Reviewerly/exHarmony huggingface dataset:
Inclusive Education Management in the Development of Cognitive Intelligence of Children

Early childhood cognitive intelligence that has not been fully developed in inclusive schools needs serious attention, considering that this age is a crucial growth period and the golden age of children. This study aims to analyze and understand inclusive education management in children's cognitive intelligence in RA Al-Iman, and RA Tania. This research uses a qualitative approach with a case study. The data collection technique is done through interviews, observation, and documentation. The data analysis was carried out circularly, starting from the display power, data reduction, and concluding. The results showed that inclusive education in developing children's cognitive intelligence in both institutions was planned and systematic, starting from identifying the needs of inclusive education, designing inclusive education, implementing inclusive education that leads to cognitive intelligence, and assessing inclusive education. This research has implications for the importance of implementing an egalitarian education and refers to equal rights so that children can mingle, play, and get the same education without any differences.
An Approach for a Next-Word Prediction for Ukrainian Language

Text generation, in particular, next-word prediction, is convenient for users because it helps to type without errors and faster. Therefore, a personalized text prediction system is a vital analysis topic for all languages, primarily for Ukrainian, because of limited support for the Ukrainian language tools. LSTM and Markov chains and their hybrid were chosen for next-word prediction. Their sequential nature (current output depends on previous) helps to successfully cope with the next-word prediction task. The Markov chains presented the fastest and adequate results. The hybrid model presents adequate results but it works slowly. Using the model, user can generate not only one word but also a few or a sentence or several sentences, unlike T9.
Spatial Perception Enhancement in Assembly Training Using Augmented Volumetric Playback

Conventional training and remote collaboration systems allow users to see each other’s faces, heightening the sense of presence while sharing content like videos or slideshows. However, these methods lack depth information and a free 3D perspective of the training content. This paper investigates the impact of volumetric playback in a Mixed Reality (MR) spatial training system. We describe the MR system in a mechanical assembly scenario that incorporates various instruction delivery cues. Building upon previous research, four spatial instruction cues were explored; “Annotation”, “Hand gestures”, “Avatar”, and “Volumetric playback”. Through two user studies that simulated a real-world mechanical assembly task, we found that the volumetric visual cue enhanced spatial perception in the tested MR training tasks, exhibiting increased co-presence and system usability while reducing mental workload and frustration. We also found that the given tasks required less effort and mental load when eye gaze was incorporated. Eye gaze on its own was not perceived to be very useful, but it helped to compliment the hand gesture cues. Finally, we discuss limitations, future work and potential applications of our system.
Blockchain-Based Privacy Protection Scheme for IoT-Assisted Educational Big Data Management

Adoption of the Internet of Things (IoT) in education brings many benefits. However, the poor implementation of access control of educational data produced by the IoT devices has brought students’ and teachers’ privacy into danger. Attackers can access educational data that they are not permitted to access and even erase the records during access. To tackle this problem, we employ blockchain technology to guarantee the integrity of access control rules and trace the records of access events. In this paper, we propose a blockchain-based access control scheme for the data produced by IoT devices. The scheme consists of three components: (1) a well-implemented data collection module that is deployed in smart classrooms, which collects and uploads data about the real-time situation inside the smart classroom to the data center; (2) a MongoDB-based data center and its control module that makes access control decisions based on the verification of the permissions of visitors, where the permissions are managed by blockchain; and (3) a customized blockchain system that stores and keeps security policy updates of the role-based access control module and records access events in a trusted way. Our analysis indicates that the proposed access control scheme guarantees the correctness of the access control process and makes the access of collected educational data auditable and responsible. Our system collectively analyzes the context of the smart classroom and is capable of detecting multiple scenarios such as absence, lateness, and gunshot. We show how the scheme preserves students’ and teachers’ privacy by carrying out extensive experimental studies. The results indicate that the proposed data management system can give correct responses as quickly as a traditional data server does while preserving privacy.
Latency Estimation Tool and Investigation of Neural Networks Inference on Mobile GPU

A lot of deep learning applications are desired to be run on mobile devices. Both accuracy and inference time are meaningful for a lot of them. While the number of FLOPs is usually used as a proxy for neural network latency, it may not be the best choice. In order to obtain a better approximation of latency, the research community uses lookup tables of all possible layers for the calculation of the inference on a mobile CPU. It requires only a small number of experiments. Unfortunately, on a mobile GPU, this method is not applicable in a straightforward way and shows low precision. In this work, we consider latency approximation on a mobile GPU as a data- and hardware-specific problem. Our main goal is to construct a convenient Latency Estimation Tool for Investigation (LETI) of neural network inference and building robust and accurate latency prediction models for each specific task. To achieve this goal, we make tools that provide a convenient way to conduct massive experiments on different target devices focusing on a mobile GPU. After evaluation of the dataset, one can train the regression model on experimental data and use it for future latency prediction and analysis. We experimentally demonstrate the applicability of such an approach on a subset of the popular NAS-Benchmark 101 dataset for two different mobile GPU.
Aspect Based Sentiment Analysis for Explicit and Implicit Aspects in Restaurant Review using Grammatical Rules, Hybrid Approach, and SentiCircle

TripAdvisor is one of the most popular e-commerce platforms in the tourism sector in Indonesia.TripAdvisor give Traveler Choice Award every year in Indonesia through user reviews.However, online text-based reviews are often associated only with evaluation scores that do not pay attention to the context and meaningful content of the review itself, either explicitly or implicitly.Moreover, the sentence structure of the review can have an impact on the goal of the target sentiment which is nothing but an aspect of the review itself.This research discusses aspect based sentiment analysis for explicit and implicit aspects.This research starting with taking the TripAdvisor website restaurant product review dataset to measure customer satisfaction based on four aspect categories of Ambience, Food, Service, and Price.Furthermore, the aspect word extraction and opinion word extraction processes in the case of explicit sentences for simple, compound, complex, and compound-complex sentence structures are carried out using grammatical rule extraction.This research also works on implicit sentence cases for simple sentence structures.Aspect categorization process uses hybrid approach.Aspect and opinion keyword extraction process uses the ELMo-Wikipedia.Then WordNet and TF-ICF are used to expand the meaning of aspect and opinion that has been taken.The last stage is the aspect based sentiment analysis process, both explicit and implicit sentences using SentiCircle.This research can produce two evaluations of sentiment classification, namely positive and negative.The results of the aspect extraction obtained an evaluation of the aspect categorization for each precision 0.82, recall 0.87, and f1measure 0.86.Meanwhile, the results of the sentiment analysis showed that the respective evaluations for precision 0.87, recall 0.92, and f1-measure 0.89.
Motional Squeezing for Trapped Ion Transport and Separation

Transport, separation, and merging of trapped ion crystals are essential operations for most large-scale quantum computing architectures. In this Letter, we develop a theoretical framework that describes the dynamics of ions in time-varying potentials with a motional squeeze operator, followed by a motional displacement operator. Using this framework, we develop a new, general protocol for trapped ion transport, separation, and merging. We show that motional squeezing can prepare an ion wave packet to enable transfer from the ground state of one trapping potential to another. The framework and protocol are applicable if the potential is harmonic over the extent of the ion wave packets at all times. As illustrations, we discuss two specific operations: changing the strength of the confining potential for a single ion and separating same-species ions with their mutual Coulomb force. Both of these operations are, ideally, free of residual motional excitation.
Is Blockchain Technology Secure to Work On?

Technology is revolutionizing and making positive as well as negative impacts on social animals. The social animal's behavior gets affected due to instant change in their lives. A concept of blockchain technology or distributed ledger transforms the way of their living. This technology is well known for its immutable and distributed architecture. The government focuses on such technologies rather than centralized network so that they can avoid central server banking crimes. These technologies allow social animals to stay secured and updated in an online process of information transfer. These are very famous for bitcoin, ripple, and ether transactions; however, mentioned transactions are just applications of distributed ledger. This chapter discusses the features, pros, cons, challenges, and future scope of distributed ledger. Further, it discusses the applications of blockchain technology in different sectors like identity management, smart cities, privacy protection, travel industry, electronic voting, finance, health industry, smart contracts, and hospitality.
FAKE NEWS DETECTION OF SOCIAL MEDIA NEWS IN BLOCKCHAIN FRAMEWORK

Social media news are most important in today's worlds, it puts positive or negative influence on social views.There is a wide propagation of fake news on social media so it will be difficult to believe on the news.Fake news has negative impacts on individuals as well as on society.Information spreads rapidly over the social media and so there is a need of mechanism which detects and stops the spreading of fake news.Therefore, detection of fake news is the need of time and also a challenging problem.The goal of this proposed research work is to detect fake news and minimize spreading of the fake news.In the proposed research a machine learning approach is used for detection of fake news with blockchain framework.In first section a supervised machine learning techniques is design to identify the trustiness of specific news while blockchain framework revoke the malicious activity of spreading fake news.A blockchain environment is created with mining, smart contract as well as Proof of Work (PoW) of consensus.The current systematic review broadly focuses on the various methods to detect fake news in social media.After partial implementation of system, performance evaluation has done with traditional blockchain framework.It is found that 10% less time for transaction verification by consensus in P2P environment over the existing systems.
STROKE PREDICTION USING MACHINE LEARNING ALGORITHMS

Stroke is a destructive illness that typically influences individuals over the age of 65 years age.Prediction of stroke is a time consuming and tedious for doctors.Therefore, the project mainly aims at predicting the chances of occurrence of stroke using the emerging Machine Learning techniques.Five different algorithms are used and a comparison is made for better accuracy.Aim is to create an application with a userfriendly interface which is easy to navigate and enter inputs.
Exploiting Local Temporal Characteristics via Multinomial Decomposition Algorithm for Real-Time Activity Recognition

With the current expansion of Internet of Things (IoT), tracking human physical activity through wearable devices is becoming increasingly frequent. A human activity recognition (HAR) module is needed to localize and classify the patterns of acquired signals. Although this topic has been a talking point in the past decades, the trade-off between efficiency, reliability, and computational complexity is still an open research challenge. In this work, we propose a novel recognition process, based on online feature vector computation followed by a multinomial decomposition algorithm (MDA). Specifically, the temporal characteristics of performed activities are encoded over local segments. Afterwards, a low-cost algorithm divides the multinomial classification into different stages, where the encoded patterns feed a neural network at each stage to classify the corresponding segment. The proposed approach has been evaluated on five public datasets and compared with a set of state-of-the-art methods. Experimental results show that with a few hundreds of floating point operations per second (flops), our solution can achieve competitive performance in terms of accuracy. The effects of the sliding window length, the sampling frequency, the size of the training set, and overfitting have been studied. A prototype was developed to display the behavior of this solution in real world conditions.
The CSTR System for Multilingual and Code-Switching ASR Challenges for Low Resource Indian Languages

This paper describes the CSTR submission to the Multilingual and Code-Switching ASR Challenges at Interspeech 2021.For the multilingual track of the challenge, we trained a multilingual CNN-TDNN acoustic model for Gujarati, Hindi, Marathi, Odia, Tamil and Telugu and subsequently fine-tuned the model on monolingual training data.A language model built on a mixture of training and CommonCrawl data was used for decoding.We also demonstrate that crawled data from YouTube can be successfully used to improve the performance of the acoustic model with semi-supervised training.These models together with confidence based language identification achieve the average WER of 18.1%, a 41% relative improvement compared to the provided multilingual baseline model.For the code-switching track of the challenge we again train a multilingual model on Bengali and Hindi technical lectures and we employ a language model trained on CommonCrawl Bengali and Hindi data mixed with in-domain English data, using a novel transliteration method to generate pronunciations for the English terms.The final model improves by 18% and 34% relative compared to our multilingual baseline.Both our systems were among the top-ranked entries to the challenge.
Plaintext-Related Dynamic Key Chaotic Image Encryption Algorithm

To address the problems of the high complexity and low security of the existing image encryption algorithms, this paper proposes a dynamic key chaotic image encryption algorithm with low complexity and high security associated with plaintext. Firstly, the RGB components of the color image are read, and the RGB components are normalized to obtain the key that is closely related to the plaintext, and then the Arnold transform is used to stretch and fold the RGB components of the color image to change the position of the pixel points in space, so as to destroy the correlation between the adjacent pixel points of the image. Next, the generated sequences are independently encrypted with the Arnold-transformed RGB matrix. Finally, the three encrypted images are combined to obtain the final encrypted image. Since the key acquisition of this encryption algorithm is related to the plaintext, it is possible to achieve one key per image, so the key acquisition is dynamic. This encryption algorithm introduces chaotic mapping, so that the key space size is 10180. The key acquisition is closely related to the plaintext, which makes the ciphertext more random and resistant to differential attacks, and ensures that the ciphertext is more secure after encryption. The experiments show that the algorithm can encrypt the image effectively and can resist attack on the encrypted image.
Needs and opportunities for seismic early warning prior to aftershocks for search and rescue teams: An in-depth analysis of practitioners’ perceptions

During their postseismic interventions, Urban Search and Rescue (USAR) teams are particularly exposed to the risk of collapse of buildings due to aftershocks.Risk reduction requires the capacity building of rescuers to anticipate incoming ground motions and to quickly protect themselves, which makes the concept of earthquake early warning (EEW) particularly interesting.In parallel to the recent scientific advances in EEW, it is crucial to understand what are the real expectations and needs of USAR teams in terms of protection against this risk, and to what extent EEW solutions could meet them.In this study, we conduct a survey to collect insights from 104 USAR rescuers (32 teams represented from 11 countries).Results highlight that aftershocks are a major concern for rescuers, and that the current tools and procedures do not allow an effective protection.In this context, we find that the concept of EEW is very favorably received by the respondents, who consider different types of possible actions upon receipt of an early warning.Depending on the time available, the perceive benefits range from simply reducing the surprise effect to moving teams into safe areas and stopping dangerous operations.Combined with the opportunities offered by the miniaturization of seismic monitoring means and by the high level of interoperability of USAR teams on an international scale, this study also provides a basis for the functional specifications of future solutions of EEW useful to all USAR teams, as well as for the definition of their modalities of engagement on the field.
Speech2Video: Cross-Modal Distillation for Speech to Video Generation

This paper investigates a novel task of talking face video generation solely from speeches. The speech-to-video generation technique can spark interesting applications in entertainment, customer service, and human-computer-interaction industries. Indeed, the timbre, accent and speed in speeches could contain rich information relevant to speakers' appearance. The challenge mainly lies in disentangling the distinct visual attributes from audio signals. In this article, we propose a light-weight, cross-modal distillation method to extract disentangled emotional and identity information from unlabelled video inputs. The extracted features are then integrated by a generative adversarial network into talking face video clips. With carefully crafted discriminators, the proposed framework achieves realistic generation results. Experiments with observed individuals demonstrated that the proposed framework captures the emotional expressions solely from speeches, and produces spontaneous facial motion in the video output. Compared to the baseline method where speeches are combined with a static image of the speaker, the results of the proposed framework is almost indistinguishable. User studies also show that the proposed method outperforms the existing algorithms in terms of emotion expression in the generated videos.
Bidirectional LSTM with saliency-aware 3D-CNN features for human action recognition

Deep convolutional neural network (DCNN) and recurrent neural network (RNN) have been proved as an imperious research area in multimedia understanding and obtained remarkable action recognition performance. However, videos contain rich motion information with varying dimensions. Existing recurrent based pipelines fail to capture long-term motion dynamics in videos with various motion scales and complex actions performed by multiple actors. Consideration of contextual and salient features is more important than mapping a video frame into a static video representation. This research work provides a novel pipeline by analyzing and processing the video information using a 3D convolution (C3D) network and newly introduced deep bidirectional LSTM. Like popular two-stream convent, we also introduce a two-stream framework with one modification; that is, we replace the optical flow stream by saliency-aware stream to avoid the computational complexity. First, we generate a saliency-aware video stream by applying the saliency-aware method. Secondly, a two-stream 3D-convolutional network (C3D) is utilized with two different types of streams, i.e., RGB stream and saliency-aware video stream, to collect both spatial and semantic temporal features. Next, a deep bidirectional LSTM network is used to learn sequential deep temporal dynamics. Finally, time-series-pooling-layer and softmax-layers classify human activity and behavior. The introduced system can learn long-term temporal dependencies and can predict complex human actions. Experimental results demonstrate the significant improvement in action recognition accuracy on different benchmark datasets.
Distributionally Robust Multilingual Machine Translation

Multilingual neural machine translation (MNMT) learns to translate multiple language pairs with a single model, potentially improving both the accuracy and the memoryefficiency of deployed models.However, the heavy data imbalance between languages hinders the model from performing uniformly across language pairs.In this paper, we propose a new learning objective for MNMT based on distributionally robust optimization, which minimizes the worst-case expected loss over the set of language pairs.We further show how to practically optimize this objective for large translation corpora using an iterated best response scheme, which is both effective and incurs negligible additional computational cost compared to standard empirical risk minimization.We perform extensive experiments on three sets of languages from two datasets and show that our method consistently outperforms strong baseline methods in terms of average and per-language performance under both many-to-one and one-to-many translation settings.
Biometric Systems De-Identification: Current Advancements and Future Directions

Biometric de-identification is an emerging topic of research within the information security domain that integrates privacy considerations with biometric system development. A comprehensive overview of research in the context of authentication applications spanning physiological, behavioral, and social-behavioral biometric systems and their privacy considerations is discussed. Three categories of biometric de-identification are introduced, namely complete de-identification, auxiliary biometric preserving de-identification, and traditional biometric preserving de-identification. An overview of biometric de-identification in emerging domains such as sensor-based biometrics, social behavioral biometrics, psychological user profile identification, and aesthetic-based biometrics is presented. The article concludes with open questions and provides a rich avenue for subsequent explorations of biometric de-identification in the context of information privacy.