Reviewerly/exHarmony huggingface dataset:
Securing Copyright of Digital Video by Exploiting Quantized DC coefficients

The scheme developed the method to protect the copyright of digital video while encoding the video through MPEG standard. The technique suggested to insert the same watermark at two different locations in intra-coded frame. As these frames have highest degree of luminance part as comparision to Bi-directional(B-frames) and Predicting-frames (P-frames), therefore a little alteration to such frames doesn’t degraded the perceptibility of proposed video watermarking method. The scheme also tested the robustness by applying intentional and unintenional attacks which provides satisfactory results for most of the attack A reasonable size of watermark bits can also be adjusted in a single I-frames as it contains the sufficient number of quantized discrete cosine transform block therefore great size of watermark can be accomodated in the video object.
“Memory” for Sustainable Society

In today's advanced information society, the amount of data being generated is exploding, and within a few years, the annual amount of data generated is expected to exceed 100ZB [1] . The data is not only created by people, but also by the machines. These huge amounts of data are being actively used to create new value and business. However, at present, only a small fraction of this data is stored. If more data could be stored and utilized as information and knowledge, people's lives would become enriched. For example, if we can store people’s memories and retrieve them when needed, we can use them to treat dementia, pass on the knowledge of experts, and do many other things.
Quantum-inspired algorithm for Vehicle Sharing Problem

Recent hardware developments in quantum technologies have inspired a myriad of special-purpose hardware devices tasked to solve optimization problems. In this paper, we explore the application of Fujitsu's quantum-inspired CMOS-based Digital Annealer (DA) in solving constrained routing problems arising in transportation and logistics. More precisely in this paper, we study the vehicle sharing problem and show that the DA as a QUBO solver can potentially fill the gap between two common methods: exact solvers like Cplex and heuristics. We benchmark the scalability and quality of solutions obtained by DA with Cplex and with a greedy heuristic. Our results show that the DA is a general QUBO solver that is more robust than heuristics, and more scalable than Cplex. Our methodology and framework which focus on QUBO problems is also applicable to other quantum-inspired and fully quantum devices that are undergoing development.
Implementation of ELECTRE II Algorithm to Analyze Student Constraint Factors in Completing Thesis

This study aims to determine the obstacles that cause students to be slow in completing final assignments during the pandemic at STIKI Indonesia. Data were obtained from the results of student questionnaires with a sample of 30 students. There are five attributes used, namely Environment (A1), Less Capable of Time Management (A2), Lecturer Influence (A3), and Online Communication (A4). The method used in this research is ELECTRE II. The research results show that the Less Capable of Time Management factor (A2) is the most dominant alternative, followed by the Lecturer Influence factor (A3) in the second place. The results of this research are intended to provide an evaluation for universities, especially divisions related to final assignments, in paying attention to the obstacles constraint by students to complete the final task and can provide the right solution so that student progress can be carried out smoothly.
Beauty Related Graduate Fields Activation Methods for International Students

In order to seek activation methods for beauty related graduate level international students, this research was conducted by analyzing the current status of graduate level beauty departments and conducting focus group interviews (FGI) in two separate groups: 8 professors and 12 international students from 7 different graduate schools and the results are as follows. In 2020, current count of active beauty related graduate level departments are 74, with 298 international students representing 29 graduate schools. The biggest motivation for international students attending beauty related graduate school was to become a beauty professional through self development, thus preparing themselves to become a competitive candidate in the job market. The familiarity between the student and professor and department affinity influenced students decision to attend graduate school. Most of the students experienced challenges due to language barriers, and responded that having mentors, assistants and professors speaking their native language would be needed. The recognition of merit scholarships, visa and career continuation policies were low amongst international students. Therefore, in order to further activate and promote beauty related graduate level programs, the needs of international students have to be met by increasing the number of hands-on classes following the latest trends, career continuation programs and opportunities to increase familiarity amongst professors and students. In addition, language programs support, native class assistants and upperclassmen mentorship have to be inter-connected to increase class understanding. At the same time, subsidizing Korean level test and supporting the test process have to be further monitored and promoted to ensure diversified support reaches the international students. With such efforts, beauty related graduate programs will be able to recruit more international students and help the operation of the university while help build more international relationships.
Modeling of global solar radiation in Algeria based on geographical and all climatic parameters

The design of photovoltaic or solar systems and estimating their performance require knowledge of the intensity of solar radiation. The measurement of this parameter for some sites in Algeria is unfortunately not obvious. However, researchers are moving towards the modeling, estimation and prediction. To model the global solar radiation in Algeria, we must take into account the geographical and climatic parameters such as sunshine duration, relative humidity, temperature, latitude site, etc... In this study, the modeling of daily global solar radiation on a horizontal plane according to the parameters mentioned above is based on the statistical linear regression technique. The daily data used in the development and validation of models are extracted from the database of O. N. M (National Office of Meteorology, Dar el Beida. Algeria) for 2001-2005. We test the proposed models on two sites such as Djelfa and Ain-Bessam. For each site, validity and performance of the model will be studied based on the number of parameters introduced in the analytical expressions and results are discussed in terms of statistical errors as: R, MBE and RMSE between the measured global solar radiation and global solar radiation estimated. It was found that air temperature and relative humidity are indeed important climatic parameters for the prediction of solar radiation.
A Tool for Security Requirements Recommendation using Case-Based Problem Domain Ontology

With the significant increase of various cyber threats, the strategies of the attacks are becoming more diverse. In particular, more attention needs to be paid to Advanced Persistent Threat (APT) attacks since these attacks are continuously performed on a specific target for an apparent purpose through numerous tactics and techniques without being discovered for a long time. Although it is difficult to detect and respond to such APT attacks, it is more challenging to elicit security requirements that sufficiently reflect these complex characteristics for proactive defense. To address this problem, we propose a tool that recommends security requirements for APT attacks using the Case-Based Problem Domain Ontology specialized for APT attacks.
Building Recognition Based on Improved Faster R-CNN in High Point Monitoring Image

In this study, the Faster R-CNN model is trained to recognize the building based on the monitoring video image data captured by the high-resolution camera (high point) of the 40 meter communication tower. Aiming at the problem that the building target in the foreground is small and the detection effect is not good, the Faster R-CNN network structure is improved, and the low-level features and high-level features are used to detect the target in different scales. The experimental results show that the average accuracy of this method is 75.58%, which can effectively detect buildings. It can be applied to the law enforcement and inspection of illegal occupation of land, improve the work efficiency of government departments, and reduce the cost of manual inspection.
Improving the Performance of Vietnamese–Korean Neural Machine Translation with Contextual Embedding

With the recent evolution of deep learning, machine translation (MT) models and systems are being steadily improved. However, research on MT in low-resource languages such as Vietnamese and Korean is still very limited. In recent years, a state-of-the-art context-based embedding model introduced by Google, bidirectional encoder representations for transformers (BERT), has begun to appear in the neural MT (NMT) models in different ways to enhance the accuracy of MT systems. The BERT model for Vietnamese has been developed and significantly improved in natural language processing (NLP) tasks, such as part-of-speech (POS), named-entity recognition, dependency parsing, and natural language inference. Our research experimented with applying the Vietnamese BERT model to provide POS tagging and morphological analysis (MA) for Vietnamese sentences,, and applying word-sense disambiguation (WSD) for Korean sentences in our Vietnamese–Korean bilingual corpus. In the Vietnamese–Korean NMT system, with contextual embedding, the BERT model for Vietnamese is concurrently connected to both encoder layers and decoder layers in the NMT model. Experimental results assessed through BLEU, METEOR, and TER metrics show that contextual embedding significantly improves the quality of Vietnamese–Korean NMT.
Comprehensive Evaluation Model of Environmental Quality in Ecological Reserve

The environmental quality of the ecological protection zone directly affects the water quality and air quality of the area, thereby affecting the quality of people’s life. This paper constructs the evaluation index system of the environmental quality of the ecological protection area and determines the classification standards of each index, proposes the extension evaluation method of the environmental quality of the ecological protection area, and uses the entropy weight method to determine the weight of each level of index. Its purpose is to understand the development trend of environmental quality in all aspects of the ecological reserve through the dynamic evaluation of the environmental quality of the ecological reserve, so as to take corresponding countermeasures to promote the healthy development of the area. The constructed model was used to dynamically evaluate the environmental quality of the ecological reserve in Zhangjiakou City from 2008 to 2019. The research results show that the environmental quality gaps in different aspects of the ecological reserve of Zhangjiakou City are relatively large, and the overall regional ecological environment characteristic value is decreasing year by year, reflecting that the environmental quality of the ecological reserve of the region is getting better year by year.
OKSP: A Novel Deep Learning Automatic Event Detection Pipeline for Seismic Monitoring in Costa Rica

Small magnitude earthquakes are the most abundant but the most difficult to locate robustly and well due to their low amplitudes and high frequencies usually obscured by heterogeneous noise sources. They highlight crucial information about the stress state and the spatio-temporal behavior of fault systems during the earthquake cycle, therefore, its full characterization is then crucial for improving earthquake hazard assessment. Modern deep learning algorithms along with the increasing computational power and efficiency are exploiting the continuously growing seismological databases, worldwide, allowing scientists to improve the completeness for earthquake catalogs, systematically detecting and locating smaller magnitude earthquakes and reducing the errors introduced mainly by human intervention through traditional approaches in seismological observatories. In this work, we introduce OKSP, a novel deep learning automatic earthquake detection pipeline for seismic monitoring in Costa Rica. Using Kabré supercomputer from the Costa Rica High Technology Center, we applied OKSP to the day before and the first 5 days following the Puerto Armuelles, M6.5, earthquake that occurred on 26 June, 2019, along the Costa Rica-Panama border and found 1100 more earthquakes previously unidentified by the Volcanological and Seismological Observatory of Costa Rica. From these events, a total of 23 earthquakes with magnitudes below 1.0 occurred a day to hours prior to the mainshock, shedding light about the rupture initiation and earthquake interaction leading to the occurrence of this productive seismic sequence. Our observations show that for the study period, the model was 100% exhaustive and 82% precise, resulting in an F1 score of 0.90. This effort represents the very first attempt for automatically detecting earthquakes in Costa Rica using deep learning methods and demonstrates that, in the near future, earthquake monitoring routines will be carried out entirely by AI algorithms.
Assessment of 3-Dimensional Hand Pose by PosePrior Network for Images

Deep learning is a function of artificial intelligence which imitates the human brain. In this work, we introduced a technique which estimates 3D HPE for images. This technique overcomes uncertainties caused by missing of depth information in images. Finally, we introduce a deep neural network that acquires knowledge about 3D articulation of a Network –Implicit method. This network gives good 3D hand pose estimation along with the detection of key points in images. We have compared our results to previous experiments, our proposed system gives impressive results. We present a dataset of large-scale 3D HPE that depends on the synthetic human hand models to train the networks. Along with other datasets, sign language recognition is also taken as another dataset to illustrate the viability of 3D HPE on images.
M&amp;M: Recognizing Multiple Co-evolving Activities From Multi-source Videos

The wide deployment of surveillance systems has shown the necessity to recognize human activities in videos. Existing work achieved high recognition accuracy for single- person activities and some group activities. In this paper, we identify a more challenging issue: recognizing multiple co- evolving but asynchronous activities from a set of videos captured by multiple cameras that share overlapped views. To address this issue, we design a system M&M to fuse objects and 2D skeletons from multi-source videos to reconstruct the complete 3D view-invariant model of multiple activity scenarios, which enables accurate recognition of cross-source activities. By embedding the 3D model with a concise graph representation, we propose an efficient recognition method in a bottom-up manner to achieve high accuracy and good scalability to changing complexity of the captured scenario. We collect and release a dataset containing correlated multi-source videos for multiple co-evolving activities, and evaluate our design on it. Experimental results show that M&M achieves 91.2% average accuracy for 10 types of coevolving activities.
SCAT: Stride Consistency with Auto-regressive regressor and Transformer for hand pose estimation

The current state-of-the-art monocular 3D hand pose estimation methods are mostly model-based. For instance, MANO is one of the most popular hand parametric models, which can depict hand shapes and poses. It is widely adopted for estimating hand poses in images and videos. However, MANO is a parametric model derived from scanned hand data with limited shapes and poses which constrains its capability in depicting in-the-wild shape and pose variations. In this paper, we propose a 3D hand pose estimation approach which does not depends on any parametric hand models yet can still accurately estimate in-the-wild hand poses. Our approach (Stride Consistency with Autoregressive regressor and Transformer, SCAT) offers a new representation for measuring hand poses. The new representation includes a mean shape hand template and its 21 hand joint offsets depicting the 3D distances between the hand template and the hand that needs to be estimated. Besides, SCAT can generate a robust and smooth linear mapping between visual feature maps and the target 3D off-sets, ensuring inter-frame smoothness and removing motion jittering. We also introduce an auto-regressive refinement procedure for iteratively refining the hand pose estimation. Extensive experiments show that our SCAT can generate more accurate and smoother 3D hand pose estimation results compared with the state-of-the-art methods.
Generating Data for Real World Time Series Application with GRU-Based Conditional GAN

Khare, Priyanshi Wadhvani, Rajesh Gyanchandani, Manasi Brahma, BanalaxmiThe access to sufficient amount of data has always challenged researchers to productively effectuate their solutions. One of the promising solutions can be generation of data using generative adversarial networks (GAN). This paper is focused on generating realistic data for different time series applications using GAN. The approach adopted here uses GRU-based GAN with conditional input for data generation. The data generated using GAN can contribute in the formation of larger datasets. The time component plays a major role in forecasting in various domains so it is crucial to target data related to time series. The competence of the data generated has been judged by using it to train the most prominent time series forecasting models and then testing it using real data. The linear regression model, ARIMA model, and GRU-based forecasting model are chosen for carrying out the experiment. The similarity between the actual data and generated data is also demonstrated using Wilcoxon signed-rank test as the datasets used here are nonparametric. The experimentation has been executed on three real world datasets from different domains.
Evaluation of Face Recognition with Wearing Mask or Not

Due to the COVID-19 pandemic, wearing a mask (masking) has become common in daily life, which also affects the face recognition system. The recognition system has to consider the face as input first to memorize the face of a person. Normally, the memorization process usually and only requires a clear facial face. However, when the face is masked, the system might be confused. In this work, we design a framework for a face recognition system to increase the accuracy of recognition of masked face, which can also classify the face into masked or not masked.
NetEase Cloud Dataset: Active User Identification and Deep Neural Network Based CTR

The online short-video platform has become the new emerging market of the online social media era. NetEase Cloud Music is one of the popular music apps that is transformed into the latest form of music app that contains short-videos. This study has self-defined the user active level and using linear regression to analyze the mathematical relationship of active level with user features. Neural Network is a great model when dealing with a significant amount of data. The online-short video click-through rate (CTR) is calculated using the approach of the neural network. Mean-Squared error is also using by changing the number of variables to find the minimum error. The goal of this paper is to stress the relationship between active user level defined by ourselves and the FNN prediction. It is also essential to calculate the CTR of users with further research on the Reco system.
The Typical Non-Linear Code over Large Alphabets

We consider the problem of describing the typical (possibly) non-linear code of minimum distance bounded from below over a large alphabet. We concentrate on block codes with the Hamming metric and on subspace codes with the injection metric. In sharp contrast with the behavior of linear block codes, we show that the typical non-linear code in the Hamming metric of cardinality $q^{n-d+1}$ is far from having minimum distance d, i.e., from being MDS. We also give more precise results about the asymptotic proportion of block codes with good distance properties within the set of codes having a certain cardinality. We then establish the analogous results for subspace codes with the injection metric, showing also an application to the theory of partial spreads in finite geometry.
Human Pose Manipulation and Novel View Synthesis using Differentiable Rendering

We present a new approach for synthesizing novel views of people in new poses. Our novel differentiable renderer enables the synthesis of highly realistic images from any viewpoint. Rather than operating over mesh-based structures, our renderer makes use of diffuse Gaussian primitives that directly represent the underlying skeletal structure of a human. Rendering these primitives gives results in a high-dimensional latent image, which is then transformed into an RGB image by a decoder network. The formulation gives rise to a fully differentiable framework that can be trained end-to-end. We demonstrate the effectiveness of our approach to image reconstruction on both the Human3.6M and Panoptic Studio datasets. We show how our approach can be used for motion transfer between individuals; novel view synthesis of individuals captured from just a single camera; to synthesize individuals from any virtual viewpoint; and to re-render people in novel poses. Code and video results are available at https://github.com/GuillaumeRochette/HumanViewSynthesis.
Uniform Textual Feedback Analysis for Effective Sentiment Analysis

In this paper, a machine learning-based methodology is proposed to measure the users’ sentiments expressed in textual feedback. The methodology uses collaborative filtering to evaluate the degree of positivity or negativity for every important aspect from each user’s perspective participating in the feedback analysis, hence proposing a uniform feedback analysis. Key aspects of a particular item or an issue are identified through topic modeling and taking into account the syntactic and semantic properties of words after processing the merged document obtained from all the feedbacks. Aggregate sentiment of an item is evaluated by considering the importance and sentiments of key aspects. This methodology can be used to analyze textual feedbacks of any domain with very little domain-dependent information. In this paper, feedbacks of two different domains have been analyzed and presented. Results show that the performances of the same items of different brands can be compared easily.