Reviewerly/exHarmony huggingface dataset:
Cryptosystem Identification Scheme Based on ASCII Code Statistics

In the field of information security, block cipher is widely used in the protection of messages, and its safety naturally attracts people’s attention. The identification of the cryptosystem is the premise of encrypted data analysis. It belongs to the category of attack analysis in cryptanalysis and has important theoretical significance and application value. This paper focuses on the extraction of ciphertext features and the construction of cryptosystem identification classifiers. The main contents and innovations of this paper are as follows. Firstly, inspired by language processing, we propose the feature extraction scheme based on ASCII statistics of ciphertexts which decrease the dimension of data preprocessing. Secondly, on the basis of previous work, we increase the types of block ciphers to eight, encrypt plaintext of the same sizes as experimental objects, and recognize the cryptosystem. Thirdly, we use two machine learning classifiers to perform classification experiments including random forest and SVM. The experimental results show that our scheme can not only improve the identification accuracy of 8 typical block cipher algorithms but also shorten the experimental time and reduce the computation load by greatly minimizing the dimension of the feature vector. And the various evaluation indicators obtained by the scheme have been greatly improved compared with the existing published literature.
Statistical Graph Signal Recovery Using Variational Bayes

This brief investigates the problem of Graph Signal Recovery (GSR) when the topology of the graph is not known in advance. In this brief, the elements of the weighted adjacency matrix is statistically related to normal distribution and the graph signal is assumed to be Gaussian Markov Random Field (GMRF). Then, the problem of GSR is solved by a Variational Bayes (VB) algorithm in a Bayesian manner by computing the posteriors in a closed form. The posteriors of the elements of weighted adjacency matrix are proved to have a new distribution which we call it Generalized Compound Confluent Hypergeometric (GCCH) distribution. Moreover, the variance of the noise is estimated by calculating its posterior via VB. The simulation results on synthetic and real-world data shows the superiority of the proposed Bayesian algorithm over some state-of-the-art algorithms in recovering the graph signal.
Affording Enjoyment in VR Games: Possibilities, Pitfalls, and Perfection

In this paper, we present research that aims to understand affordances and inhibitors of enjoyment in virtual reality (VR) video games. We apply the GameFlow model to analyse VR and non-VR versions of the same games to identify differences in enjoyment in VR games. Our approach involves conducting qualitative analysis on video game reviews, using GameFlow as a theoretical foundation. We report on our analysis of the games Superhot and Skyrim. We find that affordances are largely consistent between VR and non-VR versions of the same games, with a few key differences related to Feedback, Control, Player Skills, and Immersion. We conclude that GameFlow is applicable to VR games, with the addition of a Comfort element to describe player comfort while playing.
Sifu - a cybersecurity awareness platform with challenge assessment and intelligent coach

Abstract Software vulnerabilities, when actively exploited by malicious parties, can lead to catastrophic consequences. Proper handling of software vulnerabilities is essential in the industrial context, particularly when the software is deployed in critical infrastructures. Therefore, several industrial standards mandate secure coding guidelines and industrial software developers’ training, as software quality is a significant contributor to secure software. CyberSecurity Challenges (CSC) form a method that combines serious game techniques with cybersecurity and secure coding guidelines to raise secure coding awareness of software developers in the industry. These cybersecurity awareness events have been used with success in industrial environments. However, until now, these coached events took place on-site. In the present work, we briefly introduce cybersecurity challenges and propose a novel platform that allows these events to take place online. The introduced cybersecurity awareness platform, which the authors call Sifu, performs automatic assessment of challenges in compliance to secure coding guidelines, and uses an artificial intelligence method to provide players with solution-guiding hints. Furthermore, due to its characteristics, the Sifu platform allows for remote (online) learning, in times of social distancing. The CyberSecurity Challenges events based on the Sifu platform were evaluated during four online real-life CSC events. We report on three surveys showing that the Sifu platform’s CSC events are adequate to raise industry software developers awareness on secure coding.
NeuroMAX

Convolutional neural networks (CNNs) require high throughput hardware accelerators for real time applications owing to their huge computational cost. Most traditional CNN accelerators rely on single core, linear processing elements (PEs) in conjunction with 1D dataflows for accelerating convolution operations. This limits the maximum achievable ratio of peak throughput per PE count to unity. Most of the past works optimize their dataflows to attain close to a 100% hardware utilization to reach this ratio. In this paper, we introduce a high throughput, multi-threaded, log-based PE core. The designed core provides a 200% increase in peak throughput per PE count while only incurring a 6% increase in area overhead compared to a single, linear multiplier PE core with same output bit precision. We also present a 2D weight broadcast dataflow which exploits the multi-threaded nature of the PE cores to achieve a high hardware utilization per layer for various CNNs. The entire architecture, which we refer to as NeuroMAX, is implemented on Xilinx Zynq 7020 SoC at 200 MHz processing clock. Detailed analysis is performed on throughput, hardware utilization, area and power breakdown, and latency to show performance improvement compared to previous FPGA and ASIC designs.
Overview of PyQt5

Hello! Welcome to Modern PyQt! When you build an application, whether for desktop, mobile, or embedded devices, your goal is to create a seamless experience for the user. If you are designing a program for others to use, then you should always consider how the user might interact with the software to solve their problems. Applications such as web browsers, word processors, and video players are all created to enrich the end user’s life, helping them achieve some task simply and efficiently.
Assessing and Justifying the Reasonableness of Answers to Open-Ended Problems

Engineering students have little practice solving the ill-defined problems they will encounter as professional engineers. Our research team has created an Open-ended Modeling Problem (OEMP) for students to begin practicing engineering judgment. This study investigates one aspect of engineering judgment, evaluating what is a good enough solution, or reasonableness. After solving an assigned OEMP, students were asked if their solutions were reasonable and to justify their answer. This paper examines the different justifications students provided and the assignment factors that may have led to those responses. This work has implications for designing future ill-structured problems and engaging students in engineering judgment.
Self-Adaptive Software Systems in Contested and Resource-Constrained Environments: Overview and Challenges

Self-adaptive approaches are a promising to address the dynamic and uncertain nature of the environments where today's complex systems operate. In particular, systems operating in military environments, during crises or under unexpected conditions need to address critical concerns related to resource sparsity and the unstable and uncertain nature of exchanged information. Despite a plethora of self-adaptive and autonomic approaches proposed in the last decade, very few have been designed for or evaluated in contested environments, where adversarial action in the communications domain leads to stale or incomplete information, or in resource-constrained environments, where resources are either limited or required by a large number of components. These conditions are where self-adaptability to aid human operators is needed the most. To better understand self-adaptation in contested and resource-constrained environments, we conducted a systematic literature review of publications over the last decade. We conduct our review through the lens of a military environment, where contention, both physical and from a resource, be it computational or communication based is at its peak. We followed the systematic literature review methodology and analysed 238 primary studies. We identified that the most frequent application domains are those where failures are frequent and costly, namely, cloud computing, web services and applications, and servers. Despite this, less than 3% of the papers considered constrained resources and stale or incomplete information and a significant focus was on developing centralised solutions instead of distributed ones throughout all papers. Very few papers (4.6%) considered environments where the information about the system components was not readily available. No papers evaluated the systems running in contested and resource-constrained environments. We present an analysis of the self-adaptive systems that consider incomplete or stale information and constrained resources, discuss their limitations and identify future areas of research. Critical research gaps include the lack of evaluation of self-adaptive approaches, including the lack of standards or formalisms to allow for the comparison of various approaches. In addition, there is a need to consider more self-* properties and non-functional requirements in order to make sure the designed system is resilient. This paper presents our review findings in detail, examines how self-adaptation happens in contested and resource-constrained environments, and discusses the identified research gaps.
Unsupervised Video Satellite Super-Resolution by Using Only a Single Video

Recent studies have shown that deep-learning (DL)-based methods lead to improved performance in video satellite super-resolution (SR). However, the vast majority of prior work is supervised, which is restricted to artificially generated training data (e.g., predetermined bicubic downsampling). Unfortunately, in the real world, the low-resolution (LR) satellite video frames rarely obey these restrictions. To solve this problem, we resort to unsupervised learning and propose a video satellite SR method by using only a single video. The single video SR (SingleVSR) method takes advantage of the power of DL without relying on prior high-resolution (HR) and LR pairs. In the training phase, the LR frames are alternately processed by both downsampling network (i.e., Net <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LR</sub> ) and upsampling network (i.e., Net <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">HR</sub> ). The losses obtained by LR frames and network outputs are used to optimize both Net <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LR</sub> and Net <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">HR</sub> . In the testing phase, the trained Net <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">HR</sub> is applied to generate the SR results of LR frames. In contrast to the existing video satellite SR methods, our SingleVSR does not require any assumption on degradation or any additional training data except for the single video to be tested. Experiments performed on Jilin-1 and OVS-1 satellite videos demonstrate the superiority of the proposed method.
Tungsten skarn mineral resource assessment of the Great Basin region of western Nevada and eastern California

A new quantitative mineral resource assessment for tungsten, a critical mineral commodity with highly concentrated production and a moderate risk of global supply disruption, was conducted for the Great Basin region of western Nevada and eastern California. This assessment was part of a larger effort focusing on three regions in the United States and represents the first study of domestic tungsten resources and mineral potential in over twenty years. By integrating geology, bedrock and stream sediment geochemistry, geophysics, and remote sensing data with recently developed software tools and analyses, estimates of undiscovered tungsten skarn deposits in permissive tracts are combined with grade and tonnage distributions of known deposits to generate probabilistic estimates of undiscovered resources. Identified resources in the Great Basin region total 168 thousand metric tons (kt) of tungsten trioxide (WO 3 ), including 116 kt of past production and 52 kt remaining in place. Consistent with the historic significance of the Great Basin region as a past producer containing a large portion of U.S. identified resources, undiscovered resources are likely to occur adjacent to known deposits and prospects and at unexplored depths. Undiscovered deposits are estimated to contain median resources of 940 kt of WO 3 with a 90% probability of at least 420 kt and a 10% probability of at least 1.7 million metric tons (Mt), of which 240 kt to 1.1 Mt may be economic to extract. Based on a 20-year average price, median recoverable undiscovered resources are estimated at 570 kt WO 3 equivalent with a net present value of $3 billion U.S. dollars. The methods, data, results, and economic significance of the assessment contribute to a scientific understanding of a critical mineral resource with direct implications for policy and land management decisions. • Tungsten is a critical mineral commodity for the United States and other countries. • The Great Basin hosts globally important tungsten skarn deposits and prospects. • Integration of legacy and newly acquired datasets reveals mineralized areas. • A recoverable resource estimate of 570,000 t WO 3 is presented for the first time. • Newly developed grade and tonnage models and resource simulation tools are used.
Research on Software Reuse for Satellite Control Software Based on Product-Line Technology

The reuse strategy for current satellite control software, which gives the percentage of reuse property less than 30%, is based on software snippets. With the rapid growth of satellite R&D missions, the development method for satellite control software is changing to the way that consists of standard-hardware assembly and software-defined function. The role of on-board software becomes more important, and requirements on software become stricter as well as the decreasing development period. The software development ability is currently constraining the satellite development. Based on the problem aforementioned, it is the tendency to strengthen the software productization and improve the software reuse rate. This paper introduces the reuse method based on software product line technology for control software of low-Earth orbit (LEO) satellite, and analyzes the key techniques including satellite domain analysis, software architecture modeling, component design and variability management. The application for new satellite constellation is demonstrated and verified that more than 80% of the reusable core asserts can be realized, while comprehensive and systematic software reuse can be achieved for specific domain.
A Fully Convolutional Sequence Learning Approach for Cued Speech Recognition from Videos

Cued Speech constitutes a sign-based communication variant for the speech and hearing impaired, which involves visual information from lip movements combined with hand positional and gestural cues. In this paper, we consider its automatic recognition in videos, introducing a deep sequence learning approach that consists of two separately trained components: an image learner based on convolutional neural networks (CNNs) and a fully convolutional encoder-decoder. Specifically, handshape and lip visual features extracted from a 3D-CNN feature learner, as well as hand position embeddings obtained by a 2D-CNN, are concatenated and fed to a time-depth separable (TDS) block structure, followed by a multi-step attention-based convolutional decoder for phoneme prediction. To our knowledge, this is the first work where recognition of cued speech is addressed using a common modeling approach based entirely on CNNs. The introduced model is evaluated on a French and a British English cued speech dataset in terms of phoneme error rate, and it is shown to significantly outperform alternative modeling approaches.
Liver Pathological States Identification with Self-organization Models Based on Ultrasound Images Texture Features

The article deals with the new possibilities in the development of diagnostic decision support systems on the example of normal and pathology states separation in diffuse liver diseases based on statistical features of ultrasound image texture. It is proposed to use the difference conversions of the original grayscale matric, which makes the results of image processing independent of variations in the original grayscale settings. Based on grey-level co-occurrence matrices, it was proposed to calculate a number of new indicators, which allowed to effectively distinguish the class textures of liver norm and pathology. The indicators’ definitions are protected by a patent of Ukraine. The liver state classifiers are built using the GMDH Shell DS software in the form of analytical expressions and an authors’ algorithm in the form of a forest, whose trees are built according to the Group Method of Data Handling principles. The work was performed on data, had provided by the Nuclear Medicine and Diagnostic Radiology Institute of the National Academy of Medical Sciences of Ukraine.
Privacy is not Free: Energy-Aware Federated Learning for Mobile and Edge Intelligence

In mobile and edge intelligence systems, federated learning (FL) enables local data training and learning model sharing without obtaining actual data from mobile and edge users, which are data owners. Data training processes are performed at the user side with only trained gradients passed to an aggregator, i.e., learning server. The learning server continually trains and updates corresponding learning models by collecting gradients. The updated learning models are delivered back to the mobile and edge users for improved data training results. Despite the advantages of federated learning in preserving privacy, the local data training process will consume an adequate amount of energy from the perspective of mobile and edge users. In mobile and edge intelligence systems, mobile users may not always prefer to apply federated learning to reduce energy consumption. There is a trade-off between applying federated learning to reserve data privacy and updating actual data for learning servers to train. In this work, a Markov decision process (MDP) based system model is proposed for mobile and edge users to make federated learning decisions to optimize long-term performance in terms of utility function consists of data training reward and data processing delay. A deep reinforcement learning approach is proposed to solve the MDP problem in highly dynamic systems with a large state space.
S3QLRDF: Property Table Partitioning Scheme for Distributed SPARQL Querying of large-scale RDF data

The proliferation of the semantic web in the form of Resource Description Framework (RDF) demands an efficient, scalable, and distributed storage along with a highly available and fault-tolerant parallel processing strategy. More precisely, the rapid growth of RDF data raises the need for an efficient partitioning strategy over distributed data management systems to improve SPARQL query performance regardless of its pattern shape with minimized pre-processing time. In this context, we propose a new relational partitioning scheme called Property Table Partitioning (PTP) for RDF data, that further partitions existing Property Table into multiple tables based on distinct properties (comprising of all subjects with non-null values for those distinct properties) in order to minimize input data and join operations. In this paper, we introduce a distributed RDF data management system called S3QLRDF, which is built on top of Spark and utilizes SQL to execute SPARQL queries over PTP schema. We perform an extensive experimental evaluation with respect to preprocessing costs and query performance, using Lehigh University Benchmark (LUBM) and Waterloo SPARQL Diversity Test Suite (WatDiv) datasets with up to 1.4 billion triples. Our results demonstrate that S3QLRDF outperforms state-of-the-art distributed RDF management systems.
ASIC Accelerator in 28 nm for the Post-Quantum Digital Signature Scheme XMSS

This paper presents the first 28 nm ASIC implementation of an accelerator for the post-quantum digital signature scheme XMSS. In particular, this paper presents an architecture for a novel, pipelined XMSS Leaf accelerator for accelerating the most compute-intensive step in the XMSS algorithm. This paper then presents the ASIC designs for both an existing non-pipelined accelerator architecture and the novel, pipelined XMSS Leaf accelerator. In addition, the performance of the 28 nm ASIC is compared to the same designs on 28 nm Artix-7 FPGA. The novel pipelined XMSS Leaf accelerator is 25% faster compared to the non-pipelined version in the ASIC, and both accelerator architectures have a 10 × lower power consumption than on the FPGA. The evaluation shows that the pipelining increases the frequency by 1.7× on the FPGA but only 1.2× on the ASIC, due to the critical path in the ASIC being in the memory. The non-pipelined XMSS Leaf accelerator is shown to have a significantly better area-delay and energy-delay metric on the ASIC, while the pipelined accelerator wins out in these metrics on the FPGA. Consequently, this work shows the different architectural decisions that need to be made between FPGA and ASIC designs, when selecting how to best implement post-quantum cryptographic accelerators in hardware.
Mobile keystroke dynamics for biometric recognition: An overview

In the relatively recent past, the analysis of keystroke dynamics for biometric recognition purposes has intrigued researchers, since practical evidences have shown differences in the typing behaviours of distinct subjects. This area of research has become even more appealing since the emergence and evolution of mobile smartphones, given their pervasiveness and intensive use in real-life applications. In addition, unlike hard keyboards used with computers, mobile smartphones offer the possibility of exploiting embedded sensors to augment the information acquired when typing on their soft keyboards. This study discusses the state of the art of keystroke-dynamics-based automatic recognition system, exclusively when dealing with mobile devices, for both verification and identification purposes. In more details, the databases employed, the features selected, the methodologies implemented, and the performance achieved through the technological advances introduced in the last years, are here overviewed.