Reviewerly/exHarmony huggingface dataset:
Assessment of Psychological Behavior Prediction Using Decision making trial and Evaluation Laboratory Method

Depression, bipolar disorder, or personality disorder Personality changes due to mental illness such as disorders may occur. Body like urinary tract infections it can also be caused by diseases. Basically, psychology helps people a lot, because people explain why they do what they do can with this kind of professional insight, a Psychologists stress their decision-making in people to help improve management and behavior can Alternative: Role ambiguities, role conflict, job satisfaction, face satisfaction and anxiety. Evaluation Preference: Role ambiguity, role conflict, job satisfaction, faces satisfaction and anxiety. From the result it is seen that Facet satisfaction and is got the first rank whereas is the Job satisfaction got is having the lowest rank. The value of the dataset for Range of Psychological Behavior Prediction in DEMATEL Method shows that it results in Facet satisfaction and top ranking.
Performance Analysis of Memetic algorithm Using VIKOR Method

A memetic algorithm is a variant of the traditional genetic algorithm is expansion. This is a local search technique Uses, which are pre-merged Reduces chance. A simplified data Cryptographic analysis of an encryption standard is NP-hard Can be formulated as a joint problem. Memetic algorithms (MAs) are global search Evolution uses local search rather than algorithms are the means. MAs are evolutionary mechanisms are, they are local search to refine individuals using processes. Dolphins, Elegans, Hamsterster, Ca-Grqc. The size of population, the number of generations, Size of the mating pool, Tournament size, the probability of crossover, the probability of mutation. From the end based on the number of generations are the result seen and got the first Rank, whereas the probability of mutation got having the lowest rank. The Value of dataset for Memetic Algorithm in VIKOR method shows that it results in the number of generations and top ranking.
Assessing the Sentiment of Book Characteristics Using Machine Learning NLP Models

This paper presents a new approach for processing the entire books with Natural Language Processing algorithms. In particular, we proposed methods to evaluate books in terms of assessing the intensity of the book’s soft features, such as fantastic, touching, suspenseful, etc. Using Bag of Words and TF/IDF, we embedded books and conducted classification experiments to determine the most appropriate parameters for classifying the intensity of features. The obtained results showed, that in the considered problem the Random Forests algorithm fitted the best, achieving accuracy of 95% and F1 measure of 89%. The evaluation also included the selection of the best converter and data aggregation method.
Human Activity Recognition for Online Examination Environment Using CNN

Human Activity Recognition (HAR) is an intelligent system that recognizes activities based on a sequence of observations about human behavior. Human activity recognition is essential in human-to-human interactions to identify interesting patterns. It is not easy to extract patterns since it contains information about a person’s identity, personality, and state of mind. Many studies have been conducted on recognizing human behavior using machine learning techniques. However, HAR in an online examination environment has not yet been explored. As a result, the primary focus of this work is on the recognition of human activity in the context of an online examination. This work aims to classify normal and abnormal behavior during an online examination employing the Convolutional Neural Network (CNN) technique. In this work, we considered two, three and four layered CNN architectures and we fine-tuned the hyper-parameters of CNN architectures for obtaining better results. The three layered CNN architecture performed better than other CNN architectures in terms of accuracy.
Text Line Segmentation in Historical Newspapers

This paper deals with page segmentation into individual text lines used as an input of a line-based OCR system. This task is usually solved in one step which directly identifies text lines in whole documents. However, a direct approach may jeopardize the reading order of the lines and thus deteriorate the overall transcription result. We propose a novel approach which decomposes this problem into two steps: text-block and text-line segmentation. The particular tasks are handled by algorithms based on fully convolutional neural networks. The proposed method is evaluated on two standard corpora, Europeana and RDCL 2019, and on a novel dataset created from data available in Porta fontium portal. This dataset is freely available for research purposes.
CamCarv - Expose the Source Camera at the Rear of Seam Insertion

It is well known that photo response non-uniformity (PRNU) noise based source attribution helps to verify the camera used to take an image. Recent advances in content-aware image resizing method such as seam carving allow an image to be resized while the critical content is retained. In this paper, we propose identifying the source camera from seam inserted images using blocks as small as 20 $$\times $$ 20. In particular, the correlation is computed between the noise residue of the seam inserted image and the camera PRNU constructed using different numbers of im-ages. We found that different correlation patterns with the camera PRNUs are ob-served, depending on whether the image is taken by that camera or not. Addition-ally, based on this observation, features are extracted from the correlation patterns which are then weighted and combined to form a decision metric for source cam-era identification. We demonstrate by our experimental results that our approach is effective in identifying the source camera in seam insertion images.
An Empirical Study of Adversarial Domain Adaptation on Time Series Data

Domain-adversarial learning allows a machine learning model to be trained with supplementary data from a different domain. This enables applications in various time series domains. Although several domain-adversarial models have been proposed in the past, there is a lack of empirical results with different types of time series. This paper provides an empirical analysis with multiple models, datasets and evaluation objectives. Two models known from literature are evaluated in combination with four public datasets: An RNN-based model (VRADA) is contrasted with a newer CNN-based one (CoDATS). The datasets include indoor climate, gas sensors, human activity and physiological data. Our experiments explicitly consider different dataset sizes, similarities between domains and the scenario of multisource training. It is found that CoDATS is very suitable for univariate datasets and performs well even with small datasets. Multivariate datasets can only benefit from the adversarial domain adaptation if the number of data points is large enough. VRADA was found to outperform CoDATS in modeling multivariate datasets. The multisource training available in CoDATS appears promising. A correlation is shown between the similarity of domains and prediction performance.
Evolutionary Approach to Melodic Line Harmonization

The paper presents a novel evolutionary algorithm (EA) for melodic line harmonization (MLH) - one of the fundamental tasks in music composition. The proposed method solves MLH by means of a carefully constructed fitness function (FF) that reflects theoretical music laws, and dedicated evolutionary operators. A modular design of the FF makes the method flexible and easily extensible. The paper provides a detailed analysis of technical EA implementation, its parameterization, and experimental evaluation. A comprehensive study proves the algorithm’s efficacy and shows that constructed harmonizations are not only technically correct (in line with music theory) but also nice to listen to, i.e. they fulfill aesthetic requirements, as well. The latter aspect is verified and rated by a music expert - a harmony teacher.
Forced Movement Extensions of the Particle Swarm Optimizers with Inertia Weight

This paper investigates some ideas for extending the IPSO method. We design an IPSO-based procedure that combines the adaptive coefficients and forced particle movements. We also propose a new criterion for the evaluation of the forced movement. Then, using a set of benchmark functions, we test the performance of algorithm variants based on the designed procedure. Our experiments show that optimizers with forced movement mechanisms consistently find better values of the global best solutions. Moreover, the addition of time adaptive coefficients might benefit the algorithm’s performance.
Short Texts Representations for Legal Domain Classification

This work presents the results of comparison text representations used for short text classification with SVM and neural network when challenged with imbalanced data. We analyze both direct and indirect methods for selecting the proper category and improve them with various representation techniques. As a baseline, we set up a BOW method and then use more sophisticated approaches: word embeddings and transformer-based. The study were done on a dataset from a legal domain where the task was to select the topic of the discussion with the layer. The experiments indicate that fine-tuned pre-trained BERT model for this task gives the best results.
Optimization of Parameterized Behavior Trees in RTS Games

Introduction of Behavior Trees (BTs) impacted the field of Artificial Intelligence (AI) in games, by providing flexible and natural representation of non-player characters (NPCs) logic, manageable by game-designers. Recent trends in the field focused on automatic creation of AI-agents: from deep- and reinforcement-learning techniques to combinatorial (constrained) optimization and evolution of BTs. In this paper, we present a novel approach to semi-automatic construction of AI-agents, that mimic and generalize given human gameplays by adapting and tuning of expert-created BT under a developed similarity metric between source and BT gameplays. To this end, we formulated mixed discrete-continuous optimization problem, in which topological and functional changes of the BT are reflected in numerical variables, and constructed a dedicated hybrid-metaheuristic. The performance of presented approach was verified experimentally in a prototype real-time strategy game. Carried out experiments confirmed efficiency and perspectives of presented approach, which is going to be applied in a commercial game.
Business Roundup

BUSINESS Business Roundup ShareShare onFacebookTwitterWechatLinked InRedditEmail C&EN, 2023, 101 (3), p 15January 23, 2023Cite this:C&EN 101, 3, 15AbstractChemours has picked Villers-Saint-Paul, France, as the site for a $200 million capacity expansion of its Nafion ion-exchange materials, used in water electrolyzers that produce hydrogen. The firm said last fall that it was considering sites in the US and Europe. Orica and Saudi Chemical will work together to build nitric acid and ammonium nitrate plants in Saudi Arabia. Saudi Chemical says the planned facility will be the first of its kind in the region. Novozymes has entered a partnership to supply enzymes to Carbios, which has developed an enzymatic process to break down polyethylene terephthalate into its raw materials, terephthalic acid and ethylene glycol. Carbios’s first commercial-scale plant using its technology is set to open in France in 2025. PureCycle Technologies has selected the Port of Antwerp-Bruges as the location of the first European plant to use its solvent-based process to recycle polypropylene. The company’s first plant is underView: PDF | Full Text HTML
Experience and prospects of digital twins application in public healthcare

Digitalization of healthcare opens up opportunities for applying new approaches to the organization of medical care, improving its quality, optimizing the use of healthcare resources. Digital twin is one of the new approaches, a virtual model of a physical object with dynamic bidirectional connections between a physical object and its corresponding twin in a digital environment. Digital twins allow to study, create and test new hypotheses, conduct experiments and research in silico. T h e a i m o f t h e s t u d y is to describe the existing Russian and foreign experience in the practical application of digital twins in the field of healthcare. M a t e r i a l s a n d m e t h o d s . The literature review was conducted in Pubmed and Elibrary databases. Publications describing ethical and regulatory aspects, industrial, engineering or logistics applications were excluded. The articles have passed two rounds of peer review. R e s u l t s . Digital twins are used in clinical medicine to test new methods of treatment and the body’s response to them, to simulate the work of organs and tissues. The application of the technology for the development of precision therapy in cardiology, oncology, endocrinology, gastroenterology, orthodontic treatment, rehabilitation, intensive care is described. The digital twin can be used by health authorities to model patient behavior, predict the spread of the infectious process, the work of a medical institution and its structural units in order to optimize resources. C o n c l u s i o n s . The article provides an overview of technology and the main areas of application of digital twins for personalized medicine, public health and smart healthy cities. The prospects for using digital twins lie in the field of creating smart “healthy” cities and regions.
Present and Future in the Digit(al)ization of Judicial Procedures in Romania in a European ContextPresent and Future in the Digit(al)ization of Judicial Procedures in Romania in a European Context

In our study, we review some current trends in the digitalization of court activities, with an emphasis on Romania in a European Union context. We analyse the past and current state of the Romanian digitization and digitalization of courts, in the framework of major court activity types that may be digitized and digitalized. We also present the most recent attempts by the European Commission to facilitate the digital transition of court activities and the financing and normative aspects of this transition. Finally, we conclude by presenting proposed Romanian efforts to achieve the digital transition and some of their inadequacies.
Case Study Discovering the Potential for Algorithmic DecisionMaking on Setting GDPR Fines

Administrative fines for GDPR infringements are growing rapidly in number, yet companies are presented with an opaque process on how these fines are issued by the data protection authorities (DPAs). In particular, one principle described within the guidelines issued by the European Data Protection Board (EDPB) requires a case-by-case assessment, which is potentially offsetting the automation of administrative fines in the future. This paper is challenging this principle through algorithmic arguments. The suggested approach has its benefits in terms of scalability. Yet, this approach may well receive funded critics due to potential clashes with other principles.
HDR fusion algorithm based on PCNN and over exposure correction

For traditional HDR fusion algorithm based on mapping functions, the artificial determination of the over exposure region often ignores the spatial distribution and the relation of pixels of the image brightness. In this paper, a stimulated HDR fusion algorithm based on pulse coupled neural network is proposed. For the proposed algorithm, first obtain the external stimuli according to the maximum image brightness, calculate whether the pixel “fires” or not and determine the over exposure region. Then alter the value of external stimuli to perform iteration and construct judgment matrix to make sure that every pixel in the image “fires”. The image irradiance is linear with the illuminance and exposure time. Obtain the brightness mapping function using linear fitting, and synthetize HDR image with over exposure correction algorithm to eliminate artifacts. The algorithm mainly has the following advantages: 1. It fully considers the spatial distribution of the image brightness. 2. For the imaging of different scenes, it self-adapts to separate exposure regions. 3. It performs mapping according to the imaging principles, and obtains the mapping function without the exposure time. 4. It reduces the quantitative error of synthetizing image, and the artifacts in the transition region. The algorithm proposed in this paper is experimented under multiple scenes, and the image entropy and spatial frequency are improved greatly.
Interminable Flows: A Generic, Joint, Customizable Resiliency Model for Big-Data Streaming Platforms

The examiner of cloud computing systems in the last few years observes that there is a trend of the emergence of a new Big Data framework almost every year. Since Hadoop was developed in 2007, new frameworks followed it such as Spark, Storm, Heron, Apex, Flink, Samza, Kafka...etc. Each framework is developed in a certain way to target and achieve certain objectives better than other frameworks do. However, there are few common functionalities and aspects that are shared between these frameworks. One vital aspect all these frameworks strive to achieve is better reliability and faster recovery time in case of failures. This is particularly crucial for streaming systems (compared to batch processing systems) where events are processed and monitored online in real time, and any delay in data delivery will cause a major inconvenience to the users. Another observation is that some reliability implementations are redundant between different frameworks. Encapsulating these implementations into one layer and make it shared between different applications will benefit more than one framework without the burden of re-implementing the same reliability approach in each single framework. These observations motivated us to present Warden, a generic, multi-framework, flexible, customizable, low overhead protocol to ensure the resiliency of streaming applications running on streaming Big Data frameworks. Most reliability protocols carry out one rigid fault tolerance approach targeted towards one system at a time. It is more challenging to provide a reliability approach that is pluggable in multiple Big Data frameworks at a time and can achieve low overheads comparable with single targeted framework approaches, yet is flexible and customizable by its users to make it tailored towards their objectives. The genericity is attained by providing an interface that can be used in different applications from different frameworks. The low overhead is achieved by providing faster application finish times with and without failures. The customizability is fulfilled by providing the users the options to choose between two delivery semantics (Exactly Once / At Most Once) combined with two fault tolerance guarantees (Crash Failures / Byzantine Failures). To the best of our knowledge, such approach was never tried on multiple streaming frameworks before. We built a prototype of Warden on Flink and Samza (with Kafka) streaming frameworks. Our evaluations highlight the effectiveness of our approach in the presence of failures and without failures compared to other fault tolerance techniques (such as checkpointing).
Evaluation of Block Chain and Artificial Intelligence using SPSS Statistics Analysis

Block chain is a peer-to-peer network of all transactions in is a decentralized ledger. This generation By using, individuals have a mandatory settlement authority Transactions can be confirmed without needing to Possible applications might also consist of monetary transactions, settlement trades, voting, and lots of other troubles. Block chain Defined: Block chain is a shared, immutable ledger is, in an enterprise network Recording of transactions and assets Simplifies the monitoring process An asset can be tangible (a house, car, money land) or intangible (hyper properties, patents, copyrights, branding). Bit coin and Ethereum are popular examples of block chains. Everyone is authorized to connect with the block chain and transact on it. Most intelligence procedures by Using machines, especially computers systems. In particular sets of AI Expert systems, herbal language Includes processing, speech recognition and gadget imaginative and prescient. Unlike trendy databases that store records in a centralized, relational desk, block chain is an open, peer-to-peer (P2P) network that supports communal hobby as opposed to a centralized controlling entity. In block chain, facts is accumulated in organizations known as blocks Artificial Intelligence (AI) allows machines to Research from Experience, New Adaptation to inputs and Performing human duties Most AI examples you listen approximately these days From chess-gambling computers to self-driving deep knowledge – up to cars acquisition and natural language Highly dependent on processing.. The selections AI takes at each step are determined by using previously accumulated data and a selected set of rules. If planned properly, those errors may be reduced to 0.
Efficient multi-object detection and Visual Impairment by Artificial Intelligence

Freedom and autonomy means making your own choices, Refers to the ability to act the way you want without needing help. Blind people in everyday life they face many difficulties. They need help. To make users more independent, Connected to the mirror using artificial intelligence a prototype was developed to describe the images from the camera. Some functions face, Object and color recognition, all machine learning and image recognition. Place different objects in front of the device by the teachers to check whether they can be identified Practice tests were conducted. The results were satisfactory to prove the concept of the device. Visually impaired people in daily life due to visual impairment they face many difficulties. In day-to-day consumption, transactions alone are difficult. Similar shapes and by them different products of sizes cannot be identified. They need help from others in everyday life, especially in shopping. Also, in the payment process, it is difficult for the visually impaired to distinguish the denomination(s) of the banknotes. Also cheating and Such as supply of improper materials Many acquired problems occur. The app will enable users to identify canned foods and distinguish denominations of currency notes to complete cash transactions effectively and efficiently. With voice and resonance on smart phones the application can be installed.
Blind image deconvolution via an adaptive weighted TV regularization

Blind image deconvolution has attracted growing attention in image processing and computer vision. The total variation (TV) regularization can effectively preserve image edges. However, due to lack of self-adaptability, it does not perform very well on restoring images with complex structures. In this paper, we propose a new blind image deconvolution model using an adaptive weighted TV regularization. This model can better handle local features of image. Numerically, we design an effective alternating direction method of multipliers (ADMM) to solve this non-smooth model. Experimental results illustrate the superiority of the proposed method compared with other related blind deconvolution methods.