Reviewerly/exHarmony huggingface dataset:
On the Asymptotic Capacity of <i>X</i>-Secure <i>T</i>-Private Information Retrieval With Graph-Based Replicated Storage

The problem of private information retrieval with graph-based replicated storage was recently introduced by Raviv, Tamo and Yaakobi. Its capacity remains open in almost all cases. In this work the asymptotic (large number of messages) capacity of this problem is studied along with its generalizations to include arbitrary T -privacy and X-security constraints, where the privacy of the user must be protected against any set of up to T colluding servers and the security of the stored data must be protected against any set of up to X colluding servers. A general achievable scheme for arbitrary storage patterns is presented that achieves the rate (ρ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">min</sub> -X -T )/N, where N is the total number of servers, and each message is replicated at least ρ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">min</sub> times. Notably, the scheme makes use of a special structure inspired by dual Generalized Reed Solomon (GRS) codes. A general converse is also presented. The two bounds are shown to match for many settings, including symmetric storage patterns. Finally, the asymptotic capacity is fully characterized for the case without security constraints (X = 0) for arbitrary storage patterns provided that each message is replicated no more than T + 2 times. As an example of this result, consider PIR with arbitrary graph based storage (T = 1, X = 0) where every message is replicated at exactly 3 servers. For this 3-replicated storage setting, the asymptotic capacity is equal to 2/ν <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> (G) where ν <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> (G) is the maximum size of a 2-matching in a storage graph G[V, E]. In this undirected graph, the vertices V correspond to the set of servers, and there is an edge uv ∈ E between vertices u, v only if a subset of messages is replicated at both servers u and v.
Investigating the “Socio” in Socio-Technical Development: The Case for Psychological Safety in Agile Information Systems Development

One constitutional part of project management is the management of teams, their actions, and their social mechanisms. Team processes, behavior, and agile practices used by team members play important parts in the success of projects. To reap benefits from these highly interactive and social-focused practices, team members need to feel safe to speak freely. We propose a model that conceptualizes the effects of psychological safety and (social) agile practices on team performance. The proposed model combines recent research from organizational psychology and agile information systems development to provide a better understanding of the team-level effects. Our findings from three case studies conducted in two large insurance companies and one software development company suggest that social agile practices positively influence psychological safety, transparency, communication, and ultimately productivity.
To Tune or Not to Tune?

This experimental study presents a number of issues that pose a challenge for practical configuration tuning and its deployment in data analytics frameworks. These issues include: 1) the assumption of a static workload or environment, ignoring the dynamic characteristics of the analytics environment (e.g., increase in input data size, changes in allocation of resources). 2) the amortization of tuning costs and how this influences what workloads can be tuned in practice in a cost-effective manner. 3) the need for a comprehensive incremental tuning solution for a diverse set of workloads. We adapt different ML techniques in order to obtain efficient incremental tuning in our problem domain, and propose Tuneful, a configuration tuning framework. We show how it is designed to overcome the above issues and illustrate its applicability by running a wide array of experiments in cloud environments provided by two different service providers.
Thermal power forecasting of solar power tower system by combining mechanism modeling and deep learning method

Concentrating solar power systems are proliferating around the world. However, the non-schedulable characteristic of thermal output presents considerable challenges to safe utilization of solar heat. So, seasonable forecasting of thermal output is going to be essential to both power grid and plant. In this paper, a hybrid forecasting method based on mechanism modeling and deep learning method is proposed. By mechanism modeling, the major meteorological factors used for forecasting are identified accurately, avoiding the subjectivity of chosen of model input features. By convolutional neural network and long short-term memory network, the input features are reconstructed, and the spatial-temporal coupling characteristics between meteorological factors are fully explored. Finally, the thermal power is output by fully connected layers. As a case study, the thermal power of a Solar Two-like concentrating solar power plant based on meteorological conditions of Zhangbei is forecasted, and the mean values of root mean square error and mean absolute error are 5.061 MWt and 2.871 MWt on the whole year, respectively. The results show that the methods proposed in paper can be effectively used for thermal power forecasting of concentrating solar power system.
Investigating Methods for Integrating Unmanned Aerial Systems in Search and Rescue Operations

Unmanned aerial systems (UAS) are increasingly being used in search and rescue (SAR) operations to assist in the discovery of missing persons. UAS are useful to first responders in SAR operations due to rapid deployment, high data volume, and high spatial resolution data collection capabilities. Relying on traditional manual interpretation methods to find a missing person in imagery data sets containing several hundred images is both challenging and time consuming. To better find small signs of missing persons in large UAS datasets, computer assisted interpretation methods have been developed. This article presents the results of an initial evaluation of a computer assisted interpretation method tested against manual methods in a simulated SAR operation. The evaluation performed focused on using resources available to first responders performing SAR operations, specifically: RGB data, volunteers, and a commercially available software program. Results from this field test were mixed, as the traditional group discovered more objects but required more time, in man hours, to discover the objects. Further field experiments, based on the capabilities of current first responder groups, should be conducted to determine to what extent computer assisted methods are useful in SAR operations.
Two new matrix-variate distributions with application in model-based clustering

Two matrix-variate distributions, both elliptical heavy-tailed generalization of the matrix-variate normal distribution, are introduced. They belong to the normal scale mixture family, and are respectively obtained by choosing a convenient shifted exponential or uniform as mixing distribution. Moreover, they have a closed-form for the probability density function that is characterized by only one additional parameter, with respect to the nested matrix-variate normal, governing the tail-weight. Both distributions are then used for model-based clustering via finite mixture models. The resulting mixtures, being able to handle data with atypical observations in a better way than the matrix-variate normal mixture, can avoid the disruption of the true underlying group structure. Different EM-based algorithms are implemented for parameter estimation and tested in terms of computational times and parameter recovery. Furthermore, these mixture models are fitted to simulated and real datasets, and their fitting and clustering performances are analyzed and compared to those obtained by other well-established competitors.
Contextual word representations

Advances in how programs treat natural language words have a big impact in AI.
Analysis of a Consensus Protocol for Extending Consistent Subchains on the Bitcoin Blockchain

Currently, an increasing number of third-party applications exploit the Bitcoin blockchain to store tamper-proof records of their executions, immutably. For this purpose, they leverage the few extra bytes available for encoding custom metadata in Bitcoin transactions. A sequence of records of the same application can thus be abstracted as a stand-alone subchain inside the Bitcoin blockchain. However, several existing approaches do not make any assumptions about the consistency of their subchains, either (i) neglecting the possibility that this sequence of messages can be altered, mainly due to unhandled concurrency, network malfunctions, application bugs, or malicious users, or (ii) giving weak guarantees about their security. To tackle this issue, in this paper, we propose an improved version of a consensus protocol formalized in our previous work, built on top of the Bitcoin protocol, to incentivize third-party nodes to consistently extend their subchains. Besides, we perform an extensive analysis of this protocol, both defining its properties and presenting some real-world attack scenarios, to show how its specific design choices and parameter configurations can be crucial to prevent malicious practices.
Factors Influencing Consumers’ Intention to Adopt Fashion Robot Advisors: Psychological Network Analysis

Drawing upon the theory of human–robot interaction (HRI), this study examined the relations among perceived characteristics of fashion robot advisors (FRAs), consumers’ negative preconceptions toward robots, and positive dispositions toward technology to identify network differences in adoption and nonadoption groups. For interviews, pretests, and main data collection, we presented video clips of FRAs as stimuli. Based on the data ( n = 464) collected via an online survey, we conducted psychological network analysis to explore defining factors that differentiate adoption and nonadoption groups. The results indicate that perceived characteristics of social intelligence, humanlikeness, and knowledgeableness combined with a positive disposition of technological self-efficacy lead to adoption of FRAs. This study contributes to the literature on the theory of HRI and technology acceptance models, particularly in fashion retail sectors. Furthermore, this study provides a new graphical approach to networks that conceptualizes shoppers’ adoption of technology as a complex interplay of psychological attributes.
Evaluating the Security Impact of Healthcare Web Applications Through Fuzzy Based Hybrid Approach of Multi-Criteria Decision-Making Analysis

Continuous data breaches targeting the invaluable medical records have become a nemesis for the healthcare organizations. A secure and effective information security model in healthcare web applications can gain and enhance the respect as well as revenue of the healthcare organizations. For achieving this goal, a multi-criteria decision methodology can be a milestone. The authors have used a hybrid integrated Fuzzy Analytical Hierarchy Process-Technique for Order of Preference by Similarity to Ideal Solution (Fuzzy AHP-TOPSIS) method for evaluating various information security factors of a web application in order to provide effective and useful results for the developers and researchers. Furthermore, every calculation needs a validation and scientific proof in our case the study assesses the evaluated result on software of hospital from Varanasi, India. The results and ideology of this study will definitely help the practitioners in developing secure and effective information security within a web application. Moreover, the empirical analysis conducted in our research has attempted to etch a systematic path for the developers who can focus on the most prioritized factors for assured and concrete information security within a web application.
Path planning of mobile robot based on adaptive ant colony algorithm

The traditional ant colony algorithm has some problems, such as low search efficiency, slow convergence speed and local optimum. To solve those problems, an adaptive heuristic function is proposed, heuristic information is updated by using the shortest actual distance, which ant passed. The reward and punishment rules are introduced to optimize the local pheromone updating strategy. The state transfer function is optimized by using pseudo-random state transition rules. By comparing with other algorithms’ simulation results in different simulation environments, the results show that it has effectiveness and superiority on path planning.
Efficacy of Modern Neuro-Evolutionary Strategies for Continuous Control Optimization

We analyze the efficacy of modern neuro-evolutionary strategies for continuous control optimization. Overall the results collected on a wide variety of qualitatively different benchmark problems indicate that these methods are generally effective and scale well with respect to the number of parameters and the complexity of the problem and are relatively robust with respect to the setting of hyper-parameters. The comparison of the most promising methods indicates that the OpenAI-ES algorithm outperforms or equals the other algorithms on all considered problems. Moreover, we demonstrate how the reward functions optimized for reinforcement learning methods are not necessarily effective for evolutionary strategies and vice versa. This finding has important consequences since it implies that the comparison performed to date are biased toward one or the other class of algorithm and since it might lead to reconsideration of the relatively efficacy of the two classes of algorithms
Fitting a Kalman Smoother to Data

This paper considers the problem of fitting the parameters in a Kalman smoother to data. We formulate the Kalman smoothing problem with missing measurements as a constrained least squares problem and provide an efficient solution method based on sparse linear algebra. We then introduce the Kalman smoother tuning problem, which seeks to adjust parameters in the Kalman smoother to achieve low prediction error on held out measurements. We derive a Kalman smoother auto-tuning algorithm, which is based on the proximal gradient method, that finds good, if not the best, parameters for a given dataset. Central to our method is the computation of the gradient of the prediction error on the held out measurements with respect to the parameters of the Kalman smoother; we describe how to compute the gradient at little to no additional cost. We demonstrate the method on population migration within the United States as well as data collected from a smartphone's IMU+GPS system while driving.
Double SHA-256 Hardware Architecture With Compact Message Expander for Bitcoin Mining

In the Bitcoin network, computing double SHA-256 values consumes most of the network energy. Therefore, reducing the power consumption and increasing the processing rate for the double SHA256 algorithm is currently an important research trend. In this paper, we propose a high-data-rate low-power hardware architecture named the compact message expander (CME) double SHA-256. The CME double SHA-256 architecture combines resource sharing and fully unrolled datapath technologies to achieve both a high data rate and low power consumption. Notably, the CME algorithm utilizes the double SHA-256 input data characteristics to further reduce the hardware cost and power consumption. A review of the literature shows that the CME algorithm eliminates at least 9.68% of the 32-bit XOR gates, 16.49% of the 32-bit adders, and 16.79% of the registers required to calculate double SHA-256. We synthesized and laid out the CME double SHA-256 using CMOS 0.18 μm technology. The hardware cost of the synthesized circuit is approximately 13.88% less than that of the conventional approach. The chip layout size is 5.9 mm×5.9 mm, and the correctness of the circuit was verified on a real hardware platform (ZCU 102). The throughput of the proposed architecture is 61.44 Gbps on an ASIC with Rohm 180nm CMOS standard cell library and 340 Gbps on a FinFET FPGA 16nm Zynq UltraScale+ MPSoC ZCU102.
Using Games to Study Law of Motions in Mind

The development of games had been seen as an essential field of study in artificial intelligence.Although game concepts have been adopted in various problem domains, understanding the nature of game playing's underlying mechanism has been limited.This study investigates such a mechanism by adopting a logistic model of game outcome uncertainty where a measure of game refinement had been derived.Application of such a measure to the popular board and sports games have been conducted to determine its implication in determining sophisticated design and has defined the harmonic balance between skill and chance.A theoretical analysis using practical data confirms the effectiveness of the proposed model, where reasonable game length is determined based on the definition of a gamified experience.The model is further expanded with the analogy of physics in mind.The law of motions in the game is formalized by considering the derivatives of the game progress model, where the link between game refinement theory and flow theory is identified and discussed.In essence, the sophisticated game's underlying mechanism leads to a more natural yet pleasurable human experience.
A Penalty Strategy Combined Varying-Parameter Recurrent Neural Network for Solving Time-Varying Multi-Type Constrained Quadratic Programming Problems

To obtain the optimal solution to the time-varying quadratic programming (TVQP) problem with equality and multitype inequality constraints, a penalty strategy combined varying-parameter recurrent neural network (PS-VP-RNN) for solving TVQP problems is proposed and analyzed. By using a novel penalty function designed in this article, the inequality constraint of the TVQP can be transformed into a penalty term that is added into the objective function of TVQP problems. Then, based on the design method of VP-RNN, a PS-VP-RNN is designed and analyzed for solving the TVQP with penalty term. One of the greatest advantages of PS-VP-RNN is that it cannot only solve the TVQP with equality constraints but can also solve the TVQP with inequality and bounded constraints. The global convergence theorem of PS-VP-RNN is presented and proved. Finally, three numerical simulation experiments with different forms of inequality and bounded constraints verify the effectiveness and accuracy of PS-VP-RNN in solving the TVQP problems.
The Provable Security of Ed25519: Theory and Practice

A standard requirement for a signature scheme is that it is existentially unforgeable under chosen message attacks (EUF-CMA), alongside other properties of interest such as strong unforgeability (SUF-CMA), and resilience against key substitution attacks.Remarkably, no detailed proofs have ever been given for these security properties for EdDSA, and in particular its Ed25519 instantiations. Ed25519 is one of the most efficient and widely used signature schemes, and different instantiations of Ed25519 are used in protocols such as TLS 1.3, SSH, Tor, ZCash, and WhatsApp/Signal. The differences between these instantiations are subtle, and only supported by informal arguments, with many works assuming results can be directly transferred from Schnorr signatures. Similarly, several proofs of protocol security simply assume that Ed25519 satisfies properties such as EUF-CMA or SUF-CMA.In this work we provide the first detailed analysis and security proofs of Ed25519 signature schemes. While the design of the schemes follows the well-established Fiat-Shamir paradigm, which should guarantee existential unforgeability, there are many side cases and encoding details that complicate the proofs, and all other security properties needed to be proven independently.Our work provides scientific rationale for choosing among several Ed25519 variants and understanding their properties, fills a much needed proof gap in modern protocol proofs that use these signatures, and supports further standardisation efforts.
TAaMR: Targeted Adversarial Attack against Multimedia Recommender Systems

Deep learning classifiers are hugely vulnerable to adversarial examples, and their existence raised cybersecurity concerns in many tasks with an emphasis on malware detection, computer vision, and speech recognition. While there is a considerable effort to investigate attacks and defense strategies in these tasks, only limited work explores the influence of targeted attacks on input data (e.g., images, textual descriptions, audio) used in multimedia recommender systems (MR). In this work, we examine the consequences of applying targeted adversarial attacks against the product images of a visual-based MR. We propose a novel adversarial attack approach, called Target Adversarial Attack against Multimedia Recommender Systems (TAaMR), to investigate the modification of MR behavior when the images of a category of low recommended products (e.g., socks) are perturbed to misclassify the deep neural classifier towards the class of more recommended products (e.g., running shoes) with human-level slight images alterations. We explore the TAaMR approach studying the effect of two targeted adversarial attacks (i.e., FGSM and PGD) against input pictures of two state-of-the-art MR (i.e., VBPR and AMR). Extensive experiments on two real-world recommender fashion datasets confirmed the effectiveness of TAaMR in terms of recommendation lists changing while keeping the original human judgment on the perturbed images.
Forecasting Day-Ahead Hourly Photovoltaic Power Generation Using Convolutional Self-Attention Based Long Short-Term Memory

The problem of Photovoltaic (PV) power generation forecasting is becoming crucial as the penetration level of Distributed Energy Resources (DERs) increases in microgrids and Virtual Power Plants (VPPs). In order to improve the stability of power systems, a fair amount of research has been proposed for increasing prediction performance in practical environments through statistical, machine learning, deep learning, and hybrid approaches. Despite these efforts, the problem of forecasting PV power generation remains to be challenging in power system operations since existing methods show limited accuracy and thus are not sufficiently practical enough to be widely deployed. Many existing methods using long historical data suffer from the long-term dependency problem and are not able to produce high prediction accuracy due to their failure to fully utilize all features of long sequence inputs. To address this problem, we propose a deep learning-based PV power generation forecasting model called Convolutional Self-Attention based Long Short-Term Memory (LSTM). By using the convolutional self-attention mechanism, we can significantly improve prediction accuracy by capturing the local context of the data and generating keys and queries that fit the local context. To validate the applicability of the proposed model, we conduct extensive experiments on both PV power generation forecasting using a real world dataset and power consumption forecasting. The experimental results of power generation forecasting using the real world datasets show that the MAPEs of the proposed model are much lower, in fact by 7.7%, 6%, 3.9% compared to the Deep Neural Network (DNN), LSTM and LSTM with the canonical self-attention, respectively. As for power consumption forecasting, the proposed model exhibits 32%, 17% and 44% lower Mean Absolute Percentage Error (MAPE) than the DNN, LSTM and LSTM with the canonical self-attention, respectively.