Reviewerly/exHarmony huggingface dataset:
PENINGKATAN KEDISIPLINAN SISWA SMK NEGERI 2 SOE MELALUI PENGEMBANGAN PRESENSI ONLINE TERHUBUNG SMS GATEWAY DAN WHATSAPP

Online Attendance list connected to whatsapp and SMS gateway will make teachers and parents to be easier in controlling student attendance quickly and accurately. This kind of attendance list is applied at SMK Negeri 2 Soe. This system aims to provide information related to the students attendance of SMK Negeri 2 Soe every day until the recap every month. The method used is the Rapid Application Development (RAD) method. Rapid Application Development (RAD) is a software development method with an object-oriented approach to system development. There are 3 stages in this community partnership program, namely the planning stage for the data retrieval related to the development of the system, the stage of system design including fingerprint recording and online attendance list program trials as well as the implementation stage where students use this fingerprint press on a shift according to before and after school hour.. The results obtained shows that students, teachers and parents can easily and quickly increase the students’attendance of SMK Negeri 2 Soe. The process of controlling student attendance is easier and it has an impact on the discipline of SMK Negeri 2 students which is increasing.
Solving a fixed number of equations over finite groups

Abstract We investigate the complexity of solving systems of polynomial equations over finite groups. In 1999 Goldmann and Russell showed $$\mathrm {NP}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>NP</mml:mi> </mml:math> -completeness of this problem for non-Abelian groups. We show that the problem can become tractable for some non-Abelian groups if we fix the number of equations. Recently, Földvári and Horváth showed that a single equation over groups which are semidirect products of a p -group with an Abelian group can be solved in polynomial time. We generalize this result and show that the same is true for systems with a fixed number of equations. This shows that for all groups for which the complexity of solving one equation has been proved to be in $$\mathrm {P}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>P</mml:mi> </mml:math> so far, solving a fixed number of equations is also in $$\mathrm {P}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>P</mml:mi> </mml:math> . Using the collecting procedure presented by Horváth and Szabó in 2006, we furthermore present a faster algorithm to solve systems of equations over groups of order pq .
Action Detection Based on 3D Convolution Neural Network with Channel Attention Mechanism

In this paper, we propose an action detection model, a simple yet effective combination of channel attention mechanism with 3D convolution neural network, by which to enhance the performance of feature extraction in the video. Channel attention module uses the channel information in the feature extraction process of our 3D convolutional network to efficaciously pick out the features that are essential for the task and suppress useless ones. The proposed model can effectively promote the spatiotemporal feature representation power in the video. We embed our feature extraction model into the R-C3D framework to test the performance of our method by conducting comparative experiments on the THUMOS'14 dataset. Experimental results indicate that the proposed method can authentically enhance the accuracy of action detection.
Distribution-Dependent Weighted Union Bound

In this paper, we deal with the classical Statistical Learning Theory’s problem of bounding, with high probability, the true risk R(h) of a hypothesis h chosen from a set H of m hypotheses. The Union Bound (UB) allows one to state that PLR^(h),δqh≤R(h)≤UR^(h),δph≥1−δ where R^(h) is the empirical errors, if it is possible to prove that P{R(h)≥L(R^(h),δ)}≥1−δ and P{R(h)≤U(R^(h),δ)}≥1−δ, when h, qh, and ph are chosen before seeing the data such that qh,ph∈[0,1] and ∑h∈H(qh+ph)=1. If no a priori information is available qh and ph are set to 12m, namely equally distributed. This approach gives poor results since, as a matter of fact, a learning procedure targets just particular hypotheses, namely hypotheses with small empirical error, disregarding the others. In this work we set the qh and ph in a distribution-dependent way increasing the probability of being chosen to function with small true risk. We will call this proposal Distribution-Dependent Weighted UB (DDWUB) and we will retrieve the sufficient conditions on the choice of qh and ph that state that DDWUB outperforms or, in the worst case, degenerates into UB. Furthermore, theoretical and numerical results will show the applicability, the validity, and the potentiality of DDWUB.
Deep Learning in Social Media Analysis

Social media analytics keep on collecting the information from different media platforms and then calculating the statistical data. Twitter is one of the social network services which has ample amount of data where many users used post significant amounts of data on a regular basis. Handling such a large amount of data using traditional tools and technologies is very complicated. One of the solutions to this problem is the use of machine learning and deep learning approaches. In this chapter, the authors present a case study showing the use of Twitter data for predicting the election result of the political parties.
Explaining Deep Learning-Based Classification of Textual Tweets

Social media platforms like Twitter, Facebook, Instagram allow users to share content, express their views, and generate a massive volume of sentiment-rich data. The process of sentiment analysis (SA) classifies a sentiment, opinion, blog, or update into three categories, namely positive, negative, and neutral. Nowadays, deep learning-based classification for such unstructured datasets has become popular due to its high performance. Hence, the proposed model builds a deep learning-based classifier to categorize the sentiments. Nevertheless, the classifier proves to be accurate; the question of why a particular statement is predicted as positive or negative sentiment remains unanswered. In the paper, we propose an explainable sentiment analysis model to explain the outcome of a classifier’s instance using a model-agnostic local interpretable model. For experimental purposes, two instances classified as positive and negative sentiments, respectively, have been explained successfully by local interpretable model-agnostic explanations (LIME) explainer along with their analysis.
ICT Based Training Path of Sports Humanistic Spirit under Internet Technology

modern education is based on the scientific outlook on development, taking human development as the core of the work and adhering to the concept of people-oriented education. With the continuous improvement of Internet technology, the advantages of the "Internet + education" model have emerged. Through the Internet technology and the integration of sports teaching work, will not only contribute to the solution of the problems in present sports teaching work, also can stimulate students' learning enthusiasm, real progress on training students' humanistic spirit. In fact, by campus teaching organic integration with the Internet technology, to promote the students' cultural quality, moral level and humanistic spirit, it has become a focus of attention in current education work. Under the current situation, the "Internet + education" model is also shoulding the burden of cultivating its humanistic spirit on the basis of improving students' physique and entertainment.
Real-time estimation of distance between people and/or objects in video surveillance

Social distancing is not just about keeping away from loved ones, shutting schools or working from home. A strict physical separation must be maintained in order to prevent the spread of COVID-19. Yet, front-line workers in healthcare, public safety, retail, manufacturing and transportation and logistics have found it particularly challenging to maintain the 1,5 to 2 meters separation from other people while doing their everyday jobs. In this paper we have presented a simple real-time method for distance estimation with any kind of camera between people applicable either in close quarters or open spaces. The aim is to help easily and non obtrusively alert if the correct distance between people is not maintained. The initial results are promising and the error between the real and the measured values is in statistical margin. At the same time, the proposed system monitors people without facial recognition. The data collected is anonymous and do not require the use of facial recognition or any other personal information.
Real-Time Multiple Object Tracking with Discriminative Features

Tracking-by-detection methods track multiple objects by detecting the objects of interest in each frame and associating the detected objects with the tracks. By allowing object detection and appearance embedding to be learned in a shared network, recent tracking-by-detection methods can implement the tracking task in real time with the power of deep neural networks. However, they just focus on the detection stage and do not take advantage of the embedding features well in the association stage. In this paper, we exploit the discriminative embedding features in the association stage to improve the tracking performance. By combing the embedding features with the bounding boxes to associate the detected objects with the tracks, the number of identity switches during tracking can be reduced. Further, after associating the detected objects with the tracks, the embedding feature of each track is not only updated according to the associated object, but also learned to distinguish the similar detected objects. The experiments show that our method can achieve competitive tracking performance in real time compared to the state-of-the-art tracking methods.
A novel method for 3D reconstruction of blood vessels

Abstract With the development of computer technology, computer-aided diagnosis has been greatly improved. Computer-aided diagnosis can help doctors diagnose with medical imaging and other technique. Cardiovascular disease(CVD) lead to death with high probability. The computer-aided toward blood vessel is necessary. In this paper, we propose a novel method for 3D reconstructing of blood vessels. We calculate the volume data of blood vessels using an implicit modeling method based on the scanned blood vessel images, and establish a 3D model for blood vessels. Compared with existing methods, our method can obtain the surface and internal information of blood vessels accurately based on the implicit function method. The experimental results reflect that our method can implement 3D reconstruction of blood vessels with high accuracy and smoothness.
D2NE: Deep Dynamic Network Embedding

In this paper, we propose a new approach named D2NE, short for Deep Dynamic Network Embedding, to learn the vertex representations for dynamic networks. The algorithm utilizes the graph attention mechanism to refresh embeddings efficiently, in which each update associate with local information only. To address the missing data, which is a common phenomenon in real-world networks, we model the auxiliary side information to capture more information on vertex relations. D2NE is highly efficient to refresh embeddings for dynamic networks, even with standard real-world sparse networks. We conduct extensive experiments on several real-world dynamic networks to validate the performance of D2NE in the link prediction task. Both quantitative results and qualitative analysis verify the effectiveness and rationality of our D2NE method.
Extracting Relevant Social Geo-Tagged Photos for Points of Interest

Geo-tagged photos of Point of Interest (POI) have played a vital role in many location-based applications. It is of importance to extract relevant geo-tagged photos to build an adequate collection of geo-tagged photos for the POI. This paper proposes a novel approach to extract relevant photos for POI from social media without using visual analysis. In our approach, we apply survival analysis to estimate the probability of existing relevant geo-tagged photos which has the POI name in title or tags or description, based on a radius distance from the POI location. Then, we use distinctive tags between the POI and its neighbor POIs to extract other relevant geo-tagged photos within a specific distance inside of which the existent probability of relevant geo-tagged photos is considerable. We present the effectiveness of our method on a case study with two POIs using publicly available data from Flickr.
A Study on the Spatiotemporal Distribution of POI Tourists

POI is a kind of geographical location information that can reflect the individual interest points of consumers. By sampling the POI photo data in Shanghai within 24 hours and combining with latitude and longitude information, this study analyzes the distribution of tourists in different time periods, and summarizes the behavior of tourists under the line.
A Forensics System for Live-Audio Watermarking

A common problem in audio forensics is the difficulty to authenticate an audio recording. You can have an argument against any audio probe. In this paper we propose a system that can take audio probes which can be used with success in a court. The recording will be bulletproof due to the use of a reference protocol based on an external entity, the company that provides the recording system. Using an external cloud-based application, for the recorded person and for the recording institution, the resulted audio-recording will be 100% authenticated in terms of integrity check. All editing by means of deleting/inserting audio fragment longer than 5 ms will be detected.
IOT - Overview, Implementation and Upcoming Challenges

IoT is a chain of Physical Objects that are installed with sensors, software, and other technologies for the goal of connecting and transferring data with other devices and systems over the Internet. In many research, it has been found that the data and communication between the Internet of Things are insecure. Also, the data which is transferred from one end to the other end is accessible for a shorts distance. A discovered protocol for Data Integrity in IoT communication. A protocol for providing an environmental monitoring solution and a system that facilitates the practical experimentation of the IoT solutions. After the data is secured with the protocols and it has received an environment for the experiments, the message has to be sent over a long distance.
Innovation and Creativity in Language Studies

This paper is based on “Innovation and Creativity in Language Studies”. As we all know language is very important for all of us including nature to human being. So this paper will help us to know how we can make the language studies creative which will be more attractive to people to know about language studies. As we all know language studies is offered by the Linguistics department. Linguistics is scientific study of language. It involves language analysis like language form, language meaning and etc. So this paper will tell you how the creativity and innovative idea will work in language studies. If we want to make language studies a catchy object then it must be attractive to all. As we know a boring paper never attract people so we have to make it more n more attractive by innovative and creative ideas. Practical work is more effective than a theoretical work so if we make the language studies classes practically then it will be more acceptable to all. We also can use mobile because nowadays each n everyone has smartphone in their hand. Here mainly we will discuss about the English Language. We will talk how to make the classroom or teaching skills more funny and interesting.
Marketing of Library Collections and Services in the Twenty-first Century Environment: The Use of Social Media Technologies

Marketing is an important component of libraries and other information centres. In the past, few approaches where used for marketing library collections and services. However, the use of social media tools in marketing library collection and services have over the past many years promise to be an important channel for marketing library collections and services. The purpose of this chapter therefore is to dig answers on the challenges and opportunities of using social media technologies in marketing library services and collections in 21st century environment. The chapter start with a snapshot overview of the importance of marketing library services and collection, highlight social media tools that can be used for marketing library collection and services. In addition, the chapter examines and presents the challenges facing libraries and other information centres in marketing the collection and services in 21st century environment. The methodology that will be used for this chapter is literature review comprising of journal articles, books, conference papers and newspapers articles.
Fast and simple quantum state estimation

We present an iterative method to solve the multipartite quantum state estimation problem. We demonstrate convergence for any informationally complete set of generalized quantum measurements in every finite dimension. Our method exhibits fast convergence in high dimension and strong robustness under the presence of realistic errors both in state preparation and measurement stages. In particular, for mutually unbiased bases and tensor product of generalized Pauli observables it converges in a single iteration. We show outperformance of our algorithm with respect to the state-of-the-art of maximum likelihood estimation methods both in runtime and fidelity of the reconstructed states.
A Deep Marginal-Contrastive Defense against Adversarial Attacks on 1D Models

Deep learning algorithms have been recently targeted by attackers due to their vulnerability. Several research studies have been conducted to address this issue and build more robust deep learning models. Non-continuous deep models are still not robust against adversarial, where most of the recent studies have focused on developing attack techniques to evade the learning process of the models. One of the main reasons behind the vulnerability of such models is that a learning classifier is unable to slightly predict perturbed samples. To address this issue, we propose a novel objective/loss function, the so-called marginal contrastive, which enforces the features to lie under a specified margin to facilitate their prediction using deep convolutional networks (i.e., Char-CNN). Extensive experiments have been conducted on continuous cases (e.g., UNSW_NB15 dataset) and discrete ones (i.e, eight-large-scale datasets [32]) to prove the effectiveness of the proposed method. The results revealed that the regularization of the learning process based on the proposed loss function can improve the performance of Char-CNN.
Comparative Analysis of Machine Learning Algorithms for Hybrid Sources of Textual Data: In Development of Domain Adaptable Sentiment Analysis Model

Sentiment classification is the task of categorizing the text into different opinionated categories positive, negative and neutral depending upon the user’s post on social media in a particular domain. In social media, Twitter is the most popular platform for classification but with the limitations of number of words to be posted by individual which produces the inaccurate classification of dataset. Hence, in this paper, we are trying to increase the performance of the sentiment classification model by collecting the textual data on the same domain from different sources. To validate the same, different state-of-the-art machine learning classification algorithms have been applied and analyzed that shows the better results for hybrid data in comparison with the single-source Twitter (microblog) data and also articulated the most suitable algorithms for the hybrid textual sources of data.
Deep Convolutional Network for Steganalysis of HUGO, WOW, and UNIWARD algorithms

This paper presents a novel convolutional neural network (CNN) architecture for image steganalysis. We trained the CNN to detect distortions introduced by the state-of-the-art embedding algorithms: WOW, HUGO, and UNIWARD. We used a large database of test images and a large number of stegomessages, which resulted in a six billion-sample training set. The experimental results show that the resultant CNN, which was trained on such a large dataset, outperforms state-of-the-art steganalysis algorithms in terms of total error rate.
Segmentation of cancer cell in histopathologic images of breast cancer and lesion area in skin cancer images using convolutional neural networks

Background: Pathological analysis plays an important role in the diagnosis, prediction and planning of cancer treatment. Using digital pathology, ie, scanning and storing digital parts of patient tissue, tools for analyzing these complex images now can be developed. Doctors use a computer diagnostic system from an intelligent assistant to accurately diagnose. These systems have great benefits in improving treatment efficacy. Methods: In this study, the deep neural network classifier has been used with the help of the Tensor Flow framework and the use of the Keras-library. Input images are initially transmitted from a low pass filter to reduce noise effects. The pre-processed images are then imported into a convolutional neural network. Results: The results of the research reveal a significant difference in the accuracy values between different methods with the proposed method, which in some cases indicates an increase of more than 14.18% in the accuracy of the diagnosis. Another advantage of the proposed method is to provide high sensitivity to histopathologic images, which shows an increase of 12 to 18 percent compared to other studies. The reason for this is the excellence of extracting high-level features through convolutional neural network, which is accompanied by a reduction in the size of the feature vector. Conclusion: The results showed a accuracy of %98.6 for skin lesions and %96.1 accuracy for breast cancer histopathologic findings, which offers promising results compared to the results of other studies.
Cognitive Processing of Miscommunication in Interactive Listening: An Evaluation of Listener Indecision and Cognitive Effort

Purpose The purpose of the current study was to evaluate the social and cognitive underpinnings of miscommunication during an interactive listening task. Method An eye and computer mouse–tracking visual-world paradigm was used to investigate how a listener's cognitive effort (local and global) and decision-making processes were affected by a speaker's use of ambiguity that led to a miscommunication. Results Experiments 1 and 2 found that an environmental cue that made a miscommunication more or less salient impacted listener language processing effort (eye-tracking). Experiment 2 also indicated that listeners may develop different processing heuristics dependent upon the speaker's use of ambiguity that led to a miscommunication, exerting a significant impact on cognition and decision making. We also found that perspective-taking effort and decision-making complexity metrics (computer mouse tracking) predict language processing effort, indicating that instances of miscommunication produced cognitive consequences of indecision, thinking, and cognitive pull. Conclusion Together, these results indicate that listeners behave both reciprocally and adaptively when miscommunications occur, but the way they respond is largely dependent upon the type of ambiguity and how often it is produced by the speaker.