Reviewerly/exHarmony huggingface dataset:
Towards Job-Transition-Tag Graph for a Better Job Title Representation Learning

Works on learning job title representation are mainly based on Job-Transition Graph, built from the working history of talents.However, since these records are usually messy, this graph is very sparse, which affects the quality of the learned representation and hinders further analysis.To address this specific issue, we propose to enrich the graph with additional nodes that improve the quality of job title representation.Specifically, we construct Job-Transition-Tag Graph, a heterogeneous graph containing two types of nodes, i.e., job titles and tags (i.e., words related to job responsibilities or functionalities).Along this line, we reformulate job title representation learning as the task of learning node embedding on the Job-Transition-Tag Graph.Experiments on two datasets show the interest of our approach.
Hand-Written Characters Recognition using Siamese Network Design

Hand-written documents are being digitalized and various traditional approaches have been merged to automatically recognize the syntax and semantic of target statement. However, real time applications such as bank data processing and security are still challenging due to the variation in human hand-written document. Similarly, traditional assistive scheme requires large amount of data to learn new features and their pattern. To deal with this, one-shot learning scheme is employed to recognize hand-written characters using Siamese network architecture. The Siamese network can compute the similarity or difference between two data samples and also effective to process unseen data that has not been previously trained. Additionally, convolutional neural network architecture is fused up to extract significant features during training and provides the stability in learning. The performance is validated through standard evaluation measures operated on the public dataset.
Certifying machine learning models against evasion attacks by program analysis

Machine learning has proved invaluable for a range of different tasks, yet it also proved vulnerable to evasion attacks, i.e., maliciously crafted perturbations of inputs designed to force mispredictions. In this article we propose a novel technique to certify the security of machine learning models against evasion attacks with respect to an expressive threat model, where the attacker can be represented by an arbitrary imperative program. Our approach is based on a transformation of the model under attack into an equivalent imperative program, which is then analyzed using the traditional abstract interpretation framework. This solution is sound, efficient and general enough to be applied to a range of different models, including decision trees, logistic regression and neural networks. Our experiments on publicly available datasets show that our technique yields only a minimal number of false positives and scales up to cases which are intractable for a competitor approach.
Bloomberg as a Component of Research

The authors raise the problem of providing research work of students, postgraduates, and university teachers with up-to-date information on the functioning of national and international market structures, prove the need and effectiveness of using modern informational and analytical systems, in particular, Bloomberg Professional Services. The authors consider the options for using Bloomberg Professional Services in educational activities, and also offer three formats for research work using Bloomberg Terminal, assessing the effectiveness of such research.
ARABIC MULTI-MODAL SYSTEM BASED ON VOICE AND MACHINE VISION

In this paper, the development of a multi-modal human assistant was tackled. This assistant could help humans based on a given utterance together with the help of machine vision. The utterance, Modern Standard Arabic (MSA) or Egyptian dialect, could be a question about something in the assistant's environment or a request that the assistant can accomplish by coupling with a robot in the upcoming work. The utterance was processed through a mix of previously used techniques such as natural language processing (NLP), sentence similarity, and pattern matching rather than using each one alone. The techniques are tweaked to evolve an algorithm that can deal with an utterance even if two languages or more are existing.
Federated Learning in the Bubl Platform to Enhance the Privacy of Personal Patient Data

Data privacy and security are currently an important societal topic that rightfully garners much attention. In an effort to make people the owners of their personal data, the Bubl platform will provide its users with a secure personal data vault. This platform will have a focus on medical and healthcare data, because of its sensitive nature. Gathering important insights from users’ data could be useful, but due to the high privacy and security standards required by Bubl, it becomes impossible to deploy standard Machine Learning (ML) techniques. These methods require centralization of all training data, which is not allowed. This problem can be solved using techniques from the research field of Privacy Preserving Machine Learning (PPML). Therefore, a system to facilitate PPML within the Bubl platform is developed. More specifically, we employ a technique called Federated Learning (FL). In our FL implementation in the Bubl platform, we focus on minimizing Random-Access Memory (RAM) usage to adhere to the constraints posed by the small computational budgets of the data vaults. Challenges that arise are non-Independent and Identically Distributed (IID) data and the fact that patient vaults contain very few data samples. The latter is the main focus of this research as it is underdeveloped in the FL literature. Currently, we are still working on acquiring the results which are expected in the coming months. At this moment, only preliminary results are discussed that reflect on the effect of the number of clients and the distribution of non-IID data on the ML performance.
Matching Bistatic Target Responses in Radar Networks to Enable Vectorial Velocity Estimation

Multiple sensors in a radar network can be jointly evaluated to process the bistatic Doppler information of a target obtained under different aspect angles. This enables the estimation of the target's vectorial velocity in a single snapshot. As a precursor to the joint evaluation, the monostatic and bistatic Doppler components need to be matched for each target in the range-Doppler map. In this work, a new approach for the target matching in the case of a radar-repeater network is presented. The matching is performed based on the correlation of monostatic and bistatic steering vectors extracted from the range-Doppler map for each receive channel of the radar. After validating the matched responses, the vectorial target velocity is determined. The approach is successfully tested with a 77 GHz radar and two repeaters by measurements of rotating cylinders.
Xatu

The performance of Adaptive Bitrate (ABR) algorithms for video streaming depends on accurately predicting the download time of video chunks. Existing prediction approaches (i) assume chunk download times are dominated by network throughput; and (ii) apriori cluster sessions (e.g., based on ISP and CDN) and only learn from sessions in the same cluster. We make three contributions. First, through analysis of data from real-world video streaming sessions, we show (i) apriori clustering prevents learning from related clusters; and (ii) factors such as the Time to First Byte (TTFB) are key components of chunk download times but not easily incorporated into existing prediction approaches. Second, we propose Xatu, a new prediction approach that jointly learns a neural network sequence model with an interpretable automatic session clustering method. Xatu learns clustering rules across all sessions it deems relevant, and models sequences with multiple chunk-dependent features (e.g., TTFB) rather than just throughput. Third, evaluations using the above datasets and emulation experiments show that Xatu significantly improves prediction accuracies by 23.8% relative to CS2P (a state-of-the-art predictor). We show Xatu provides substantial performance benefits when integrated with multiple ABR algorithms including MPC (a well studied ABR algorithm), and FuguABR (a recent algorithm using stochastic control) relative to their default predictors (CS2P and a fully connected neural network respectively). Further, Xatu combined with MPC outperforms Pensieve, an ABR based on deep reinforcement learning.
Efficient linear-time ranking and unranking of derangements

In this paper, we propose linear-time algorithms for ranking and unranking derangements. Myrvold and Ruskey proposed the linear-time ranking and unranking of permutations that use a novel approach to convert a random generation algorithm into an unranking algorithm. Our algorithms achieve the exact linear-time ranking and unranking of derangements by applying a method specialized for derangements. We use a computational model that enables arithmetic operations and random access for O(log⁡n!)=O(nlog⁡n) bits to be executed in constant time.
Survey of Arabic Machine Translation, Methodologies, Progress, and Challenges

For the longest time, translation was a labor-intensive process that required just human effort. While human translation remains the most reliable method of textual content translation, it takes longer and is more expensive if done for each individual piece of information. Several Machine Translation (MT) approaches have recently emerged to facilitate the migration of any content across languages, especially for the low resource languages such as the Arabic Language. Given that Arabic is one of the world's most widely spoken languages, the task of Arabic machine translation has recently gotten a lot of interest from the scientific community. Indeed, the amount of study devoted to these low resources languages has resulted in some significant accomplishments; however, the status of Arabic MT systems falls short of the quality obtained for other languages. As a result, this survey examines the origins and main development timeline of MT approaches, investigates the significant branches, and categorizes different study orientations. In addition, it gives a comprehensive overview of the key research works that have been completed in the field of Arabic Neural MT (ANMT) and discusses possible future research prospects in this discipline.
Physical Education Teaching Based on Human Model Design and Computer Simulation of Human Two-Dimensional Moving Image

The traditional gait analysis is mainly used in the research of joint dynamics and electromyography, which is helpful to develop a medical feedback system. The system judges the leg injury by analyzing the patient’s gait, so as to provide effective treatment. In order to study how computer technology can be more appropriately introduced into physical education, this paper puts forward a new design method of manikin. The application of some key implementation technologies of computer simulation of two-dimensional motion images of human body is conducive to the scientization of physical education teaching and training. The experiment analyzes the daily movement of human body. MEMS technology is a technology to obtain human inertial data by simulating human motion with one or more computers. By integrating the collected data, different characteristics can be obtained. Then, classify these feature processing algorithms, and finally, identify the actions performed by the human body. Finally, the advantages of this recognition method in recognition accuracy are verified.
Experimental Analysis on Detection of Emotions by Facial Recognition using Different Convolution Layers

This Emotions have been playing a crucial part in the social, physical, and mental life. Hence identifying the emotions by recognizing the facial expression of the user is of utmost importance. This information obtained will help in various recommendation systems like music, products, books etc. In this paper, the emotions are detected by using the facial data and the results are compared between CNN model and DCNN model. The dataset used was FER-2013, which included 28,000 annotated photos in the training set, 3,500 in the training set, and 3,500 in the test set. In FER-2013, each image is assigned to one of seven emotions: happy, sad, angry, afraid, surprise, disgust, and neutral. The parameters chosen are applied to three different models created by altering Convolution Layers, demonstrating that as the number of layers grow, accuracy improves.
Multi-prediction metropolis hastings resampling filtering algorithm based on CUDA

Aiming at the problems of accuracy, speed reduction and estimation accuracy loss caused by MH instead of sequential importance resampling in Metropolis Hastings resampling particle filter algorithm, this paper proposes a parallel Metropolis hasting filter algorithm based on a multi-prediction framework, which loads particles Filtering shifts from resampling to prediction and update steps. The overhead of the Multi-prediction framework can be easily compensated by parallel implementation. This algorithm reduces global sequential operations by adding local parallel computing. Simulation experiments prove that the real-time performance and state estimation accuracy of this method have been improved.
The Decline of America

In the United States, often we read ever more frequently and emphatically that the future of America is in question. We are told the country is heading toward fiscal disaster and unable to pay for its extensive military involvements worldwide simultaneously. Another issue of preoccupation is its current heavy commitments to social programs and investments in education, infrastructure, and research. We are told that the argument for these expenses is needed to compete with China and other rivals. If current trends continue, there are predictions that the American economic and military dominance will be lost, and the era of the American empire will be over. Besides the large number of articles dealing with this issue, the authors present a mind genomics study and address their conclusions in this chapter.
The Impact of Community Support during Covid-19 in Meeting Needs, Developing Academic Commitment and Volunteer Values among Gifted Students at the Intermediate Stage in Poor Environments

The current research aimed at developing a program based on community support and assessing its impact during the covid-19 pandemic in meeting needs, developing academic commitment and volunteering values among gifted students in the intermediate stage in poor environments. The quasi-experimental method was used for the experimental and control groups. The sample consisted of 41 gifted students (21 experimental group, 20 control group) and the experimental group underwent a community support program that lasted for 14 weeks. Measures of academic needs, academic commitment, and volunteer values were administered before starting and after completing the program. The results revealed that there were statistically significant differences between the performance of the two groups on the needs scale, academic commitment, and volunteer values in favor of the experimental group. This indicated the impact of community support during the pandemic in meeting needs, developing academic commitment, and volunteering values. The results of the Eta square indicated a large effect size for the community support program. In light of the results, the researchers recommended forming working groups to continue developing the community support program and reapply it on other samples. Keywords: Community support, needs, study commitment, volunteer values, gifted, poor environments
Micro-Expression Recognition Based on Attribute Information Embedding and Cross-modal Contrastive Learning

Facial micro-expressions recognition has attracted much attention recently. Micro-expressions have the characteristics of short duration and low intensity, and it is difficult to train a high-performance classifier with the limited number of existing micro-expressions. Therefore, recognizing micro-expressions is a challenge task. In this paper, we propose a micro-expression recognition method based on attribute information embedding and cross-modal contrastive learning. We use 3D CNN to extract RGB features and FLOW features of micro-expression sequences and fuse them, and use BERT network to extract text information in Facial Action Coding System. Through cross-modal contrastive loss, we embed attribute information in the visual network, thereby improving the representation ability of micro-expression recognition in the case of limited samples. We conduct extensive experiments in CASME II and MMEW databases, and the accuracy is 77.82% and 71.04%, respectively. The comparative experiments show that this method has better recognition effect than other methods for microexnression recognition.
A study on the generalizability of Oculomotor Plant Mathematical Model

The Oculomotor plant mathematical model (OPMM) is a dynamic system that describes a human eye in motion. In this study, we focus on an anatomically inspired homeomorphic model where every component is a mathematical representation of a certain biological phenomenon of a real oculomotor plant. This approach estimates internal state of oculomotor plant from recorded eye movements. In the past, the utility of such models was shown to be useful in biometrics and gaze contingent rendering via eye movement prediction. In previous studies, an implicit underlying assumption was that a set of parameters estimated for a certain subject should remain consistent in time and generalize to unseen data. We note a major drawback of the prior work, as it operated under this assumption without explicit validation. This work creates a quantifiable baseline for the specific OPMM where the generalizability of the model parameters is the foundational property of their estimation.
Critical Analysis on the Necessity and Techniques Used in Internal Control in Accounting Operations

The study’s main objectives are to evaluate the necessity of internal control processes and assess the tools and methods used in internal control processes. We used the practitioners polices, procedures, tools and techniques to have better internal control. The size of the business also plays a significant role in the effectiveness of internal control techniques. The cost should be less than the benefits (efficiency, effectiveness and customer satisfaction) for the organization. Before implementing any techniques, any organization needs to conduct a self-induced investigation, testing, and preliminary experiments.
Mass Mahjong Decision System Based on Transfer Learning

In this paper, we propose a transfer learning to solve the problem of lacking in data and the difficulty in constructing models effectively, which is typically represented by Mass Mahjong in the field of imperfect information. Design and implement the Mass Mahjong Discard model based on transfer learning. The previously well-trained Blood Mahjong Discard model on a large dataset is migrated to Mass Mahjong Discard model in a similar domain. In the subsequent model optimization, a self-play based approaching is used to improve the Mass Mahjong Discard model. The experimental results show that the transfer learning-based Mass Mahjong Discard model performs well in the situation of less data, and can fit the Mass Mahjong Discard rule. And the model won the second prize in the Mass Mahjong event of the National University Computer Gaming Competition in 2021.
An application of the delay line differentiator for pulse characterization

Various applications of the delay line discriminator (DLD) have been described in the open literature – primarily for use as part of a waveform classifier. In this work, we document the performance of the DLD when employed as a radar waveform estimator, assuming that a preliminary algorithm has detected the rising and falling pulse edges. We examine some of the issues associated with the waveform estimation problem, in particular the effects of signal-to-noise ratio (SNR) and DLD parameter settings on the waveform estimation. In the process, we outline analytic techniques used for classification of linear FM, constant frequency, and phase modulated waveforms.
Comparison of gesture classification methods with contact and non-contact sensors for human-computer interaction

Classification of signals that are received from the human body and control systems is one of the most important subjects of the machine learning application. In this study, classification algorithms were used to classify electromyography and depth sensor data. First, electromyography and joint angle data were obtained from software developed in Python environment. Five different types of movements have been identified for classification and thousand different samples have been collected as training for each of these movements. Support Vector Machine, Random Forest, and K-Nearest Neighbour algorithms were used for classification. To measure success algorithms, results have been compared for achieving criteria. The results show which of three different algorithms was the most successful on two different sensors. While Random Forest provides the best results for non-contact sensor, K- Nearest Neighbour produces the best results for contact sensors. This paper evaluated the classification success of two different sensors. The results will be utilized in online classification to control a graphical user interface.
Medical Image Denoising Processing Application Technology Based on Combined Filtering

Abstract—In recent years, with the continuous progress and wide application of science and technology, image processing technology has been More and more scholars pay close attention to it. Image processing technology refers to a series of operations such as digitization, encoding, transmission, and analysis of images. Obviously, the restoration of degraded images is also an important research field, such as aerospace, remote sensing images, biological sciences and other fields, which are very popular. In this paper, the inverse filter models are studied. Through MATLAB simulation, it is found that the recovery effect of these filter models is not ideal in the case of noise, so a combined algorithm is proposed. Combining the spatial filter and the inverse filter wave, the purpose is to filter out some noise through the spatial filter before the image is fed into the inverse filter. Through the MATLAB simulation, it is found that the denoised image processed by the combined algorithm has a better effect.