Reviewerly/exHarmony huggingface dataset:
Ordered Gradient Approach for Communication-Efficient Distributed Learning

The topic of training machine learning models by employing multiple gradient-computing workers is attracting great interest recently. Communication efficiency in such distributed learning settings is an important consideration, especially for the case where the needed communications are expensive in terms of power usage. We develop a new approach which is efficient in terms of communication transmissions. In this scheme, only the most informative worker results are transmitted to reduce the total number of transmissions. Our ordered gradient approach provably achieves the same order of convergence rate as gradient descent for nonconvex smooth loss functions while gradient descent always requires more communications. Experiments show significant communication savings compared to the best existing approaches in some cases.
Easy-to-Deploy Living Activity Sensing System and Data Collection in General Homes

Emergence of smart appliances and high performance IoT devices is promoting studies on more functional and intelligent home services using these devices. Especially, in developed countries including Japan with aging population and declining birthrate, it is urgent to develop technologies to monitor living situations of residents including elderly persons and improve their quality of life (QoL) through home services based on the activity recognition technology. However, activity recognition systems in general require many types/number of sensors and hence they are difficult to deploy and operate. In this paper, we propose a system consisting of low-cost and easy-to-deploy sensors based on energy harvesting that can collect data of resident's activities of daily living (ADL) for months without maintenance. The system was deployed in 10 homes of senior citizens where we collected ADL data for two months each. We also estimated the ADLs from the collected data by using long short-term memory (LSTM), a deep learning model. As a result, ADLs could be estimated at high recall rate of 82.4% on average and hence we found that the proposed system has high applicability to actual services.
Secured Communication of Text and Audio using Image Steganography

Data security has gained an utmost importance due to the unprecedented increase in the data generated over the internet, which is a basic necessity of any application in information technology. Steganography is a specialty of science to manage and conceal a piece of important information inside image, audio, video or text documents. Steganography is a method and study of composing a secret communication such that only the source and the targeted beneficiary will realize the concealed information. The data concealed or covered process starts with the steganography method by identifying a network canopy and redundant parts, i.e. only permitted persons would be able to change without affecting the quality of that medium. The encoding method generates a Stego medium by substituting these redundant bits with concealed message content. In the proposed work, an image is employed as the cover file and the data is kept secure with the assistance of the symmetric key, which the sender or source and the specified receiver or person must process. The proposed methodology has performed better with an improvement in PSNR and SNR values than the existing wavelet transform methodology as showed in experimental results. The proposed work is implemented using MATLAB.
BaSa: A Technique to Identify Context based Common Tokens for Hindi Verses and Proses

Activating multilingual options on websites in this digital era has enabled the opportunity to generate text in different languages. Hindi has turned to be the significant language in different domains and mainly in the government domain. Proses (proses) and verses (verses) are generally used as an input data source to perform different operations like classification, clustering and so on. Carrying out different natural language processing (NLP) steps is a prerequisite for implementing machine learning algorithms. In this paper, the BaSa technique is proposed to identify context based common tokens from given corpora. Common tokens present at different levels of Natural language processing (NLP) are computed. It will give guidelines in the form for researchers working in Hindi NLP.Corpus of 820 proses and 710 verses is processed. It is observed that percentage of number of tokens reduced at each stage of NLP is almost same for proses and verses.
GeoDenStream: An improved DenStream clustering method for managing entity data within geographical data streams

Data streams have become an integral part of the rapidly evolving modern information landscape in various application domains. Stream clustering, and in particular density-based clustering, has emerged as one of the most commonly used data stream analysis tasks. Several density-based stream clustering methods have been proposed; chief among them is DenStream. Existing DenStream clustering methods usually preserve only the key summary descriptors about each cluster such as the center and radius. Such approach is not suitable for streams that observe discrete entities, since the clustering process does not maintain the entity-level composition of each cluster over time. The primary challenge we explore in this paper is therefore how existing DenStream clustering methods can be enhanced to support entity-based stream mining in geographical space. In view of this consideration, this paper presents GeoDenStream, a spatiotemporal entity-based stream clustering method. Building on DenStream, GeoDenStream is particularly suitable for clustering discrete entities due to its ability to track the relationship between entities and clusters over time and its ability to recover data that has been incorrectly labeled as noise. Memory efficiency in GeoDenStream is achieved by using a combination of data pruning and indexing. The performance of GeoDenStream was evaluated with both synthetic and real-world stream data from a popular social media platform (Twitter). The results of these evaluations show that GeoDenStream is able to efficiently handle memory constraints, overlapping data points, and false noise.
Data Selection for Multi-Task Learning Under Dynamic Constraints

Learning-based techniques are increasingly effective at controlling complex systems.However, most work done so far has focused on learning control laws for individual tasks.Simultaneously learning multiple tasks on the same system is still a largely unaddressed research question.In particular, no efficient state space exploration schemes have been designed for multi-task control settings.Using this research gap as our main motivation, we present an algorithm that approximates the smallest data set that needs to be collected in order to achieve high performance across multiple control tasks.By describing system uncertainty using a probabilistic Gaussian process model, we are able to quantify the impact of potentially collected data on each learning-based control law.We then determine the optimal measurement locations by solving a stochastic optimization problem approximately.We show that, under reasonable assumptions, the approximate solution converges towards the exact one.Additionally, we provide a numerical illustration of the proposed algorithm.
Real Time Face Recognition System with Deep Residual Network and KNN

Human Face Recognition is the technique to determine the individuals using facial images. In this recent era, human face recognition will be effective to improve security issues. In this research area has a plentiful applications such as biometric, traffic control, information security, law application, digital identification, surveillance system. In this paper, our aim is to consider live streaming on surveillance system to detect human face from real time video feed to improve security issues in university area. Here, we used facial measurements known as embedding's calculation from faces and a network architecture named deep residual network is used with classification model KNN (k nearest neighboring). After this study, we found 91.05% accuracy.
Enhanced Braille Display Use of OCR and Solenoid to Improve Text to Braille Conversion

There are total 37 million blind people across the world. Out of that 15 million are from India. These people are unable to access the documents or electronics media which are not available in Braille scripts. Here we are implementing enhanced Braille system that helps blind people to read text or content. We scanned image form camera, processed that image by image processing techniques and same will be converted into text using OCR. The detected text will be given to raspberry pi which recognize every character and convert it into Braille code. With the help of solenoid, we are displaying that Braille code on Braille.
Progressive Multistage Learning for Discriminative Tracking

Visual tracking is typically solved as a discriminative learning problem that usually requires high-quality samples for online model adaptation. It is a critical and challenging problem to evaluate the training samples collected from previous predictions and employ sample selection by their quality to train the model. To tackle the above problem, we propose a joint discriminative learning scheme with the progressive multistage optimization policy of sample selection for robust visual tracking. The proposed scheme presents a novel time-weighted and detection-guided self-paced learning strategy for easy-to-hard sample selection, which is capable of tolerating relatively large intraclass variations while maintaining interclass separability. Such a self-paced learning strategy is jointly optimized in conjunction with the discriminative tracking process, resulting in robust tracking results. Experiments on the benchmark datasets demonstrate the effectiveness of the proposed learning framework.
Leveraging maximum entropy and correlation on latent factors for learning representations

Many tasks involve learning representations from matrices, and Non-negative Matrix Factorization (NMF) has been widely used due to its excellent interpretability. Through factorization, sample vectors are reconstructed as additive combinations of latent factors, which are represented as non-negative distributions over the raw input features. NMF models are significantly affected by latent factors’ distribution characteristics and the correlations among them. And NMF models are faced with the challenge of learning robust latent factor. To this end, we propose to learn representations with an awareness of the semantic quality evaluated from the aspects of intra- and inter-factors. On the one hand, a Maximum Entropy-based function is devised for the intra-factor semantic quality. On the other hand, the semantic uniqueness is evaluated via inter-factor correlation, which reinforces the aim of semantic compactness. Moreover, we present a novel non-linear NMF framework. The learning algorithm is presented and the convergence is theoretically analyzed and proved. Extensive experimental results on multiple datasets demonstrate that our method can be successfully applied to representative NMF models and boost performances over state-of-the-art models.
Clockwork: Tracking Remote Timing Attacks

Timing leaks have been a major concern for the security community. A common approach is to prevent secrets from affecting the execution time, thus achieving security with respect to a strong, local attacker who can measure the timing of program runs. However, this approach becomes restrictive as soon as programs branch on a secret. This paper focuses on timing leaks under remote execution. A key difference is that the remote attacker does not have a reference point of when a program run has started or finished, which significantly restricts attacker capabilities. We propose an extensional security characterization that captures the essence of remote timing attacks. We identify patterns of combining clock access, secret branching, and output in a way that leads to timing leaks. Based on these patterns, we design Clockwork, a monitor that rules out remote timing leaks. We implement the approach for JavaScript, leveraging JSFlow, a state-of-the-art information flow tracker. We demonstrate the feasibility of the approach on case studies with IFTTT, a popular IoT app platform, and VJSC, an advanced JavaScript library for e-voting.
Kitchen Task Assessment Dataset for Measuring Errors due to Cognitive Impairments

With the demographic change towards ageing population, the number of people suffering from neurodegenerative diseases such as dementia increases. As the ratio between young and elderly population changes towards the seniors, it becomes important to develop intelligent technologies for supporting the elderly in their everyday activities. Such intelligent technologies usually rely on training data in order to learn models for recognising problematic behaviour. One problem these systems face is that there are not many datasets containing training data for people with dementia. What is more, many of the existing datasets are not publicly available due to privacy concerns. To address the above problems, in this paper we present a sensor dataset for the kitchen task assessment containing normal and erroneous behaviour due to dementia. The dataset is recorded by actors, who follow instructions describing normal and erroneous behaviour caused by the progression of dementia. Furthermore, we present a semantic annotation scheme which allows reasoning not only about the observed behaviour but also about the causes of the errors.
Automatic Detection and Characterization of Parasite Eggs using Deep Learning Methods

Parasitic infection can be detected by analyzing the microscopic image of fecal slide. The number of parasite eggs found in the slide will be used to determine the degree of infection. Opisthorchis Vivertini (OV) and Minute Intestinal Flukes (MIF) are among the most common parasites found in southeast Asia. OV affects liver whereas MIF affect small intestine. The egg of both parasites have almost the same size and shape which make it difficult to discriminate between them, even for experts. The detection and classification of parasite eggs in the micro-scopic image of the fecal slides can be seen as object detection problem. This work investigates the application of state-of-the-art object detectors to this task. The experimental results show that these deep learning models are capable of correctly detecting and classifying the parasite eggs.
Chinese Sign Language Recognition Based on DTW-Distance-Mapping Features

Sign language is an important communication tool between the deaf and the external world. As the number of the Chinese deaf accounts for 15% of the world, it is highly urgent to develop a Chinese sign language recognition (CSLR) system. Recently, a novel phonology- and radical-coded CSL, taking advantages of a limited and constant number of coded gestures, has been preliminarily verified to be feasible for practical CSLR systems. The keynote of this version of CSL is that the same coded gesture performed in different orientations has different meanings. In this paper, we mainly propose a novel two-stage feature representation method to effectively characterize the CSL gestures. First, an orientation-sensitive feature is extracted regarding the distances between the palm center and the key points of the hand contour. Second, the extracted features are transformed by a dynamic time warping- (DTW-) based feature mapping approach for better representation. Experimental results demonstrate the effectiveness of the proposed feature extraction and mapping approaches. The averaged classification accuracy of all the 39 types of CSL gestures acquired from 11 subjects exceeds 93% for all the adopted classifiers, achieving significant improvement compared to the scheme without DTW-distance-mapping.
Automatic Text Summarization and Keyword Extraction using Natural Language Processing

The process of gaining and absorbing the knowledge from various sources is a time-consuming process where people, mainly youth spend time surfing over the internet for relevant information. The proposed system mainly focuses on scraping the data from websites and providing the summary as well as keywords from the information extracted from various websites giving the user flexibility to select the website of their choice. The proposed system for the text summarization and keyword extraction undergoes a sequence of steps starting from data extraction from a website link, removal of outliers and irrelevant information, emphasizing on the importance of particular data extracted from the website and creating a summary of the extracted data. For the selection of relevant information from the extracted data, it is necessary to use natural language processing. The proposed project helps its users to reduce their surfing time and gives summary prepared from multiple website links and documents or keywords from a particular website or a document.
Token Economy: A New Form Economy with Decentralized Mutual Trust and Collective Governance

The token economy is a form of decentralized digital economy with the support of blockchain. In the token economy, the responsibility, rights and interests in the real economy can be solidified on the blockchain to form true digital commitments with consensus, tamper-resistance, traceability and verification. These commitments can not only enhance the mutual trust between economic entities, but also realize the isomorphism of real economic activities and digital economic activities. Furthermore, they are conducive to improving the protection of property rights, strengthening the market's allocation of resources, realizing the fair distribution of benefits, strengthening the government's penetrating supervision, and creating a mutually beneficial business environment. Therefore, the token economy is expected to become a new form of digital economy with mutual trust and collective governance. To better illustrate the token economy, this paper introduces the concepts of blockchain, token and token economy, presents the reason why token economy could create a new form of digital economy with mutual trust and collective governance, and finally advances some suggestions on how to develop the token economy.
SoloGAN: Multi-domain Multimodal Unpaired Image-to-Image Translation via a Single Generative Adversarial Network

Despite significant advances in image-to-image (I2I) translation with generative adversarial networks (GANs), it remains challenging to effectively translate an image to a set of diverse images in multiple target domains using a pair of generator and discriminator. Existing multimodal I2I translation methods adopt multiple domain-specific content encoders for different domains, where each domain-specific content encoder is trained with images from the same domain only. Nevertheless, we argue that the content (domain-invariance) features should be learned from images among all of the domains. Consequently, each domain-specific content encoder of existing schemes fails to extract the domain-invariant features efficiently. To address this issue, we present a flexible and general SoloGAN model for efficient multimodal I2I translation among multiple domains with unpaired data. In contrast to existing methods, the SoloGAN algorithm uses a single projection discriminator with an additional auxiliary classifier and shares the encoder and generator for all domains. As such, the SoloGAN model can be trained effectively with images from all domains so that the domain-invariance content representation can be efficiently extracted. Qualitative and quantitative results over a wide range of datasets against several counterparts and variants of the SoloGAN model demonstrate the merits of the method, especially for challenging I2I translation tasks, i.e., tasks that involve extreme shape variations or need to keep the complex backgrounds unchanged after translations. Furthermore, we demonstrate the contribution of each component using ablation studies.
Sentiment Classification on Thai Social Media Using a Domain-Specific Trained Lexicon

Social media contains many valuable documents which can be used to retrieve insights into any targeted topic. Sentiment analysis is a task to extract opinions of people out of their writing. Although a method using a sentiment lexicon is suitable for sentiment classification on social media documents, a lexicon created without consideration of any specific topic domain may not reflect actual intention in the domain of the considered documents. This study compares a sentiment lexicon created from documents in a specific domain with a generic lexicon. The experimental results show that using a specific lexicon improves classification performance, particularly on documents having non-neutral sentiment.
Real Time Patient Activity Monitoring and Alert System

In the current scenario, there is a need for patient monitoring systems in intensive care units (ICU) in hospitals. This system intimates doctors and nurses about the patient's physiological conditions. However, this system doesn't have a remote monitoring capability, which necessitates constant onsite attendance by the support of personnel doctors or Nurses. The proposed “Real-Time Patient Activity Monitoring and Alert System” is a system which consists of both hardware and software components at both the patients and doctor ends, to monitor the activities of the patient is normal or abnormal using a Web camera and temperature sensor. In case if the activity of the patient is abnormal, then the alert notification is sent to the doctor of that particular patient. The paper proposes a method to compare, capture, and generate alert messages regarding the patient's condition using the sensors and GSM module. Thus, the proposed system tries to provide an efficient monitoring system for effective health care services for the patients of ICU.
A content-based technique for linking dual language news articles in an archive

To retrieve a specific news article from a vast archive containing multilingual news articles against a user query or based on similarity among news articles is a challenging task. The task becomes even further complicated when the archive contains articles from a low resourced and morphologically complex language like Urdu, along with English new articles. The article proposes a content-based (lexical) similarity measure, that is, Common Ratio Measure for Dual Language (CRMDL), for linking digital news articles published in various online news sources. The similarity measure links Urdu-to-English news articles during the preservation process using an Urdu-to-English lexicon. A literature review showed that an Urdu-to-English lexicon did not exist, and therefore, the first task was to build a lexicon from multiple sources. The proposed similarity measure, that is, CRMDL, is evaluated rigorously on different data sets, of varying sizes, to assess the effectiveness. The experimental results show that the proposed measure is feasible and effective for similarity computation between Urdu and English news articles, which can obtain, on average, 50% precision and 67% recall. The performance can be improved sufficiently by managing the limitations summarised in the study.