Reviewerly/exHarmony huggingface dataset:
Self-Dual Codes, Symmetric Matrices, and Eigenvectors

We introduce a consistent and efficient method to construct self-dual codes over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$GF(q)$ </tex-math></inline-formula> using symmetric matrices and eigenvectors from a self-dual code over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$GF(q)$ </tex-math></inline-formula> of smaller length where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$q \equiv 1 \pmod 4$ </tex-math></inline-formula> . Using this method, which is called a ‘symmetric building-up’ construction, we improve the bounds of the best-known minimum weights of self-dual codes with lengths up to 40, which have not significantly improved for almost two decades. We focus on a class of self-dual codes, which includes double circulant codes. We obtain 2967 new self-dual codes over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$GF(13)$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$GF(17)$ </tex-math></inline-formula> up to equivalence. We also compute the minimum weights of quadratic residue(QR) codes that were previously unknown. These are a [20, 10, 10] QR self-dual code over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$GF(23)$ </tex-math></inline-formula> , [24, 12, 12] QR self-dual codes over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$GF(29)$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$GF(41)$ </tex-math></inline-formula> , and a [32, 16, 14] QR self-dual code over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$GF(19)$ </tex-math></inline-formula> . They have the highest minimum weights so far.
Smart Speakers for Inclusion: How Can Intelligent Virtual Assistants Really Assist Everybody?

Smart speakers equipped with intelligent virtual assistants allow people to look for information, complete tasks and control other devices without using their hands and eyes, just their voice. Humans can finally use natural language utterances and be fully understood, without being forced to learn the machine language or to handle more or less complicated interaction techniques. Their potential in terms of inclusive design is therefore very high. However, it is important not to fall into the opposite problem, that is, to limit their use to the voice/auditory channel only, excluding all those who can't or don't want to use it. In this paper, the authors analyze the current situation, highlighting the peculiarities of these systems and the reasons why they are quickly gaining ground. Then, they focus on the potential interaction issues and on the challenges still open. After studying the main use cases relating to people with disabilities, elderly and accessibility, the authors can draw a list of suggestions addressing the inclusive design of virtual assistants and smart speakers.
Authentic versus synthetic: An investigation of the influences of study settings and task configurations on search behaviors

Abstract In information seeking and retrieval research, researchers often collect data about users' behaviors to predict task characteristics and personalize information for users. The reliability of user behavior may be directly influenced by data collection methods. This article reports on a mixed‐methods study examining the impact of study setting (laboratory setting vs. remote setting) and task authenticity (authentic task vs. simulated task) on users' online browsing and searching behaviors. Thirty‐six undergraduate participants finished one lab session and one remote session in which they completed one authentic and one simulated task. Using log data collected from 144 task sessions, this study demonstrates that the synthetic lab study setting and simulated tasks had significant influences mostly on behaviors related to content pages (e.g., page dwell time, number of pages visited per task). Meanwhile, first‐query behaviors were less affected by study settings or task authenticity than whole‐session behaviors, indicating the reliability of using first‐query behaviors in task prediction. Qualitative interviews reveal why users were influenced. This study addresses methodological limitations in existing research and provides new insights and implications for researchers who collect online user search behavioral data.
Autosymmetry of Incompletely Specified Functions

Autosymmetric Boolean functions are “regular functions” that are rather frequent in the set of Boolean functions describing standard circuits. Autosymmetry is typically exploited for improving the synthesis time and the quality of the optimized circuits. This paper studies in not a naive way, for the first time, the autosymmetry of incompletely specified functions, i.e., Boolean functions with don't care conditions. The theory of autosymmetry for completely specified functions is extended to the incompletely specified case and a new heuristic algorithm is provided for the detection of autosymmetry. The experimental results validate the theoretical study and show that the 77 % of the considered benchmarks has an improved autosymmetry degree.
Data structures for categorical path counting queries

Consider an ordinal tree T on n nodes, each of which is assigned a category from an alphabet [σ]={1,2,…,σ}. We preprocess the tree T in order to support categorical path counting queries, which ask for the number of distinct categories occurring on the path in T between two query nodes x and y. For this problem, we propose a linear-space data structure with query time O(nlg⁡lg⁡σlg⁡w), where w=Ω(lg⁡n) is the word size in the word-RAM. As shown in our proof, from the assumption that matrix multiplication cannot be solved in time polynomially faster than cubic (with only combinatorial methods), our result is optimal, save for polylogarithmic speed-ups. For a trade-off parameter 1≤t≤n, we propose an O(n+n2t2)- word, O(tlg⁡lg⁡σlg⁡w) query time data structure. We also consider c- approximate categorical path counting queries, which must return an approximation to the number of distinct categories occurring on the query path, by counting each such category at least once and at most c times. We describe a linear-space data structure that supports 2- approximate categorical path counting queries in O(lg⁡n/lg⁡lg⁡n) time. Next, we generalize the categorical path counting queries to weighted trees. Here, a query specifies two nodes x,y and an orthogonal range Q. The answer to thus formed categorical path range counting query is the number of distinct categories occurring on the path from x to y, if only the nodes with weights falling inside Q are considered. We propose an O(nlg⁡lg⁡n+(n/t)4)- word data structure with O(tlg⁡lg⁡n) query time, or an O(n+(n/t)4)- word data structure with O(tlgϵ⁡n) query time. For an appropriate choice of the trade-off parameter t, this implies a linear-space data structure with O(n3/4lgϵ⁡n) query time. We then extend the approach to the trees weighted with vectors from [n]d, where d is a constant integer greater than or equal to 2. We present a data structure with O(nlgd−1+ϵ⁡n+(n/t)2d+2) words of space and O(tlgd−1⁡n(lg⁡lg⁡n)d−2) query time. For an O(n⋅polylogn)- space solution, one thus has O(n2d+12d+2⋅polylogn) query time. The inherent difficulty revealed by the lower bound we proved motivated us to consider data structures based on sketching. In unweighted trees, we propose a sketching data structure to solve the approximate categorical path counting problem which asks for a (1±ϵ)- approximation (i.e. within 1±ϵ of the true answer) of the number of distinct categories on the given path, with probability 1−δ, where 0<ϵ,δ<1 are constants. The data structure occupies O(n+ntlg⁡n) words of space, for the query time of O(tlg⁡n). For trees weighted with d- dimensional weight vectors (d≥1), we propose a data structure with O((n+ntlg⁡n)lgd⁡n) words of space and O(tlgd+1⁡n) query time. All these problems generalize the corresponding categorical range counting problems in Euclidean space Rd+1, for respective d, by replacing one of the dimensions with a tree topology.
White-Box Traceable Attribute-Based Encryption with Hidden Policies and Outsourced Decryption

We address three practical problems of Attribute-Based Encryption (ABE) in this paper: performance, accountabil-ity and privacy. To do so, we present a novel Ciphertext-Policy Attribute-Based Encryption (CP-ABE) approach, which combines white-box accountability, hidden policies and outsourced decryption. In contrast to existing schemes, the proposed construction is not only more flexible, but also efficient enough to be used in more resource-constrained environments. This is because our construction is designed around efficient Type III bilinear maps and relies on a dedicated proxy to perform computationally ex-pensive operations. Furthermore, we provide an implementation of the proposed design. The conducted evaluation demonstrates the practicality of the approach under realistic assumptions.
Correlation Power Analysis and Higher-Order Masking Implementation of WAGE

WAGE is a hardware-oriented authenticated cipher, which has the smallest (unprotected) hardware cost (for 128-bit security level) among the round 2 candidates of the NIST lightweight cryptography (LWC) competition. In this work, we analyze the security of WAGE against the correlation power analysis (CPA) on ARM Cortex-M4F microcontroller. Our attack detects the secret key leakage from power consumption for up to 12 (out of 111) rounds of the WAGE permutation and requires 10,000 power traces to recover the 128-bit secret key. Motivated by the CPA attack and the low hardware cost of WAGE, we propose the first optimized masking scheme of WAGE in the t-strong non-interference (SNI) security model. We investigate different masking schemes for S-boxes by exploiting their internal structures and leveraging the state-of-the-art masking techniques. To practically demonstrate the effectiveness of masking, we perform the test vector leakage assessment on the 1-order masked WAGE. We evaluate the hardware performance of WAGE for 1, 2, and 3-order security and provide a comparison with other NIST LWC round 2 candidates.
Schema-Backed Visual Queries over Europeana and Other Linked Data Resources

We describe and demonstrate the process of extracting a data-driven schema of the Europeana cultural heritage Linked data resource (with actual data classes, properties and their connections, and cardinalities) and application of the extracted schema to create a visual query environment over Europeana. The extracted schema information allows generating SHACL data shapes describing the actual data endpoint structure. The schema extraction process can be applied also to other data endpoints with a moderate data schema size and a potentially large data triple count, as e.g., British National Bibliography Linked data resource.
Convolutional Neural Network Based Intelligent Advertisement Search Framework for Online English Newspapers

Background: Instant access to desired information is the key element for building an intelligent environment creating value for people and steering towards society 5.0. Online newspapers are one such example which provide instant access to information anywhere and anytime on our mobiles, tablets, laptops, desktops, etc. But when it comes to searching for a specific advertisement in newspapers, online newspapers do not provide easy advertisement search options. Also, there are no specialized search portals which can provide for keyword-based advertisement search across multiple online newspapers. As a result, to find a specific advertisement in multiple newspapers, a sequential manual search is required across a range of online newspapers. Objective: This research paper proposes a keyword-based advertisement search framework to provide an instant access to the relevant advertisements from online English newspapers in a category of reader’s choice. Method: First, an image extraction algorithm is proposed which can identify and extract the images from online newspapers without using any rules on advertisement placement and/or size. It is followed by a proposed deep learning Convolutional Neural Network (CNN) model named ‘Adv_Recognizer’ which is used to separate the advertisement images from non-advertisement images. Another CNN Model, ‘Adv_Classifier’, is proposed, which classifies the advertisement images into four pre-defined categories. Finally, Optical Character Recognition (OCR) technique is used to perform keyword-based advertisement searches in various categories across multiple newspapers. Results: The proposed image extraction algorithm can easily extract all types of well-bounded images from different online newspapers and this algorithm is used to create ‘English newspaper image dataset’ of 11,000 images, including advertisements and non-advertisements. The proposed ‘Adv_Recognizer’ model separates advertisement and non-advertisement images with an accuracy of around 97.8%. and the proposed ‘Adv_Classifier’ model classifies the advertisements in four predefined categories exhibiting an accuracy of around 73.5%. Conclusion: The proposed framework will help newspaper readers in performing exhaustive advertisement searches across a range of online English newspapers in a category of their own interest. It will also help in carrying out advertisement analysis and studies.
Echocardiography image segmentation using <scp>semi‐automatic</scp> numerical optimisation method based on wavelet decomposition thresholding

Abstract We propose optimisation based algorithm using wavelet decomposition for segmentation of echocardiography images. The objective of the proposed method is to find an optimal threshold value for segmentation. This threshold value is used for separating the left ventricle by segmenting wavelet decomposed coefficients. The proposed method evaluates the optimal value of threshold using a nonlinear derivative free optimising algorithm. Its objective function is the contrast property of GLCM. The contrast would be highest at the correct position of the boundary between image regions. Accordingly, optimization task is formulated as a maximization problem. The proposed method is an iterative process for finding optimum threshold. Segmentation is carried out using this optimised threshold value. Further, the exact left ventricle contour is extracted using morphological operations and user provided location input. The proposed method is observed to be performing better and found to have 94% accuracy compared to ground truth labels.
Reflections on

At UC Davis, in late 2010, we noticed that developers frequently debated the topic of code readability and style. Practitioners often expressed strong, off-the-cuff judgments regarding the read- ability of code, but mostly disagreed on the precise attributes of readability. While developers clearly cared about the factors they believed related to readability, the concept was not well defined.
Single Depth View Based Real-Time Reconstruction of Hand-Object Interactions

Reconstructing hand-object interactions is a challenging task due to strong occlusions and complex motions. This article proposes a real-time system that uses a single depth stream to simultaneously reconstruct hand poses, object shape, and rigid/non-rigid motions. To achieve this, we first train a joint learning network to segment the hand and object in a depth image, and to predict the 3D keypoints of the hand. With most layers shared by the two tasks, computation cost is saved for the real-time performance. A hybrid dataset is constructed here to train the network with real data (to learn real-world distributions) and synthetic data (to cover variations of objects, motions, and viewpoints). Next, the depth of the two targets and the keypoints are used in a uniform optimization to reconstruct the interacting motions. Benefitting from a novel tangential contact constraint, the system not only solves the remaining ambiguities but also keeps the real-time performance. Experiments show that our system handles different hand and object shapes, various interactive motions, and moving cameras.
Convolutional Neural Networks for Automatic Detection of Focal Cortical Dysplasia

Focal cortical dysplasia (FCD) is one of the most common epileptogenic lesions associated with cortical development malformations. However, the accurate detection of the FCD relies on the radiologist professionalism, and in many cases, the lesion could be missed. In this work, we solve the problem of automatic identification of FCD on magnetic resonance images (MRI). For this task, we improve recent methods of Deep Learning-based FCD detection and apply it for a dataset of 15 labeled FCD patients. The model results in the successful detection of FCD on 11 out of 15 subjects.
Autofeeding System for Assembling the CBCs on Automobile Engine Based on 3-D Vision Guidance

Assembling crankshaft bearing caps (CBCs) on automobile engine is a vital and complicated task that manual work is usually inefficient and become more prone to error. In this article, an autofeeding system with vision and line laser combined with an industrial object location and classification method is proposed for assembling the CBCs. First, an improved fully convolution one-stage network is used to obtain 2-D localization and detect the placement direction and order of a group of CBCs in the CBCs' image. Based on the 2-D location information, the line laser projects the laser on the surface of the CBCs at two different positions, and the images are captured accordingly. Then, an improved Steger algorithm is proposed to extract the centerline of the laser light bar, and the two laser images are merged into one laser image to calculate the height and pose information of the CBCs. The experimental results demonstrate that our method has achieved high real-time performance, accuracy, and robustness. After comparison and application in the factory, it is illustrated that the proposed autofeeding system improves the productivity and saving manpower.
A Deep Learning-based Approach to 5G-New Radio Channel Estimation

In this paper, we present a deep learning-based technique for channel estimation. By treating the time-frequency grid of the channel response as a low-resolution 2D-image, we propose a 5G-New Radio Convolutional Neural Network, called NR-ChannelNet, which can be properly trained to predict the channel coefficients. Our study employs a 3GPP-compliant 5G-New Radio simulator that can reproduce a realistic scenario by including multiple transmitting/receiving antenna schemes and clustered delay line channel model. Simulation results show that our deep learning approach can achieve competitive performance with respect to traditional techniques such as 2D-MMSE: indeed, under certain conditions, our new NR-ChannelNet approach achieves remarkable gains in terms of throughput.
Dissecting FLOPs Along Input Dimensions for GreenAI Cost Estimations

The term GreenAI refers to a novel approach to Deep Learning, that is more aware of the ecological impact and the computational efficiency of its methods. The promoters of GreenAI suggested the use of Floating Point Operations (FLOPs) as a measure of the computational cost of Neural Networks; however, that measure does not correlate well with the energy consumption of hardware equipped with massively parallel processing units like GPUs or TPUs. In this article, we propose a simple refinement of the formula used to compute floating point operations for convolutional layers, called $$\alpha $$ -FLOPs, explaining and correcting the traditional discrepancy with respect to different layers, and closer to reality. The notion of $$\alpha $$ -FLOPs relies on the crucial insight that, in case of inputs with multiple dimensions, there is no reason to believe that the speedup offered by parallelism will be uniform along all different axes.
A novel bearing-only localization for generalized Gaussian noise

A maximum likelihood estimator for bearing only source localization problem is addressed. Bearing measurements are assumed to be corrupted by additive generalized Gaussian noise, making the proposed estimator capable of keeping its performance high for a range of noise densities, i.e., from highly impulsive to uniform noises. Most previous works rely on small noise approximation and adopt recursive methods; therefore they need a good initial guess and high computational load. The novelty of this paper is to propose a non-iterative importance sampling based method to find a solution to the unconstrained lp-norm minimization problem for highly non-linear cost functions not known before. Our estimator is simple, does not need derivation or matrix multiplication operations, and requires generating a fair number of random particles.
A Video-based Fall Detection Network by Spatio-temporal Joint-point Model on Edge Devices

Tripping or falling is among the top threats in elderly healthcare, and the development of automatic fall detection systems are of considerable importance. With the fast development of the Internet of Things (IoT), camera vision-based solutions have drawn much attention in recent years. The traditional fall video analysis on the cloud has significant communication overhead. This work introduces a fast and lightweight video fall detection network based on a spatio-temporal joint-point model to overcome these hurdles. Instead of detecting falling motion by the traditional Convolutional Neural Networks (CNNs), we propose a Long Short-Term Memory (LSTM) model based on time-series joint-point features, extracted from a pose extractor and then filtered from a geometric joint-point filter. Experiments are conducted to verify the proposed framework, which shows a high sensitivity of 98.46% on Multiple Cameras Fall Dataset and 100% on UR Fall Dataset. Furthermore, our model can achieve pose estimation tasks simultaneously, attaining 73.3 mAP in the COCO keypoint challenge dataset, which outperforms the OpenPose work by 8%.
Few-Shot Learning for Tamil Handwritten Character Recognition Using Deep Siamese Convolutional Neural Network

Optical Character Recognition (OCR) is at the forefront of numerous applications such as digitalization of legal and legacy documents, automatic form processing, writer identification in forensic intelligence. Most of these applications seldom have sufficient training samples in order to achieve an accuracy worthy of real-time deployments. Inspired by the demonstrated performance of Siamese Neural Networks (SNN) in various fields such as Computer vision, Natural Language Processing, Signal processing etc., in this paper, we explore the application of SNN for Tamil Handwritten character recognition. The Siamese-CNN learning is implemented using cross-entropy loss and subsequently used to validate the few-shot learning. It achieved an optimal accuracy of 83.39% for n-way-40-shot learning. Rigorous experiments were conducted all through and the results are indicative of a promising new direction for the development of efficient Indic OCR models using Siamese networks.