Reviewerly/exHarmony huggingface dataset:
Expert System Insemination

Abstract Insemination is a method used as a program by injecting sperm directly into the cervix so fertilization can occur. According to world health, the success rate of this program ranges from 20-30%, allowing several couples to choose this alternative. The process of applying artificial insemination requires the right pregnancy results, which require proper identification. System identification technology can be done quickly to assist in the identification of fertility that must be performed by an obstetrician. System development in this research is an expert system to determine the success of insemination programs for pregnancy. This system makes it easier for couples to consult and provide information to couples about insemination activities based on their partner’s condition. This expert system is made with a Hybrid method, where reading data from this method is done by forward chaining and certainty factors. The forward chaining method aims to read the cycle of couples who will carry out insemination and certainty factors, namely the method used to determine the level of success in the pregnancy program that will be obtained by the couple.
Mode of Linguistic-and-methodological Competence Development in the Course of Professional Training of Prospective Foreign Language Teachers

The article deals with the mode of lin-guistic-and-methodological competence&nbsp; development&nbsp; in&nbsp; prospective&nbsp; foreign&nbsp; language teachers in the context of the analysis of particular disciplines of the general training cycle. The content analysis of educational and professional&nbsp; curricula&nbsp; and&nbsp; working&nbsp; plans&nbsp; of educational disciplines for prospective foreign language teachers is carried out; a number of disciplines&nbsp; of&nbsp; the&nbsp; general&nbsp; training&nbsp; cycle&nbsp; are analyzed in the context of their influence on linguistic-and-methodological competence development&nbsp; in&nbsp; prospective&nbsp; foreign&nbsp; language teachers. Potential&nbsp; opportunities&nbsp; of&nbsp; the&nbsp; cycle&nbsp; of professional and practical training for linguistic-and-methodological competence&nbsp; development, as well as their interaction on the basis of cross-curricular relations, are revealed.&nbsp;The features of the process of prospective foreign language teachers training, according&nbsp; to&nbsp; the&nbsp; character&nbsp; of&nbsp; the&nbsp; disciplines,&nbsp; practice/training,&nbsp; forms&nbsp; and&nbsp; methods&nbsp; of&nbsp; cooperation with prospective foreign language teachers in the course of their training are found out. As&nbsp; a&nbsp; result&nbsp; of&nbsp; the&nbsp; given&nbsp; research&nbsp; it&nbsp; is&nbsp; discovered that the disciplines under consideration contribute to the development of linguistic-and-methodological&nbsp; competence&nbsp; of&nbsp; prospective&nbsp; foreign&nbsp; language&nbsp; teachers&nbsp; providing future&nbsp; professionals&nbsp; with&nbsp; sociocultural&nbsp; and social&nbsp; knowledge,&nbsp; skills&nbsp; and&nbsp; competencies which&nbsp; not&nbsp; only&nbsp; ensure&nbsp; their&nbsp; adaptation&nbsp; to&nbsp; a foreign&nbsp; language&nbsp; environment,&nbsp; but&nbsp; also&nbsp; contribute to successful performance of teaching activities.
Optimizing Fast Near Collision Attack on Grain Using Linear Programming

In 2018, an attack named fast-near-collision attack (FNCA) was proposed, which is an improved version of near-collision attack (NCA) on Grain-v1, one of the three hardware-oriented finalists of the eSTREAM project. FNCA is designed as a key recovery attack and takes a divide-and-conquer strategy that needs a merging phase. We propose an improved FNCA where the merging phase is optimized by a linear programming based strategy. It decreases the candidates of the internal state vectors (ISVs) in each step of merging and has a reduction in the overall time complexity. Since the merging phase is vital for a divide-and-conquer strategy, where the most of bits of the full internal state are recovered, other analyses on Grain family with FNCA can get optimized by our method in varying degrees. This paper offers an experiment on a reduced Grain and a theoretical analysis on Grain-v1 to confirm the results. In the case of the reduced Grain of an 80-bit internal state, the time complexity is 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">37.1096</sup> , which has a 27.8% reduction. For Grain-v1, its theoretical time complexity is around 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">73.4</sup> , which is reduced by 79.4% compared with the original one.
A Hybrid Mechanism for Helicopters

This study successfully provides the empirical practicability of a hybrid mechanism for helicopters. A turbine engine and a set of electricity power systems can operate simultaneously and/or independently as a symmetric structure. The latter power source works as an immediate supplementary device if the former one has malfunction. We look forward to promoting this experimental evidence in the helicopter industry. The ultimate purpose of this manuscript is to decrease the incidents of crashes and save people’s lives.
Entanglement, photon statistics and Wehrl entropy for a time-dependent qubit–field system in the presence of phase decoherence effect

We investigate the effect of qubit motion in terms of speed and acceleration on the variation in dynamic behavior of Wehrl entropy, second order correlation function and qubit–field entanglement. We consider three different coherent states of low-power potential (CSLPP) through the value of its exponent, specifically, the infinite-square, triangular, and harmonic potential wells. The relationship between the photon statistics quantified by the second order correlation function and nonclassical correlation of the qubit–field system is explored as the system evolves. The results show that the proposed scheme is very sensitive to the value of the exponent for both a moving and a stationary qubit. A high correlation between the qubit and CSLPP field is achieved by a proper choice of exponent, qubit speed, and acceleration.
Problems of managing interactions of general education institutions with higher education institutions

The article is dedicated to solving the complex problem of managing interaction between secondary and higher education institutions. The scientific approaches to studying the problem of interaction between secondary and higher education institutions in domestic and foreign studies are analyzed, the essential characteristics of the interaction management of secondary and higher educational institutions are determined, the criteria, indicators and the levels of effectiveness of interaction management of general education are determined. Studying the theoretical foundations of management of educational institutions and methodological approaches to the development of education and management of pedagogical systems made it possible to conclude that it is advisable to use a systematic, synergetic, optimization, activity, functional, functional to study the essence of managing the interaction between secondary and higher education institutions. The results of practical implementation of the developed technology of interaction management of secondary and higher education institutions, which is based on the management principles of mutual responsibility, coordination, awareness, flexibility, professional competence of the management subjects; contains target, content, functional and accompanying components; provided with an optimal control and diagnostic complex. On the basis of generalization of the obtained data, the socio-psychological factors that significantly influence the effectiveness of the interaction management of general and higher education institutions were identified: objective (peculiarities of economic development and functioning of the education system as a social branch; rigid administrative structure of levels of management of educational institutions; insufficient bases; governing the activities of subjects of interaction management of secondary and higher education institutions) and subjective (relation to equator schools to implement interaction, motivation of teaching staff to active interaction in the position of the subject of general and higher education institutions, students lack interest in the particular field of professional knowledge and as a consequence - low mobilization readiness conscious professional choice).
Application Of Hill Cipher And LSB + 1 Methods For Messaging In Messages Inpicture

Abstract This research discusses encryption and security description of files in images with hill cipher cryptography and steganogography using the lsb + 1 method, testing the results of cryptographic and stegano data security applications that are equipped with cryptography, then from the results the trial was done with the hill cipher method, by displaying data in the form of files that want to do the data security. The results of the DIPA trial with a file size of 17.2 MB after being encrypted into RNPF with a file size of 17.2 MB.
SINGLE-LEVEL METHOD OF BEHAVIORAL ONLINE TESTING OF DISTRIBUTED INFORMATION SYSTEMS

The article considers a single-level method of behavioral online testing with recognition of the reference behavior of DIS. The method features are the evolu-tionary search for reference behavior in the flow of DIS functioning, paralleliza-tion of operations of check recognition and evolutionary development of check populations. The method allows to reduce the complexity and time of check, to make it compatible with the real functioning of the DIS.
BK.Synapse

Training neural networks efficiently is a thoroughly-researched topic that plays an important role in their adoption. Major advancements have been made, including the use of multiple nodes to further decrease training time. However, training at scale usually means adding on multiple layers of complex deployment logic and parallelization concerns, distracting researchers from the core of their algorithms. This paper presents a framework called BK.Synapse that can facilitate distributed training while maintaining clarity, simplicity, and user-friendliness. The design is modular, allowing flexible and easy deployment on a variety of hardware specifications. The framework is benchmarked in a case study: training a neural network for an object detection problem. Our results show a good amount of improvements over conventional training, with very few modifications to the existing codebase. The resulting model also performs relatively well upon further testing.
Development of Visualization Scoreboard for Four Disciplines Execution Visualization of Journal Publication on Mobile Devices

This study aims to develop scoreboard visualization as Four Disciplines Execution tools on mobile devices, to improve the smooth processing of the journal publication.By using a scoreboard that can be accessed on a mobile device, the journal editor team can always monitor the achievement of the process that has been obtained, and see what should do to maintain the journal to be published in the certain time.The research was conducted using Multimedia Development Life Cycle according to Luther.The results show that this mobile-based scoreboard visualization can be used properly.The results of this study can be a basic consideration for completing tools to monitor an activity in journal management to avoid the delay in publishing a journal.
“Where is My Parcel?” Fast and Efficient Classifiers to Detect User Intent in Natural Language

We study the performance of customer intent classifiers designed to predict the most popular intent received through ASOS.com Customer Care Department, namely “Where is my order?”. These queries are characterised by the use of colloquialism, label noise and short message length. We conduct extensive experiments with twowell established classification models: logistic regression via n-grams to account for sequences in the dataand recurrent neural networks that perform the extraction of these sequential patterns automatically. Maintaining the embedding layer fixed to GloVe coordinates, a Mann-Whitney U test indicated that the F1 score on aheld out set of messages was lower for recurrent neural network classifiers than for linear n-grams classifiers (M1=0.828, M2=0.815; U=1,196, P=1.46e-20), unless all layers were jointly trained with all other network parameters (M1=0.831, M2=0.828, U=4,280, P=8.24e-4). This plain neural network produced top performance on a denoised set of labels (0.887 F1) matching with Human annotators (0.889 F1) and superior to linear classifiers (0.865 F1). Calibrating these models to achieveprecision levels above Human performance (0.93 Precision), our results indicate a small difference in Recall of 0.05 for the plain neural networks (training under 1hr), and 0.07 for the linear n-grams (training under 10min), revealing the latter as a judicious choice of model architecture in modern AI production systems.
Individuals with Autism Spectrum Disorders in the Digitized Work Environment – Review of Technological Solutions Supporting Integration

The aim of the article is to examine the opportunities for employees with ASD, arising from assistive technology in order to integrate work environment. The possible solution to a communication problem is replacing the interpersonal communication between employees with electronic forms of communication, communicators, chats, chatbots. Furthermore, there is the implementation of wearable electronic systems monitoring stress level and facilitating effective stress control. In the future, developing the work environment according to ‘smart workplace concept’ is expected, by connecting sensors recording human body parameters in a network with sensors recording ambient physical parameters and also with the controllers of its values, adjusting it dynamically. As a result, distracting factors influencing work performance could be eliminated, improving the work comfort of people with ASD and their collaborators.
Adversarial Learning for Cross-Modal Retrieval with Wasserstein Distance

This paper presents a novel approach for cross-modal retrieval in an Adversarial Learning with Wasserstein Distance (ALWD) manner, which aims at learning aligned representation for various modalities in a GAN framework. The generator projects the image and the text features into an aligned representation space, while the discriminator ensures that the image and text features are not too far from each other, in a way which would maintain the semantic relation between the input samples. That is, ALWD reformulates the cross-modal retrieval as an image-text domain adaptation problem aiming at reducing domain discrepancy. To learn domain invariant representations, a domain critic network is adopted to estimate Wasserstein distance between different modal distributions and the feature extractor network is optimized to minimize the Wasserstein distance under an adversarial manner. Meanwhile, ALWD introduces additive margin softmax function to make sure the learned representations should also be discriminative in label prediction. Furthermore, a structure preservation constraint is imposed to keep local structure consistent during the learning process. Extensive comparison experiments on three widely used datasets demonstrate that ALWD outperforms the state-of-the-art cross-modal retrieval methods.
Circumscribed Douglas-Peucker Polygonal Approximation for Curvilinear Obstacle Representation

ECD (Exact Cell Decomposition) based path planning is not applicable in curvilinear obstacles environment. Therefore, after the curvilinear obstacles are approximated to the polygons by using DP (Douglas-Peucker) algorithm, which is a polygon approximation algorithm, the ECD method is applied. However, there is a case of not including all the existing obstacles' area and ignoring the outer area, when it comes to the curvilinear obstacles, approximated to the polygons by using the DP algorithm. In this case, path planning of ECD method cannot guarantee the clearance. This paper proposes a CDP (Circumscribed DP) algorithm to solve this problem. The CDP algorithm has a disadvantage of having more inner area than the DP algorithm, but it can guarantee the clearance because of the fact the algorithm always has 0(%) of outer area (OA). In order to confirm this, the polygonal approximation of DP and CDP algorithms was compared in the same curvilinear obstacles and the result was as the following: When each ε value is 0.05, 0.08, 0.11(m), each result of the inner area ratio (IA) was 2.45, 4.89, 7.19(%) by DP algorithm, 16.3, 18.39, 32.58(%) by CDP algorithm, and result of the outer area ratio (OA) was 0.7, 1.17, 1.54(%) by DP Algorithm, 0, 0, 0(%) by CDP Algorithm. Also, it can be confirmed that the CDP algorithm has always guaranteed for clearance.
A Robust Table Detection Method for Distortion in Image Acquired from Camera

In this paper, the robust table detection method for distortion is proposed in image acquired by camera. Images acquired by the camera contain distortion due to the curvature of the paper and it makes difficult to detect the table in images. In order to address this issue, a method of dividing the frame line of the table in the vertical direction and the horizontal direction and detecting the frame lines by curve approximation in each direction is proposed. Dividing the frame lines in each direction, it is possible to simplify the multiple curve detection. As a result, the lines detection accuracy in the curved surface image was 99.4%. In addition, similar results were obtained for curved images rotated by 90°. This method can cope with distortion in both the vertical and horizontal directions. From these results, it was confirmed the effectiveness of the proposed method.
Bird's Eye Navigation (BEN) - A Pixelwise Mapping System Used in Swarming of Vehicular Robots

BEN is a smart system which is based on the combination of Computer Vision, Image Processing and Drone. BEN is also applicable for Indoor System Navigation. This system can differentiate between objects even if they are very close to each other, especially at a certain location where GPS Navigation fails to serve the purpose. As the drone hovers above a certain location at a particular altitude, the camera installed on the drone can take the video footage from that height and upload it to the server where further analysis is done. In the server, it is pre-programmed in such a way that it will be able to track the robots' positions and the individual targets with high accuracy. As per the algorithm used, the co- ordinate pixels of the robots enable the server to understand distance between them. The video footage is taken and the corresponding analysis is done in real time. BEN also calculates the angle between them in order to rotate them along a certain angle to make the robots face each other in a desired position. After initializing the targets, BEN finds the smallest distance between the robots and their individual targets. After that, it creates a smallest possible path between the nearest robot and their corresponding target. Now the ground-based robots follow that specific path and reach the target's position. If it detects anything suspicious, the other robots are called to reach that particular location. Then these robots will again follow the smallest possible path to get to that destination. This entire tracking system is based on the `OpenCV Boosting Tracking Algorithm'. For each and every rotation or movement made by the robots, their new position and the angle of rotation is checked using a magnetometer. As a whole, we will be able to locate all the ground-based robots and guide them to reach their corresponding nearby targets using the eyes of the system named as `BEN'.
Clustering of subsample means based on pairwise L1 regularized empirical likelihood

To classify a vast amount of strata or subsamples with unknown families of distributions according to their strata-means, a clustering approach is developed based on pairwise $$L_1$$ regularized empirical likelihood. Under such a clustering approach, all possible contradictory conclusions are ruled out automatically. On the contrary, the decision rules based on many existing pairwise comparison procedures can generate contradictory results. Moreover, under certain mild conditions, the proposed clustering method enjoys the consistency property that with probability going to one, all strata are classified correctly. An exterior point algorithm is presented for the clustering. The applications of the proposed methods are demonstrated using stock market data and microarray data of breast cancer patients.
Interdisciplinary Explorations of Digital Touch

This chapter introduces and reflects on the multimodal, sensory and interdisciplinary methodological stance of this book, and the InTouch project more broadly. We introduce our main framework, which combines multimodality and sensory ethnography. We outline the collaborations and interdisciplinary dialogues that we have engaged with to explore digital touch, and argue that this approach brings different aspects of touch to the fore in ways that are productive for research and design. Finally, the different ways in which we use prototyping to gain access to, and to generate, digital touch experiences and imaginations for the purposes of research is outlined.
Automatic Detection of Protective Movement Behavior with MoCap and sEMG Data for Chronic Pain Rehabilitation

Physical rehabilitation is an important part of chronic pain (CP) management. Physiotherapists provide guidance and intervention on the exercises based on the CP patient's affective states. One important clue the physiotherapist uses to understand their patient's affective state is the presence and type of protective movement behavior. As rehabilitation is transferring from clinical settings to home-based environments, technology should provide similar service by automatically detecting the protective behavior exhibited by patients and use it as a cue to inform and adapt the support. Our research focuses on the detection of protective behavior from a deep learning (DL) perspective and using MoCap and EMG data. Based on the knowledge learned from a wider-relevant literature and the specific characteristic of protective behavior, we aim to automatically detect protective behavior with deep learning approaches and further learn its configuration pattern with explainable models. Our initial studies have demonstrated interesting accuracy improvements and also provided important knowledges about the temporal and configurational characteristics of protective behavior.
Data Analytics in the Electronic Games

This paper aims at the use of data analytics methods in mobile games. The main goal was to predict future purchases of players in the selected mobile game. The result presents the information about whether the player is going to buy any of the offered bonus packages or not. This information is crucial for marketing and possible ways of monetization. From the perspective of data analytics, the goal is the creation of a classification model in line with the CRISP-DM methodology. We used the following algorithms in the modeling phase: Random forest, Naive Bayes, Linear regression, XGBoost, and Gradient Boosting. All generated models were evaluated by contingency tables, which presented models accuracy as the ration between successfully predicted values to all predicted samples. The results are plausible and have the potential to be deployed into practice as a baseline model or support for personalized marketing activities.
Automatic Coloring of Terms in Presentation Slides Using Word Vectors

This paper proposes a new method of coloring terms in presentation slides. The proposed method consists of two ranking methods. One is based on TF-IDF and the other is based on cosine similarity between a term and a presentation title. The proposed method tries to extract important terms from slides written in Japanese. After that, these terms are colored. It is shown the proposed method colors terms more properly than the previous one.