Reviewerly/exHarmony huggingface dataset:
Why Reliability for Computing Needs Rethinking

Offering high quality services/products has been of paramount importance for both communications and computations. Early on, both of these were in dire need of practical designs for enhancing reliability. That is why John von Neumann proposed the first gate-level method (using redundancy to build reliable systems from unreliable components), while Edward F. Moore and Claude E. Shannon followed suit with the first device-level scheme. Moore and Shannon's prescient paper also established network reliability as a probabilistic model where the nodes of the network were considered to be perfectly reliable, while the edges could fail independently with a certain probability. The fundamental problem was that of estimating the probability that (under given conditions) two (or more) nodes are connected, the solution being represented by the well-known reliability polynomial (of the network). This concept has been heavily used for communications, where big strides were made and applied to networks of: roads, railways, power lines, fiber optics, phones, sensors, etc. For computations the research community converged on the gate-level method proposed by von Neumann, while the device-level scheme crafted by Moore and Shannon-although very practical and detailed-did not inspire circuit designers and went under the radar. That scheme was built on a thought-provoking network called hammock, exhibiting regular brick-wall near-neighbor connections. Trying to do justice to computing networks in general (and hammocks in particular), this paper aims to highlight and clarify how reliable different types of networks are when they are intended for performing computations. For doing this, we will define quite a few novel cost functions which, together with established ones, will allow us to meticulously compare different types of networks for a clearer understanding of the reliability enhancements they are able to bring to computations. To our knowledge, this is the first ever ranking of networks with respect to computing reliability. The main conclusion is that a rethinking/rebooting of how should we design reliable computing systems, immediately applicable to networks/arrays of devices (e.g., transistors or qubits), is both timely and needed.
Intelligent Attendance System with Face Recognition using the Deep Convolutional Neural Network Method

Abstract Recording student attendance in lectures can be done in several ways, namely giving initials on the attendance sheet or by the lecturer calling each student and then giving a checkmark on the attendance sheet or attendance recording system. This method is inefficient because it is done repeatedly at every meeting, resulting in reduced lecturing time. Some researchers are trying to develop various ways to overcome this, such as using fingerprints, Internet of Things devices, cards with RFID technology, QR codes, and smartphones. However, these technologies require many devices, and they may be costly. The purpose of this research is to develop an intelligent attendance system with facial recognition technology that can identify many people simultaneously without having to make direct contact using the Deep Convolutional Neural Network method. The system is then tested and analyzed for its accuracy in identifying and recording student attendance. The results of research conducted on 16 students in a lecture show that the system can be used to record student attendance with an accuracy of 81.25% in the condition that the student facing forward, 75.00% in the student condition facing sideways, and 43.75% in the student condition facing down.
Estimating Algorithmic Information Using Quantum Computing for Genomics Applications

Inferring algorithmic structure in data is essential for discovering causal generative models. In this research, we present a quantum computing framework using the circuit model, for estimating algorithmic information metrics. The canonical computation model of the Turing machine is restricted in time and space resources, to make the target metrics computable under realistic assumptions. The universal prior distribution for the automata is obtained as a quantum superposition, which is further conditioned to estimate the metrics. Specific cases are explored where the quantum implementation offers polynomial advantage, in contrast to the exhaustive enumeration needed in the corresponding classical case. The unstructured output data and the computational irreducibility of Turing machines make this algorithm impossible to approximate using heuristics. Thus, exploring the space of program-output relations is one of the most promising problems for demonstrating quantum supremacy using Grover search that cannot be dequantized. Experimental use cases for quantum acceleration are developed for self-replicating programs and algorithmic complexity of short strings. With quantum computing hardware rapidly attaining technological maturity, we discuss how this framework will have significant advantage for various genomics applications in meta-biology, phylogenetic tree analysis, protein-protein interaction mapping and synthetic biology. This is the first time experimental algorithmic information theory is implemented using quantum computation. Our implementation on the Qiskit quantum programming platform is copy-left and is publicly available on GitHub.
T1K+: A Database for Benchmarking Color Texture Classification and Retrieval Methods

In this paper we present T1K+, a very large, heterogeneous database of high-quality texture images acquired under variable conditions. T1K+ contains 1129 classes of textures ranging from natural subjects to food, textile samples, construction materials, etc. T1K+ allows the design of experiments especially aimed at understanding the specific issues related to texture classification and retrieval. To help the exploration of the database, all the 1129 classes are hierarchically organized in 5 thematic categories and 266 sub-categories. To complete our study, we present an evaluation of hand-crafted and learned visual descriptors in supervised texture classification tasks.
Fusion of Machine Learning and Privacy Preserving for Secure Facial Expression Recognition

The interest in Facial Expression Recognition (FER) is increasing day by day due to its practical and potential applications, such as human physiological interaction diagnosis and mental disease detection. This area has received much attention from the research community in recent years and achieved remarkable results; however, a significant improvement is required in spatial problems. This research work presents a novel framework and proposes an effective and robust solution for FER under an unconstrained environment; it also helps us to classify facial images in the client/server model along with preserving privacy. There are a lot of cryptography techniques available but they are computationally expensive; on the other side, we have implemented a lightweight method capable of ensuring secure communication with the help of randomization. Initially, we perform preprocessing techniques to encounter the unconstrained environment. Face detection is performed for the removal of excessive background and it detects the face in the real-world environment. Data augmentation is for the insufficient data regime. A dual-enhanced capsule network is used to handle the spatial problem. The traditional capsule networks are unable to sufficiently extract the features, as the distance varies greatly between facial features. Therefore, the proposed network is capable of spatial transformation due to the action unit aware mechanism and thus forwards the most desiring features for dynamic routing between capsules. The squashing function is used for classification purposes. Simple classification is performed through a single party, whereas we also implemented the client/server model with privacy measurements. Both parties do not trust each other, as they do not know the input of each other. We have elaborated that the effectiveness of our method remains unchanged by preserving privacy by validating the results on four popular and versatile databases that outperform all the homomorphic cryptographic techniques.
Edge Intelligence Based Identification and Classification of Encrypted Traffic of Internet of Things

A detection model of Internet of Things encrypted traffic based on edge intelligence is proposed in the paper, which can reduce the communication times of distributed Internet of Things gateways in the process of edge intelligence as well as the encrypted traffic detection model establishment time, in order to solve the problems that it is difficult to carry out efficient classification and accurate identification of the encrypted traffic of Internet of Things. In this paper, four new classification and identification methods for encrypted traffic are put forward, namely time-sequence behavior analysis, dynamic behavior analysis, key behavior analysis and two-round filtering analysis. The experimental results show that when the sample size is 1600, the encrypted traffic detection model establishment time is less than 100 seconds, and the accuracy of all the four new traffic classification methods is more than 92% and the recall rates of them are more than 83%.
Path-Planning for Mobile Robots Using a Novel Variable-Length Differential Evolution Variant

Mobile robots are currently exploited in various applications to enhance efficiency and reduce risks in hard activities for humans. The high autonomy in those systems is strongly related to the path-planning task. The path-planning problem is complex and requires in its formulation the adjustment of path elements that take the mobile robot from a start point to a target one at the lowest cost. Nevertheless, the identity or the number of the path elements to be adjusted is unknown; therefore, the human decision is necessary to determine this information reducing autonomy. Due to the above, this work conceives the path-planning as a Variable-Length-Vector optimization problem (VLV-OP) where both the number of variables (path elements) and their values must be determined. For this, a novel variant of Differential Evolution for Variable-Length-Vector optimization named VLV-DE is proposed to handle the path-planning VLV-OP for mobile robots. VLV-DE uses a population with solution vectors of different sizes adapted through a normalization procedure to allow interactions and determine the alternatives that better fit the problem. The effectiveness of this proposal is shown through the solution of the path-planning problem in complex scenarios. The results are contrasted with the well-known A* and the RRT*-Smart path-planning methods.
Abstract argumentation and the rational man

Abstract Abstract argumentation has emerged as a method for non-monotonic reasoning that has gained popularity in the symbolic artificial intelligence community. In the literature, the different approaches to abstract argumentation that were refined over the years are typically evaluated from a formal logics perspective; an analysis that is based on models of economically rational decision-making does not exist. In this paper, we work towards addressing this issue by analysing abstract argumentation from the perspective of the rational man paradigm in microeconomic theory. To assess under which conditions abstract argumentation-based decision-making can be considered economically rational, we derive reference independence as a non-monotonic inference property from a formal model of economic rationality and create a new argumentation principle that ensures compliance with this property. We then compare the reference independence principle with other reasoning principles, in particular with cautious monotony and rational monotony. We show that the argumentation semantics as proposed in Dung’s seminal paper, as well as other semantics we evaluate, with the exception of naive semantics and the SCC-recursive CF2 semantics, violate the reference independence principle. Consequently, we investigate how structural properties of argumentation frameworks impact the reference independence principle and identify cyclic expansions (both even and odd cycles) as the root of the problem. Finally, we put reference independence into the context of preference-based argumentation and show that for this argumentation variant, which explicitly models preferences, reference independence cannot be ensured in a straight-forward manner.
Improving Interpretability for Cyber Vulnerability Assessment Using Focus and Context Visualizations

Risk scoring provides a simple and quantifiable metric for decision support in cyber security operations, including prioritizing how to address discovered software vulnerabilities. However, scoring systems are often opaque to operators, which makes scores difficult to interpret in the context of their own networks, each other, or in a broader threat landscape. This interpretability challenge is exacerbated by recent applications of artificial intelligence (AI) and machine learning (ML) for vulnerability assessment, where opaque machine reasoning can hinder domain experts' trust in the decision-support toolkit or the actionability of its outputs. In this paper, we address this challenge through a combination of visualizations and analytics that complement existing techniques for vulnerability assessment. We present a study toward designing more interpretable visual encodings for decision support for vulnerability assessment. In particular, we consider the problem of making datasets of known vulnerabilities more interpretable at multiple scales, inspired by focus and context principles from the information visualization design community. The first scale considers individually scored vulnerabilities by using an explainable AI (XAI) toolkit for an ML risk-scoring model and by developing new visualizations of CVSS score features. The second scale uses an embedding for vulnerability descriptions to cluster potentially similar vulnerabilities. We outline use cases for these tools and discuss opportunities for applying XAI concepts to cyber risk and vulnerability management.
A New Distributional Ranking Loss With Uncertainty: Illustrated in Relative Depth Estimation

We propose a new approach for the problem of relative depth estimation from a single image. Instead of directly regressing over depth scores, we formulate the problem as estimation of a probability distribution over depth and aim to learn the parameters of the distributions which maximize the likelihood of the given data. To train our model, we propose a new ranking loss, Distributional Loss, which tries to increase the probability of farther pixel's depth being greater than the closer pixel's depth. Our proposed approach allows our model to output confidence in its estimation in the form of standard deviation of the distribution. We achieve state of the art results against a number of baselines while providing confidence in our estimations. Our analysis show that estimated confidence is actually a good indicator of accuracy. We investigate the usage of confidence information in a downstream task of metric depth estimation, to increase its performance.
An Object Detection Method Combining Multi-Level Feature Fusion and Region Channel Attention

In deep convolutional neural networks, the introduction of attention mechanism can effectively improve the detection accuracy of multi-scale objects, such as SENet and CBAM.But they use the first-order pooling of feature map to compute the channel weights, which leads to the unreasonable weights allocation.Due to the first-order information can not represent the feature distribution, the differences between channels unable to be distinguished in detail.In this work, we propose a novel object detection method combining multi-level feature fusion and region channel attention (ODMC).First, the positive and negative phase information of multi-level features are fused based on CReLU to enhance the semantic of low-level features and location information of high-level ones.Second, as for the channels after feature fusion, the regional information of feature maps is used to optimize the weight allocation, which can help accurately focus on important channels and suppress irrelevant channels.Finally, the objects are classified and located according to the enhanced features.Extensive experiments on PASCAL VOC and MS COCO benchmark show that ODMC achieves significant improvements over the comparable state-of-the-art detection models with high efficiency.
Multi-GMTI fusion for Doppler blind zone suppression using PHD fusion

For ground moving target indication (GMTI) sensor tracking, the existence of the Doppler blind zone (DBZ) seriously deteriorates tracking performance. In order to minimize the adverse effects of the DBZ factor, this paper puts forward the idea of using sensor fusion technique to suppress the DBZ masking problem. First, we derive the probability hypothesis density (PHD) fusion under the generalized covariance intersection (GCI) framework and its Gaussian mixture (GM) implementation for fusing local PHDs from the local trackers. However, we find that there is the problem of cardinality underestimation (CUE) in the original PHD fusion, which is exacerbated when targets are masked by the DBZ. After analyzing this problem in detail, we propose an improved PHD fusion algorithm through operations such as scale coefficient correction, GM component partition, and fused label correction. Finally, the feasibility and effectiveness of the proposed fusion are verified through numerical examples, and it is proved that it alleviates the CUE problem and is significantly better than local trackers.
Autonomous Graph Mining Algorithm Search with Best Speed/Accuracy Trade-off

Graph data is ubiquitous in academia and industry, from social networks to bioinformatics. The pervasiveness of graphs today has raised the demand for algorithms that can answer various questions: Which products would a user like to purchase given her order list? Which users are buying fake followers to increase their public reputation? Myriads of new graph mining algorithms are proposed every year to answer such questions - each with a distinct problem formulation, computational time, and memory footprint. This lack of unity makes it difficult for a practitioner to compare different algorithms and pick the most suitable one for a specific application. These challenges - even more severe for non-experts - create a gap in which state-of-the-art techniques developed in academic settings fail to be optimally deployed in real-world applications. To bridge this gap, we propose AutoGM, an automated system for graph mining algorithm development. We first define a unified framework UnifiedGM that integrates various message-passing based graph algorithms, ranging from conventional algorithms like PageRank to graph neural networks. Then UnifiedGM defines a search space in which five parameters are required to determine a graph algorithm. Under this search space, AutoGM explicitly optimizes for the optimal parameter set of UnifiedGM using Bayesian Optimization. AutoGM defines a novel budget-aware objective function for the optimization to incorporate a practical issue - finding the best speed-accuracy trade-off under a computation budget - into the graph algorithm generation problem. Experiments on real-world benchmark datasets demonstrate that AutoGM generates novel graph mining algorithms with the best speed/accuracy trade-off compared to existing models with heuristic parameters.
Exploitation of a New Short-Term Multimodel Photovoltaic Power Forecasting Method in the Very Short-Term Horizon to Derive a Multi-Time Scale Forecasting System

The relentless spread of photovoltaic production drives searches of smart approaches to mitigate unbalances in power demand and supply, instability on the grid and ensuring stable revenues to the producer. Because of the development of energy markets with multiple time sessions, there is a growing need of power forecasting for multiple time steps, from fifteen minutes up to days ahead. To address this issue, in this study both a short-term-horizon of three days and a very-short-term-horizon of three hours photovoltaic production forecasting methods are presented. The short-term is based on a multimodel approach and referred to several configurations of the Analog Ensemble method, using the weather forecast of four numerical weather prediction models. The very-short-term consists of an Auto-Regressive Integrated Moving Average Model with eXogenous input (ARIMAX) that uses the short-term power forecast and the irradiance from satellite elaborations as exogenous variables. The methods, applied for one year to four small-scale grid-connected plants in Italy, have obtained promising improvements with respect to refence methods. The time horizon after which the short-term was able to outperform the very-short-term has also been analyzed. The study also revealed the usefulness of satellite data on cloudiness to properly interpret the results of the performance analysis.
Explainable Early Stopping for Action Unit Recognition

A common technique to avoid overfitting when training deep neural networks (DNN) is to monitor the performance in a dedicated validation data partition and to stop training as soon as it saturates. This only focuses on what the model does, while completely ignoring what happens inside it. In this work, we open the “black-box” of DNN in order to perform early stopping. We propose to use a novel theoretical framework that analyses meso-scale patterns in the topology of the functional graph of a network while it trains. Based on it, we decide when it transitions from learning towards overfitting in a more explainable way. We exemplify the benefits of this approach on a state-of-the art custom DNN that jointly learns local representations and label structure employing an ensemble of dedicated subnetworks. We show that it is practically equivalent in performance to early stopping with patience, the standard early stopping algorithm in the literature. This proves beneficial for AU recognition performance and provides new insights into how learning of AUs occurs in DNNs.
Awareness of Secure Coding Guidelines in the Industry - A First Data Analysis

Software needs to be secure, in particular, when deployed to critical infrastructures.Secure coding guidelines capture practices in industrial software engineering to ensure the security of code.This study aims to assess the level of awareness of secure coding in industrial software engineering, the skills of software developers to spot weaknesses in software code, avoid them, and the organizational support to adhere to coding guidelines.The approach draws on well-established theories of policy compliance, neutralization theory, and securityrelated stress and the authors' many years of experience in industrial software engineering and on lessons identified from training secure coding in the industry.The paper presents the questionnaire design for the online survey and the first analysis of data from the pilot study.
Data Visualization Practitioners’ Perspectives on Chartjunk

Chartjunk is a popular yet contentious topic. Previous studies have shown that extreme minimalism is not always best, and that visual embellishments can be useful depending on the context. While more knowledge is being developed regarding the effects of embellishments on users, less attention has been given to the perspectives of practitioners regarding how they design with embellishments. We conducted semi-structured interviews with 20 data visualization practitioners, investigating how they understand chartjunk and the factors that influence how and when they make use of embellishments. Our investigation uncovers a broad and pluralistic understanding of chartjunk among practitioners, and foregrounds a variety of personal and situated factors that influence the use of chartjunk beyond context. We highlight the personal nature of design practice, and discuss the need for more practice-led research to better understand the ways in which concepts like chartjunk are interpreted and used by practitioners.