Reviewerly/exHarmony huggingface dataset:
Integrated Photonic Synaptic Interconnect Processor for Acceleration of Deep Neural Network Training

Our neuromorphic work on analog photonic synaptic signal processing relies on holographically induced refractive index gratings in a photorefractive semi-insulating III/V layer on silicon photonics to boost inference and training performance and efficiency of Neural Networks.
Solar panels with sun position tracking system

Abstract This article presents the development of an automated system that includes a set of solar panels, which with the help of special drives and software orientates the position for maximum power generation. The development of drives is based on Dynamo software and Revit. The problem was posed to address the issue of global warming and reduce fuel consumption for electricity generation. Principle of operation of solar panels.
Current trends in the development of interactive methods in teaching foreign languages

Abstract The subject of research covered in this paper is a method of teaching a foreign language as a means of enhancing students’ cognitive activity as well as development of their creative and intellectual abilities. The interactive method of teaching is observed as a form of the cognitive activity reflecting relevant phenomena of the modern reality. The paper is mainly focused on the methodology of conducting classes using such an interactive teaching method as a language game in order to develop lexical, grammatical and phonetic competence of students. The potential of a language game as an interactive teaching method (communicative approach) is demonstrated through studying headlines of French newspapers in the context of teaching the French language to linguistic students. Various interpretations of the “language game” concept have been compiled on the basis of up-to-date academic literature. Several levels of language game comprehension and translation are analyzed including the following ones: word-formation, idiomatic and phonetic. Examples of newspaper headlines are provided in order to scrutinize lexical and stylistic figures of speech (e.g. personification, metaphor, comparison, oxymoron, pun) as well as set expressions and syntactic constructions. Methodological guidelines for applying a language game as an interactive teaching method are offered. The paper is aimed at teachers of higher professional education institutions.
Beyond Words: Comparing Structure, Emoji Use, and Consistency Across Social Media Posts

Social media content analysis often focuses on just the words used in documents or by users and often overlooks the structural components of document composition and linguistic style. We propose that document structure and emoji use are also important to consider as they are impacted by individual communication style preferences and social norms associated with user role and intent, topic domain, and dissemination platform. In this paper we introduce and demonstrate a novel methodology to conduct structural content analysis and measure user consistency of document structures and emoji use. Document structure is represented as the order of content types and number of features per document and emoji use is characterized by the attributes, position, order, and repetition of emojis within a document. With these structures we identified user signatures of behavior, clustered users based on consistency of structures utilized, and identified users with similar document structures and emoji use such as those associated with bots, news organizations, and other user types. This research compliments existing text mining and behavior modeling approaches by offering a language agnostic methodology with lower dimensionality than topic modeling, and focuses on three features often overlooked: document structure, emoji use, and consistency of behavior.
Differences and Similarities Learning for Unsupervised Feature Selection

In this paper, a novel feature selection algorithm, named Feature Selection with Differences and Similarities (FSDS), is proposed. FSDS jointly exploits sample differences from global structure and similarities from local structure. To reduce the disturbance from noisy feature, a row-wise sparse constraint is also merged into the objective function. FSDS, then combines the underlying subspace features with original feature to construct a more reliable feature set. Furthermore, a joint version of FSDS (FSDS2) is introduced. To optimize the proposed two-step FSDS and the joint version FSDS2 we also design two efficient iterative algorithms. Experimental results on various datasets demonstrate the effectiveness of the proposed algorithms.
Forgetful AI: AI and the Right to Erasure under the GDPR

Artificial Intelligence and, specifically, Machine Learning, depends on data for its development and continuous evolution. Frequently, the information used to train Machine Learning algorithms is personal data and, thereby, subject to the rules contained within the GDPR. If the necessary requirements are fulfilled, Article 17 of the GDPR grants to the data subject the right to request from the controller the erasure of personal data concerning him/her. In this paper we will study the impact of the right to erasure under the GDPR in the development of Artificial Intelligence in the European Union. We will assess whether datasets, mathematical models and the results of applying such models to new data need to be erased, pursuant to a valid request from the data subject. We will also analyse the challenges created by this erasure, how they can be minimized and the most adequate legal interpretations to ensure seamless AI development that is also compatible with the principles of privacy and data protection currently in force within the European Union. Keywords: Artificial Intelligence, GDPR, Right to Erasure
John Horton Conway FRS

John Horton Conway lived to discover the Mathematics behind problems, always working to isolate a pure, essential kernel of truth. He loved to communicate these simple truths to others, often changing the way they thought. Everything John touched turned to Mathematics, and to very beautiful Mathematics. John was generous with his mathematical riches; he gave them to everyone that showed interest — whether at the coffee house, at the sushi restaurant, or in Mathematics departments. He was a magnet for all mathematicians, and he welcomed all who came to him. John would find a way to start with some simple calculation, a game or puzzle and turn it into whatever he wanted to explain. John often chose problems about games and recreational topics, but the insights he derived changed our understanding of several very serious branches of Mathematics.
Social Media Analytics: Techniques, Tools, Platforms a Comprehensive Review

To determine which social media analytics tools, techniques, and platforms were developed in recent times, this paper reviews tools, techniques, and platforms related to social media analytics. In this paper, we talk about the tools used to deal with various social media data (social networking, media, etc.). In the past decade, there has been advancement in the technologies used to deal with social media as there has been an increase in the number of people using social media to share information and also the development of the new social media platforms that have let to increase in the amount of data that we have to deal with. Social media platforms have a considerable number of users across the world, which is overgrowing. These people are sharing information through these sources. There is a large quantity of social data comprising of data related to users, videos, web-based relations, and interactions, etc. which needs to be analyzed. Therefore analyzing social media data has become a significant activity for researchers, mainly due to the availability of the web-based API from social media platforms like twitter, facebook [], Gmail, etc. This has also led to the development of data services, software tools for analyzing social media data. In this paper, there is a detailed review of the leading software tools and techniques that are used for scraping, cleaning, and analyzing social media data.
A Submodular Optimization-Based VAE-Transformer Framework for Paraphrase Generation

Paraphrase plays an important role in various Natural Language Processing (NLP) problems, such as question answering, information retrieval, conversation systems, etc. Previous approaches mainly concentrate on producing paraphrases with similar semantics, namely fidelity, while recent ones begin to focus on the diversity of generated paraphrases. However, most of the existing models fail to explicitly emphasize on both metrics above. To fill this gap, we propose a submodular optimization-based VAE-transformer model to generate more consistent and diverse phrases. Through extensive experiments on datasets like Quora and Twitter, we demonstrate that our proposed model outperforms state-of-the-art baselines on BLEU, METEOR, TERp and n-distinct grams. Furthermore, through ablation study, our results suggest that incorporating VAE and submodularity functions could effectively promote fidelity and diversity respectively.
Comparative research on two methods of straight line extraction based on sub-pixel

In recent years, machine vision technology are widely used in the industrial production process. In this paper, We studied two straight line extraction methods. Traditional method generally use the canny algorithm and sub-pixel edge detecting algorithm to detect sub-pixel edge of the image, and then use least-squares method to fit the geometric information of the edge of the image and fulfil measurement. It was found that the image collected in the actual measurement environment is often affected by the environment and produces one or other interference information, such as dust and hair interference, which affects the extraction of image edges and the accuracy of the measurement, resulting in measurement failure. We search the sub-pixel precision edge by the caliper tool method, and then use the method of RANSAC to fit straight line and get the corresponding geometric information. Finally, the distance information of the two straight line sub-pixel edges is obtained by distance calculation, and compared with the traditional method. Through the comparison of experimental data, the caliper tool method has significantly improved the measurement accuracy and robustness of the system, and achieved a better result.
Problems of science popularization exemplified by modern Russian practice

Abstract Science popularization is a specific public activity of scientists, the purpose of which is education and self-realization. The article emphasizes the fact that this activity is self-organized in Russia, and the key role in its development is played by technical and technological improvement and the Internet. Media development contributes to the uncontrollable spread of pseudoscientific and unscientific theories as well as their criticism. The study is focused on the showization of science, which is presented to the public in the form of spectacle and entertainment. The forms of popularization of science in sociocultural practice begin to fulfill the function of intellectual leisure for the public. Popularization in the context of media development enables scientific knowledge to become public knowledge by means of publishing popular science literature, organizing online and offline lecture halls, web portals with scientific and educational content, which is evidence of the democratization of science in general and the activities of the scientist in particular. The activities of popularizers in modern Russia are consolidating, traditions are being developed, shaped by stable rituals, and recognizable authoritative “stars” are appearing. Thus, the popularization of science seems to be an ambiguous phenomenon of modern Russian culture, which reveals the features of a centralizing sociocultural institution.
The Application of Urban AR Technology in Cultural Communication and Innovation

With the development of science and technology, today’s society has gradually entered the Internet-led system, and based on the demand for creating a more perfect intelligent experience for users, augmented reality technology has become a hot spot for the development of the modern intelligent industry. AR applications such as AR games, AR smart furniture, AR navigation systems, etc. are constantly emerging. As technology evolves and matures, AR applications are beginning to shift from 2D data information (text or image-based descriptions) to 3D integration objects. The development of technology has provided more ways and possibilities for cultural communication and innovation. City around the globe is ready for the next step for their evolution to augmented cities. This paper aims to design an application of Location-based AR technology for exploring cities and discuss its important role in cultural communication and innovation.
Comparative Study of RSA with Optimized RSA to Enhance Security

Asymmetric crypto algorithms are a robust technology used in the communication of texts on the channel to reduce security risks. Mathematical approaches are one of the drawbacks and it’s because they involve a larger amount of calculation which contributes to the need for enhanced use of computing power. This paper aims to refine the algorithm for RSA encoding and therefore enhance information security, reliability and availability. The results show the information security efficiency and usability of the RSA algorithm. We can also see that when performing encoding and decoding, time, space, processor and network output are lower than other RSA solutions since computing is performed on the client and server.
Risk-Sensitive Piecewise-Linear Policy Iteration for Stochastic Shortest Path Markov Decision Processes

A Markov Decision Process (MDP) is commonly used to model a sequential decision-making problem where an agent interacts with an uncertain environment while looking for minimizing the expected cost accumulated along the process. If the process horizon is infinite, a discount factor \(\gamma \) \(\in \) [0, 1] is used to indicate the importance the agent gives to future states. If the agent’s mission is to achieve a goal state, the process becomes a Stochastic Shortest Path MDP (SSP-MDP), the in fact model used for probabilistic planning in AI. Although several efficient solutions have been proposed to solve SSP-MDPs, there are little research carried out when we consider the “risk” in such processes. A Risk Sensitive MDP (RS-MDP) allows modeling the agent’s risk-averse and risk-prone attitudes, by including a risk and a discount factor in the MDP definition. The proof of convergence of known solutions based on dynamic programming adapted for RS-MDPs, such as risk-sensitive value iteration (VI) and risk-sensitive policy iteration (PI), rely on the discount factor. However, when solving an SSP-MDP we look for a proper policy, i.e. a policy that guarantees to reach the goal while minimizing the accumulated expected cost, which is naturally modeled without discount factor. Besides, it has been shown that the discount factor can modify the chosen risk attitude when solving a risk sensitive SSP-MDP. Thus, in this work we aim to formally proof the convergence of the PI algorithm for a Risk Sensitive SSP-MDP based on operators that use a piecewise-linear transformation function, without a discount factor. We also run experiments in the benchmark River domain showing how the intended risk attitude, in an interval of extreme risk-averse and extreme risk-prone, varies with the discount factor \(\gamma \), i.e. how an optimal policy for Risk Sensitive SSP-MDP can go from being a risk-prune policy to a risk-averse one, depending on the discount factor.
Gender Identification for Coloring Black and White Portrait with cGan

This paper proposes a method to color black & white portrait images with gender recognition model and condition generation network (cGan). As a cGan model essentially, a method named pix2pix interprets image style transformation as a translation process from input pixels to target pixels. Lots of input to target image pairs should be prepared for model training for different application scenarios. When pix2pix is applied to color black & white portraits, there are obvious differences in color and saturation between portraits of different genders while the ability of gender recognition isn’t owned by pix2pix in essence. Therefore, a gender recognition model is added to the network to produce more realistic and gender specific color portraits. The experiments show that our work has achieved better results than some other common methods.
A Study on Effective Clustering Methods and Optimization Algorithms for Big Data Analytics

High-dimensional information is labeled through massive dimensions of structures, disseminates advanced difficulties that to be understood in around all on its part these periods. As the dimensions of datasets grow, the model data representations development into sparse and density of range growing into the extra task. It cannot achieve suitable consequences however, handling higher-dimensional data. However, falling the dimensional subspace similarly progresses extremely challenging issues. This broadside conveys a limited about effective clustering, Swarm Intelligence algorithms, and optimization approaches. It consistently focuses on a comprehensive summary of clustering performances with their advantages and disadvantages specified in recent works. Optimization methods and their comparative studies are deliberated with their performances. The scope of the forthcoming exertion to include the popular clustering approaches and optimization algorithms are used to find the performance analysis over different real-world datasets and it also deliberated after the work.
Applying Model Fusion to Augment Data for Entity Recognition in Legal Documents

Named entity recognition for legal documents is a basic and crucial task, which can provide important knowledge for the related tasks in the field of wisdom justice. However, it is still difficult to augment the labeled data of named entities for legal documents automatically. To address this issue, we propose a novel data augmentation method for named entity recognition by fusing multiple models. Firstly, we train a total of ten models by conducting 5-fold cross-training on the small-scale labeled datasets based on Bilstm-CRF and Bert-Bilstm-CRF models separately. Next, we try to apply single-model fusion and multi-model fusion modes, in which, single-model fusion is to vote on the prediction results of five models of the same baseline, while multi-model fusion is to vote on the prediction results of ten models with two different baselines. Further, we take the identified entities with high correctness in the multiple experimental results as effective entities, and add them to the training set for the next training. Finally, we conduct the different experiments on two public datasets and our built judicial dataset separately, which shows the experimental results using data augmentation are close to those based on 5 times of labeled dataset, and obviously better than those on the initial small-scale labeled datasets.
Data Fusion Protocols for Cloud Infrastructures

In this paper will be presented a new class of security protocols dedicated to Cloud infrastructures. Fusion technology in different types of data and management processes, will be described. Especially, will be presented an impact of description, interpretation and analysis processes as well as securing processes. Security processes, which are based on fusion methodology also include data sharing technologies. Data fusion protocols can be used in different infrastructures, so the impact of these processes will be analysed and presented.
Integrated educational projects as a methods of human capital development in the knowledge economy

Abstract Human capital is linked to intellectual capital, so professional education is an important element in the process of training future professionals ready to develop innovations. The synergy of university education and work-based learning is necessary for the development of human capital. The creation of integrated educational projects takes into account this feature. To study the impact of these projects on human capital development, the authors conducted a four-stage research. According to the results of the study, universities want to realize their scientific potential and prepare in-demand young professionals, businesses need to hire experienced professionals focused on innovation. The needs of a young specialist are obtaining relevant knowledge and practical skills for employment. Integrated educational projects bring together numerous audiences, contribute to meeting their needs by connecting academic education, practice-oriented knowledge and practical skills through various learning formats. These projects contribute to the development of human capital, as the participants-students of these projects are more prepared for professional activities. Universities with the help of such projects develop a partner network, increase their own attractiveness and the overall level of knowledge of students. Business companies receive young professionals with practical skills and developed creative thinking.
Text Entry in Virtual Reality: Implementation of FLIK Method and Text Entry Testbed

We present a testbed for testing text entry techniques in virtual reality, and two experiments employing the testbed. The purpose of the testbed is to provide a flexible and reusable experiment tool for text entry studies, in such a way to include studies from a variety of sources, more specifically to this work, from virtual reality text entry experiments. Our experiments evaluate common text entry techniques and one novel one that we have dubbed the Fluid Interaction Keyboard (FLIK). These experiments not only serve as a way of validating the text entry test-bed, but also contribute the results of these studies to the pool of research related to text entry in virtual reality.
Emotions Synthesis Using Spatio-Temporal Geometric Mesh

Emotions can be synthesized in virtual environments through spatial calculations that define regions and intensities of displacement, using landmark controllers that consider spatio-temporal variations. This work presents a proposal for calculating spatio-temporal mesh for 3D objects that can be used in the realistic synthesis of emotions in virtual environments. his technique is based on calculating centroids by facial region and uses classification by machine learning to define the positions of geometric controllers making the animations realistic.
Fuzzy expert system for the impact of emotional eating in people with T2DM

Nowadays, Type2 Diabetic Mellitus (T2DM) is the most hazardous diseases in both developed and developing countries and it increases the risks of creating kidney disease, eye disease, nerve damage, blood vessel damage and heart disease. It can be controlled with proper diet and regular exercise but the people with diabetes are often failed to implement it. Emotional eating is the tendency of its victims to react to stressful, difficult feelings by eating, even when not feeling physical hunger. But the role of emotions in eating behaviour is often not addressed when discussing the diet and weight with people with diabetes. Therefore, the aim of the paper is design a fuzzy expert system for the effect of emotional eating in people with diabetes to assist the individuals.