Reviewerly/exHarmony huggingface dataset:
Menzerath-Altmann Law in Consecutive and Simultaneous Interpreting: Insights into Varied Cognitive Processes and Load

Notwithstanding theoretical simulations of distinctive cognitive processes and load of consecutive (CI) and simultaneous interpreting (SI), quantitative linguistic inquiry into their outputs is needed for solid empirical evidence. As a fundamental law of quantitative linguistics, Menzerath–Altmann Law (MAL) mirrors the economic processing of linguistic information and complex dynamic language system. Given its extensive validation at various linguistic levels and predictive power of its parameters in register, language and authorship differentiation, MAL is worthy of being applied to interpreting studies. We endeavour to investigate whether interpreted languages follow the MAL and reveal varied cognitive load of CI versus SI, as manifested by different MAL fitting models. Results show that (1) both CI and SI outputs follow the MAL; (2) SI processing involves more diversified structural information and shows a greater tendency of shortening the clauses of a sentence with increased sentence length, than CI processing, expressed by significantly higher a and lower b in SI models than that in CI models. Our findings suggest the disparate language representations are shaped by cognitive capacity limitations and interpreting modalities, and reveal how language system dynamically re-regulates and reorganizes the linguistic information to accommodate environmental settings from the perspective of synergetic linguistics.
Machine Learning-Based Automated Tool to Detect Sinhala Hate Speech in Images

Social media platforms have emerged rapidly with technological advancements. Facebook, the most widely used social media platform has been the primary reason for the spread of hatred in Sri Lanka in the recent past. When a post with Sinhala hate content is reported on Facebook, it is translated to the English language before the review of the moderators. In most instances, the translated content has a different context compared to the original post. This results in concluding that the reported post does not violate the established policies and guidelines concerning hate content. Hence, an effective approach needs to be in place to address the aforementioned problem. This research project proposes a solution through an automated tool that is capable of detecting hate content presented in Sinhala phrases extracted from Facebook posts/memes. The tool accepts an image that contains Sinhala texts, extracts the text using a Convolutional Neural Network (CNN) model, preprocesses the text using Natural Language Processing (NLP) techniques, analyzes the preprocessed text to identify hate intensity level and finally classifies the text into four main domains named Political, Race, Religion and Gender using a text classification model.
Video Summarization Based on Fusing Features and Shot Segmentation

Video summarization is a technique that creates short summaries from original videos while retaining the main representative information. Traditional video summarization models based on deep learning mostly use frames as the basic processing unit, which cannot handle long videos due to hardware limitations. In this paper, we compress the frame-level features into shot-level features using a feature extractor based on Convolutional Neural Network (CNN), which can improve the training accuracy and reduce computation. At the same time, we propose a feature fusion algorithm based on the capsule network, which combines the RGB features and Light Flow features of the video into the deep features with adaptive weights to enhance the original video features. Experiment results on two benchmark datasets (TVsum and SumMe) validate the effectiveness of our method.
Palmprint Recognition with Fixed Number of SURF Keypoints

In this paper the palmprint recognition problem was studied using SURF (Speeded Up Robust Features) keypoints. The main setback when using SURF is that it computes, for the same set of parameters, very different number of keypoints for each image in the dataset, making the matching procedure inefficient. To solve this impediment, we use an algorithm that tunes a SURF parameter for approximately computing the same number of keypoints for all the images in the dataset. This idea was tested on five palmprint datasets. The palmprint recognition results using this approach are very good, better than those obtained in two recent papers.
Deep Reinforcement Learning With Part-Aware Exploration Bonus in Video Games

Reinforcement learning algorithms rely on carefully engineering environment rewards that are extrinsic to agents. However, environments with dense rewards are rare, motivating the need for developing reward functions that are intrinsic to agents. Curiosity is a type of successful intrinsic reward function, which uses the prediction error as an reward signal. In prior work, the prediction problem used to generate intrinsic rewards is optimized in the pixel space rather than a learnable feature space to avoid randomness caused by feature changes. However, these methods ignore small but important elements of the states that are often associated with locations of the character, which makes it impossible to generate accurate internal rewards for efficient exploration. In this article, we first demonstrate the effectiveness of introducing prior learned features for existing prediction-based exploration methods. Then, an attention map mechanism is designed to discretize learned features, thereby updating the learned feature and meanwhile reducing the impact of randomness on intrinsic rewards caused by the learning process of features. We verify our method on some video games from the standard reinforcement learning Atari benchmark, achieving clear improvements over random network distillation, which is one of the most advanced exploration methods, in almost all Atari games.
Foreground Information-Aware Image Superresolution Reconstruction for Image Processing IoT Systems in Smart City

In recent years, with the rise of Internet of Things (IoT), a majority of smart technologies, such as autonomous vehicles, smart healthcare, and urban surveillance, require a huge number of images of high quality and resolution. Currently, image superresolution reconstruction technologies are widely used for obtaining high quality images. Unfortunately, the existing methods generally focus on the whole image without highlighting foreground information and lack visual focus. Also, they have low utilization of shallow features and numerous training parameters. In this paper, we propose a feature extraction module that focuses on foreground information: the parallel attention module (PAM). PAM computes channel and spatial attention in parallel, inputs the obtained attention values into a cascaded gated network, and dynamically adjusts the weights of both using nonuniform joint loss to focus on image foreground information and detail features to improve the reconstructed image’s foreground sharpness. To further improve the performance, we propose to connect multiple PAM modules in series with skip connections and call it PAMNet. PAMNet can better leverage the shallow residual features, and the reconstructed images are closer to ground truth. Thereby, the applications in the urban image processing IoT systems can obtain high-resolution images more quickly and precisely. The comprehensive experimental results show that PAMNet performs better than the state-of-the-art technologies.
CLASSIFICATION OF ROBOTIC PLATFORMS AND READY-MADE TECHNICAL SOLUTIONS FOR TEACHING PUPILS THE ROBOTICS FUNDAMENTALS

The use of robotics systems in education also known as "Educational robotics", "Robotics in education" is gaining popularity as a trend. This is evidenced by the increase in the number of publications on this topic in scientific journals and conducting an experiment at the national level on "Methodological system of teaching the basics of technology and robotics as part of STEM-education" by the Ministry of Education and Science of Ukraine. The rapid development of the robotics industry necessitates training of appropriately qualified specialists. However, school remains a traditional place to master basic competencies needed to determine the professional orientation of a person, and familiarity with robotics increases the chances of students choosing this area of activity in the future. Teachers mainly choose the most common technical tools for teaching robotics, which has both advantages and disadvantages. It is important to develop a classification of educational technical solutions that already exist and are used in the process of teaching the basics of robotics at school. The paper analyzes sets of components used in teaching the basics of robotics for school-aged children, explores 84 different technical solutions that can be used in the process of learning robotics, and identifies common and distinctive features. As a result of the research a classification of robotic platforms and ready-made technical solutions was offered for use by teachers in the process of teaching students the basics of robotics. In the perspective of further research, it is planned to develop methodological recommendations for the selection of content and means of teaching the basics of robotics to secondary school pupils.
Path Planning Algorithm For Mobile Robot based on Deep Learning

Robot path planning in viewing the angle of the camera is one of the most critical challenges of mobile robot path planning in dynamic environments. This study was done to avoid obstacles in the scope of a monocular camera using a Convolutional Neural Network. Due to the lack of an available dataset, first of all, a dataset for path planning has been made. This dataset is divided into five categories; straight, left, right, Spin left, and spin right. After creating this dataset, the proposed network for robot's path planning was designed based on the CaffeNet network. With the difference that by making changes in some layers, the network has become more profound and faster. The proposed network was trained on the dataset and tested in several environments. Finally, the performance of the designed network in terms of training time, Consumption memory, and accuracy has been evaluated and compared with several other networks. The results show that the proposed network is the most suitable network for path planning operations on the presented dataset regarding accuracy and speed among the compared networks.
Measuring Semantic Similarity between Services Using Hypergraphs

We propose in this paper a new HyperGraph-based Similarity Algorithm (HGSA) to measure the semantic similarity between services with focus on Web services. HGSA relies on a term-similarity function that considers several parameters such as node's local density, depth, number of relation factors, and link strength. This function is leveraged to build a similarity matrix between Web services. Computing the similarity between Web services is reduced to applying the Hungarian method to this matrix. We study the complexity of the proposed algorithm and provide experiments that show that it outperforms other related approaches in terms of accuracy.
Introduction to Cryptography in Blockchain

Since ancient times, there has been a need to protect information considered confidential or secret, with the aim of keeping it out of the reach of a possible enemy or adversary. This has been done in various ways: by preventing physical access to it, by disguising its nature, or by using cryptographic procedures to transform it to prevent access to its content. The aim of cryptography is not to hide the existence of a message, but rather to hide its meaning, a process known as encryption.
Face Recognition by Artificial Neural Network Using MATLAB

Facial Recognition considering one of the most difficult operations due to the unruly amount of datasets. However, human could easy to recognize an emotion while inconceivably for a computers. Artificial Neural Networks (ANN) provides an exceedingly smart solution in terms of recognition performance when deal might as well with the data. In this paper human faces have been detected through artificial neural network using MATLAB simulation to find out the impression via recognizing the expression of the faces obtained from the database that containing 266 samples with various expressions within the wide ages. Consequently, many pre-classified datasets such as Japanese Female Facial Expression (JFFE), Face and Gesture Recognition (FG-NET), Face Expression Recognition Dataset 2013 (FER-2013), and Cohn Kanade Dataset (CK +) were studied to achieve a comprehensive model that could contribute the scientific research. The study investigated an obtained dataset to demonstrate the efficiency and solidarity of the proposed through to focus positively on the facial impression and its fluctuations. The result clearly shows that LEARN Gradient Descent with Momentum weight (LEARNGDM) is the best learning function to get an accomplishment with an average error equal to 0.01257, validation ratio 97.462, and 98.67232 precision.
Face mask detection and recognition using an IoT enabled PDMS-Ag e-skin sensor that works in contact and non-contact modes

The COVID-19 pandemic has already spread over 200 countries in a few months and taken a toll on many lives. At this critical time, there is a need to follow some precautions to control the virus spreading rapidly through direct and indirect contact. The World Health Organization (WHO) has already recommended the importance of face masks for protection from the virus. Hence, one of the prime changes we have had to incorporate in our lives is wearing a face mask. This work reports the development of Ag particles containing polydimethylsiloxane (PDMS) based e-skin sensor, which generates signals on touch (contact mode) or proximity (non-contact mode) near the sensor. These signals are retrieved using IoT. The signals indicate a person's presence, which activates face mask detection using deep learning. This model is an IoT and Machine Learning-based system. When a human touches or places a hand near the PDMS-Ag sensor, this model performs face masks detection. This model is also suitable for security purposes. Since controlling the number of new COVID-19 cases is the need of the hour, we are using face mask detection in this study.
Centralized Admission Process: An E-Governance Approach for Improving the Higher Education Admission System of Bangladesh

Higher education is very important for the overall growth of a country because, through higher education, a country can get more qualified personnel in important positions. So, it is vital to make the entry process into higher education as simple and straightforward as possible. Using a centralized admission system is a big step toward that simplicity. The current admission process in Bangladesh is very chaotic which takes quite a bit of financial and physical toll on the students who want to pursue higher education. Since the process is decentralized, it becomes the government to analyze the admission data each year and take action based on the data. In this paper, we would like to look at how other countries have solved this issue and propose a system that can fit perfectly with the educational system of Bangladesh and make the admission process very streamlined. Our proposed should also make it very easy for the Government of Bangladesh to monitor and analyze admission-related data very easily.
Shortest Path Planning with an Energy-Constrained Robot

In this paper, we study the problem of shortest path planning for a mobile robot that does not possess unlimited energy for traveling. The robot can travel at most B distance with its battery fully charged. The objective of this energy-constrained robot is to travel from location S to location G in the presence of k charging stations while minimizing the incurred travel cost. As the robot is constrained by its energy, it needs to stop at one or more charging stations in order to recharge its battery. To solve the stated problem, we have proposed a variant of the classical A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> search algorithm that plans the path of the robot in such a way that it moves as much distance as possible with a full recharge (the maximum being B). Furthermore, it chooses the one to be the next charging station that minimizes the extra distance that needs to be covered to reach it on top of the shortest distance from S to G. We have designed novel heuristic functions that guide the search towards such charging stations where the deviation from the shortest path without the constraint is the minimum. We prove that the proposed approach is complete. Results show that our proposed algorithm finds an optimal solution while taking a negligible time to execute.
Post-quantum Key Escrow for Supervised Secret Data Sharing on Consortium Blockchain

Consortium blockchain has been widely used in different management scenarios (i.e., digital finance), where normal members want to keep their on-chain data private while supervision peers want to reveal the on-chain private data under certain circumstances like financial regulation and judicial forensics, and key escrow is an idea to solve the problem. Since current key escrow schemes heavily rely on traditional asymmetric encryption and decryption algorithms that are vulnerable to attacks from quantum computers, we design and implement the first post-quantum (PQ) key escrow system for consortium blockchains (i.e., PQ-KES4Chain), which is integrated with all the PQ public-key encryption/KEM candidate algorithms in the current round of NIST call for national standard. Furthermore, we provide chaincodes, related APIs together with client codes for further development. And we perform a detailed security analysis on the system design and a full evaluation on the performance of PQ-KES4Chain including the time of chaincodes execution and the on-chain storage space. We further discuss the implications of our findings, which could be helpful for the developers of PQ KEM algorithms and applications.
Blockchain data anti-counterfeiting sharing model of Power Material Alliance based on attribute-based encryption

In order to ensure the privacy protection and data security in the current power supply chain data sharing mechanism, inspired by the ability of attribute-based encryption technology to effectively realize the secure sharing and access control of data on the cloud. Proposes an anti-counterfeiting sharing model based on attribute-based encryption for the Blockchain of the Electric Power Material Alliance. First, in the private key generation phase, the data user entrusts multiple nodes to participate in the joint calculation and store part of the private key, while other data users cannot obtain the complete key, thereby improving the efficiency of private key generation; Finally, by constructing a shared chain with on-chain and off-chain collaborative computing and storage functions, the effective integration of attribute-based encryption and blockchain systems is realized. Security analysis and experimental simulation results show that the proposed model has certain optimizations in terms of key generation calculation efficiency and actual business scenarios, and meets the needs of power supply and application.
A text-based mining approach for real estate policy impact monitoring and analysis

Text mining has been widely used in economic analysis. Besides extracting individual opinion from a single text, it is more important to accurately aggregate group opinions from mass text files. This research proposed a text mining framework for constructing policy impact indicators to monitoring the public opinions on real estate market policies. A real estate domain dictionary has been developed based on the released policy announcements in Chinese real estate government websites. And corresponding public opinion index are constructed to evaluate the impact of real estate policies on the target market. In addition, we have improved the handling of emotional words in the public opinion dictionary. Word2Vec and So-PMI models are used to identify the polarity of trend words, and the score of each sentiment words is calculated based on its co-occurrences with the trends words using a modified label propagation algorithm. Beijing real estate market is taken as the test bed. Empirical study show that the policy public opinion index explains a significant part of Beijing housing price changes. In-sample and out-of-sample forecasts of Beijing housing prices indicate that the policy public opinion index is capable of increase the prediction accuracy.
Research on Natural Language Extraction Method Based on Deep Learning Technology

NLP (Natural Language Processing) has become one of the research hotspots of machine learning. Text classification is an important branch of NLP processing technology. This article uses journal papers as experimental data to study Chinese text classification and proposes two classification models. One is the Chinese text classification algorithm PRE-TF-IDF. The traditional word frequency algorithm only considers the appearance frequency of the word when weighting the word, and does not consider the position of the word in the text. The PRE-TF-IDF algorithm improves the accuracy of text classification by adding weight preprocessing and word density weighting on the basis of the TF-IDF (Term Frequency Inverse Document Frequency) algorithm. The other is the text classification model CNNSVM(Convolutional Neural Network and Support Vector Machine Classifier)based on the combination of convolutional neural network and support vector machine. On the basis of the traditional convolutional neural network model, an attention mechanism is added, while the parameters of the model are simplified, and a support vector machine-based classifier is used to replace the softmax layer in the traditional model to achieve text classification. The extraction effect of feature words is improved, and the problem of weak generalization ability of the softmax layer is solved.
Automation of Kpi Calculation for Leadership Personnel of Higher Educational Institutions

This article is devoted to the problem of automating the calculation of key performance indicators of university executives. The article considers indicators for evaluating the activities of teachers based on the electronic portfolio of pedagogical staff. A mathematical model and a generalized algorithm for calculating the KPI of pedagogical and executive staff are presented. As an example, the process of forming a report on the scientific and methodological activities of pedagogical staff is considered.
Enhance Object Tracking on Augmented Reality Markerless using FAST Corner Detection

Markerless-based is the method in Augmented Reality (AR) to augmentation 3D objects in the frame. One of the challenges in markerless AR is to extract features as input data training on markerless AR. One of algorithm for feature extraction of object target is FAST corner detection in image processing. This paper uses FAST corner detection for feature extraction on Augmented Reality Markerless based with the aim to improve performance of augmentation objects in AR. The User Defined Target and Extended Tracking are to improve convenience and reliability of the AR. The aim is can trace 3D objects even the 3D object invisible. The results show that maximum environment for the best augmentation is intensity in of 80-190 cd.