Reviewerly/exHarmony huggingface dataset:
Proposing total quality management as a buffer between global software development challenges and project success

Purpose This study aims to empirically investigate the relationship between different global software development (GSD) challenges (management, process, social, technical and environmental challenges) and software project success. Further, the study examines the moderating role of total quality management (TQM) between the relationship of GSD challenges and success of software projects. Design/methodology/approach Using two field studies, the authors collected data form software developers working in globally distributed teams. In study 1 ( n = 194), relationship of different dimensions of GSD challenges (management, process, social, technical and environmental challenges) and project success was examined. In study 2 ( n = 138), moderating role of TQM was examined on the relationship of GSD challenges and project success. Findings The results of study 1 indicate that there is a negative relationship between all dimensions of GSD challenges and project success. Findings of study 2 confirmed that TQM practices moderate the negative relationship between GSD challenges and project success. Practical implications The findings of the study provide guidelines to the project managers of software industry to mitigate GSD challenges using TQM practices. Originality/value Study adds in the literature of TQM, GSD challenges and project success by (1) empirically investigating the relationship between different GSD challenges and software project success and (2) by examining the moderating role of TQM practices on relationship of GSD challenges and project success in global software development industry.
Tabu Annealing Lion Swarm Optimization Algorithm

Lion swarm optimization is a swarm intelligence algorithm that simulates the behaviors of lion king, lioness and cub. Lion king is the individual who occupies the best position in the lion group, but only a single position update is carried out in each iteration, so the search is not careful enough. To solve the above problem, a tabu annealing lion swarm optimization algorithm is proposed, which uses the memory tabu annealing algorithm with tabu list to enhance the lion king's optimization ability. The experimental comparisons on CEC2013 test function show that the proposed tabu annealing lion swarm optimization algorithm has faster convergence speed, higher accuracy and is more effective.
Simple Constructions from (Almost) Regular One-Way Functions

Two of the most useful cryptographic primitives that can be constructed from one-way functions are pseudorandom generators (PRGs) and universal one-way hash functions (UOWHFs). In order to implement them in practice, the efficiency of such constructions must be considered. The three major efficiency measures are: the seed length, the call complexity to the one-way function, and the adaptivity of these calls. Still, the optimal efficiency of these constructions is not yet fully understood: there exist gaps between the known upper bound and the known lower bound for black-box constructions.A special class of one-way functions called unknown-regular one-way functions is much better understood. Haitner, Harnik and Reingold (CRYPTO 2006) presented a PRG construction with semi-linear seed length and linear number of calls based on a method called randomized iterate. Ames, Gennaro and Venkitasubramaniam (TCC 2012) then gave a construction of UOWHF with similar parameters and using similar ideas. On the other hand, Holenstein and Sinha (FOCS 2012) and Barhum and Holenstein (TCC 2013) showed an almost linear call-complexity lower bound for black-box constructions of PRGs and UOWHFs from one-way functions. Hence Haitner et al. and Ames et al. reached tight constructions (in terms of seed length and the number of calls) of PRGs and UOWHFs from regular one-way functions. These constructions, however, are adaptive.In this work, we present non-adaptive constructions for both primitives which match the optimal call-complexity given by Holenstein and Sinha and Barhum and Holenstein. Our constructions, besides being simple and non-adaptive, are robust also for almost-regular one-way functions.
A System and Its Implementation Based on FPGA for Video JPEG2000 Codec and Network Transmission

This paper brings forward a system and its implementation based on FPGA (Field-Programmable Gate Array) for video JPEG2000 codec and network transmission. According to configuring instructions from upper monitors of the encoding terminal and the decoding terminal, the FPGAs implemented configuring and then controlling of JPEG2000 codec chips and network communication chips. The encoding terminal converted analog video signal to digital signal for JPEG2000 codec's encoding, then sent coded data to the network communication chip to generate network messages which were then sent out to the Ethernet network. The decoding terminal received network messages from the Ethernet network through the network communication chip on it, then sent coded data to the JPEG2000 codec for decoding, finally converted digital video to analog signal for displaying. In this design, the system gave full play to the functions of each part, met real-time requirements in video or image encoding, decoding, and network transmission requirements.
Preconditioned Plug-and-Play ADMM with Locally Adjustable Denoiser for Image Restoration

Plug-and-Play priors recently emerged as a powerful technique for solving inverse problems by plugging a denoiser into a classical optimization algorithm. The denoiser accounts for the regularization and therefore implicitly determines the prior knowledge on the data, hence replacing typical handcrafted priors. In this paper, we extend the concept of Plug-and-Play priors to use denoisers that can be parameterized for nonconstant noise variance. In that aim, we introduce a preconditioning of the ADMM algorithm, which mathematically justifies the use of such an adjustable denoiser. We additionally propose a procedure for training a convolutional neural network for high quality nonblind image denoising that also allows for pixelwise control of the noise standard deviation. We show that our pixelwise adjustable denoiser, along with a suitable preconditioning strategy, can further improve the Plug-and-Play ADMM approach for several applications, including image completion, interpolation, demosaicing, and Poisson denoising.
Variance Reduced Stochastic Proximal Algorithm for AUC Maximization

Stochastic Gradient Descent has been widely studied with classification accuracy as a performance measure. However, these stochastic algorithms are not applicable when non-decomposable pairwise performance measures are used, such as Area under the ROC curve (AUC), a standard performance metric used when the classes are imbalanced. Several algorithms have been proposed for optimizing AUC as a performance metric, one of the recent being a Stochastic Proximal Gradient Algorithm (SPAM). However, the downside of stochastic gradient descent is that it suffers from high variance leading to very slow convergence. Several variance reduced methods have been proposed with faster convergence guarantees than vanilla stochastic gradient descent to combat this issue. Again, these variance reduced methods are not applicable when non-decomposable performance measures are used. In this paper, we develop a Variance Reduced Stochastic Proximal algorithm for AUC Maximization (VRSPAM) that combines the two areas of analyzing non-decomposable performance metrics with and optimization efforts to guarantee faster convergence. We perform an in-depth theoretical and empirical analysis to demonstrate that our algorithm converges faster than existing state-of-the-art algorithms for the AUC maximization problem.
CAD3: Edge-facilitated Real-time Collaborative Abnormal Driving Distributed Detection

Speeding, slowing down, and sudden acceleration are the leading causes of fatal accidents on highways. Anomalous driving behavior detection can improve road safety by informing drivers who are in the vicinity of dangerous vehicles. However, detecting abnormal driving behavior at the city-scale in a centralized fashion results in considerable network and computation load, that would significantly restrict the scalability of the system. In this paper, we propose CAD3, a distributed collaborative system for road-aware and driver-aware anomaly driving detection. CAD3 considers a decentralized deployment of edge computation nodes on the roadside and combines collaborative and context-aware computation with low-latency communication to detect and inform nearby drivers of unsafe behaviors of other vehicles in real-time. Adjacent edge nodes collaborate to improve the detection of abnormal driving behavior at the city-scale. We evaluate CAD3 with a physical testbed implementation. We emulate realistic driving scenarios from a real driving data set of 3,000 vehicles, 214,000 trips, and 18 million trajectories of private cars in Shenzhen, China. At the microscopic (road) level, CAD3 significantly improves the accuracy of detection and lowers the number of potential accidents caused by false negatives up to four times and 24 times as compared to distributed standalone and centralized models, respectively. CAD3 can scale up to 256 vehicles connected to a single node while keeping the end-to-end latency under 50 ms and a required bandwidth below 5 mbps. At the mesoscopic (driver-trip) level, CAD3 performs stable and accurate detection over time, owing to local RSU interaction. With a dense deployment of edge nodes, CAD3 can scale up to the size of Shenzhen, a megalopolis of 12 million inhabitant with over 2 million concurrent vehicles at peak hours.
Direct measurement of density-matrix elements using a phase-shifting technique

A direct measurement protocol allows reconstructing specific elements of the density matrix of a quantum state without using quantum state tomography. However, the direct measurement protocols to date are primarily based on weak or strong measurements with an ancillary pointer, which interacts with the investigated system to extract information about the specified elements. Here, we present a direct measurement scheme based on phase-shifting operations which do not need ancillary pointers. In this method, estimates of at most six expectation values of projective observables suffice to determine any specific element of an unknown quantum density matrix. A concrete quantum circuit to implement this direct measurement protocol for multiqubit states is provided, which is composed of just single-qubit gates and two multiqubit controlled-phase gates. This scheme is also extended for the direct measurement of the density matrix of continuous-variable quantum states. Our method can be used in quantum information applications where only partial information about the quantum state needs to be extracted, for example, problems such as entanglement witnessing, fidelity estimation of quantum systems, and quantum coherence estimation.
Accelerating the Activation Function Selection for Hybrid Deep Neural Networks – FPGA Implementation

Much of the work in literature about hardware implementations of deep neural networks illustrates the multiplication of input signal with weights and summing up the data. Work in this paper is focused on the hardware implementation of non-linear functions (activation functions), along with hardware rendition of the automatic selection of activation function for each layer in the neural network in order to increase the accuracy. We have used Field Programmable Gate Array (FPGA) based hardware development platform to add in the advantages of the power efficiency and edge deployment. Our novel hardware design modules to this extent are capable of accelerating the entire process of activation function selection and activation function output generation along with its derivative. The tabulated results in this paper, for power and resource utilization generated through Xilinx® Vivado platform by targeting our design modules towards Avnet® Ultra96 v2 Evaluation board prove the sheer hardware novelty in terms of less hardware foot print and energy efficiency in comparison to the GPU (Graphics Processing Unit) and CPU (Central Processing Unit) based executions along with FPGA based implementations of few activation functions reported in literature.
DeExam: A Decentralized Exam Administration Model using Public Blockchain

In exams, which are intended to evaluate capability, it is practically difficult to detect errors or mistakes made by test providers and scorers, as the correct/incorrect decision logic is often not disclosed. Therefore, we propose a sustainable exam administration model with enhanced reliability and transparency, utilizing a public blockchain, without a centralized authority. The blockchain records the hash values of the correct answers, decision condition, and decision result, which cannot be tampered with afterward. We implemented our proposed method using Bitcoin Core and used it to conduct a CTF (Capture The Flag) competition with over 100 participants. No fatal flaws were observed, thereby confirming the viability of the method.
Face occlusion detection algorithm based on yolov5

Abstract The current face-mask recognition detection algorithm during the epidemic only distinguishes between wearing or not wearing a mask. Such detection often has certain loopholes, such as using other objects to cover their mouths and noses instead of masks to cheat the detection. To address such problems, this paper proposes a YOLOv5 based face occlusion detection algorithm, which is modified based on the YOLOv5 algorithm by improving the loss function as DIoU and increasing the experimental samples by introducing multiple data sets to improve the object detection effect. The experimental results show that the improved YOLOv5 algorithm has improved the object detection effect for different kinds of face occlusions, which verifies the method’s effectiveness.
Verification of the photogrammetric approach on the torsion test method for timber beams

The photogrammetric approach was proposed in this study to evaluate torsional behaviour of glulam timber beams during the torsion test. The main objectives were to investigate the applicability of the photogrammetric approach and to monitor more details of angles of twist of glulam timber beams. Experiments were conducted and the twists on long and short sides of the cross section were measured using the photogrammetric approach and inclinometer sensors, which were used to validate the accuracy. The test results showed that the optical system allowed monitoring the rotations of the samples at different locations which would not be possible to obtain using the traditional techniques. The results also showed that there is a non-linearity from the measurement of the shear displacements along the long side of the cross-section. However, the average rotation shows clear agreements with the inclinometer readings.
Integrated spectral analysis of ASTER and gamma-ray spectrometric data to delineate alteration minerals for uranium exploration in Gabal Khashm El-Risha area, North Eastern Desert, Egypt

The main topic of this study is to integrate the spectral analyses of ASTER (VNIR-SWIR) with gamma-ray spectrometric data for mapping the alteration zones that affect in the distribution of the radioactive elements in Gabal Khashm El-Risha area, North Eastern Desert, Egypt. The spectral transformation techniques such as band ratios and minimum noise fraction in addition to mixture tuned matched filtering applicable to ASTER data are used to identify these altered zones. The applied techniques revealed approximately rapprochement between specific alteration minerals (i.e. hematite, goethite, muscovite, epidote, kaolinite and alunite) and the high potentiality of radioelements. The present work utilizes gamma ray spectrometric data (colored radioelements, contour maps, ratio maps and F-parameter) and correlated with the interpreted ASTER maps to allocate the possible zones of uranium mineralization. The associated abnormal level of radioactivity exhibits 10.5 and 24.2 ppm for eU and eTh respectively and mainly restricted to the syenogranites, which significantly have higher hematite content rather than the other exposures. Field-work and laboratory analysis validated the results obtained from ASTER data combined with gamma-ray spectrometric data.
Distributed Computing with the Cloud

Motivated by cloud storage (à la Dropbox, Google Drive, etc.), we investigate distributed computing in message passing networks that contain a passive node that can only store and share data, and does not carry out any computations. Using basic primitives of collaborative transmission of a file from and to the cloud, we implement more complex tasks where the goal is to combine input values: e.g., each node holds a vector (or a matrix) as input and the sum (or product) of all the inputs should be stored in the cloud. We present near-optimal algorithms for these tasks. Finally we consider applications such as federated learning and file deduplication in this new model. Our results show that utilizing both node-cloud and node-node communication links can substantially speed up computation with respect to systems where processors communicate either only through the cloud or only through the network links.
Impact of Transfer Learning on Siamese Networks for Face Recognition with Few Images Per Class

Being trained on huge volumes of labeled data, Convolutional Neural Networks (CNNs) exhibit high accuracy in the face recognition task. The quality of data-collecting sensors, interclass and intraclass variations in the training data directly affect the accuracy and generalization of the model. State-of-the-art accurate biometric systems are trained on huge volumes of labelled data utilizing powerful computers. The problem arises when we want to deploy a biometric-recognition system locally on devices having limited computational power and, in an application, where collecting high numbers of images for people is costly and unfeasible. In this work, authors experimentally examine different approaches such as transfer learning and one-shot learning and assess their impact on dealing with fewer amounts of training data compared to conventional methods such as CNNs and traditional approaches.
Deep Quantile Regression for Unsupervised Anomaly Detection in Time-Series

Abstract Time-series anomaly detection receives increasing research interest given the growing number of data-rich application domains. Recent additions to anomaly detection methods in research literature include deep neural networks (DNNs: e.g., RNN, CNN, and Autoencoder). The nature and performance of these algorithms in sequence analysis enable them to learn hierarchical discriminative features and time-series temporal nature. However, their performance is affected by usually assuming a Gaussian distribution on the prediction error, which is either ranked, or threshold to label data instances as anomalous or not. An exact parametric distribution is often not directly relevant in many applications though. This will potentially produce faulty decisions from false anomaly predictions due to high variations in data interpretation. The expectations are to produce outputs characterized by a level of confidence. Thus, implementations need the Prediction Interval (PI) that quantify the level of uncertainty associated with the DNN point forecasts, which helps in making better-informed decision and mitigates against false anomaly alerts. An effort has been made in reducing false anomaly alerts through the use of quantile regression for identification of anomalies, but it is limited to the use of quantile interval to identify uncertainties in the data. In this paper, an improve time-series anomaly detection method called deep quantile regression anomaly detection (DQR-AD) is proposed. The proposed method go further to used quantile interval (QI) as anomaly score and compare it with threshold to identify anomalous points in time-series data. The tests run of the proposed method on publicly available anomaly benchmark datasets demonstrate its effective performance over other methods that assumed Gaussian distribution on the prediction or reconstruction cost for detection of anomalies. This shows that our method is potentially less sensitive to data distribution than existing approaches.
Fuzzy Granulation Approach to Face Recognition

In this paper, a new approach to face description is proposed. The linguistic description of human faces in digital pictures is generated within a framework of fuzzy granulation. Fuzzy relations and fuzzy relational rules are applied in order to create the image description. By use of type-2 fuzzy sets, fuzzy relations, and fuzzy IF-THEN rules, an image recognition system can infer and explain its decision. Such a system can retrieve an image, recognize, and classify – especially a human face – based on the linguistic description.
Privacy Protection Technology of Maritime Multi-agent Communication Based on Part-Federated Learning

Federated learning is a machine learning model based on distributed data sets, which builds a global model under the premise of ensuring the privacy and data security of participants. Because of this characteristic, federated learning is very suitable for maritime communication systems with large amounts of distributed data. However, the data set of maritime multi-agent communication system is different from the general data set, and the data distribution is not uniform, which increases the deviation of the model. In this paper, we propose a Part-Federated Learning (PFL) method which combines the advantages of split learning to improve the classical federated learning. This method, only uploading some parameters in the local model to the cloud server as shared parameters, reduces the communication cost of distributed learning, improves the privacy of the algorithm to the data, and has better performance in processing non-IDD distributed data. We optimize the proportion of shared parameters of PFL by considering the convergence of the algorithm and the communication cost. Finally, we verify the advantages of the algorithm in processing non-IID data through experiments, simulate the process of parameter optimization, and prove the feasibility of the algorithm.
Object Oriented Modelling of Cloud Voting System

The proliferation of Information and Communication Technology (ICT) has led to commencement of Electronic Governance (i.e. E-Governance) throughout the globe, which is very much essential for delivery of cost effective services to the populace in a developing country like India. This approach of governance has eased the entire administration and its processes and sub-processes which has smoothened its implementation in real-life scenarios. In a democratic country like India, to maintaingovernance over the state it is essential to establish a government which is selected through a free and fair voting process where citizens give their mandate by exercising their voting right. Voting is one of the crucial processes of administration which should represent the actual mandate of people without any foul play, corruption or manipulation. The selection of deserving contesting candidate and publishing voting results to citizens in a timely manner is the top most priority of administration. Unfortunately, due to certain flaws of the voting system like poll booth jam, proxy voting, manipulation of voters through non state players, etc the smooth conduction of voting process and subsequent declaration of voting result are highly hampered thereby leading to repoll for that contesting seat which becomes an extra financial and logistic burden over the state and tax payers. To provide an efficient flawless voting system, in this paper authors have proposed a single window based Cloud Voting System where the identity of valid voters will be verified through multiple security checkposts. Further to simulate its real world implementation,we have performed its Object Oriented Modeling (OOM) using Use Case Diagrams of the proposed system.