Reviewerly/exHarmony huggingface dataset:
Delta Activation Layer exploits temporal sparsity for efficient embedded video processing

Activation sparsity can improve compute efficiency and resource utilization in sparsity-aware neural network accelerators. While spatial sparsification of activations is a popular topic in DNN literature, introducing and exploiting spatio-temporal sparsity is a topic much less explored in DNN literature. However, it is in perfect resonance with the trend in DNNs, to shift from static-data signal processing (e.g., image processing) to stream-data real-time signal processing (e.g., video and audio) in embedded edge devices. Towards the goal of exploiting temporal sparsity, in this paper, we introduce a new DNN layer (called Delta Activation Layer), whose sole purpose is to promote both spatial as well as temporal sparsity of activations during training with time-distributed data. One may use the Delta Activation Layer either during vanilla training or during a refinement phase. We have implemented the Delta Activation Layer as an extension of the standard TensorFlow-Keras (2.0) library and applied it on training deep neural networks with several datasets including the Human Action Recognition (UCF101) dataset. We report an almost 3x improvement of activation sparsity, with recoverable loss of model accuracy after prolonged training. All source code for this project is available upon request for academic purposes.
Design and Implementation of Web-based Smart Class Routine Management System for Educational Institutes

Smart Class Routine Management System (SCRMS) is a web application that able to generate an error-free and effective class schedule automatically.At present, most of the academic institutes like schools, colleges, and universities follow the traditional manual process to create class routines.Thus, there is a high probability of making errors in overlapping timetable period or slot and definitely a valuable amount of time is wasted as there are many things to be considered.SCRMS comes with the intension to provide better and dynamic timetable management service and to reduce the workload of the routine management team.It will assist in the automatic management of all periods.The user or the routine management team simply needs to create a schedule for a batch, and the system will automatically configure all connected timetables (Teachers, Class Rooms, and Labs).SCRMS also contains an embedded database which stores the records of every batch, classroom, lab and teacher with their respective tables.In this system, class distribution will be more reliable and accurate.This web application is secured and really easy to use.In this application, the teacher's routine will be generated automatically and the student's class routine will be provided by an admin.In this paper, the SCRMS is designed and developed as a web application by PHP language with MySQL database.The programming languages and tools used to develop this system are PHP, Laravel, Bootstrap, MySQL, JavaScript, HTML, CSS, Ajax, and JQuery which are very efficient in making such dynamic web-based system.Since, the developed system is flexible, scalable, and modular, does not just meet the needs of a specific department/ institute but can be adapted and used by any other departments/ organizations.
One-Shot Phase Retrieval Method for Interferometry Using a Multi-Stage Phase-Shifting Network

In this letter, we proposed a Multi-Stage phase-shifting network (MSPS-Net) based on Multi-Stage Progressive Image Restoration for one-shot phase retrieval interferometry. This network generates three interferograms with specific phase shifts from interferograms recorded in a single frame excellently, metrics such as PSNR and SSIM indicate that the generated phase shift interferograms have high similarity to the real one. Further, By analyzing different types of aberrations, RMS value of error map retrieved by the proposed method is less than, which shows higher accuracy than latest phase-shifting network method built by Encoder and Decoder. All the experiment results show that our solution is available for the problem of high-accuracy dynamic phase measurement and has excellent generalization ability.
STUDENTS’ ENTHUSIASM IN LEARNING IN THE COVID-19 OUTBREAK

The purpose of this study is to determine student excitement for studying English during the Covid-19 outbreak, as well as the ways in which teachers encourage student enthusiasm for learning English throughout the outbreak. A qualitative research design was adopted in this study. The subjects of this study are students of class XI in MAN 1 Model Bengkulu class XI A, totaling 30 students. The study's findings revealed that pupils in class XI MAN 1 Model Bengkulu are enthusiastic about learning English. Based on observations and interviews with students from MAN 1 Model Bengkulu class XI. As indicated by the reduction in the English value of class XI MAN 1 Model Bengkulu pupils during the Covid-19 outbreak, the findings show that most students have a low level of excitement when studying. The observation checklist and interview used to observe the activities of students and teachers in the English class. To analyzing the data, the researcher was apply (1) data reduction; (2) data display; and (3) drawing and verifying conclusionTeachers use various strategies so that the enthusiasm of students in learning English during the Covid-19 outbreak continues to increase, namely by using interesting learning videos and giving quizzes to students. Keywords: Student Enthusiasm, Learning English, Covid-19 Outbreak.
SV - VLSP 2021: Combine Attentive Statistical Pooling-based Xvector and Pretrained ECAPA-TDNN for Vietnamese Text-Independent Speaker Verification

Recently, Xvectors and ECAPA-TDNN have been considered state-of-the-art models in designing speaker verification systems. This paper proposes a novel approach that combines Attentive statistic pooling-based Xvector and pre-trained ECAPA-TDNN for Vietnamese speaker verification. Experiments are conducted on various recent Vietnamese speech datasets. The results portrayed that our proposed combination outperformed all constitutive models with 4% to 37% relative EER improvement and ranked second place in Task 2 of the 2021 VLSP Speaker Verification competition.
Image Recognition and Analysis: A Complex Network-Based Approach

In existing image recognition algorithms, the position and sequence of image pixels are key factors that affect the accuracy of image recognition. Therefore, the topological invariance of complex networks has led to the recognition that applying complex networks to image recognition analysis will significantly reduce the impact of images on classification recognition accuracy when rotation, translation, and scaling occur. However, most studies on image classification by complex networks have focused on a single network, lacking dynamic evolution with the networks among them. In this paper, we propose a new complex network classification method that combines complex networks and convolutional neural networks(CNN) to train classification using deep learning. We show that the method has high classification accuracy and distinct network features and compares well with a single complex network approach. In addition, to make the distribution of the degree histogram of the image more uniform and concentrated, the original formula for calculating the power value was optimized to reduce the influence of the radius parameter on the power value.
VR facial expression tracking via action unit intensity regression model

Virtual Reality (VR) Head-Mounted Displays (HMDs), also known as VR headsets, are powerful devices that provide interaction between people and the virtual 3D world generated by a computer.For an immersive VR experience, the realistic facial animation of the participant is crucial.However, facial expression tracking has been one of the major challenges of facial animation.Existing face tracking methods often rely on a statistical model of the entire face, which is not feasible as occlusions arising from HMDs are inevitable.In this paper, we provide an overview of the current state of VR facial expression tracking and discuss bottlenecks for VR expression re-targeting.We introduce a baseline method for expression tracking from single view, partially occluded facial infrared (IR) images, which are captured by the HP reverb G2 VR headset camera.The experiment shows good visual prediction results for mouth region expressions from a single person.
A proposed approach for the key generation in cryptography to enrich the data confidentiality while sharing data over the network

Data sharing over network has been threat in the digital world. To provide the security to the data being passed over the network various system has been defined. Most widely used systems are cryptography and steganography. Cryptography is the art of converting the data to some another format which will not be understandable by the intruders. The most important phase while sharing data over network using cryptography is the key generation and key distribution. General cryptography techniques either uses public key or uses public and private key both. The major focus of the propose method is not to commute keys with the data being shared over network. Proposed approach defines a method which will get feedback from the receiver and system will identify whether the receiver is undeniable or not. If the receiver is undeniable then only the data will be decrypted to the original to the original format. In this paper we have surveyed many traditional algorithms related to symmetric key and asymmetric key cryptography. Many mathematical operations are being applied to generate the complex keys to ensure security. This approach defines the complexity with almost no mathematical complexity and using no traditional approach for the key generation which are widely known.
Discovering Transferable Forensic Features for CNN-Generated Images Detection

Visual counterfeits (We refer to CNN-generated images as counterfeits throughout this paper.) are increasingly causing an existential conundrum in mainstream media with rapid evolution in neural image synthesis methods. Though detection of such counterfeits has been a taxing problem in the image forensics community, a recent class of forensic detectors – universal detectors – are able to surprisingly spot counterfeit images regardless of generator architectures, loss functions, training datasets, and resolutions [61]. This intriguing property suggests the possible existence of transferable forensic features (T-FF) in universal detectors. In this work, we conduct the first analytical study to discover and understand T-FF in universal detectors. Our contributions are 2-fold: 1) We propose a novel forensic feature relevance statistic (FF-RS) to quantify and discover T-FF in universal detectors and, 2) Our qualitative and quantitative investigations uncover an unexpected finding: color is a critical T-FF in universal detectors. Code and models are available at https://keshik6.github.io/transferable-forensic-features/ .
A Study on Human Transiting Based on Big Data and Web Semantics

In the progress of globalization, the transnational human traffic is spreading globally. It damages national economy and social order as well as infringes on the basic human rights of the victims, which has aroused general concern all over the world, becoming global issues. One of the important features in human being traffic is the factor of globalization. A destination-source model works as a deterrent which is applied in the identification of smuggling and trafficking of illegal immigrants. The related results show that the employer penalty and market wage will influence the amount of smuggling and trafficking immigrants. Tax offered by legal unskilled workers at destination countries provides financial support for the inland monitoring of illegal immigrants. The improved SVM (supported vector machine) is proposed to study online textual data used for advertisement classification, with the purpose of discerning underlying human trafficking patterns on the network and recognizing suspicious advertisements, a concern of law-enforcement agencies.
Research on the Intelligent Obstacle Avoidance and Path Planning Strategy of UAV based on Multi-Sensor Fusion

To overcome the defects of inaccurate obstacle positioning caused by a single sensor, we propose the autonomous recognition of obstacles based on multiple sensors, regenerate the intelligent perception and obstacle avoidance strategy of safe flight path, realize timely, efficient and emergency obstacle avoidance and improve the stability and safety of UAV.
Out of the Shadows: Analyzing Anonymous’ Twitter Resurgence during the 2020 Black Lives Matter Protests

Until recently, there had been little notable activity from the once prominent hacktivist group, Anonymous. The group, responsible for activist-based cyber attacks on major businesses and governments, appeared to have fragmented after key members were arrested in 2013. In response to the major Black Lives Matter (BLM) protests that occurred after the murder of George Floyd, however, reports indicated that the group was back. To examine this apparent resurgence, we conduct a large-scale study of Anonymous affiliates on Twitter. We first use machine learning to identify a significant network of more than 33,000 Anonymous accounts and use topic modelling of tweets collected from these accounts to find evidence of sustained interest in topics related to BLM. We then use sentiment analysis on tweets focused on these topics, finding evidence of a united approach amongst the group, with positive tweets typically being used to express support towards BLM and negative tweets typically being used to criticize police actions. Finally, we examine the presence of automation in the network, identifying indications of bot-like behavior across the majority of Anonymous accounts. These findings show that whilst the group has seen a resurgence during the protests, bot activity may be responsible for exaggerating the extent of this resurgence.
Learning Folklore Using English Language Teaching (ELT) Materials for Students

Folklore is a medium in learning English because it has a context of stories that are close to language learners to make it easier to understand. This study aims to determine and correct errors in learning English based on folklore. The method used in this research is English Language learning using a descriptive and evaluation method. researchers use descriptive and evaluative methods. In the use of descriptive methods, the research process begins with drafting basic assumptions and thought rules to be used in research. In the evaluative method, the process of using this method is to evaluate the teaching materials in the use of folklore. The results obtained from this study are knowing weaknesses and errors in learning, especially writing, reading, and speaking to 20 students. Researchers also provide guidance to correct errors in writing, reading and speaking. Keywords: English Language Teaching (ELT), Folklore, Research and Development.
AN EFFICIENT AND SECURED GRAPHICAL AUTHENTICATION SYSTEM

Authentication through the use of passwords usually allow users to access resources in a computer or automated installations. However, remembering such passwords is often a problem for most users due to complex nature of most stong password. As a viable alternative, graphical passwords were introduced to complement the security problems in this method of authentication. Therefore, this papr proposes the concept of a graphical password authentication system using hybrid approach. This approach that allows users to identity a number of images during the registration process and then requested to draw a pattern on each image in a pre-defined order to complete the system approach. This process is represented via a three-stage scheme which consists of the Registration phase, the Login phase and the Authentication phase (RLA). The evaluation was carried out using the comparative study that involves generation of secure graphical password. This methodology is often refered to as Educating Users to Generate Secure Graphical Password Secrets (EUSGPAS). The evaluation involved 200 purposively selected participants who were made to use the new system over six weeks. Experimental results of ESGAS and EUSGPAS indicated a mean success rate of 100%, 97%, 100% and 99%, 96%, 98.9% with a mean completion time of 29.71s and 30.89s respectively. These results indicated the merits of the proposed method over the EUSGPAS approach.
Mezzo: An Adaptive, Real-Time Composition Program for Game Soundtracks

Mezzo is a computer program designed that procedurally writes Romantic-Era style music in real-time to accompany computer games. Leitmotivs are associated with game characters and elements, and mapped into various musical forms. These forms are distinguished by different amounts of harmonic tension and formal regularity, which lets them musically convey various states of markedness which correspond to states in the game story. Because the program is not currently attached to any game or game engine, “virtual” gameplays were been used to explore the capabilities of the program; that is, videos of various game traces were used as proxy examples. For each game trace, Leitmotivs were input to be associated with characters and game elements, and a set of ‘cues’ was written, consisting of a set of time points at which a new set of game data would be passed to Mezzo to reflect the action of the game trace. Examples of music composed for one such game trace, a scene from Red Dead Redemption, are given to illustrate the various ways the program maps Leitmotivs into different levels of musical markedness that correspond with the game state.
A Novel Approach to Quantum Circuit Partitioning

Quantum synthesis presents an effective method of circuit optimization, but scales exponentially with the number of qubits in the circuit. This problem can be addressed by partitioning the circuit into blocks with a limited number of qubits. Existing partitioning algorithms make large trade-offs to achieve either high speed or quality. We propose a method of circuit partitioning which is competitive with existing algorithms for both metrics. The proposed method is compared with two existing methods across common circuit architectures, matching an exhaustive solution in performance and a fast solution on time.
SENPAI: Supporting Exploratory Text Analysis through Semantic &amp; Syntactic Pattern Inspection

Analyzing language for social computing tasks requires looking beyond individual words. For example, the word “please” generally signals politeness, but more so together with modal verbs (“could you please...”) than without (“please do this.”). Combining semantics and syntax into rich textual patterns is essential to capturing these nuances. What are the relevant patterns for a task, and how to find them? NLP practitioners choose patterns informed by theory, and find them through computational models. However, few tools allow identifying rich patterns without NLP expertise. We introduce SENPAI, a novel tool that discovers combined semantic and syntactic patterns. SENPAI fuses neural embeddings, dependency parsing, and graph mining to surface patterns directly from data. We apply SENPAI to measure credibility, politeness, and sentiment in text. Quantitatively, models powered by SENPAI perform similarly to theoretically-motivated ones. Qualitatively, SENPAI discovers patterns that are interpretable and meaningful. SENPAI enables building computational models without NLP expertise and discovering new linguistic constructs.
DME: An Adaptive and Just-in-Time Weighted Ensemble Learning Method for Classifying Block-Based Concept Drift Steam

This study proposes a novel incremental learning algorithm called distribution matching ensemble (DME) in context of adaptive weighted ensemble learning. In particular, DME estimates the distribution of each received data block by Gaussian mixture model (GMM) and reserves the corresponding distribution information, as well it maintains a group of classifiers in a buffer. When we receive a new data block which is required to be predicted, the similarity between its distribution and each reserved distribution will be calculated by Kullback-Leibler (KL) divergence, and then the similarities can be used to guide the weight assignment of each corresponding classifier to further make adaptive ensemble decision. DME gets rid of the underlying hypothesis that the most recent labeled data block always has the most similar distribution with the current unlabeled data block. In addition, to avoid infinite extension of ensemble buffer during incremental learning, we also develop two dynamic classifier update rules. Experiments results on some synthetic and real-world streaming datasets show that the proposed DME algorithm is able to track and adapt to various types of concept drift just in time. Especially, on data stream with frequent reoccurring drifts, the DME significantly outperforms to several state-of-the-art algorithms, indicating its superiority.
Adaptive neuro fuzzy Inference systems in identification, modeling and control: The state-of-the-art

Adaptive Neural Fuzzy Inference Systems ANFIS have an increasing tendency to be used in scientific research and practical applications. The digitization of production and the emergence of Industry 4.0 enabled the development of this trend, primarily due to the ability to adapt to the task by integrating artificial neural networks and fuzzy logic, which can potentially use the advantages of both techniques in unique frameworks. This approach facilitated the modeling, data analysis, classification and control processes. The advantage of the ANFIS, compared to conventional methods, is reflected in the ability to predict the output based on a set of inputs and on the rule base. Also, these systems are suitable, because they provide the possibility to adjust the parameters of the control system. This paper presents the structure of the ANFIS system and gives a detailed review of the achievements so far, through a comparative analysis, where some possible spheres of interdisciplinary application are highlighted. Possibilities for variations, improvements and innovations of the algorithm, as well as reducing the complexity of the network architecture itself, are discussed. Proposals for some new, as yet unused combinations with metaheuristic optimization methods are presented. Finally, important guidelines are provided on when and where it is useful to apply ANFIS systems.
Text Summarization of Hadits in Indonesian Language Using The Combination of Fuzzy Logic Scoring And Latent Semantic Analysis (LSA)

The method used to solve this problem is Fuzzy Logic Scoring and Latent Semantic Analysis (LSA). Summarizing documents can help increase the amount of information contained in the document. And the machine can automatically display an important information and users can also help understand the understanding of large information in documents. This research works to summarize a document automatically by using the concept Fuzzy Logic Scoring and Latent Semantic Analysis (LSA) methods.
Social Modeling via Logic Programming in City of Gangsters

City of Gangsters is a commercial strategy game with significant social modeling mechanics: it is a tycoon management game, where the player needs to work their social connections with a network of roughly 1200 NPCs to get things done, and NPC opinions about the player modulate the player's ability to succeed. We found logic programming to be well suited to our knowledge representation problem, including the need to perform inferences over a relationship network with more than a thousand active characters, and to provide the player with meaningful feedback about the consequences of their actions in the social space. In this paper we present the technical details of this social modeling problem, the details of our logic programming implementation, and how this interacts with the game's design and its social and material economies.