Reviewerly/exHarmony huggingface dataset:
Understanding Human-AI Cooperation Through Game-Theory and Reinforcement Learning Models

For years, researchers have demonstrated the viability and applicability of game theory principles to the field of artificial intelligence.Furthermore, game theory has been shown as a useful tool for researching human-machine interaction, specifically their cooperation, by creating an environment where cooperation can initially form before reaching a continuous and stable presence in a human-machine system.Additionally, recent developments in reinforcement learning artificial intelligence have led to artificial agents cooperating more efficiently with humans, especially in more complex environments.This research conducts an empirical study to understand how different modern reinforcement learning algorithms and game theory scenarios could create different cooperation levels in human-machine teams.Three different reinforcement learning algorithms (Vanilla Policy Gradient, Proximal Policy Optimization, and Deep Q-Network) and two different game theory scenarios (Hawk Dove and Prisoners dilemma) were examined in a large-scale experiment.The results indicated that different reinforcement learning models interact differently with humans with Deep-Q engendering higher cooperation levels.The Hawk Dove game theory scenario elicited significantly higher levels of cooperation in the human-artificial intelligence system.A multiple regression using these two independent variables also found a significant ability to predict cooperation in the human-artificial intelligence systems.The results highlight the importance of social and task framing in human-artificial intelligence systems and noted the importance of choosing reinforcement learning models.
Automatic Classification of Locomotion in Sport: A Case Study from Elite Netball.

Abstract In team sport Human Activity Recognition (HAR) using inertial measurement units (IMUs) has been limited to athletes performing a set routine in a controlled environment, or identifying a high intensity event within periods of relatively low work load. The purpose of this study was to automatically classify locomotion in an elite sports match where subjects perform rapid changes in movement type, direction, and intensity. Using netball as a test case, six athletes wore a tri-axial accelerometer and gyroscope. Feature extraction of player acceleration and rotation rates was conducted on the time and frequency domain over a 1s sliding window. Applying several machine learning algorithms Support Vector Machines (SVM) was found to have the highest classification accuracy (92.0%, Cohen’s kappa Ƙ = 0.88). Highest accuracy was achieved using both accelerometer and gyroscope features mapped to the time and frequency domain. Time and frequency domain data sets achieved identical classification accuracy (91%). Model accuracy was greatest when excluding windows with two or more classes, however detecting the athlete transitioning between locomotion classes was successful (69%). The proposed method demonstrated HAR of locomotion is possible in elite sport, and a far more efficient process than traditional video coding methods.
Hierarchical Reinforcement Learning By Discovering Intrinsic Options

We propose a hierarchical reinforcement learning method, HIDIO, that can learn task-agnostic options in a self-supervised manner while jointly learning to utilize them to solve sparse-reward tasks. Unlike current hierarchical RL approaches that tend to formulate goal-reaching low-level tasks or pre-define ad hoc lower-level policies, HIDIO encourages lower-level option learning that is independent of the task at hand, requiring few assumptions or little knowledge about the task structure. These options are learned through an intrinsic entropy minimization objective conditioned on the option sub-trajectories. The learned options are diverse and task-agnostic. In experiments on sparse-reward robotic manipulation and navigation tasks, HIDIO achieves higher success rates with greater sample efficiency than regular RL baselines and two state-of-the-art hierarchical RL methods.
Perspectives On Published Energy Sources And Smart Energy Supplies

This article presents a perspective of the several modern alternative energy generation technologies. Moreover, authors facilitate the case study of the application of an emerging energy blockchain (EBC) technology and the Published Energy Sources (PES). A methodical analysis utilizes the EBC input parameters as follows: power generation such as multi-energy complementarity, energy storage, and the smart grid power that has a smart meter and/or control within the EBC system. On the other hand, the EBC technology has several variables as output that includes the following: (1) power consumption focusing on renewable energies; (2) technology enabling financial saving and earning method; (3) peer to peer energy transaction according to the EBC platform.
A User-Defined Gesture Set for Natural Interaction in a Smart Kitchen Environment

Mid-air gestures have long been used as a natural way for controlling smart kitchen appliances, like range hoods, but gesture interaction in the smart kitchen remains limited due to creation by system developers rather than users. To make gesture-based interaction more natural for users, the study aimed to find user-defined gestures suitabie for the smart kitchen. We conducted a structured process based on participatory design. Participants were invited to design gestures and evaluate the quality of gesture vocabulary. We collected the user-defined gestures and evaluate them based on participants' preferences. The experiment results indicated a series of most suitable gestures for target tasks. These gestures were designed for the user-defined gesture set for natural interaction in a smart kitchen environment.
Blockchains: Improve the Scalability and Efficiency of Conventional Blockchain by Providing a Lightweight Block Mining and Communication Algorithm

To provide secure and reliable services using the internet of things (IoT) in the smart cities/villages is a challenging and complex issue. A high throughput and resilient services are required to process vast data generated by the smart city/villages that felicitates to run the applications of smart city. To provide security and privacy a scalable blockchain (BC) mechanism is a necessity to integrate the scalable ledger and transactions limit in the BC. In this paper, we investigated the available solutions to improve its scalability and efficiency. However, most of the algorithms are not providing the better solution to achieve scalability for the smart city data. Here, proposed and implemented a hybrid approach to improve the scalability and rate of transactions on BC using practical Byzantine fault tolerance and decentralized public key algorithms. The proposed Normachain is compares our results with the existing model. The results show that the transaction rate got improved by 6.43% and supervision results got improved by 17.78%.
Automatic punctuation restoration with BERT models

We present an approach for automatic punctuation restoration with BERT models for English and Hungarian. For English, we conduct our experiments on Ted Talks, a commonly used benchmark for punctuation restoration, while for Hungarian we evaluate our models on the Szeged Treebank dataset. Our best models achieve a macro-averaged F1-score of 79.8 in English and 82.2 in Hungarian. Our code is publicly available.
Big-Parallel-ETL: New ETL for Multidimensional NoSQL Graph Oriented Data

Abstract The quantitative explosion of digital data derived from social networks, smart devices, IoT sensors, etc is eventuated by the Big Data concept considered as a very important aspect in the performance improvement of traditional decision-making systems since it reveals serious challenges to be addressed. Therefore, the main purpose of this research paper is the integration of NoSQL Graph-oriented Data into Data Warehouse to deal with Big Data challenges especially with the absence of similar approaches to the best of our knowledge. In this paper, we propose a new approach called Big-Parallel-ETL that aims to adapt the classical ETL process (Extract-Transform-Load) with Big Data technologies to accelerate data handling based on the famous MapReduce concept characterized by its efficient parallel processing feature. Our solution proposes a set of detailed Algorithms based on several rules able to conceive rapidly and efficiently the target multidimensional structure (dimensions and facts) from the NoSQL Graph oriented database.
Super-k: A piecewise linear classifier based on Voronoi tessellations

Voronoi tessellations are used to partition the Euclidean space into polyhedral regions, which are called Voronoi cells. Labeling the Voronoi cells with the class information, we can map any classification problem into a Voronoi tessellation. In this way, the classification problem changes into a query of just finding the enclosing Voronoi cell. In order to accomplish this task, we have developed a new algorithm which generates a labeled Voronoi tessellation that partitions the training data into polyhedral regions and obtains interclass boundaries as an indirect result. It is called Supervised k-Voxels or in short Super-k. We are introducing Super-k as a foundational new algorithm and opening the possibility of a new family of algorithms. In this paper, it is shown via comparisons on certain datasets that the Super-k algorithm has the potential of providing comparable performance of the well-known SVM family of algorithms.
Monte Carlo simulations of the switching processes in the superconducting quantron-based neuron

Abstract We study the response function of a superconducting single-contact interferometer (quantron or rf-SQUID), which is the building block for adiabatic neuron with a nonlinear transfer characteristic. It is shown that the intrinsic shunt capacitance of the junction leads to distortions in the response function. At the same time the contact resistance leads to the suppression of these vibrations. The response function of the rf-SQUID at finite temperature is calculated by Monte Carlo method.
Improvement of the Human Action Recognition Algorithm by the Pre-processing of Input Data

Abstract The paper presents an approach to recognizing human actions using an additional preprocessing stage of input data. The growing volumes of video information do not always allow support the quality of data at a high level; this can cause limitations in the further processing of digital data. In this regard, it becomes urgent to introduce an additional stage of image enhancement into the algorithm for recognizing actions in video. The proposed method includes three main steps: image enhancement, constructing a descriptor, and classification. The presented image enhancement stage is based on the combined local and global image processing in the frequency domain. The basic idea in using local alfa-rooting method is to apply it to different disjoint blocks with different sizes. To solve the problem of constructing a descriptor, a three-dimensional microblock dense difference (3D DMD) algorithm is used, which provides a highly oriented representation of image regions by tightly capturing microblocks within each region in several orientations and scales. 3D DMD has several advantages over other methods: higher efficiency compared to existing methods; minimal computational costs when using an integrated image; low dimension; ease of implementation; does not require settings. The presented modification allows to increase productivity by 2-4%.
Teletrabalho na perspectiva da Saúde Ocupacional

Introduction/ background/ objectives The possibility of doing work at home dates back several centuries; however, technological development has allowed for the creation of new jobs based on it and eventually fully executed outside the employer’s premises. In addition, the pandemic situation associated with SARS-COV-2 (COVID-19) has encouraged many institutions to initiate or intensify telework. The purpose of this review was to summarize the most relevant data about the topic, in the context of Occupational Health. Methodology This is a Review, initiated through a survey conducted in April 2020, in the databases “CINALH plus with full text, Medline with full text, Database of Abstracts of Reviews of Effects, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, Cochrane Methodology Register, Nursing and Allied Health Collection: comprehensive, MedicLatina, SCOPUS and RCAAP”. Content Telework is working at home, or at least outside the employer’s conventional facilities, but in a previously stipulated and approved location, using technology. In this review, data on the historical evolution of the work were summarized, highlighting the various Industrial Revolutions. Some national and especially international statistics were also included in the context of Telework. The main sub-types of Telework are also briefly described and what are the desirable characteristics in teleworkers and telemanagers. Finally, the main advantages and disadvantages mentioned in the consulted bibliography are highlighted. Conclusions Generally speaking, the perception of advantages and disadvantages depends on the employee’s characteristics (such as personality, flexibility, autonomy, competence, need for socialization, posture and commitment to work); characteristics of the employer (management, malleability, valorization of human resources, means and procedures); the tasks themselves (ease or not of being teleworked); family characteristics (number of elements, age of children and relational quality); the household (size, lighting, furniture, equipment, area exclusive or not for the Telework); traffic, pollution and urban safety; as well as consequences for the relationship with the company, work and personal life (family and society); that is, the same aspect can be considered both an advantage and a disadvantage, depending on the global context. Furthermore, in reality, Telework has such a diversity of conditions and characteristics, that consensus conclusions are not possible. It is necessary for each institution to analyze the advantages and disadvantages of inserting the Telework and, if it decides to proceed, have a sense of what are the factors that can enhance and compromise the success of the project.
Early Diagnosis of Alzheimer’s Disease using Unsupervised Clustering

Alzheimer's disease (AD) is a progressive brain disorder and a very common form of dementia. Neuroimaging techniques, such as Magnetic Resonance Imaging (MRI), produce detailed 3-dimensional images of the brain showing insights for amyloid deposits and inflammatory alterations as disease markers. The early diagnosis of AD using MRI provides a good chance for patients to prevent further brain deterioration by stopping the loss of nerve cells. This paper explores the use of unsupervised clustering approaches for the early diagnosis of AD. Though it is very common to use classification techniques for identifying medical diseases, the lack or the inaccuracies of labeled data can generate a problem. In this work, the k-means and k-medoids are compared while employing the Voxel Based Morphometry (VBM) features extracted from the MRI images. The effect of choosing certain local regions of interest (ROIs) for the analysis is also compared to the global whole-brain analysis. The results show that the proposed approach can perform an early diagnosis of AD with an accuracy of 76%.
Deep Reinforcement Learning with embedded LQR Controllers

Reinforcement learning is a model-free optimal control method that optimizes a control policy through direct interaction with the environment. For reaching tasks that end in regulation, popular discrete-action methods are not well suited due to chattering in the goal state. We compare three different ways to solve this problem through combining reinforcement learning with classical LQR control. In particular, we introduce a method that integrates LQR control into the action set, allowing generalization and avoiding fixing the computed control in the replay memory if it is based on learned dynamics. We also embed LQR control into a continuous-action method. In all cases, we show that adding LQR control can improve performance, although the effect is more profound if it can be used to augment a discrete action set.
Dual-Level Collaborative Transformer for Image Captioning

Descriptive region features extracted by object detection networks have played an important role in the recent advancements of image captioning. However, they are still criticized for the lack of contextual information and fine-grained details, which in contrast are the merits of traditional grid features. In this paper, we introduce a novel Dual-Level Collaborative Transformer (DLCT) network to realize the complementary advantages of the two features. Concretely, in DLCT, these two features are first processed by a novelDual-way Self Attenion (DWSA) to mine their intrinsic properties, where a Comprehensive Relation Attention component is also introduced to embed the geometric information. In addition, we propose a Locality-Constrained Cross Attention module to address the semantic noises caused by the direct fusion of these two features, where a geometric alignment graph is constructed to accurately align and reinforce region and grid features. To validate our model, we conduct extensive experiments on the highly competitive MS-COCO dataset, and achieve new state-of-the-art performance on both local and online test sets, i.e., 133.8% CIDEr-D on Karpathy split and 135.4% CIDEr on the official split. Code is available at https://github.com/luo3300612/image-captioning-DLCT.
WallTokens: Surface Tangibles for Vertical Displays

Tangibles can enrich interaction with digital surfaces. Among others, they support eyes-free control or increase awareness of other users' actions. Tangibles have been studied in combination with horizontal surfaces such as tabletops, but not with vertical screens such as wall displays. The obvious obstacle is gravity: tangibles cannot be placed on such surfaces without falling. We present WallTokens, easy-to-fabricate tangibles to interact with a vertical surface. A WallToken is a passive token whose footprint is recognized on a tactile surface. It is equipped with a push-handle that controls a suction cup. This makes it easy for users to switch between sliding the token or attaching it to the wall. We describe how to build such tokens and how to recognize them on a tactile surface. We report on a study showing the benefits of WallTokens for manipulating virtual objects over multi-touch gestures. This project is a step towards enabling tangible interaction in a wall display context.
New IGIP Curriculum for Advanced Training of Engineering University Teachers

The discussion of the new Curriculum for pedagogical training of teachers of technical disciplines, prior to its approval by the IGIP Executive Committee in the fall of 2019 is considered. The previous version of this Curriculum was approved in 2013. Over the past period, there have been significant changes in the use of ICT in training, discussed in the proposals of the National IGIP Offices, in the proceedings of the Annual IGIP Conferences, including the International Conference ICL-IGIP held in Tallinn on 23–25 September, 2020. At this Conference, IGIP President Hanno Hortsch in his presentation has published the IGIP curriculum in the form of a table (now we present it in translation into Russian) and reported about his vision of its use. The authors formulate their point of view on the topic basing on the overview of the presentations given at this Conference and relevant articles published in the latest issues of the journal “Higher education in Russia”.
Role of the Kazakhstan-German Cooperation in Improving Scientific Tools for Evaluation of Vocational Education Programs

The work carried out within the framework of the German-Kazakh project “GeKaVoс – Transfer of dual training in logistics, mechatronics and sustainable energy supply to Kazakhstan” can be considered as one of the crucial aspects of the contribution to improving the quality of system of technical and vocational education (TVET), the achievement of high rates of employment of graduates of the TVET system, improve the ratio of supply and demand in the labor market. As the analysis of the TVET system in Kazakhstan presented in the article shows, different forms of cooperation between participants of the educational process in the country (corporate partnership, industrial clusters, patronage of large and medium-sized enterprises over educational institutions, etc.) contribute to the development of a market-oriented education system. Monitoring and evaluation of the effectiveness of technical and vocational education, as well as identifying opportunities to improve its quality and scope require understanding of its nature, its functions, goals and key characteristics, the application of effective scientific tools to assess vocational technical education and training of trainers (tutors) (TVET). The paper shows that the success of the transfer of educational programs depends primarily on the willingness to work together of all the stakeholders, the creation of systematic structures of interaction in the development of educational programs of different levels, improving methods of forecasting demand not only for certain professions, but also for professional and pre-professional competencies. The purpose of scientific evaluation of the dual system and workplace training is to prove its effectiveness as an educational strategy that provides students with real work experience, where they can apply social and technical skills and develop them.