Reviewerly/exHarmony huggingface dataset:
Software professionals' information needs in continuous integration and delivery

Continuous integration and delivery consolidate several activities, ranging from frequent code changes to compiling, building, testing, and deployment to customers. During these activities, software professionals seek additional information to perform the task at hand. Developers that spend a considerable amount of time and effort to identify such information can be distracted from doing productive work. By identifying the types of information that software professionals seek, we can better understand the processes, practices, and tools that are required to develop a quality product on time. A better understanding of the information needs of software practitioners has several benefits, such as staying competitive, increasing awareness of the issues that can hinder a timely release, and building a visualization tool that can help practitioners to address their information needs. We conducted a multiple-case holistic study with 5 different companies (34 unique participants) to identify information needs in continuous integration and delivery. This study attempts to capture the importance, frequency, required effort (e.g., sequence of actions required to collect information), current approach to handling, and associated stakeholders with respect to identified needs. We identified 27 information needs associated with different stakeholders (i.e., developers, testers, project managers, release team, and compliance authority). The identified needs were categorized as testing, code & commit, confidence, bug, and artifacts. We discussed whether the information needs were aligned with the tools used to address them.
Accelerating Convolutional Neural Network Inference Based on a Reconfigurable Sliced Systolic Array

Convolutional neural networks (CNNs) have achieved great successes on many computer vision tasks, such as image recognition, video processing, and target detection. In recent years, many hardware designs have been devoted to accelerating CNN inference. In order to further speed up CNN inference and reduce data waste, this work proposed a reconfigurable sliced systolic array: 1) Depending on the number of network nodes in each layer, the slice mode could be dynamically configured to achieve high throughput and resource utilization. 2) To take full advantage of convolution reuse and weight reuse, this work designed a tile-column sliding (TCS) processing dataflow. 3) A four-stage for loop algorithm was employed, which divides the CNN calculation into several parts based on the input nodes and output nodes. The entire CNN inference is carried out using integer-only arithmetic originated from TensorLite. Experimental results prove that these strategies lead to significant improvement in inference performance and energy efficiency.
Fault injection attacks on SoftMax function in deep neural networks

Softmax is commonly used activation function in neural networks to normalize the output to probability distribution over predicted classes. Being often deployed in the output layer, it can potentially be targeted by fault injection attacks to create misclassification. In this extended abstract, we perform a preliminary fault analysis of Softmax against single bit faults.
High-Performance Systolic Array Montgomery Multiplier for SIKE

In theory, the speed of quantum computers is much faster than classical computers, which poses a threat to the Public Key Cryptography (PKC) that are currently in use. Post Quantum Cryptography (PQC) is a class of cryptography based on complex mathematical problems that are difficult to be attacked by quantum computers. The Supersingular Isogeny Key Encapsulation (SIKE) protocol is one of candidate algorithms for the US National Institute of Standards and Technology (NIST) PQC standardization process and survived to the Round 3. In this paper, we reconstruct the systolic array based Montgomery multiplier architecture for SIKE, using a three-stage pipeline that results in frequency improvement of 21.4%. The proposed multiplier consumed fewer DSP resources than the state-of-the-art SIKE designs and has a speed increase up to 12.7%.
Interpretation of the SUS questionnaire in Mexican sign language to evaluate usability an approach

The software development is influenced for the acceptance of the users, this has been researched in usability, therefore, there has been development in tools to evaluate the usability of a software. These tools give the developers a score of usability for the software, based in efficiency, effectiveness and satisfaction. An important point in the use of these tools, is to obtain the results of the user without biasing the user, this is accomplished following a standard and a questionnaire. The questions in these tools, for example the SUS questionnaire, have been translated to other language, but in the case of deaf users there are not in their language (Mexican Sign Language). The purpose of this paper is the study of an interpretation of the SUS questionnaire to determine if that bias the user or improve the quality of the test, in the case of deaf users.
Blockchain Adoption, Implementation and Integration in Healthcare Application Systems

This paper examines the state of the research in the field of blockchain implementation and integration in healthcare. Seven papers retrieved from ACM Digital Library were classified into three categories, including adoption, implementation, and integration. Based on this brief literature review, we conclude blockchain adoption has been covered thoroughly. Our primary contribution is an up-to-date classification of current research papers in the field of blockchain technology in Healthcare. We also identify understudied research areas, potential areas for future research work. There may be a need for additional research in the areas of blockchain implementation and integration for healthcare systems.
High-throughput configurable SIMON architecture for flexible security

As the Internet of Things applications become mission-critical and their data more valuable, it becomes more and more essential to paramount their security. The security can be improved by using emerging light-weight ciphers. SIMON is a relatively recent family of light-weight ciphers which is proposed by the National Security Agency optimized for hardware platforms. In this paper, we propose an optimized hardware architecture for SIMON for high-throughput resource-constrained applications with multiple levels of security. Moreover, a configurable architecture with different operating modes is introduced for utilizing in applications requiring higher resistance to fault and power attacks. This architecture also supports an ultra-high-throughput mode for different key sizes. Implementation results of the proposed architectures on both ASIC and FPGA are reported. The comparison results show that our proposals outperform similar architectures in terms of some design metrics, e.g. throughput, which make it suitable for IoT applications. Finally, we implement practically our architecture on the Digilent-ZYBO board and report the experimental results.
Optimal Strategy for Cyberspace Mimic Defense Based on Game Theory

Traditional defensive techniques are usually static and passive, and appear weak to confront highly adaptive and stealthy attacks.As a novel security theory, Cyberspace Mimic Defense (CMD) creates asymmetric uncertainty that favors the defender.CMD constructs multiple executors which are diverse functional equivalent variants for the protected target and arbitral mechanism.In this way, CMD senses the results of current running executors and changes the attack surface.Although CMD enhances the security of systems, there are still some critical gaps with respect to design a defensive strategy under costs and security.In this paper, we propose a dual model to dynamically select the number of executors being reconfigured according to the states of the executors.First, we establish a Markov anti-attack model to compare the effects of CMD under different types of attack.Then, we use a dynamic game of incomplete information to determine the optimal strategy, which achieves the balance of the number of reconfiguration and security.Finally, experimental results show that our dual model reduces defensive costs while guarantees security.
Evaluating the performance of Android based Cross-Platform App Development Frameworks

Mobile applications have become a necessity in people’s daily lives, and more are being developed at this time. To increase the efficiency in developing mobile apps, cross-platform mobile app development framework is needed. Currently there are various types of mobile app development frameworks with their respective approaches, be it native apps, mobile web apps, widget based, bridging with javascript, and others. However, user adoption and retention to a mobile app is highly influenced by good user experience, which can be quantified by application performance. In this research, a series of experiment are conducted to measure the performance of several Android based mobile app development frameworks based on five parameters, namely cpu usage, memory usage, response time, frame rate and app size. The results of this study show Native Android application that is used as the baseline gives the best performance and then followed by widget based cross-platform development framework, i.e. Flutter.
Low Rank Representation on Product Grassmann Manifolds for Multi-view Subspace Clustering

Clustering high dimension multi-view data with complex intrinsic properties and nonlinear manifold structure is a challenging task since these data are always embedded in low dimension manifolds. Inspired by Low Rank Representation (LRR), some researchers extended classic LRR on Grassmann manifold or Product Grassmann manifold to represent data with non-linear metrics. However, most of these methods utilized convex nuclear norm to leverage a low-rank structure, which was over-relaxation of true rank and would lead to the results deviated from the true underlying ones. And, the computational complexity of singular value decomposition of matrix is high for nuclear norm minimization. In this paper, we propose a new low rank model for high-dimension multi-view data clustering on Product Grassmann Manifold with the matrix tri-factorization which is used to control the upper bound of true rank of representation matrix. And, the original problem can be transformed into the nuclear norm minimization with smaller scale matrices. An effective solution and theoretical analysis are also provided. The experimental results show that the proposed method obviously outperforms other state-of-the-art methods on several multi-source human/crowd action video datasets.
Run-Time Norms Synthesis in Multi-objective Multi-agent Systems

Norms represent behavioural aspects that are encouraged by a social group of agents or the majority of agents in a system. Normative systems enable coordinating synthesised norms of heterogeneous agents in complex multi-agent systems autonomously. In real applications, agents have multiple objectives that may contradict each other or contradict the synthesised norms. Therefore, agents need a mechanism to understand the impact of a suggested norm on their objectives and decide whether or not to adopt it. To address these challenges, a utility-based norm synthesis (UNS) model is proposed which allows the agents to coordinate their behaviour while achieving their conflicting objectives. UNS proposes a utility-based case-based reasoning technique, using case-based reasoning for run-time norm synthesising in a centralised approach, and a utility function derived from the objectives of the system and its operating agents to decide whether or not to adopt a norm. The model is evaluated using a traffic junction scenario and the results show its efficacy to optimise multiple objectives while adopting synthesised norms.
An autoregressive generation model for producing instant basketball defensive trajectory

Learning basketball tactic via virtual reality environment requires real-time feedback to improve the realism and interactivity. For example, the virtual defender should move immediately according to the player's movement. In this paper, we proposed an autoregressive generative model for basketball defensive trajectory generation. To learn the continuous Gaussian distribution of player position, we adopt a differentiable sampling process to sample the candidate location with a standard deviation loss, which can preserve the diversity of the trajectories. Furthermore, we design several additional loss functions based on the domain knowledge of basketball to make the generated trajectories match the real situation in basketball games. The experimental results show that the proposed method can achieve better performance than previous works in terms of different evaluation metrics.
Online parallel framework for real-time visual tracking

Discriminative correlation filter (DCF) based tracking algorithms have recently achieved excellent performance in challenging factors such as rotations and distractors. However, in case with fast motions and full occlusion, these trackers are not always effective and easily drift. In contrast, Siamese matching networks are insensitive to fast motions, but suffer from distractors. In this work, we propose an efficient real-time parallel framework for robust visual tracking. By incorporating DCF and Siamese network into the tracking framework to complement each other and better distinguish target objects from background clutters. Additionally, to improve tracking results and prevent target drift, we introduce an effective tracker switch method to select suitable tracker in each frame by considering the confidence estimation as well as motion smoothness, and examines the impact among all trackers over time. To validate the effectiveness of our method, we perform comprehensive experiments on three popular benchmark datasets, namely OTB2013, OTB2015, VOT2016, and TC128. The experimental results demonstrate that the proposed algorithm exhibits favorably results and real-time tracking speed against state-of-art trackers.
Geology, fluid inclusion and H-O-S-Pb isotope study of the Ganhegou epithermal gold deposit in Yanbian, NE China: Implications for ore genesis and mineralization process

The Yanbian mining district is located at the eastern end of the Central Asian orogenic belt and northeast of the western Pacific continental margin metallogenic belt. It is a porphyry-epithermal Cu-Au mining district related to Mesozoic continental volcanic eruptions and hypabyssal magmatism. To explore the genesis and mineralization process of epithermal gold deposits, we conducted a systematic study of the geology, fluid inclusions and H-O-S-Pb isotopes of the Ganhegou gold deposit, which is expected to be revealed as a medium-sized deposit in the latest exploration. We found the following: (1) The deposit occurs in Early Cretaceous continental andesites of the Jingouling Formation and granodiorite porphyry. The ore belts are controlled by NW-trending volcanic faults. The mineralization process can be divided into the quartz-hematite-arsenopyrite-pyrite stage (I), grey-white quartz-pyrite stage (II), smoky quartz-polymetallic sulfide stage (III), milky quartz-adularia stage (IV) and carbonate stage (V). Gold precipitated in stage III. (2) Vapour-rich-, liquid-rich-, pure-liquid- and pure-vapour-type FIs developed in quartz in stages II, III and IV, and the average homogenization temperatures in these stages are 325 ℃, 273 ℃ and 229 ℃, respectively. The ore-forming fluids belonged to the H2O-NaCl ± CO2 system. (3) The δ18OH2O and δDH2O values of stage II-IV range from −9.9 to −5.2‰ and −121.9 to −119.8‰, respectively, indicating that the ore-forming fluids were driven by intermediate-felsic magmatic water and mixed with meteoric water during mineralization. (4) The δ34S values of pyrite in stages II and III range from −5.8 to −3.4‰, whereas the values of 206Pb/204Pb, 207Pb/204Pb and 208Pb/204Pb are 15.529–15.551, 18.239–18.257 and 38.075–38.153, respectively, implying that the ore-forming materials were derived from Neoproterozoic lower crust. These characteristics indicate that Ganhegou is a low-sulfidation epithermal gold deposit. Mineralization occurred via several main processes: (i) The ore-forming fluids migrated along volcanic faults to suitable positions driven by intermediate-felsic magmatic water. (ii) In stage I, the quartz-arsenopyrite-hematite assemblage formed under relatively high oxidation conditions. (iii) In stage II, the pyrite-hessite-galena-quartz veins formed in the near-neutral environment, which was slightly reduced relative to that of stage I due to the fluid boiling. (iv) In stage III, the gold-bearing quartz veins formed due to the fluid boiling and mixing. (v) In stage IV, the precipitation of quartz-adularia-pyrite veins occurred under near-neutral conditions (pH≈5). (iv) Small-scale carbonate veinlets formed in stage V, signalling the end of mineralization.
Secure cloud-of-clouds storage with space-efficient secret sharing

Cloud storage services are top-rated, but there are often concerns about the security of the files there stored. Clouds-of-clouds or multi-clouds are being explored in order to improve that security. The idea is to store the files in several clouds, ensuring integrity and availability. Confidentiality, however, is obtained by encrypting the files with block ciphers that do not provide provable security. Secret sharing allows distributing files among the clouds providing information-theoretic security/secrecy. However, existing secret sharing schemes are space-inefficient (the size of the shares is much larger than the size of the secret) or purely theoretical. In this paper, we propose the first practical space-efficient secret sharing scheme that provides information-theoretic security, which we denominate PRactical Efficient Secret Sharing (PRESS). Moreover, we present the Secure CloUD storage (SCUD) service, a new cloud-of-clouds storage service that leverages PRESS to provide file confidentiality. Additionally, SCUD provides data integrity and availability, leveraging replication.
Design of PID Controller Based on ELM and Its Implementation for Buck Converters

Buck converter is a kind of converter device with high efficiency, wide adjustable output voltage, small loss, small size and light weight. Its circuit has nonlinearity and will exhibit abundant nonlinear phenomena with the change of circuit parameters, resulting in unstable output voltage and being susceptible to load and external disturbances. Therefore, a control method based on Extreme Learning Machine (ELM) combined with a proportionalintegral-derivative (PID) controller is proposed in this paper and used for output voltage control of buck converter. Firstly, the mathematical model of buck converter in Continuous Conduction Mode (CCM) is established by the state space averaging method. Then the PID tuning algorithm is designed in combination with ELM, and the stability analysis of the model is carried out. Finally, the simulation experiment is carried out under different disturbances. By comparing with the open-loop control strategy, the effectiveness of the proposed ELM-PID control strategy is verified, indicating that the proposed method can achieve the stability of output voltage and good dynamic response.
Two-Branch Relational Prototypical Network for Weakly Supervised Temporal Action Localization

As a challenging task of high-level video understanding, weakly supervised temporal action localization has attracted more attention recently. With only video-level category labels, this task should indistinguishably identify the background and action categories frame by frame. However, it is non-trivial to achieve this in untrimmed videos, due to the unconstrained background, complex and multi-label actions. With the observation that these difficulties are mainly brought by the large variations within background and actions, we propose to address these challenges from the perspective of modeling variations. Moreover, it is desired to further reduce the variations, or learn compact features, so as to cast the problem of background identification as rejecting background and alleviate the contradiction between classification and detection. Accordingly, in this paper, we propose a two-branch relational prototypical network. The first branch, namely action-branch, adopts class-wise prototypes and mainly acts as an auxiliary to introduce priori knowledge about label dependencies and be a guide for the second branch. Meanwhile, the second branch, namely sub-branch, starts with multiple prototypes, namely sub-prototypes, to enable a powerful ability of modeling variations. As a further benefit, we elaborately design a multi-label clustering loss based on the sub-prototypes to learn compact features under the multi-label setting. The two branches are associated using the correspondences between two types of prototypes, leading to a special two-stage classifier in the s-branch, on the other hand, the two branches serve as regularization terms to each other, improving the final performance. Ablation studies find that the proposed model is capable of modeling classes with large variations and learning compact features. Extensive experimental evaluations on Thumos14, MultiThumos and ActivityNet datasets demonstrate the effectiveness of the proposed method and superior performance over state-of-the-art approaches.
Pedestrian detection based on one-stage YOLO algorithm

Abstract Pedestrian detection is mostly used in autonomous driving scenarios, which require high real-time detection. Most of the existing algorithms are based on Two-Stage detection, with poor real-time performance. This paper proposes a pedestrian detection system based on One-Stage, and uses One-Stage-based YOLO-Tiny, YOLO and YOLO-SPP algorithms to test and analyze the system in the scene of detecting pedestrians of different sizes. The results show that YOLO and YOLO-SPP have high average confidence, and YOLO-Tiny has a fast detection speed, which is suitable for fast detection scenarios.
Round-Optimal Blind Signatures in the Plain Model from Classical and Quantum Standard Assumptions

Blind signatures, introduced by Chaum (Crypto'82), allows a user to obtain a signature on a message without revealing the message itself to the signer. Thus far, all existing constructions of round-optimal blind signatures are known to require one of the following: a trusted setup, an interactive assumption, or complexity leveraging. This state-of-the-affair is somewhat justified by the few known impossibility results on constructions of round-optimal blind signatures in the plain model (i.e., without trusted setup) from standard assumptions. However, since all of these impossibility results only hold under some conditions, fully (dis)proving the existence of such round-optimal blind signatures has remained open. In this work, we provide an affirmative answer to this problem and construct the first round-optimal blind signature scheme in the plain model from standard polynomial-time assumptions. Our construction is based on various standard cryptographic primitives and also on new primitives that we introduce in this work, all of which are instantiable from classical and post-quantum standard polynomial-time assumptions. The main building block of our scheme is a new primitive called a blind-signature-conforming zero-knowledge (ZK) argument system. The distinguishing feature is that the ZK property holds by using a quantum polynomial-time simulator against non-uniform classical polynomial-time adversaries. Syntactically one can view this as a delayed-input three-move ZK argument with a reusable first message, and we believe it would be of independent interest.