Reviewerly/exHarmony huggingface dataset:
Efficient Vehicle Detection and Distance Estimation Based on Aggregated Channel Features and Inverse Perspective Mapping from a Single Camera

In this paper a method for detecting and estimating the distance of a vehicle driving in front using a single black-box camera installed in a vehicle was proposed. In order to apply the proposed method to autonomous vehicles, it was required to reduce the throughput and speed-up the processing. To do this, the proposed method decomposed the input image into multiple-resolution images for real-time processing and then extracted the aggregated channel features (ACFs). The idea was to extract only the most important features from images at different resolutions symmetrically. A method of detecting an object and a method of estimating a vehicle’s distance from a bird’s eye view through inverse perspective mapping (IPM) were applied. In the proposed method, ACFs were used to generate the AdaBoost-based vehicle detector. The ACFs were extracted from the LUV color, edge gradient, and orientation (histograms of oriented gradients) of the input image. Subsequently, by applying IPM and transforming a 2D input image into 3D by generating an image projected in three dimensions, the distance between the detected vehicle and the autonomous vehicle was detected. The proposed method was applied in a real-world road environment and showed accurate results for vehicle detection and distance estimation in real-time processing. Thus, it was showed that our method is applicable to autonomous vehicles.
Fast image similarity search by distributed locality sensitive hashing

Approximate Nearest Neighbor (ANN) search approaches that use possible neighbors instead of exact neighbors are widely investigated by researchers in recent years. ANN approaches are usually applied in a centralized manner. However, in real world applications data is usually stored in a distributed manner. This situation led to the need for implementing ANN methods in a distributed way. In this study, our goal is to perform fast and accurate search on large size image datasets by using distributed environments. For this purpose, we propose an approach called as Randomized Distributed Hashing (RDH) which uses Locality Sensitive Hashing (LSH) in a distributed scheme. In this approach, we have randomly distributed data to different nodes on a cluster. After the distribution of data, in each node we have used same randomized hash function set for indexing the local data. Then at the query stage, the query sample is locally searched in different nodes. By exploiting from parallelism, the query time performance is significantly increased. We have a speed up of 8 for the query performance in the distributed scheme with 10 nodes. The level of Mean Average Precision (MAP) scores are quite high which are comparable to other methods. We have also investigated the usage of different and selected randomized hash functions in different nodes rather than using same indexing. We create selected hash functions according to their data division property before indexing. Since LSH is data independent method, we have obtained similar results with using same hash functions. We compared our experimental results with state-of-the-art methods given in a recent study. The proposed distributed scheme is promising for searching images in large datasets with multiple nodes.
Constructive Role of Noise for High-Quality Replication of Chaotic Attractor Dynamics Using a Hardware-Based Reservoir Computer

The identification of unconventional physical systems for neuroinspired information processing comes with much-desired capabilities for powerful data analysis. Moreover, it offers the possibility to replicate complex dynamical behavior under closed-loop operation. Robust, reliable autonomous replication of the structure of complex dynamics has remained elusive, though. The authors experimentally demonstrate the advantage of pretraining a system with additive input noise, achieving robust operation over an extended parameter range, and introduce quantifiers for the quality of replication.
Blood Bowl: A New Board Game Challenge and Competition for AI

We propose the popular board game Blood Bowl as a new challenge for Artificial Intelligence (AI). Blood Bowl is a fully-observable, stochastic, turn-based, modern-style board game with a grid-based game board. At first sight, the game ought to be approachable by numerous game-playing algorithms. However, as all pieces on the board belonging to a player can be moved several times each turn, the turn-wise branching factor becomes overwhelming for traditional algorithms. Additionally, scoring points in the game is rare and difficult, which makes it hard to design heuristics for search algorithms or apply reinforcement learning. We present the Fantasy Football AI (FFAI) framework that implements the core rules of Blood Bowl and includes a forward model, several OpenAI Gym environments for reinforcement learning, competition functionalities, and a web application that allows for human play. We also present Bot Bowl I, the first AI competition that will use FFAI along with baseline agents and preliminary reinforcement learning results. Additionally, we present a wealth of opportunities for future AI competitions based on FFAI.
Stratified squamous epithelial biopsy image classifier using machine learning and neighborhood feature selection

Squamous cell carcinoma (SCC) of oral cavity is the most common among oral cancer patients. In this paper, we have developed machine learning based automatic oral squamous cell carcinoma (OSCC) classifier named as Stratified Squamous Epithelial Biopsy Image Classifier (SSE-BIC) to categorize H&E-stained microscopic images of squamous epithelial layer in four different classes: normal, well-differentiated, moderately-differentiated and poorly-differentiated. Five classifiers are used to perform the classification by maximum voting method. Total 305 features are extracted from the images of oral mucosa which include color features, textural features, gradient features, geometrical features and tamura features. Unsupervised data mining is used for segmenting the cellular area to compute geometrical features of the cells retaining color details of the images. Feature selection has been performed by neighborhood component feature selection (NCFS) technique. Total 676 images have been used to design, train and test the classifier. A detailed performance analysis is presented with individual feature sets and hybrid feature sets with feature selection applied using individual classifiers as well as proposed classifier. The proposed classifier achieves overall accuracy of 95.56%. This can account for first level of automatic screening of the biopsy images.
Chameleon Hash Time-Lock Contract for Privacy Preserving Payment Channel Networks

Payment channel networks (PCNs) have been proposed to address the low transaction throughput of the permissionless blockchain protocols. Though the PCNs allow users to have the unlimited number of transactions in the channel without interacting with blockchain, it leaks the entire payment paths to the public. To address the payment path leakage issue, we propose a Chameleon-hash based payment protocol, called Chameleon Hash Time-Lock Contract (CHTLC). Using Chameleon-hash function in a multi-layer fashion guarantees that no user can recover the payment path if at least one intermediate payment node is honest. For the same payment path, compared with Multi-hop Hash Time-Lock Contract (MHTLC) protocol of Malavolta et al. [1], CHTLC is 5 times faster in the payment data initialisation, and the communication bandwidth is reduced significantly from 17, 000 KB to just 7.7 KB.
Effective image restoration for semantic segmentation

Recent semantic segmentation algorithms are greatly accelerated by deep convolutional neural networks (DCNNs). Although most of them perform well on normal images, they are not robust to the degenerations of images. To boost the performance of semantic segmentation on degraded images, we present an effective image restoration framework based on generative adversarial network (GAN). Firstly, we propose to jointly minimize pixel-wise cross entropy loss and our semantic-aware mean square error loss for the optimization of generative restoration network, which ensures correct semantic predictions at each pixel location. Secondly, we introduce GAN into our framework, which can benefit high-order consistency of semantic predictions. Comprehensive experimental results on image super-resolution and denoising demonstrate that our approach is able to achieve the best segmentation accuracy as well as maintaining pleasing image quality compared to other common image restoration approaches.
Hate Speech Detection using Fusion Approach

Detection of hate speech in user-generated online content has become an issue of increasing importance in recent years and is discerning for applications such as disputed event identification and sentiment analysis. Text classification for online content is a bit challenging task due to the natural language complexity and hastily generated online user microblogs including a plethora of informality and mistakes. This work introduces a system to classify tweets in three categories (i.e., racism, sexism and none). In our classification strategy, we integrate deep features extracted from Convolutional Neural Network(CNN) trained on semantic word embedding with state-of-the-art syntactic and word n-gram features. We perform comprehensive experiments on a standard dataset containing 16k manually annotated tweets. Our proposed approach outperform all other state-of-the-art approaches with a significant increase in accuracy.
Full error analysis for the training of deep neural networks

Deep learning algorithms have been applied very successfully in recent years to a range of problems out of reach for classical solution paradigms. Nevertheless, there is no completely rigorous mathematical error and convergence analysis which explains the success of deep learning algorithms. The error of a deep learning algorithm can in many situations be decomposed into three parts, the approximation error, the generalization error, and the optimization error. In this work we estimate for a certain deep learning algorithm each of these three errors and combine these three error estimates to obtain an overall error analysis for the deep learning algorithm under consideration. In particular, we thereby establish convergence with a suitable convergence speed for the overall error of the deep learning algorithm under consideration. Our convergence speed analysis is far from optimal and the convergence speed that we establish is rather slow, increases exponentially in the dimensions, and, in particular, suffers from the curse of dimensionality. The main contribution of this work is, instead, to provide a full error analysis (i) which covers each of the three different sources of errors usually emerging in deep learning algorithms and (ii) which merges these three sources of errors into one overall error estimate for the considered deep learning algorithm.
Blink as you sync

We tend to synchronize our movements to the person we are talking to during face-to-face conversation. Higher interpersonal synchrony is linked to greater empathy and more effortless interactions. This paper presents a first method and a corresponding dataset to explore synchrony in natural conversation by capturing eye and head movement using commodity smart eyewear. We present a 17 hour dataset, using Electrooculography and inertial sensing, of 42 people in conversation (21 dyads: 10 in Japanese, 10 in English, 1 in Chinese). Initial results on 18 dyads show significant interpersonal synchrony of blink and head nod behaviour during conversation (at frequencies of 0.2 to 0.5 Hz). We also find that people are more likely to synchronise blinks at around 1 Hz when conversing back-to-back than when face-to-face.
Stochastic Imputation and Uncertainty-Aware Attention to EHR for Mortality Prediction

Electronic health records (EHR) have become an important source of a patient data but characterized by a variety of missing values. Using the variational inference of Bayesian framework, variational autoencoder (VAE), a deep generative model, has been shown to be efficient and accurate to capture the latent structure of complex high-dimensional data. Recently, it has been used for missing data imputation. In this paper, we propose a general framework that incorporates effective missing data imputation using VAE and multivariate time series prediction. We utilize the uncertainty obtained from the generative network of the VAE and employ uncertainty-aware attention in imputing the missing values. We evaluated the performance of our architecture on real-world clinical dataset (MIMIC-III) for in-hospital mortality prediction task. Our results showed higher performance than other competing methods in mortality prediction task.
Learners’ Technological Acceptance of VR Content Development: A Sequential 3-Part Use Case Study of Diverse Post-Secondary Students

Web-based virtual reality (VR) development tools are in ubiquitous use by software developers, and now, university (undergraduate) students, to move beyond using, to creating new and energizing VR content. Web-based VR (WebVR), among other libraries and frameworks, have risen as a low-cost platform for users to create rich and intuitive VR content and applications. However, the success of WebVR as an instructional tool relies on post-secondary students technological acceptance (TA), the intersectionality of a user’s perceived utility (PU) and perceived ease of use (PEOU, or convenience) with said technological tool. Yet, there is a dearth of exploratory studies of students’ experiences with the AR/VR development technologies to infer their TA. To ascertain the viability of WebVR tools for software engineering undergraduates in the classroom, this paper presents a 3-case contextual investigation of 38 undergraduate students tasked with creating VR content. In each use case, students were provided increasing freedom in their VR content development parameters. Results indicated that students demonstrated elements of technological acceptance in their selection of webVR and other platforms, and not only successfully creating rich and robust VR content (PU), but also executing these projects in a short period (PEOU). Other positive externalities observed were students exhibitions of soft skills (e.g. creativity, critical thinking) and different modes of demonstrating coding knowledge, which suggest further study. Discussed are the lessons learned from the WebVR and VR/AR interventions and recommendations for WebVR instruction. This work may be helpful for both learners and teachers using VR/AR in selecting, designing, and developing coursework materials, tools, and libraries.
Universal Readout for Graph Convolutional Neural Networks

Several machine learning problems can be naturally defined over graph data. Recently, many researchers have been focusing on the definition of neural networks for graphs. The core idea is to learn a hidden representation for the graph vertices, with a convolutive or recurrent mechanism. When considering discriminative tasks on graphs, such as classification or regression, one critical component to design is the readout function, i.e. the mapping from the set of vertex representations to a fixed-size vector (or the output). Different approaches have been presented in literature, but recent approaches tend to be complex, making the training of the whole network harder. In this paper, we frame the problem in the setting of learning over sets. Adopting recently proposed theorems over functions defined on sets, we propose a simple but powerful formulation for a readout layer that can encode or approximate arbitrarily well any continuous permutation-invariant function over sets. Experimental results on real-world graph datasets show that, compared to other approaches, the proposed readout architecture can improve the predictive performance of Graph Neural Networks while being computationally more efficient.
MDS Symbol-Pair Cyclic Codes of Length $2p^s$ over $\mathbb F_{p^m}$

Let p be an odd prime, s and m be positive integers. Cyclic codes of length 2p <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</sup> over F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">pm</sub> are the ideals ((x-1) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sup> (x+1) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">j</sup> ), where 0 ≤ i, j ≤ p <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</sup> , of the principal ideal ring F <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">pm</sup> [x]/(x <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2ps</sup> - 1). Using this structure, the symbol-pair distances of all cyclic codes of length 2p <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</sup> over F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">pm</sub> are completely determined. In addition, we establish all MDS symbol-pair cyclic codes of length 2p <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</sup> over F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">pm</sub> . Some MDS symbol-pair cyclic codes are better than all the known ones. Among others, we discuss possible applications to construct quantum MDS symbol-pair codes.
A Formal Treatment of Hardware Wallets

Bitcoin, being the most successful cryptocurrency, has been repeatedly attacked with many users losing their funds. The industry’s response to securing the user’s assets is to offer tamper-resistant hardware wallets. Although such wallets are considered to be the most secure means for managing an account, no formal attempt has been previously done to identify, model and formally verify their properties. This paper provides the first formal model of the Bitcoin hardware wallet operations. We identify the properties and security parameters of a Bitcoin wallet and formally define them in the Universal Composition (UC) Framework. We present a modular treatment of a hardware wallet ecosystem, by realizing the wallet functionality in a hybrid setting defined by a set of protocols. This approach allows us to capture in detail the wallet’s components, their interaction and the potential threats. We deduce the wallet’s security by proving that it is secure under common cryptographic assumptions, provided that there is no deviation in the protocol execution. Finally, we define the attacks that are successful under a protocol deviation, and analyze the security of commercially available wallets.
Optimizing Weight Value Quantization for CNN Inference

The size and complexity of CNN models are increasing and as a result they are requiring more computational and memory resources to be used effectively. Use of a lower bit width numerical representation such as binary, ternary or several bit width has been studied extensively so as to reduce the required resources. However, the representation capability of such extremely low bit width is not always sufficient and the accuracy obtained for some CNN models and data is low. There are some prior studies that use moderate lower bit width with well-known numerical representations such as fixed point or logarithmic representation. It is not apparent, however, whether those representations are optimal for maintaining high accuracy. In this paper, we investigated the numerical quantization from the ground up, and introduced a novel "Variable Bin-size Quantization (VBQ)" representation in which quantization bin boundaries are optimized to obtain maximum accuracy for each CNN model. A genetic algorithm was employed to optimize the bin boundaries of VBQ. Additionally, since the appropriate bit width to obtain sufficient accuracy cannot be determined in advance, we attempted to use the parameters obtained by a training process using higher precision representation (FP32), and used quantization in inference only. This reduced the required large computational resource cost for training. During the process of tuning VBQ boundaries using a genetic algorithm, we discovered that the optimal distribution of bins can be approximated by an equation with two parameters. We then used simulated annealing for finding the optimal parameters of the equation for AlexNet and VGG16. As a result, AlexNet and VGG16 with our 4-bit quantization achieved top-5 accuracy at 74.8% and 86.3% respectively, which were comparable to 76.3% and 88.1% obtained by FP32. Thus, VBQ combined with the approximate equation and the simulated annealing scheme can achieve similar levels of accuracy with less resources and reduced computational cost compared to other current approaches.
Robot Path Planning Using Modified Artificial Bee Colony Algorithm

The artificial bee colony (ABC) algorithm is developed by D. Karaboga. Various researchers proved that the performance of ABC is better than other competitive algorithms, but it has some drawbacks like early convergence and stagnation. The ABC performs very well while exploring the feasible search space, but it shows poor performance for exploitation. To overcome this drawback, this paper proposed a variant of ABC, namely Arrhenius ABC (aABC) algorithm using the concept of Arrhenius equation. The aim of this paper is to improve balancing between exploration and exploitation capability of ABC. In order to check the performance of aABC, it is applied to solve the robot path planning problem. The performance of aABC compared with well-known nature-inspired algorithms like differential evolution, particle swarm optimization, and basic ABC. The aABC algorithm performs better than the other considered algorithms while solving robot path planning problem.
Interlinking open science and community-based participatory research for socio-environmental issues

This paper discusses how open science can be interlinked with community-based participatory research (CBPR) to address socio-environmental issues. By reviewing three case studies in Japan, the authors developed a theoretical framework to span these inter-actor boundaries by (1) discovering and sharing goals that actors with different interests could tackle together (the transcend method); (2) considering ethical equity with special attention to empowering marginalized (or ‘small voice’) actors; (3) developing fair data visualization based on the FAIR Data Principles and (4) facilitating dialogue. A civic tech approach, in which civic engineers develop a solution to local issues by using open governmental data and information and communication technologies, is applied. This framework will reflectively be tested using case studies.
A Survey: How Python Pitches in IT-World

This paper lights on Python amongst other different programming paradigms used in the IT World, which enhances development speed. Although, Python was conceptualized in the late 1980s and after its implementation in 1989, it has emerged as a new multi-paradigm language platform with advent of Big Data. Python includes various data structures, standard libraries with the implementation of sentiment analysis and data science code. The real aim is to provide awareness to all the programmers about various facts of python language. It tells how Python works with various commercial and social communities and provides complete and desirable results. There are many areas and applications where Python makes its own stand as compared to other programming languages.