Reviewerly/exHarmony huggingface dataset:
Vertical Jump Data from Inertial and Optical Motion Tracking Systems

Motion capture (MOCAP) is a widely used technique to record human, animal, and object movement for various applications such as animation, biomechanical assessment, and control systems. Different systems have been proposed based on diverse technologies, such as visible light cameras, infrared cameras with passive or active markers, inertial systems, or goniometer-based systems. Each system has pros and cons that make it usable in different scenarios. This paper presents a dataset that combines Optical Motion and Inertial Systems, capturing a well-known sports movement as the vertical jump. As a reference system, the optical motion capture consists of six Flex 3 Optitrack cameras with 100 FPS. On the other hand, we developed an inertial system consisting of seven custom-made devices based on the IMU MPU-9250, which includes a three-axis magnetometer, accelerometer and gyroscope, and an embedded Digital Motion Processor (DMP) attached to a microcontroller mounted on a Teensy 3.2 with an ARM Cortex-M4 processor with wireless operation using Bluetooth. The purpose of taking IMU data with a low-cost and customized system is the deployment of applications that can be performed with similar hardware and can be adjusted to different areas. The developed measurement system is flexible, and the acquisition format and enclosure can be customized. The proposed dataset comprises eight jumps recorded from four healthy humans using both systems. Experimental results on the dataset show two usage examples for measuring joint angles and COM position. The proposed dataset is publicly available online and can be used in comparative algorithms, biomechanical studies, skeleton reconstruction, sensor fusion techniques, or machine learning models.
PAA: A Blockchain-Based Parking Assistance Alliance With User Preference

In order to improve traffic conditions and environmental pollution caused by automobile exhaust, it is critical and challenging to help numerous vehicle users to obtain ideal parking spaces in terms of quality of experience (QoE), security and efficiency. The parking assistance scheme using the consortium blockchain can get a tradeoff between security and efficiency, but it is a key issue to meet the preferences of parking space seekers and design a well-operated incentive sharing mechanism. This paper proposes a Parking Assistance Alliance (PAA) scheme based on double auction for vehicular blockchain. Through truthful incentives, the PAA encourages vehicles in the alliance to provide parking information for nearby vehicle users. Further, the vehicle user’s preference is fully considered when assigning the parking space. The matching priority is introduced by incorporating the parking space supply and demand, as a reference to provide prioritized service. Theoretical proofs and simulation results show that the PAA scheme can achieve individual rationality, budget balance, and truthfulness, with high user satisfaction, and the PAA scheme deployed on the IBM Hyperledger Fabric platform outperforms the existing method with an average of 7.42% higher successful allocation ratio and 35.4% less transaction confirmation time.
Regular Expression-Based Text Classification Using mSVM and Machine Learning Techniques

Due to the high popularity of patient electronic health record systems, effective data management is very essential nowadays. There are some significant volume of unstructured format data in the medical sector. Computer-assisted classification of such data into functional classifications such as subjects or illness may save time and money for medicine and healthcare platforms, particularly successful web healthcare providers. However, categorization in the medical sector is often difficult since even simple problems need a significant portion of domain expertise. Many strategies and techniques for medical information retrieval and categorization tasks have been established during the last few decades. The general approaches are machine learning algorithms such as Naïve Bayes, SVM and random forest, but still, those results have heavy noise in the generated regular expression and text. This paper proposes a collaborative natural language processing (NLP) and machine learning-based approach to generate practical regular expressions on extensive clinical data. The first section preprocesses data with NLP algorithms such as token generation, alignment generation, key generation and patter builder. This process performs the feature extraction, and those features are carried out by module training. The modified Support Vector Machine (mSVM) and other traditional machine learning algorithms are applied to the entire test dataset. The SVM produces almost 90.80–91.90% average accuracy on test data with 15-fold cross-validation. In the result section, the overall experiment analysis has been demonstrated with various machine learning algorithms using a Weka environment.
Leveraging Semantic Facets for Automatic Assessment of Short Free Text Answers

Free text answers to short questions can reflect students' mastery of concepts and their relationships relevant to learning objectives. However, automating the assessment of free text answers has been challenging due to the complexity of natural language. Existing studies often predict the scores of free text answers in a “black box” manner without analyzing their semantic components, which at least partially limit the prediction performance. In this article, we focus on fine-grained semantic facets in free text answers that correspond to knowledge to be mastered. Using a dataset with semantic facet annotation, we first show the correspondence of semantic facet matching states and answer quality, as well as the importance of semantic facets in automatic assessment of answer quality. We then extend the work to a dataset without semantic facet annotation and demonstrate the effectiveness of proposed automated methods in assessing answer quality, including semantic facet extraction, matching state prediction based on a neural framework, and feature engineering with semantic facets. The contribution of this research is twofold: 1) the proposed methods improve state-of-the-art performance of automatic assessment of free text answers and 2) it delves into fine-grained semantic components of free text answers, making it possible to explain the scores and generate detailed feedback.
Coherent Visual Storytelling via Parallel Top-Down Visual and Topic Attention

Visual storytelling aims at producing a narrative paragraph for a given photo album automatically. It introduces more new challenges than individual image paragraph descriptions, mainly due to the difficulty in preserving coherent topics and in generating diverse phrases to depict the rich content of a photo album. Existing attention-based models that lack higher-level guiding information always result in a deviation between the generated sentence and the topic expressed by the image. In addition, these widely applied language generation approaches employing standard beam search tend to produce monotonous descriptions. In this work, a coherent visual storytelling (CoVS) framework is designed to address the above-mentioned problems. Specifically, in the encoding phase, an image sequence encoder is designed to efficiently extract visual features of the input photo album. Then, the novel parallel top-down visual and topic attention (PTDVTA) decoder is constructed via a topic-aware neural network, a parallel top-down attention model, and a coherent language generator. Concretely, visual attention focuses on the attributes and the relationships of the objects, while topic attention integrating a topic-aware neural network could improve the coherence of generated sentences. Eventually, a phrase beam search algorithm with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> -gram hamming diversity is further designed to optimize the expression diversity of the generated story. To justify the proposed CoVS framework, extensive experiments are conducted on the VIST dataset, which shows that CoVS can automatically generate coherent and diverse stories in a more natural way. Moreover, CoVS obtains better performance than state-of-the-art baselines on BLEU-4 and METEOR scores, while maintaining good CIDEr and ROUGH_L scores. The source code of this work can be found in <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://mic.tongji.edu.cn</uri> .
A Genetic-Based Virtual Machine Placement Algorithm for Cloud Datacenter

Cloud computing is a process of renting hardware, software, and platforms over the internet. Users are charged on a pay-per-use model for these services. A cloud service provider provides resources on the basis of user’s request. To efficiently allocate resources, server consolidation techniques are used. Virtual machine placement (VMP) is a server consolidation technique in which virtual machines are created and mapped to physical servers for allocation of resources. This research work explores the first fit, best fit, and genetic algorithm for mapping physical servers to virtual machines. For an optimum mapping of VMP, a hybridization of best-fit algorithm and genetic algorithm suggested the improved genetic algorithm (IGA). The performance of these VMP algorithms is evaluated with datasets obtained from Microsoft Azure, Google Cloud, and Amazon EC2. The simulation results showed that the proposed IGA algorithm improves resource utilization and decreases the execution time.
Recommender System Using Knowledge Graph and Ontology: A Survey

In recent years, Users find it challenging to choose what interests them among the many options offered due to the abundance of information. And finding those choices from data itself has also becomes very challenging task for organizations. To handle this task, recommendation system is an important field of research in computer science. Despite several efforts to make RS more efficient and personalized, it still faces issues like cold start, data sparsity, etc. And as it is designed to be readable by humans only, computer cannot process nor can interpret the data in it. Ontology facilitates the knowledge sharing, reuse, communication, collaboration, and construction of knowledge rich and intensive systems. Adding semantically empowered techniques to recommender systems can significantly improve the overall quality of recommendations. There has been a lot of interest in creating recommendations using knowledge graphs as a side information. By this, we not only overcome the issues of traditional RS but also provide a flexible structure, which naturally allows integration of multiple entities all together. It is also helpful in explanation for recommended items. So, in this survey we collected recently published research papers on this particular field to enhance RS. We provided a fine-grained information on this topic along with the explanation on how to use ontology approach for building a KG and challenges faced by both RS and KG systems. Certain crucial datasets and tools are also offered for a better understanding of accessibility.
Hierarchical Clustering for Covid-19 in Asia

Hierarchical cluster (HC) or hierarchical cluster analysis (HCA) is a type of clustering algorithm where the algorithm will group similar objects based on their similarity to groups called clusters. The objectives are that the clusters will be as different as possible from other clusters, and the objects within the cluster are mainly similar to each other. The method used for this paper is agglomerative hierarchical clustering, where the algorithm will group the objects based on their similarity with the bottom-up approach. The algorithm will measure the distance between clusters with a single-linkage approach using the dataset for covid-19 daily update obtained from https://ourworldindata.org/ and processed into clusters based on confirmed cases and total deaths data.
Modelling Baby’s Health Management System Using Rule-Based Algorithm

Abstract In Malaysia, the growth of a new-born baby is recorded in a health handbook. The data in the handbook is consistently monitored through the information attained by nurses and doctors. Details such as the baby’s weight, height, immunization schedule and growth development are all recorded into the handbook. On that account, the handbook needs to be brought when having an appointment as it is recorded manually and handwritten by nurses and doctors alike. However, in some cases the probability of losing the handbook is high, hence important information such as the child’s appointment dates and health data can be overlooked and missed by parents and caregivers. Moreover, with the loss of key data, parents may face difficulties identifying symptoms that might present on their baby. Therefore, this study has developed a system to record all of the necessary data. It was modelled using a rule-based algorithm to assess the condition of a baby based on their symptoms, the system includes a postpartum confinement module to support mother’s regarding their postpartum confinement practices. The system also serves as a reminder for parents regarding their baby’s next vaccination appointment. Results shows that the system is able to improve the disadvantages of the old manual approach, allowing for maximum data storage while reducing data entering discrepancies. By using a rule-based approach, the system can model a baby’s health state and keep track of immunisation schedules as well as deliver friendly reminders to parents and caregivers if the need arises.
Load Balancing with Hashmap in Cloud Computing Using Cloud Simulator

Cloud computing is an integrated production platform helpful in both public and private organizations. Cloud computing allows users to use a large range of computational tools, and with virtualization options. With the increasing number of Internet users, it is impossible to fulfill all their demands. This issue can be solved with optimal load balancing with cloud service provider. Load balancing prevents from overcrowding. It is a technique of distributing workload through several virtual machines, which is in the server over network to maximize resource use. It also reduces data transfer time and average overall response time. The suggested algorithm aims to uniformly spread load over all servers in a cloud network. Overall response from server and data processing time has been slashed using the proposed method.
A Comparison of Four Notions of Isomorphism-Based Security for Graphs

A graph is a powerful abstraction for representing information. We address the problem of publishing a secure version of a graph that does not leak information to an adversary who may possess prior information about portions of the graph, and may have unbounded computational power. In this context, we revisit four notions of security, all of which are based on variants of graph isomorphism, that have been proposed in two different application contexts in the literature. We compare the four notions to one another, first from the standpoint of strength, i.e., whether meeting one notion implies meeting another, and then from the standpoint of computational hardness, i.e., what the exact computational complexity is for the problem of checking whether a graph meets a notion. For the latter, we identify that for two of the notions we consider, the problem is <inline-formula><tex-math notation="LaTeX">$\mathbf{NP}\text{-complete}$</tex-math></inline-formula> , and for the two others, it is <b>ISO</b> -complete, where <b>ISO</b> is the class of problems induced by graph isomorphism. We observe that strength is not necessarily correlated to computational hardness. In summary, our work makes contributions at the foundations of an important notion of security for graphs.
Development of Automated Image Caption Generator in Real-Time Application Using Pre-trained CNN Models

Generating captions automatically to an image, which is commonly termed Automated Image Captioning (AIC), is a demanding task. The essence of both image processing and natural language processing are involved in image captioning. This paper deals with the generation of descriptive sentences to a given image. Encoder–Decoder architecture is implemented here to develop the image captioning project. Three pre-trained convolution neural network (CNN) models—EfficientNet, ResNet, and DenseNet have been compared. Long Short-Term Memory (LSTM) is used as a recurrent neural network (RNN) model, which acts as a decoder. The existing mechanisms of image captioning have been discussed by doing a literature survey. The performance of the models has also been evaluated using standard evaluation metrics. In the end, by considering one of the applications of image captioning, i.e., for visually impaired persons, the generated model is developed in the real-time device to make a prototype by interfacing camera.
Authentication Using Typing Pattern

Passwords are an antique authentication methodology. With the recent hacks and leaks, keeping credentials secure is proving to be more difficult day by day. An answer to the present drawback is to feature different factors to authentication. The foremost non-invasive of that is writing patterns. This is often referred to as Keystroke Dynamics. It is the careful temporal order information that describes precisely once every key was pressed and once it absolutely was discharged as someone is typing using a keyboard. Keystroke Dynamics uses the behavioral aspect of the way and rhythm during which different types of characters are pressed on a keyboard. The rhythms of the keystrokes of a user are measured, and a novel biometric template is developed of the user’s writing pattern for any future purpose of authentication. This paper aims to make a customizable and protractile keystroke dynamics authentication system and a dashboard that is standard and simple to use in conjunction with existing authentication systems. The investigations reveal that using typing pattern for 2 factor authentication has clocked a 50% faster rate of signing in than conventional results. The average response time of an authentication request on our system is ~100–120 ms which is a very good time for any two factor authentication system.
Relation Extraction Between Entities on Textual News Data

Relation extraction is one of the key components in information extraction. The primary step in relation extraction is entity recognition. Identifying the entities in a sentence is a highly complex task and requires multilayered models which use computationally heavy algorithms. The performance of these models was highly dependent on the natural language processing tools. Neural networks are used which use different algorithms and activation functions which are not dependent on these tools. Begin, interior, outside tags are also used as the tagging scheme for the dataset. Each of the word in the line or paragraph is tokenized, and then, each word goes through a named entity recognition process. The tokenized words are tagged in the begin, interior and outside tag format. An attempt to tackle the issue of relation extraction is done using conditional random fields to identify our entities and gated recurrent unit for the purpose of contextual data recognition and finally extract the relations between these entities. Begin, interior, and outside tagging scheme are used to figure out the type of entity, which will help us in classifying the entities better.
The Extent of the Ability of Student-Teachers to Observe Talent Indicators: A Case Study of Pupils of Basic Education Department

This study aimed at investigating the level of the student-teachers' awareness of indicators among Primary School pupils. To achieve this objective, the researcher used the descriptive analytical method. The study sample consisted of (42) fourth-year students in the Faculty of Education, Sudan University of Science and Technology who were being trained in basic schools and were selected using the purposive method. The questionnaire was designed and used to collect data related to talent indicators. The coefficient of correlation Pearson and the alpha Cronbach coefficient and test (t) were used for one group. The results revealed that the total degree of student-teachers' awareness was generally high. There were statistically significant differences between the student-teachers' awareness and pupils' cognitive, social, emotional and creative indicators. In the light of these results, it is recommended that attention should be paid to teacher preparation programs in the faculties of education, where the skills of dealing with talented pupils as well as observation skills of student-teachers should be developed.&#x0D; Keywords: degree of awareness, student-teacher, Basic Education Department, indicators, talent, basic pupils
Usage of Online Electronic Information Resources among PG Students of Alagappa University: A Study

This study investigated the use and user perception of electronic Informationresources of Alagappa University Postgraduate Students. It also examined studentsâ€™awareness of e-resources and satisfaction level; problems are faced by users while using eresources. The sample consists of 70 respondents drawn respectively from a population of 95from the college. Descriptive survey method was used to draw data through the AlagappaUniversity Postgraduate Students Library questionnaire on Electronic Information Resourcesin the study. The data collected were analyzed using simple percentages. It was found that themajority of respondents have low knowledge of e-resources, they may use the universitylibrary it will be getting a number of e-resources. The study concludes that user awareness ofe-resources and a moderate level of satisfaction
Telephone Tapping in Bangladesh: Violation of Constitutional, Human rights

Advanced technology is now within reach of everyone, due to which the number of online crimes is increasing day by day. The protection of personal information is very important because it is used for terrorist activities. Eavesdropping on the phone is a big crime all over the world. Under Bangladeshi law, eavesdropping on someone's phone is a punishable offense. The Constitution of Bangladesh places the exchange and communication of personal information of citizens on the list of fundamental rights and gives the state the responsibility to protect it. There is ambiguity in the law of Bangladesh regarding eavesdropping and phone conversations. In recent time we see a lot of political telephone conversation of the opposite party has been published on the internet in Bangladesh. The ruling party took advantage of the phone call conversation scandal.
Multimodal Feature Fusion for Illumination-Invariant Recognition of Abnormal Human Behaviors

This paper presents fusion of feature maps extracted from visible and thermal infrared surveillance videos to recognize abnormal human behaviors in low illumination conditions. In most cases, abnormal behaviors are often presented in the nighttime or darkness, where using only a visible camera could not capture data well enough to analyze. Our study addresses this problem by using additional data captured by a thermal camera to enhance the information of human appearance and motion. We assume that thermal and visible are both informative but should contribute differently at a certain area of video frames. Based on this, we propose a multimodal feature fusion approach (MFF) to produce a weight matrix to determine how much thermal or visible input should contribute to obtain the best performance. Experiment results demonstrate the effectiveness of MFF to recognize abnormal human behaviors in darkness. Particular, we gain 91.01% accuracy overall with at least 4.53% higher than individual thermal or visible data and 2.17% higher than the similar fusion-based approaches.