Reviewerly/exHarmony huggingface dataset:
Multi-task learning in under-resourced Dravidian languages

Abstract It is challenging to obtain extensive annotated data for under-resourced languages, so we investigate whether it is beneficial to train models using multi-task learning. Sentiment analysis and offensive language identification share similar discourse properties. The selection of these tasks is motivated by the lack of large labelled data for user-generated code-mixed datasets. This paper works with code-mixed YouTube comments for Tamil, Malayalam, and Kannada languages. Our framework is applicable to other sequence classification problems irrespective to the size of the datasets. Experiments show that our multi-task learning model can achieve high results compared to single-task learning while reducing the time and space constraints required to train the models on individual tasks. Analysis of fine-tuned models indicates the preference of multi-task learning over single task learning resulting in a higher weighted F1 score on all three languages. We apply two multi-task learning approaches to three Dravidian languages, Kannada, Malayalam, and Tamil. Maximum scores on Kannada and Malayalam were achieved by mBERT subjected to cross entropy loss and with an approach of hard parameter sharing. Best scores on Tamil was achieved by DistilBERT subjected to cross entropy loss with soft parameter sharing as the architecture type. For the tasks of sentiment analysis and offensive language identification, the best performing model scored a weighted F1-Score of (66.8%, 90.5%), (59%, 70%) and (62.1%,75.3%) for Kannada, Malayalam and Tamil on sentiment analysis and offensive language identification respectively.
Sensoring the Neck: Classifying Movements and Actions with a Neck-Mounted Wearable Device

Sensor technology that captures information from a user's neck region can enable a range of new possibilities, including less intrusive mobile software interfaces. In this work, we investigate the feasibility of using a single inexpensive flex sensor mounted at the neck to capture information about head gestures, about mouth movements, and about the presence of audible speech. Different sensor sizes and various sensor positions on the neck are experimentally evaluated. With data collected from experiments performed on the finalized prototype, a classification accuracy of 91% in differentiating common head gestures, a classification accuracy of 63% in differentiating mouth movements, and a classification accuracy of 83% in speech detection are achieved.
Sentiment Analysis about COVID-19 vaccine on Twitter data: Understanding Public Opinion

The COVID-19 pandemic has affected almost every segment of the worldwide population. Many vaccines were developed to cope with the ongoing COVID-19 outbreak. Indian government had initiated mass vaccination in early 2021 with objective of 100% vaccinated country. However, in spite of large number of awareness programs on the vaccination for the general public, still after more than one year 100% vaccination is not achieved. The reason for this is the hesitancy and lack of belief on the vaccination. The vaccination for children is now available and open for the general public. In order to boost the vaccination task, it is essential to understand the public opinion about the different vaccines available for adults and children. Social media platform, Twitter, is the most effective medium to know about the public perception and opinion about the vaccines. Therefore, an automated analysis of the public opinion using Twitter data is necessary to understand the mental thought process of public for the vaccine. The analysis allows the Indian governments to take some proactive measures to increase the public belief in the vaccine. Thus, the present study is intended to analyze the public sentiment on COVID-19 vaccination. 6000 real time tweets were fetched from the Twitter API with hashtag covid, covid19, vaccine, covaxin, covishield and childrenvaccine. The sentiments were analyzed and categorized into positive, negative and neutral sentiment by using TextBlob, Flair NLP, Stanza, and NLTK Vader algorithms. The performance of different algorithms was compared and evaluated. It was observed that NLTK Vader analyzed the public sentiment most correctly then the other algorithms. It was found that 34.210% sentiment were still negative about the vaccine which is a great matter of concern for the Indian governments.
Optimizing convolutional neural networks on multi-core vector accelerator

Vector Accelerators have been widely used in scientific computing. It also shows great potential to accelerate the computational performance of convolutional neural networks (CNNs). However, previous general CNN-mapping methods introduced a large amount of intermediate data and additional conversion, and the resulting memory overhead would cause great performance loss. To address these issues and achieve high computational efficiency, this paper proposes an efficient CNN-mapping method dedicated to vector accelerators, including: 1) Data layout method: establishing a set of efficient data storage and computing models for various CNN networks on vector accelerators. It achieves high memory access efficiency and high vectorization efficiency. 2) A conversion method: convert the computation of convolutional layers and fully connected layers into large-scale matrix multiplication, and convert the computation of pooling layers into row computation of matrix. All conversions are implemented by extracting rows from a two-dimensional matrix, with high data access and transmission efficiency, and without additional memory overhead and data conversion. Based on these methods, we design a vectorization mechanism to vectorize convolutional, pooling and fully connected layers on a vector accelerator, which can be applied for various CNN models. This mechanism takes full advantage of the parallel computing capability of the multi-core vector accelerator and further improves the performance of deep convolutional neural networks. The experimental results show that the average computational efficiency of the convolutional layers and full connected layers of AlexNet, VGG-19, GoogleNet and ResNet-50 is 93.3% and 93.4% respectively, and the average data access efficiency of pooling layer is 70%. Compared to NVIDIA inference GPUs, our accelerator achieves a 36.1% performance improvement, comparable to NVIDIA V100 GPUs. Compared with Matrix2000 of similar architecture, our accelerator achieves a 17-45% improvement in computational efficiency.
An Unsupervised Transfer Learning Framework for Visible-Thermal Pedestrian Detection

Dual cameras with visible-thermal multispectral pairs provide both visual and thermal appearance, thereby enabling detecting pedestrians around the clock in various conditions and applications, including autonomous driving and intelligent transportation systems. However, due to the greatly varying real-world scenarios, the performance of a detector trained on a source dataset might change dramatically when evaluated on another dataset. A large amount of training data is often necessary to guarantee the detection performance in a new scenario. Typically, human annotators need to conduct the data labeling work, which is time-consuming, labor-intensive and unscalable. To overcome the problem, we propose a novel unsupervised transfer learning framework for multispectral pedestrian detection, which adapts a multispectral pedestrian detector to the target domain based on pseudo training labels. In particular, auxiliary detectors are utilized and different label fusion strategies are introduced according to the estimated environmental illumination level. Intermediate domain images are generated by translating the source images to mimic the target ones, acting as a better starting point for the parameter update of the pedestrian detector. The experimental results on the KAIST and FLIR ADAS datasets demonstrate that the proposed method achieves new state-of-the-art performance without any manual training annotations on the target data.
A novel way to formalize stable graph cores by using matching-graphs

The increasing amount of data available and the rate at which it is collected leads to rapid developments of systems for intelligent information processing and pattern recognition. Often the underlying data is inherently complex, making it difficult to represent it by linear, vectorial data structures. This is where graphs offer a versatile alternative for formal data representation. Actually, quite an amount of graph-based methods for pattern recognition has been proposed. A considerable part of these methods rely on graph matching. In the present paper, we propose a novel encoding of specific graph matching information. The basic idea is to formalize the stable cores of individual classes of graphs – discovered during intra-class matchings – by means of so called matching-graphs. We evaluate the benefit of these matching-graphs by researching two classification approaches that rely on this novel data structure. The first approach is a distance based classifier focusing on the matching-graphs during dissimilarity computation. For the second approach, we propose to use sets of matching-graphs to embed input graphs into a vector space. The basic idea is to produce hundreds of matching-graphs first, and then represent each graph g as a vector that shows the occurrence of, or the distance to, each matching-graph. In a thorough experimental evaluation on seven real world data sets we empirically confirm that our novel approaches are able to improve the classification accuracy of systems that rely on comparable information as well as state-of-the-art methods.
A Fuzzy Logic based control system for Electric Wheelchair obstacle avoidance

This paper proposes a shared-control system for electric wheelchair (EWC) to avoid obstacles with a fuzzy logic (FL) based strategy. A fuzzy logic controller is integrated into an existent electric wheelchair to decrease the risk of crashes and collisions while navigating in a crowded room. An Arduino micro-controller connected to 3 ultrasonic sensors detects distances to obstacles around the wheelchair within a radius of 90◦. It sends data to a Raspberry pi II microprocessor on which we have implemented our Fuzzy logic based algorithm of obstacle avoidance. The data resulting from the fuzzy algorithm control the wheelchair’s linear acceleration.The software is implemented on the Unix Platform. 4 data sets are used to test the controller. Experimental results give us new adjusted acceleration values.
Snakes on a Plan: Compiling Python Functions into Plain SQL Queries

"Move your computation close to the data" is decades-old advice that is hard to follow if your code exhibits complex control flow. The runtime of such applications suffers from a continual back and forth between database-external code execution and plan-based SQL evaluation. We demonstrate the ByePy compiler which translates entire Python functions with arbitrary control flow-including deeply nested iteration-into plain recursive SQL:1999 queries. The invocation of a ByePy-compiled function enters the database engine once to execute the plan of a single query. Computation does not get much closer to the data than this. The system rewards this translation effort from Python to SQL with runtime improvements of up to an order of magnitude.
Explainable Machine Learning for Longitudinal Multi-Omic Microbiome

Over the years, research studies have shown there is a key connection between the microbial community in the gut, genes, and immune system. Understanding this association may help discover the cause of complex chronic idiopathic disorders such as inflammatory bowel disease. Even though important efforts have been put into the field, the functions, dynamics, and causation of dysbiosis state performed by the microbial community remains unclear. Machine learning models can help elucidate important connections and relationships between microbes in the human host. Our study aims to extend the current knowledge of associations between the human microbiome and health and disease through the application of dynamic Bayesian networks to describe the temporal variation of the gut microbiota and dynamic relationships between taxonomic entities and clinical variables. We develop a set of preprocessing steps to clean, filter, select, integrate, and model informative metagenomics, metatranscriptomics, and metabolomics longitudinal data from the Human Microbiome Project. This study accomplishes novel network models with satisfactory predictive performance (accuracy = 0.648) for each inflammatory bowel disease state, validating Bayesian networks as a framework for developing interpretable models to help understand the basic ways the different biological entities (taxa, genes, metabolites) interact with each other in a given environment (human gut) over time. These findings can serve as a starting point to advance the discovery of novel therapeutic approaches and new biomarkers for precision medicine.
Enabling the Next Generation of Multi-Region Applications with CockroachDB

A database service is required to meet the consistency, performance, and availability goals of modern applications serving a global user-base. Configuring a database deployed across multiple regions such that it fulfils these goals requires significant expertise. In this paper, we describe how CockroachDB makes this easy for developers by providing a high-level declarative syntax that allows expressing data access locality and availability goals through SQL statements. These high-level goals are then mapped to database configuration, replica placement, and data partitioning decisions. We show how all layers of the database, from the SQL Optimizer to Replication, were enhanced to support multi-region workloads. We also describe a new Transaction Management protocol that enables local, strongly consistent reads from any database replica. Finally, the paper includes an extensive evaluation demonstrating that CockroachDB's new declarative SQL syntax for multi-region clusters is easy to use and supports a variety of configuration options with different performance tradeoffs to benefit a variety of workloads. We also show that throughput scales linearly with the number of regions, and the new Transaction Management protocol reduces tail latency by over 10x compared to prior approaches.
TimeUnion: An Efficient Architecture with Unified Data Model for Timeseries Management Systems on Hybrid Cloud Storage

Timeseries management systems have attracted considerable attention during the last decade with the rise of IoT and performance monitoring. With the rapidly increasing data scale in the production environment, deploying timeseries management systems on cloud with cloud storage is a natural trend because of its high availability, reliability, and scalability. However, the state-of-the-art designs are not tailored for cloud environments; they suffer from the limited number of timeseries a single compute node can handle because of the imbalanced resource usage.
Advanced encryption schemes in multi-tier heterogeneous internet of things: taxonomy, capabilities, and objectives

The Internet of Things (IoT) is increasingly becoming widespread in different areas such as healthcare, transportation, and manufacturing. IoT networks comprise many diverse entities, including smart small devices for capturing sensitive information, which may be attainable targets for malicious parties. Thus security and privacy are of utmost importance. To protect the confidentiality of data handled by IoT devices, conventional cryptographic primitives have generally been used in various IoT security solutions. While these primitives provide just an acceptable level of security, they typically neither preserve privacy nor support advanced functionalities. Also, they overly count on trusted third parties because of some limitations by design. This multidisciplinary survey paper connects the dots and explains how some advanced cryptosystems can achieve ambitious goals. We begin by describing a multi-tiered heterogeneous IoT architecture that supports the cloud, edge, fog, and blockchain technologies and assumptions and capabilities for each layer. We then elucidate advanced encryption primitives, namely wildcarded, break-glass, proxy re-encryption, and registration-based encryption schemes, as well as IoT-friendly cryptographic accumulators. Our paper illustrates how they can augment the features mentioned above while simultaneously satisfying the architectural IoT requirements. We provide comparison tables and diverse IoT-based use cases for each advanced cryptosystem as well as a guideline for selecting the best one in different scenarios and depict how they can be integrated.
Salt and Pepper Noise Removal Method Based on the Edge-Adaptive Total Variation Model

The traditional median filter can handle the image salt and pepper noise better. However, when the noise intensity is large, it is often necessary to enlarge the filter window to ensure the denoising effect, but the enlarged window may also cause excessive smoothing of the image, loss of texture details, and blurred edges. In view of the strong edge preservation characteristics of variational model denoising, we propose a salt and pepper noise removal method based on the edge-adaptive total variational model. Firstly, the image is segmented into edge regions and non-edge regions by edge detection operators. Secondly, the salt and pepper noise of the image is processed using the median filter and adaptive total variation model, respectively. Lastly, the non-edge regions processed by the median filter and the edge regions processed by the adaptive total variation model are extracted for splicing. The experimental results show that the method cannot only effectively remove salt and pepper noise, but also effectively protect the main edge details of the image.
On the environmental sustainability of Ai art(s)

A wide variety of creative practitioners are currently exploring the use of Ai in their work processes, for instance in poetry, music, performance, and visual arts. In this paper, we discuss the relationship between Ai and sustainability in general but focus on the relationship between the emerging area of Ai art and sustainability in particular. We highlight the importance of pursuing research concerning the sustainability of Ai art and take initial steps towards understanding how Ai art practices may influence how we save or waste resources. Developed through our online fieldwork by analyzing the environmental impact of three specific cases of Ai artworks, we provide a conceptual approach that can be used to map the environmental sustainability of Ai art. With this paper as a basis, we hope to elicit awareness among scientific and artistic communities about the environmental sustainability of Ai art.
The Dissemination and Evaluation of Campus Ideological and Political Public Opinion Based on Internet of Things Monitoring

With the advent of the information age, the rapid development of Internet of Things technology makes monitoring methods more flexible and changeable. As an intelligent application, the development of the Internet of Things brings convenience to public opinion monitoring. However, at present, due to the high price of transmission equipment, inconvenient maintenance, information delay, and other unfavorable conditions, real-time and controllable public opinion monitoring cannot be carried out on a large scale, and there are still many deficiencies in campus ideological and political public opinion monitoring. We found an effective transmission method, explored the network energy saving, dig deep into the transmission function of the signal system, and reduced the interference and mutual influence of various transmission accessories. In the application of the Internet of Things, the network public opinion is remotely monitored, the campus public opinion information is mastered, and its dissemination and development orientation are controlled. The exploration and research of online public opinion monitoring are in line with the goal of smart campus, and its theoretical development is constantly enriched.
THE ROLE OF ECONOMIC GROWTH IN MODULATING MOBILE CONNECTIVITY DYNAMICS FOR FINANCIAL INCLUSION IN DEVELOPING COUNTRIES

This study establishes economic growth needed for supply-side mobile money drivers in developing countries to be positively related to mobile money innovations in the perspectives of mobile money accounts, the mobile phone used to send money, and the mobile phone used to receive money. The empirical evidence is based on Tobit regressions. For the negative net relationships that are computed, minimum economic growth thresholds are established above which the net negative relationships become net positive relationships. The following minimum economic growth rates are required for nexuses between supply-side mobile money drivers and mobile money innovations to be positive: (i) 6.109 percent (6.193%) of Gross Domestic Product (GDP) growth for mobile connectivity performance to be positively associated with the mobile phone used to send (receive) money and (ii) 4.590 percent (4.259%) of GDP growth for mobile connectivity coverage to be positively associated with the mobile phone used to send (receive) money.
Drawing the web structure and content analysis beyond the Tor darknet: Freenet as a case of study

The World Wide Web is the most widely used service on the Internet, although only a small part of it, the Surface Web, is indexed and accessible. The rest of the content, the Deep Web, is split between that unable to be indexed by usual search engines and content that needs to be accessed through specific methods and techniques. The latter is deployed in the so-called darknets, which have been the subject of much less study, where anonymity and privacy security services are preserved. Although there are several darknets, Tor is the most well-known and widely analyzed. Hence, the current work presents an analysis of web site connectivity, relationships and content of one of the less known and explored darknets: Freenet. Given the special features of this study, a new crawling tool, called c4darknet, was developed for the purpose of this work. This tool is, in turn, used in the experimentation that was carried out in a real distributed environment. Our results can be summarized as follows: there is great general availability of websites on Freenet; there are significant nodes within the network connectivity structure; and underage porn or child pornography is predominant among illegal content. Finally, the outcomes are compared against a similar study for the I2P darknet, showing special features and differences between both darknets.
What If Artificial Intelligence Become Completely Ambient in Our Daily Lives? Exploring Future Human-AI Interaction through High Fidelity Illustrations

As artificial intelligence (AI) has become prevalent in users' daily lives, it is becoming critical for HCI designers to envision and design future human-AI interactions. Recent studies have highlighted the importance of exploring user-centered future design directions before implementing a technology in users' lives. However, it is challenging for HCI designers to envision the societal impact of future technology that does not exist and understand potential users' perceptions. Therefore, to comprehensively envision future human-AI interactions and their impact and elicit potential users' perceptions of the future technologies, we created high-fidelity illustrations with designers and illustrators for immersive experience. Subsequently, through an online exhibition of these illustrations, we derived potential users' perceptions, expectations, and concerns about the future. Based on our findings, we explored user-centered considerations for implementing AI in users' daily lives through elaborately articulated human-AI interactions.