Reviewerly/exHarmony huggingface dataset:
Efficient 3D dense residual network and its application in human action recognition

In view of the problem that 3D-CNN can better extract the spatio-temporal features in video, but it requires a high amount of computation and memory, this paper designs an efficient 3D convolutional block to replace the 3×3×3 convolutional layer with a high amount of computation, and then proposes a 3D-efficient dense residual networks (3D-EDRNs) integrating 3D convolutional blocks for human action recognition. The efficient 3D convolutional block is composed of 1×3×3 convolutional layers for obtaining spatial features of video and 3×1×1 convolutional layers for obtaining temporal features of video. Efficient 3D convolutional blocks are combined in multiple locations of dense residual network, which not only takes advantage of the advantages of easy optimization of residual blocks and feature reuse of dense connected network, but also can shorten the training time and improve the efficiency and performance of spatial-temporal feature extraction of the network. In the classical data set UCF101, HMDB51 and the dynamic multi-view complicated 3D database of human activity (DMV action3D), it is verified that the 3D-EDRNs combined with 3D convolutional block can significantly reduce the complexity of the model, effectively improve the classification performance of the network, and have the advantages of less computational resource demand, small number of parameters and short training time.
MuST-Cinema: a Speech-to-Subtitles corpus

Growing needs in localising audiovisual content in multiple languages through subtitles call for the development of automatic solutions for human subtitling. Neural Machine Translation (NMT) can contribute to the automatisation of subtitling, facilitating the work of human subtitlers and reducing turn-around times and related costs. NMT requires high-quality, large, task-specific training data. The existing subtitling corpora, however, are missing both alignments to the source language audio and important information about subtitle breaks. This poses a significant limitation for developing efficient automatic approaches for subtitling, since the length and form of a subtitle directly depends on the duration of the utterance. In this work, we present MuST-Cinema, a multilingual speech translation corpus built from TED subtitles. The corpus is comprised of (audio, transcription, translation) triplets. Subtitle breaks are preserved by inserting special symbols. We show that the corpus can be used to build models that efficiently segment sentences into subtitles and propose a method for annotating existing subtitling corpora with subtitle breaks, conforming to the constraint of length.
Research Guides: EDUC 2110: Critical Issues in Education: Write and Cite

This guide introduces essential research tips and tools for EDUC 2110 with Dr. Thompson (Spring 2020)
Optimization of Project Scheduling Activities in Dynamic CPM and PERT Networks Using Genetic Algorithms

Projects consist of interconnected dimensions such as objective, time, resource and environment. Use of these dimensions in a controlled way and their effective scheduling brings the project success. Project scheduling process includes defining project activities, and estimation of time and resources to be used for the activities. At this point, the project resource-scheduling problems have begun to attract more attention after Program Evaluation and Review Technique (PERT) and Critical Path Method (CPM) are developed one after the other. However, complexity and difficulty of CPM and PERT processes led to the use of these techniques through artificial intelligence methods such as Genetic Algorithm (GA). In this study, an algorithm was proposed and developed, which determines critical path, critical activities and project completion duration by using GA, instead of CPM and PERT techniques used for network analysis within the scope of project management. The purpose of using GA was that these algorithms are an effective method for solution of complex optimization problems. Therefore, correct decisions can be made for implemented project activities by using obtained results. Thus, optimum results were obtained in a shorter time than the CPM and PERT techniques by using the model based on the dynamic algorithm. It is expected that this study will contribute to the performance field (time, speed, low error etc.) of other studies.
The Role of Russian Flagship Universities of Russia in the Formation of the Scientific and Human Potential of the National Centers of Technological Excellence

The increasing role of the flagship universities in regional development is driven by the consistent policy of the state in forming the centers of technological excellence. To become a regional center of excellence, a university must have a set of proven specific competencies in a particular field, both in terms of educational and research activities. This paper summarizes the advanced experience of the flagship universities in Russia in creating the centers of technological excellence on their basis. More than that, the authors identify the key directions for the development of regional flagship universities as the most important structural units, considering them as a factor in the formation of a system of technological excellence centers.
The Need for Establishment of Virtual Libraries in National Open University Study Centres in the South-East, Nigeria

The study is based on the need for the establishment of a virtual library in National Open University study centers in the South â€“ East, Nigeria. All the librarians in the NOUN library in the South- East were selected. The population of the study is 8. The case study questionnaire was used to collect data. The data collected was analyzed using mean (X) statistics to determine the level of acceptance or disagreement of items in questionnaire. The findings includes that there is a need for the establishment of virtual library National Open University of Nigeria. More so it was found that there are advantages of virtual library and it also agrees that there are challenges encountered in the establishment of the Virtual library. Possible solutions were suggested which could be used in tackling these challenges. The Virtual library is an ICT department fit for the present boom in the information world. Even though the traditional library has a say as far information storage is concerned, it cannot be compared with the virtual library which promises higher services to render and a vast community of user to serve at the same time.
Professional Competence of a Teacher As the Basis of Quality Education

The article shows the need to improve the professional competence of a teacher, since competence is the basis of quality education at any level of its receipt. The concept of professionalism of a teacher is multidimensional. However, fi rst of all, the professionalism of a teacher is determined by the fundamental nature of his knowledge and skills related to teaching children, as well as the meta-subject competencies that he has mastered and the ability to optimally organize the process of learning a subject every time in the conditions of information and digital technologies.
STUDENTS’ MOTIVATION AND PROBLEMS IN LEARNING ENGLISH IN EFFECTIVE ENGLISH CONVERSATION COURSE (EECC)

This research is aimed to know students’ motivation and problems in learning English in Effective English Conversation Course (EECC). The design of this research was descriptive qulitative. The sample was 15 students. The research instruments in this research were tests and questionnaire. The results of finding showed that 1) students’ intrinsic motivation in learning was high namely 12 students (80%).  2) students’ extrinsic motivation in learning English was high namely 12 students (80%). 3) students’ problems in learning English were in listening skill (85%), reading skill (56%), and writing skill (57%) as category high. 4) students’ ability in learning English was 66 as category average. It is recommended that the teachers pay attention to the students’ motivation and problems in learning English because it influenced to the students’ ability.
Summary of the EC-Model

This chapter provides a brief summary of all components of the model: usage, conventionalization (usualization, diffusion), entrenchment (routinization), and the forces affecting these three components. It begins by listing the different types of motor, sensory, cognitive, and interpersonal activities involved in usage. Next, it shows how the six dimensions of conformity on the collective level of conventions are related to the four types of association of the cognitive level. Also listed are the three feedback-loop processes—usualization, diffusion, and routinization—and their main effects on conventionalization and entrenchment. The summary ends with a survey of forces acting upon usage, conventionalization, and entrenchment.
An Adaptive Context Modeling Approach Using Genetic Algorithm in IoTs Environments

Internet of Things (loTs) is the future of ubiquitous and personalized intelligent service delivery. It depends on installing intelligent sensors to sense and control physical environment to generate enormous amount of data with various data types. Context aware computing is employed for transforming these sensor data into knowledge through three stages: collection, modeling and reasoning. In context modeling, raw data represents in according meaningful manner statically. Furthermore, with growth of IoTs live applications, static modeling is not convenient because of changing context data structure overtime. The work in this paper is dedicated to propose a new dynamic approach for context modeling based on genetic algorithm and satisfaction factor. In addition, flexibility indicator property and context based are defined to measure the performance of the proposed approach.
Facial Clustering in Video Data Using Deep Convolutional Neural Networks

This paper presents an automatic system that structures information in video surveillance systems based on the analysis of facial images. We describe the cluster analysis in video data using face detection in each video frame and feature extraction with pretrained deep convolutional neural networks. Different aggregation techniques to combine frame features into a single video descriptor are implemented to organize video data based on clustering techniques. An experimental study with the YouTube Faces dataset that demonstrates the most accurate algorithm matches normalized average frame feature vectors and group them with average linkage agglomerative clustering algorithm.
Mathematical programming for data classification: A short survey

Data classification problems have been intensively studied by several groups of researchers including computer scientists, statisticians, engineers, biologists. Within the context of widespread use of databases and the explosive growth in their sizes, “Big Data”, new challenges are introduced in order to permit to several organizations to take benefits and efficiently utilize their data. The main objective of this paper is to review main published works which propose mathematical programming approaches in order to solve data classification problems with Support Vector Machine (SVM).
Implementation of Image Steganography in QR Code Using Mid-Band DCT

Image steganography is taking a new turn in the present-day world. Now day’s strong technique in computer science and security of the digital content. The use of QR code is increasing day by day and this advance version of 2D barcode proves to provide higher content capacity. The cover image in previous works is used as jpeg or BMP images. These types of images assist to handle greater distortion, providing higher PSNR. In this research paper, I have implemented, image steganography on QR code as a cover image. The PSNR has to be maintained so that the message in the QR code is not lost.
Differentially Private ERM Based on Data Perturbation.

In this paper, after observing that different training data instances affect the machine learning model to different extents, we attempt to improve the performance of differentially private empirical risk minimization (DP-ERM) from a new perspective. Specifically, we measure the contributions of various training data instances on the final machine learning model, and select some of them to add random noise. Considering that the key of our method is to measure each data instance separately, we propose a new `Data perturbation' based (DB) paradigm for DP-ERM: adding random noise to the original training data and achieving ($\epsilon,\delta$)-differential privacy on the final machine learning model, along with the preservation on the original data. By introducing the Influence Function (IF), we quantitatively measure the impact of the training data on the final model. Theoretical and experimental results show that our proposed DBDP-ERM paradigm enhances the model performance significantly.
Big Data and Machine Learning for Evaluating Machine Translation

Human Evaluation of Machine Translation is the most important aspect of improving accuracy of translation output which can be used for text categorization ahead. In this article we describe approach of text classification based on parallel corpora and natural language processing techniques. A text classifier is built on multilingual texts by translating different features of the model using the Expectation Maximization Algorithm. Cross-lingual text classification is the process of classifying text into different languages during translation by using training data. The main idea underlying this mechanism is using training data from parallel corpus and applying classification algorithms for reducing the distortion and alignment errors in Machine translation. In this chapter a Classification Model is trained which directs source language to target language on the basis of translation knowledge and parameters defined. The Algorithm adopted here is Expectation Maximization Algorithm which removes ambiguity in parallel corpora by aligning source sentence to target sentence. It considers possible translations from source to target language and selects the one that fits the model on the basis of BLEU (bilingual evaluation understudy) score. The only requirement of this learning is unlabelled data in the target language. The algorithm can be evaluated accurately by running a separate classifier on different parallel corpora. We use Monolingual Corpora and Machine Translation in our study to see the effect of both the models on our parallel corpora.
AI-Enabled Digital Forensic Evidence Examination

Digital forensics is crucial for the prosecution of offenders in cyberspace, including nation-state actors and non-nation-state actors. The evidence discovered, verified, and associated during the evidence examination phase serves as the basis for digital forensic analysis and eventually the basis for the verdict of a judge and a jury. However, the current digital forensic evidence examination procedure usually takes a relatively long time, demands a great amount of resources, and requires great efforts from human experts. The biggest challenges in this procedure are accuracy and speed. Nevertheless, in some cases, delay is not allowed, as it may have significant impact upon a critical mission, especially a time-sensitive one. To address this challenge, this paper recommends an AI-based digital forensic evidence examination architecture that is empowered by contextual binding, machine learning, and human-machine teaming. In this approach, human experts are teamed up with artificial intelligence (AI) systems in conducting evidence examination. This approach certainly improves the efficiency and effectiveness of an investigation, thus successfully supporting missions.
ScopeFlow: Dynamic Scene Scoping for Optical Flow

We propose to modify the common training protocols of optical flow, leading to sizable accuracy improvements without adding to the computational complexity of the training process. The improvement is based on observing the bias in sampling challenging data that exists in the current training protocol, and improving the sampling process. In addition, we find that both regularization and augmentation should decrease during the training protocol. Using an existing low parameters architecture, the method is ranked first on the MPI Sintel benchmark among all other methods, improving the best two frames method accuracy by more than 10%. The method also surpasses all similar architecture variants by more than 12% and 19.7% on the KITTI benchmarks, achieving the lowest Average End-Point Error on KITTI2012 among two-frame methods, without using extra datasets.
Regularization Learning for Image Recognition

In order to reduce overfitting for the image recognition application, this paper proposes a novel regularization learning algorithm for deep learning. Above all, we propose a novel probabilistic representation for explaining the architecture of Deep Neural Networks (DNNs), which demonstrates that the hidden layers close to the input formulate prior distributions, thus DNNs have an explicit regularization, namely the prior distributions. Furthermore, we show that the backpropagation learning algorithm is the reason for overfitting because it cannot guarantee precisely learning the prior distribution. Based on the proposed theoretical explanation for deep learning, we propose a novel regularization learning algorithm for DNNs. In contrast to most existing regularization methods reducing overfitting by decreasing the training complexity of DNNs, the proposed method reduces overfitting through training the corresponding prior distribution in a more efficient way, thereby deriving a more powerful regularization. Simulations demonstrate the proposed probabilistic representation on a synthetic dataset and validate the proposed regularization on the CIFAR-10 dataset.
Secure Multiparty Device Pairing Using Random Peephole Generations

A secure device pairing mechanism is used to establish a trusted communication channel between unassociated wireless devices. The broadcast nature of wireless communication opens the door for man-in-the-middle (MITM) attacks, and even other subtle forms of masquerader and misfeasor attacks. This paper introduces a simple device pairing approach to tackle such attacks seamlessly. The algorithm is compatible with a multitude of devices, whereas a majority of existing algorithms are based on two devices exclusively. This approach utilizes a human visual channel as an Out-Of-Band (OOB) channel to authenticate the public keys exchanged between the devices. The interactive nature of this approach forces user attention, hence improving the reliability and consistency of the device pairing process. To do so, we introduce the concept of ‘peepholes,’ and mathematically define it before demonstrating the algorithm’s methodology. Subsequent sections also demonstrate its robustness against attacks via misfeasors, masqueraders, and men-in-the-middle.
Examining the Moderating influence of Job Complexity on the Relationships between Empowering Leadership and Organizational Innovation

Organizations consider innovation to be a critical variable between life and death of the organization. Moreover, the goals of ambitious organizations can be achieved through innovation. In the 21st century, this is one of the main resources needed to achieve sustainability and economic growth. The main objective of the current study is to examine the impact of empowering leadership on organizational innovation, besides the examination of the moderating effect of the job complexity between empowering leadership and organizational innovation. The questionnaire was utilized in this study to collect data from the population of police sector in the United Arab Emirates (UAE) by using probability random sampling. Structural Equation Modeling-Variance Based (SEM-VB) was utilized to examine the research model in this research, by using the SmartPLS 3.0 software. The result from the analysis shed lights on the impact of empowering leadership on organizational innovation. The proposed research model explained 50.7% of the organizational innovation (OI). Empowering leadership had a positive direct effect on the OI within the police sector in the UAE. Moreover, job complexity moderates (dampens) the relation between empowering leadership and the innovation of organization. The results of the current study have the potential to give further insights into the innovation of organizations strategies.
FAKTOR-FAKTOR YANG MEMPENGARUHI PENINGKATAN HASIL BELAJAR MAHASISWA

The purpose of this study is to determine the impact of the learning environment that can affect the learning outcomes of students of the Informatics and Computer Engineering Education Study Program at the Bung Hatta University. The correlational descriptive method was chosen as a research method to find out how much the contribution of the learning environment, learning motivation and infrastructure to learning outcomes. The population of this study was 28 people with proportional random sampling technique (Proposional Random Sampling) to obtain a sample of 22 people. The instrument used in data collection was done by distributing questionnaires to students who included research samples. The questionnaire distributed consisted of 25 question items having the values specified in the Likert scale model. The results of this study see that there is a contribution between the learning environment by 40.8%, 43.2% learning motivation, and 54.4% infrastructure, towards student learning outcomes in the Elementary Electronics course in the Informatics and Computer Engineering Education Study Program Faculty of Teacher Training and Bung Hatta University Education. The data collection in this study will use a Likert scale questionnaire distributed to students who take the Basic Electronics course and the data calculation results are processed using the SPSS application.
Basic Geophysics - Birth of Seismics

What does a four-tonne sphere have to do with oil?Development of portable seismometers, establishment of reflection and refraction seismics in the search for crude oil, presentation of Ludger Mintrop and John Clarence Karcher.
Global Density Clustering

This paper presents Global Density Clustering, (GDC), an algorithm that has several major advantages over the most popular existing clustering algorithms: (1) No parameters are chosen at the outset of the function; rather, the user can control the desired resolution as clustering proceeds. (2) GDC is efficient enough to work on a large dataset even when there are a sizable number of features. It is O(MN log N) where M is the number of features, i.e. the dimension, and N is the number of data points, i.e. the dataset size. It is suitable for big data. (3) GDC has the advantage of the powerful and intuitive definition of clusters as: points within a cluster are closer than distance dist to their nearest neighbor in the cluster (dist is not picked at the outset but rather that is chosen as the algorithm is progressing) and all points outside the cluster are further than dist from any point in the cluster. (4) GDC supports variable density without the plethora of special data structures such as HDBSCAN needs. (5) Other advantages are described. An essential reason that GDC has these advantages is it searches for and considers points whose nearest neighbor are furthest apart before searching for those that are closer together. It is a top-down or “global” consideration of distances that other density algorithms do from a bottom-up or “local” view. Other novel approaches to the main problems of clustering such as noisy backgrounds are described.