy and identifies the emotions of other people allowing a positive incorporation into their daily lives, especially in the school environment.