Reviewerly/exHarmony huggingface dataset:
An Axis Permutation Based on Laplacian Scores to Enhance the Visualization Performance of PCPs

Over the past decades, parallel coordinates plots (PCPs) have been extensively applied to various tasks of multivariate data visualization. However, a main disadvantage of PCPs is the almost inevitable overlap of polylines, which causes chaotic visual effects when the number of polylines is large and the sequence of parallel coordinates is not conducive to visualization. To tackle this issue, this paper proposed an axis permutation of PCPs based on Laplacian scores (LS), which can be used to enhance the visualization performance of the original PCPs. The enhanced PCP is a combination of an unsupervised feature selection technique (LS) and the visualization of multivariate data. With the new sequence of axes based on the Laplacian scores of the corresponding features, polylines of PCPs are distributed with less clutter and overlap. The experimental results demonstrated that the proposed method made great progress in enhancing the visualization performance of PCPs.
Bi-Lingual TDNN-LSTM Acoustic Modeling for Limited Resource Hindi and Marathi Language ASR

It is a need of time to build an automatic speech recognition (ASR) system for low-resource languages. India is a multilingual country with more than 5000 languages, and out of which 22 languages are official. The ASR systems for Indian languages are still in the infancy stage, mainly due to resource deficiency (e.g., lack of transcribed speech data, pronunciation lexicon, and text data). Deep neural networks (DNNs) have significantly improved the performance of the ASR system of low-resource languages. The merging data of various languages is a commonly used trend to train the multilingual DNN acoustic model. In multilingual training, hidden layers act as a global feature extractor. The multilingual ASR systems work better for similar kinds of sources and target languages. In this work, we use two low-resource Indian languages, namely Hindi and Marathi. Both languages are closely related and belong to the same Indo-Aryan family. Both languages include various common phonemes and words. Various state-of-the-art experiments were performed using different acoustic modeling and language modeling techniques. Experiments demonstrated that multilingual ASR systems consistently outperform monolingual ASR systems for both Hindi and Marathi languages.
An Investigation into Virtual Immersion Mandarin Chinese Writing Instruction with Student with Autism

Research findings show that using virtual world environments, such as Second Life (SL), for language learning can create a more comfortable yet authentic learning environment for language learners. This research focused on the influence of virtual immersion Mandarin Chinese writing instruction with a participant with autism by conducting an action research to seek possible solutions for teaching and learning needs. In this research, there were 2 cycles: traditional writing instruction (TWI) and virtual immersion writing instruction (VIWI). Three writing topics were presented to the participant. The topics were: describe the kitchen, going to the convenience store, and comparisons between different transportations. Each topic was presented to the participant during 2 class sessions, 50 min each. First session was for prewriting planning activities by using mind maps. Second session was to review the prewriting materials and then move on to the writing activity. For results, summarization and comparison of the mind maps and essays from the participant were conducted. The essays were graded then the scores were compared. Oral interviews regarding attitudes toward Mandarin Chinese learning, Mandarin Chinese writing learning, and 3D virtual reality learning were collected further to understand the participant’s thoughts in these areas. Results show that the incorporation of VIWI not only changed the participant’s attitude toward Mandarin Chinese learning, and Mandarin Chinese writing learning but also enhanced the participant’s prewriting planning skills and Mandarin Chinese essay writing skills.
Optimization and Innovation of SIFT Feature Matching Algorithm in Static Image Stitching Scheme

Abstract With the development of computer technology, people can obtain electronic images by a variety of means, but these images often need to obtain a panoramic view of a large field of view from multiple limited field of view images because the field of view is smaller than that of humans, and image stitching technology can be very good Solve the contradiction between vision and resolution. This paper studies the extraction and registration of image feature points and its related technologies, including image feature detection and extraction, point feature-based matching, and image fusion. According to the requirements of matching and image splicing, the feature points with good robustness to image deformation and occlusion are used as image features to indicate that it meets the requirements of image splicing registration. This paper studies and designs an improved algorithm to improve the accuracy of feature points extraction. Further research and analysis of the SIFT operator based on the characteristics of the invariant technology realize that the feature points extracted from the image can be used for reliable matching of an object or scene from different perspectives.
CARS: A Containerized Amazon Recommender System

With the big data boom, recommender systems that make intelligent recommendations for users have been playing an important role in today’s industry. However, existing recommender systems often overlook scalability, flexibility, and portability. They also commonly lack in-situ visualizations. To solve these problems, we present CARS: A Containerized Amazon Recommender System. CARS processes large Amazon data sets for analysis and makes product recommendations. However, its utility is not restricted to only prominent organizations like Amazon. CARS achieves scalability by taking advantage of industry-grade recommendation tools irrespective of available hardware resources. CARS runs in a completely isolated environment to promote flexibility and remote collaboration. The demonstrated implementation generates shopping recommendations from user ratings within product review data sets. CARS processes this review data using Apache Spark, a unified analytics engine for big data. The system complements recommendations with data-driven insights and interactive visualizations. In addition to these features, CARS contains a robust set of command line options to customize the results shown to the end-user, perform logging of processed data, and provide performance monitoring through Spark’s built-in web-interface. Highly portable and automated analysis of purchase data helps organizations understand the habits of their customers. CARS demonstrates the feasibility of such a system for a wide variety of users.
Sample-optimal and efficient learning of tree Ising models

We show that n-variable tree-structured Ising models can be learned computationally-efficiently to within total variation distance є from an optimal O(n lnn/є2) samples, where O(·) hides an absolute constant which, importantly, does not depend on the model being learned—neither its tree nor the magnitude of its edge strengths, on which we place no assumptions. Our guarantees hold, in fact, for the celebrated Chow-Liu algorithm [1968], using the plug-in estimator for estimating mutual information. While this (or any other) algorithm may fail to identify the structure of the underlying model correctly from a finite sample, we show that it will still learn a tree-structured model that is є-close to the true one in total variation distance, a guarantee called “proper learning.”
Homodyne Noise Characterization in Quantum Random Number Generators

Quantum random number generators (QRNGs) based on homodyne measurements of vacuum fluctuations are in high-demand as fast and secure sources of randomness for vital applications such as cryptographic protocols. Despite the high-volume of research, most implementations rely on a balanced homodyne detection (BHD) scheme with a continuous-wave local oscillator. Typically, these operate under the assumption that electronic noise levels remain constant throughout large spans of time. In this work, we propose and analyse a suitable setup for a QRNG based on BHD using a pulsed local oscillator. Contrarily to previous schemes, this implementation introduces the possibility of real-time electronic noise monitoring, which can help increase robustness against attacks. The experimental setup is characterized to assess the conditions where quantum fluctuations are dominant. Finally, we compare it with the more traditional continuous-wave implementation and highlight the advantages of each implementation.
Automating the Diagram Method to Prove Correctness of Program Transformations

We report on the automation of a technique to prove the correctness of program transformations in higher-order program calculi which may permit recursive let-bindings as they occur in functional programming languages. A program transformation is correct if it preserves the observational semantics of programs. In our LRSX Tool the so-called diagram method is automated by combining unification, matching, and reasoning on alpha-renamings on the higher-order meta-language, and automating induction proofs via an encoding into termination problems of term rewrite systems. We explain the techniques, we illustrate the usage of the tool, and we report on experiments.
Optimal Additive Quaternary Codes of Low Dimension

An additive quaternary [n,k,d]-code (length n, quaternary dimension k, minimum distance d) is a 2k-dimensional \mathbb F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -vector space of n-tuples with entries in \mathbb F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> ⊕\mathbb F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> (the 2-dimensional vector space over \mathbb F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> ) with minimum Hamming distance d. We determine the optimal parameters of additive quaternary codes of dimension k ≤ 3. The most challenging case is dimension k=2.5. We prove that an additive quaternary [n,2.5,d]-code where d <; n-1 exists if and only if 3(n-d) ≥ d/2+d/4+d/8 articular, we construct new optimal 2.5-dimensional additive quaternary codes. As a by-product, we give a direct proof for the fact that a binary linear [3m,5,2e] <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -code for e <; m-1 exists if and only if the Griesmer bound 3(m-e) ≥ e/2+e/4+e/8 is satisfied.
Administrative Justice in a Digital World

Abstract Using digital tools in administrative decision-making—from automation of relatively simple decisions to artificial intelligence judgements—both enhances and challenges the operation of administrative justice. By beginning with an understanding of digital algorithms as comprising computer code, digital data, and use context, this chapter highlights challenges for administrative justice in administrative discretion, data challenges, automating decisions and errors, information about administrative justice, appealability and accountability responsibility, and explainability. The chapter then examines legal, policy, and technological responses to strengthen administrative justice, including expanding digital rights, bolstering review rights via providing explanations and software code, and instituting organizational governance innovations and technical standards.
Avoiding Counterfeits and Achieving Privacy in Supply Chain: A Blockchain Based Approach

Counterfeiting is one of the biggest threats to the current RFID-based supply chain industry. There are several blockchain solutions suggested which preserve RFID tag uniqueness and bring all the parties to a common platform. However, they are based on some common assumptions such as an adversary may need a large number of tags to retrieve the product details. Besides, these tags can be easily cloned post supply chain and reused for counterfeiting. Lack of proper mechanisms to preserve trade secrets such as volumes and supplier relationships is also a major challenge faced by most of these systems in this competitive market. We propose a novel product ownership tracking system based on blockchain for the supply chain industry which solves these problems while allowing access to the trade secrets only to its supply chain participants. The proposed protocol is based on Shamir's threshold Scheme where a secret key is used to decrypt the trade secrets and its shares are distributed among the supply chain parties during the ownership transfer process. Product verification is done by the consumer as well with the help of the retailer at the time of sale.
Deep MammoNet: Early Diagnosis of Breast Cancer Using Multi-layer Hierarchical Features of Deep Transfer Learned Convolutional Neural Network

Deep Convolutional Neural Network (CNN) comprises of multiple convolutional layers which learn feature form input image with different levels of abstraction; In this work, we address the issue to improvise the recognition accuracy of CNN for classification of breast cancer from mammogram images. To achieve optimize classification we propose a multilayer hierarchical convolutional feature integration in deep transfer learned CNN. In Deep CNN the last layer learns significant features that are highly invariant but their spatial resolutions are too stiff to exactly confine the target. In contrast, features from earlier layers offer more exact localization and hold more fine-grained spatial subtleties for exact confinement but are less invariant. This observation recommends that reasoning with multiple layers of CNN features for breast cancer detection from mammogram images is of great importance. In this work, we proposed to integrate the features extracted from the earlier layer and the last layer of deep CNN to train and improvise the classification accuracy of breast cancer detection in the mammogram image. We also discussed that consistent improvement in accuracy is obtained by using mammogram augmentation and different weight learning factors across different layers.
In-Camera Raw Compression: A New Paradigm from Image Acquisition to Display

We propose the NeuralCAM- a new camera processing paradigm where we directly compress the sensor raw Bayer data (RAW) and apply the image signal processor (ISP) at users’ client for on-demand rendering and processing. Both RAW compression and ISP processing are built upon the deep neural network (DNN)-based approaches that fully leverage the power of data-driven method to efficiently capture the underlying statistics for improved processing capacity. Experiments have shown 3 - 4 dB PSNR gains when compared with state-of-the-art image coders in RAW format, and have also revealed superior image quality in RGB space by mimicking the Professional ISPs that often couldn’t afforded by power-constrained mobile chips.
Talking Blockchains: The Perspective of a Database Researcher

There are few topics out there, that seem to create as much confusion and discussion as blockchains. This has a multitude of reasons: (1) A large number of drastically different concepts and systems are unified under the very broad term "blockchain". (2) The topic touches a variety of different fields, including databases, distributed processing, networks, cryptography, and even economics. (3) There exists a large number of different applications of the technology.The goal of this paper is to simplify and structure the discussion of blockchain technology. We first introduce a simple formalization of the basic components, that appear again and again in a variety of blockchain systems. Second, we formalize four important execution models, that express the workflow of a large number of blockchain systems. Third, along the way, the we also discuss certain misconceptions, that constantly reappear in discussions. We believe that a common formalization of the transaction processing behavior of blockchain systems both helps beginners to get into the topic as well as can bring experts from different fields to a common denominator when discussing blockchain systems.
Wukong

Querying graph data is becoming increasingly prevalent and important across many application domains, like social networking, urban monitoring, electronic payment, and semantic webs. In the last few years, we have ben working on improving the performance of graph querying by leveraging new hardware features and system designs. Moving towards this goal, we have designed and developed Wukong, a distributed in-memory framework that provides low latency and high throughput for concurrent query processing over large and fast-evolving graph data. This article overviews our architecture and presents four systems that aim to satisfy diverse challenging requirements on graph querying (e. g. high concurrency, evolving graphs, workload heterogencity, and locality preserving). Our systems also significantly outperform state-of-the-art systems in both latency and throughput, usually by orders of magnitude.
Energy-efficient IoT based improved health monitoring system for sports persons

Nowadays, wearable technology and the Internet of Things (IoT) are transforming the healthcare sector by refining the way how devices, applications, and people connect and interact with each other. IoT applications in sports are tremendously useful to monitor health and reduce the risk factor. The battery life of wearable and accurate monitoring has been considered a significant challenge in sports medicine. Hence, in this paper, Energy Efficient IoT based Improved Health Monitoring system (EEIoT-IHMS) has been proposed for accurate and continuous sports person’s health monitoring system. This paper determines the optimal set of clusters based on sensor features, in which power usage has been minimized by duty cycling with optimized prediction accuracy. The experimental results demonstrate that the proposed (EEIoT-IHMS) enhances accuracy ratio, improves battery life, and reduces energy consumption compared to other popular methods.
Text Mining of Research Articles Using Clustering Approach

Widening of research articles publication in various streams of research is epidemic. Tracking down of an appropriate article from the research archive is considered to be vast and also time consuming. Research articles are clustered based on their respective domain and it plays an important role for researchers to retrieve articles in a faster manner. Hence a commonly practiced search mechanism, namely domain name search has been applied to retrieve appropriate documents and articles. When new domains of documents are added to the repository it’s to spot keywords and boost the corresponding domains for proper classification. Classification techniques namely Random forest classifier, SVM and TF-IDF have been used to classify articles and compare its processing time. TF-IDF (Term Frequency-Inverse Document Frequency) has been further proposed to transform the corpus into vector space model. Clustering algorithm such as K-Means and Hierarchical have been used to cluster articles. Finally, the processing time of SVM is better than random forest classifier and TF-IDF and K-Means gives a better understanding than Hierarchical algorithm.
Camera and Lidar-Based View Generation for Augmented Remote Operation in Mining Applications

Remote operation of diggers, scalers, and other tunnel-boring machines has significant benefits for worker safety in underground mining. Real-time augmentation of the presented remote views can further improve the operator effectiveness through a more complete presentation of relevant sections of the remote location. In safety-critical applications, such augmentation cannot depend on preconditioned data, nor generate plausible-looking yet inaccurate sections of the view. In this paper, we present a capture and rendering pipeline for real time view augmentation and novel view synthesis that depends only on the inbound data from lidar and camera sensors. We suggest an on-the-fly lidar filtering for reducing point oscillation at no performance cost, and a full rendering process based on lidar depth upscaling and in-view occluder removal from the presented scene. Performance assessments show that the proposed solution is feasible for real-time applications, where per-frame processing fits within the constraints set by the inbound sensor data and within framerate tolerances for enabling effective remote operation.
Explainable artificial intelligence

Artificial intelligence (AI) is rapidly reshaping business and society. Fueled and guided by big data, AI is making a profound impact on lives, livelihoods, and future opportunities. AI and big data are changing customer experiences, business operations, strategies, and entire industry sectors. State-of-the-art AI algorithms and big data outperform human experts on a range of challenging prediction problems. However, these same algorithms can be bias, fragile, and unable to explain their output. These flaws can lead to regulatory and legal issues. In this chapter, we explore the nature of AI systems and the performance measures used to evaluate their decisions. We discuss the legal imperative to broaden these metrics beyond narrow mathematical measures of optimality such that they include explanation capabilities. Making algorithms and data transparent is not enough to determine if AI decisions are discriminatory, unfair, or wrong. Explanations can ensure AI behavior is law abiding and trustworthy.
Real-Time Object Detection by Feature Map Forecast for Live Streaming Video

This paper proposes a method that jointly learns to detect objects at the current frame and forecast the next frame’s future feature map. Previous offline detectors have shown the effectiveness of utilizing future information in video object detection; however, we cannot take such an approach when dealing with live streaming videos. In contrast, we utilize the forecast feature map with the current and past frame feature maps for object detection, where forecast feature maps are learned using observation of the present and past frames. To maintain a reliable forecast, we introduce a scheduler network, which decides whether we use the forecast feature map as input or extract the feature map from the next frame. Evaluations of our proposed model on the ImageNet VID dataset demonstrate the superior performance of our model against the public benchmark at similar architectures, with achieving 65.7% mAP at 38.9 fps.
Grouped Multi-Layer Echo State Networks with Self-Normalizing Activations

We study prediction performance of Echo State Networks with multiple reservoirs built based on stacking and grouping. Grouping allows for developing independent subreservoir dynamics, which improves linear separability on readout layer. At the same time, stacking enables to capture multiple time-scales of an input signal by the hierarchy of non-linear mappings. Combining those two effects, together with a proper selection of model hyperparameters can boost ESN capabilities for benchmark time-series such as Mackey Glass System. Different strategies for determining subreservoir structure are compared along with the influence of activation function. In particular, we show that recently proposed non-linear self-normalizing activation function together with grouped deep reservoirs provide superior prediction performance on artificial and real-world datasets. Moreover, comparing to standard tangent hyperbolic models, the new models built using self-normalizing activation function are more feasible in terms of hyperparameter selection.