Reviewerly/exHarmony huggingface dataset:
The role of ethical leadership on employee commitment to the organization: The mediating role of job satisfaction and job engagement

Purpose. This research aims to investigates whether and how ethical leadership influences themediator variables (employees’ job satisfaction and job engagement) in the organization context. It alsoexamines how the mediators should influence employees’ organizational commitment. Study design. Allthe participants in the recent study are Indonesian workers who are active in the private sector, which invited them to fill out an online survey. This online survey was carried out using Google Forms. Various control and filter questions were anonymous, and random constructs were carried out to avoid bias and ensure the survey was valid. The initial empirical data were gathered from convenience sample involving 690 employees who work on financial sector. This study applied multi-item scales to all the constructs from prior studies for the concepts of ethical leadership, employee job satisfaction, job engagement, and employee commitment. A professional translator reviewed all the instruments. Subsequently, this study conducts a pilot test of the measurement items to ensure the final formal survey. Demographic parameters such as gender, age, education, and period of experience as employees were included in the questionnaire. Structural equation modeling was used to test the research hypotheses. Findings. The empirical results indicate that ethical leadership has significant and positive effects on employees’ job satisfaction and engagement. Furthermore, employees’ job satisfaction and job engagement havepartial mediators between ethical leadership and employees’ organizational commitment. Implications for practice. The result of this study can practitioners, regulators, and researchers an insight to observe the dynamic behavior to elaborate on the impact of ethical leadership in business and psychology. The managers need to build ethical standards in the workplace. As well as provide clear rules of the organization system, which possibly influence workers’ job satisfaction, engagement, and organizational commitment. The current study was limited to the private organization; there is a future need to study workers across the culture and region. The values of results. This study contributes to managers’ ethicalor unethical leadership behavior and ethical climate theory literature and, specifically, the decisionmaking process through developing and testing a model of employees’ organizational commitment. This study also provides new insights into the determinants of these variables and employees’ organizational commitment towards job satisfaction and engagement.
Coexistence of quantum key distribution and optical communication with amplifiers over multicore fiber

Abstract In this paper, the influence of classical signals on quantum key distribution (QKD) is studied over multi-core fiber (MCF) when optical amplifiers exist. Firstly, the long-distance simultaneous transmission architectures of QKD and classical signals are proposed based on advanced asymmetric sending or not sending QKD (SNS-QKD) and classical Bennett–Brassard 1984-QKD (BB84-QKD), and the segment length between optical amplifiers can be adjusted according to requirement. Then, theoretical models of spontaneous Raman scattering noise and four-wave mixing noise are established based on the proposed architectures. Next, the calculation models of the secure key rate under the influence of noises from classical signals are derived. Finally, the experimental results show that the theoretical models match well with the experimental photons, and the maximum difference between experimental and simulated noise photons is less than 2.6 dB. Simulation results show that the performance of asymmetric SNS-QKD is better than that of BB84-QKD architecture when classical signals and quantum signals are transmitted in different cores of MCF.
Blockchain enabled zero trust based authentication scheme for railway communication networks

Abstract With the introduction of emerging technologies such as cloud computing, the railway communication network has the characteristics of complex structure and blurred boundaries, which leads to a series of security threats including information leakage and malicious access. Specifically, the third-party cloud services are difficult to be supervised, and network traffic is untrustworthy. To ensure system security, we propose a zero-trust security model in this paper. Then, we introduce blockchain and Merkle tree to build a distributed identity storage scheme for guaranteeing reliable, confidential and efficient data updates, and improving authentication efficiency. Furthermore, the proxy was introduced for two-way authentication with cloud servers, so that internal and external threats could be counteracted. Moreover, reputation assessment mechanism has been adopted to reduce the possibility of nodes accessing malicious cloud services. Performance analysis demonstrated that the proposed security model is able to enhance the security, efficiency and stability of the system, and consequently can guarantee the safety and reliability of railway transportation.
Big Data Mining Analysis of Power Grid Based on Apriori Optimization

Abstract To ensure the safe and stable operation of the power grid system and strengthen the refined management level of the State Grid Corporation of China, a grid operation big data association analysis model based on Apriori association algorithm is proposed. This paper discusses the logic framework of the power big data platform, and describes the specific implementation from the functional architecture, technical architecture and other aspects. Aiming at the problem of massive heterogeneous power data and low sharing degree, the transaction matrix is pruned and compressed by setting the minimum support degree and establishing the frequent set matrix, and the implementation process of the improved algorithm on the MapReduce programming model of Hadoop platform is provided. Based on the research of Spark programming model, the improved algorithm is implemented in parallel style. The experimental results show that the efficiency of the algorithm after parallel processing has been greatly improved, which can also effectively improve the calculation amount and efficiency of power big data.
Joint remote state preparation in multi-hop network under noisy environment

Abstract Joint remote state preparation is an important method to transmit quantum information with more senders and higher security. In this paper, we present a deterministic joint remote state preparation scheme in multi-hop network with two senders and N intermediate parties, using only projective measurements and recovery operations. We describe the scheme under the framework of density matrix to investigate the performance of the scheme in noisy environment. The relation of fidelity, noise rate and the number of intermediate nodes is given for three types of noise. It is revealed that the average fidelity attains its minimum when the noise rate is at the most uncertain point, decreases monotonically as the number of intermediate nodes increases. However, in some special cases, the average fidelity of the multi-hop scheme is greater than some existing one step joint remote state preparation scheme.
Prediction of Sufficient Accuracy for Human Activity Recognition using Novel Long Short Term Memory in Compared with Decision Tree

The research study is to predict human activity recognition and analyze their strength in machine learning (ML) processes to achieve precision and also to determine performance of human activities. The framework to classify the Novel Long Short Term Memory (LSTM) and Decision Tree (DT) to perform all measures. This research study used the Novel Long Short Term Memory and Decision Tree to perform the required actions to accurately identify body movements in adjacent film sequence. Recent study findings and a threshold of 0.05%, as well as 95% confidence interval and standard parameter estimates, were castoff in a study 47 samples divided into two groups calculated using the G-power of 80%. Since the novel Long Short Term Memory (LSTM) algorithm and local invariant methods have achieved an accuracy of 93.83% in predicting the activity analysis, this research needs to obtain improved accuracy to perform activity prediction by using the Decision Tree algorithm. When applied to human activity analysis, the Novel Long Short Term Memory technique was shown to have an accuracy of 90.12 percent, with a 95% confidence interval including the value 0.045 (p 0.05). Researchers found that when comparing two algorithms for analyzing human behaviour, Long Short Term Memory (LSTM) performed much better than Decision Tree (DT).
Improving trajectory calculations by FLEXPART 10.4+ using single-image super-resolution

Abstract. Lagrangian trajectory or particle dispersion models as well as semi-Lagrangian advection schemes require meteorological data such as wind, temperature and geopotential at the exact spatiotemporal locations of the particles that move independently from a regular grid. Traditionally, these high-resolution data have been obtained by interpolating the meteorological parameters from the gridded data of a meteorological model or reanalysis, e.g., using linear interpolation in space and time. However, interpolation is a large source of error for these models. Reducing them requires meteorological input fields with high space and time resolution, which may not always be available and can cause severe data storage and transfer problems. Here, we interpret this problem as a single-image super-resolution task. That is, we interpret meteorological fields available at their native resolution as low-resolution images and train deep neural networks to upscale them to a higher resolution, thereby providing more accurate data for Lagrangian models. We train various versions of the state-of-the-art enhanced deep residual networks for super-resolution (EDSR) on low-resolution ERA5 reanalysis data with the goal to upscale these data to an arbitrary spatial resolution. We show that the resulting upscaled wind fields have root-mean-squared errors half the size of the winds obtained with linear spatial interpolation at acceptable computational inference costs. In a test setup using the Lagrangian particle dispersion model FLEXPART and reduced-resolution wind fields, we find that absolute horizontal transport deviations of calculated trajectories from “true” trajectories calculated with un-degraded 0.5∘ × 0.5∘ winds are reduced by at least 49.5 % (21.8 %) after 48 h relative to trajectories using linear interpolation of the wind data when training on 2∘ × 2∘ to 1∘ × 1∘ (4∘ × 4∘ to 2∘ × 2∘) resolution data.
WATT-EffNet: A Lightweight and Accurate Model for Classifying Aerial Disaster Images

Incorporating deep learning (DL) classification models into unmanned aerial vehicles (UAVs) can significantly augment search-and-rescue operations and disaster management efforts. In such critical situations, the UAV’s ability to promptly comprehend the crisis and optimally utilize its limited power and processing resources to narrow down search areas is crucial. Therefore, developing an efficient and lightweight method for scene classification is of utmost importance. However, current approaches tend to prioritize accuracy on benchmark datasets at the expense of computational efficiency. To address this shortcoming, we introduce the Wider ATTENTION EfficientNet (WATT-EffNet), a novel method that achieves higher accuracy with a more lightweight architecture compared to the baseline EfficientNet. The WATT-EffNet leverages width-wise incremental feature modules and attention mechanisms over width-wise features to ensure the network structure remains lightweight. We evaluate our method on a UAV-based aerial disaster image classification dataset and demonstrate that it outperforms the baseline by up to 15 times in terms of classification accuracy and 38.3% in terms of computing efficiency as measured by Floating Point Operations per second (FLOPs). Additionally, we conduct an ablation study to investigate the effect of varying the width of WATT-EffNet on accuracy and computational efficiency.
FedRule: Federated Rule Recommendation System with Graph Neural Networks

Much of the value that IoT (Internet-of-Things) devices bring to "smart" homes lies in their ability to automatically trigger other devices' actions: for example, a smart camera triggering a smart lock to unlock a door. Manually setting up these rules for smart devices or applications, however, is time-consuming and inefficient. Rule recommendation systems can automatically suggest rules for users by learning which rules are popular based on those previously deployed (e.g., in others' smart homes). Conventional recommendation formulations require a central server to record the rules used in many users' homes, which compromises their privacy and leaves them vulnerable to attacks on the central server's database of rules. Moreover, these solutions typically leverage generic user-item matrix methods that do not fully exploit the structure of the rule recommendation problem. In this paper, we propose a new rule recommendation system, dubbed as FedRule, to address these challenges. One graph is constructed per user upon the rules s/he is using, and the rule recommendation is formulated as a link prediction task in these graphs. This formulation enables us to design a federated training algorithm that is able to keep users' data private. Extensive experiments corroborate our claims by demonstrating that FedRule has comparable performance as the centralized setting and outperforms conventional solutions.
FINGER: Fast Inference for Graph-based Approximate Nearest Neighbor Search

Approximate K-Nearest Neighbor Search (AKNNS) has now become ubiquitous in modern applications, such as a fast search procedure with two-tower deep learning models. Graph-based methods for AKNNS in particular have received great attention due to their superior performance. These methods rely on greedy graph search to traverse the data points as embedding vectors in a database. Under this greedy search scheme, we make a key observation: many distance computations do not influence search updates so that these computations can be approximated without hurting performance. As a result, we propose FINGER, a fast inference method for efficient graph search in AKNNS. FINGER approximates the distance function by estimating angles between neighboring residual vectors. The approximated distance can be used to bypass unnecessary computations for faster searches. Empirically, when it comes to speeding up the inference of HNSW, which is one of the most popular graph-based AKNNS methods, FINGER significantly outperforms existing acceleration approaches and conventional libraries by 20 to 60 across different benchmark datasets.
Meteor: Improved Secure 3-Party Neural Network Inference with Reducing Online Communication Costs

Secure neural network inference has been a promising solution to private Deep-Learning-as-a-Service, which enables the service provider and user to execute neural network inference without revealing their private inputs. However, the expensive overhead of current schemes is still an obstacle when applied in real applications. In this work, we present Meteor, an online communication-efficient and fast secure 3-party computation neural network inference system aginst semi-honest adversary in honest-majority. The main contributions of Meteor are two-fold: i) We propose a new and improved 3-party secret sharing scheme stemming from the linearity of replicated secret sharing, and design efficient protocols for the basic cryptographic primitives, including linear operations, multiplication, most significant bit extraction, and multiplexer. ii) Furthermore, we build efficient and secure blocks for the widely used neural network operators such as Matrix Multiplication, ReLU, and Maxpool, along with exploiting several specific optimizations for better efficiency. Our total communication with the setup phase is a little larger than SecureNN (PoPETs’19) and Falcon (PoPETs’21), two state-of-the-art solutions, but the gap is not significant when the online phase must be optimized as a priority. Using Meteor, we perform extensive evaluations on various neural networks. Compared to SecureNN and Falcon, we reduce the online communication costs by up to 25.6 × and 1.5 ×, and improve the running-time by at most 9.8 × (resp. 8.1 ×) and 1.5 × (resp. 2.1 ×) in LAN (resp. WAN) for the online inference.
Secure Steganographic Cover Generation via a Noise-Optimization Stacked StyleGAN2

Recently, the style-based generative adversarial network StyleGAN2 yields state-of-art performance on unconditional high-quality image synthesis. However, from the perspective of steganography, the image security is not guaranteed during the image synthesis. Relying on the optimal properties of StyleGAN2, this paper proposes a noise-optimization stacked StyleGAN2 named NOStyle to generate the secure and high-quality cover (image used for data hiding). In our proposed scheme, we decompose the image synthesis into two stages with symmetrical mode. In stage-I, StyleGAN2 is preserved to generate a high-quality benchmark image. In the stage-II generator, based on the progressive mechanism and shortcut connection, we design a noise secure optimization network by which the different-scale stochastic variation (noise map) is automatically adjusted according to the results of the stage-II discriminator. After injecting the stochastic variation into different resolutions of the synthesis network, the stage-II generator obtains an intermediate image. For the symmetrical stage-II discriminator, we combine the image secure loss and fidelity loss to construct the noise loss which is used to evaluate the difference between two images generated by the stage-I generator and stage-II generator. Taking the outputs of stage-II discriminator as inputs, by iteration, the stage-II generator finally creates the optimal image. Extensive experiments show that the generated image is not only secure but high quality. Moreover, we make a conclusion that the security of the generated image is inverse proportion to the fidelity.
The adaptive market hypothesis and the return predictability in the cryptocurrency markets

This study employs robust martingale difference hypothesis tests to examine return predictability in a broad sample of the 40 most capitalized cryptocurrency markets in the context of the adaptive market hypothesis. The tests were applied to daily returns using the rolling window method in the research period from May 1, 2013 to September 30, 2022. The results of this study suggest that the returns of the majority of the examined cryptocurrencies were unpredictable most of the time. However, a great part of them also suffered some short periods of weak-form inefficiency. The results obtained validate the adaptive market hypothesis. Additionally, this study allowed the observation of some differences in return predictability between the examined cryptocurrencies. Also some historical trends in weak-form efficiency were identified. The results suggest that the predictability of cryptocurrency returns might have decreased in recent years also no significant relationship between market cap and predictability was observed.&#x0D;
A survey on different feature extraction methods for writer identification and verification

Identifying and verifying a person based on scanned images of their handwriting is a needful biometric application in historical document analysis, behavioural biometrics study, forensic science, access control, graphology, and copyrights management. Writer identification and verification are still challenging in offline and online handwriting recognition. Since the performances of handwriting biometric identification and verification systems depend on both the quality and types of chosen features, this is one of the most critical phases. This article represents a literature survey on offline and online biometric features used in different scripts for writer verification and identification techniques. Several previous efficient works on online and offline writer authentication methods for biometrics using cutting-edge hand-craft features in different levels of handwriting analysis like documents, paragraphs, words, and characters are analysed systematically to date for the first time in detail.
Concerns about Cybersecurity: The Implications of the use of ICT for Citizens and Companies

The widespread use of Information and Communication Technologies - ICT substantially increases the risks related to information security. In fact, due to the increase in the number and type of cyber attacks, Cybersecurity has become a growing concern in today's society. This phenomenon affects not only individual citizens, but also companies and even State entities. Despite the numerous advantages of this "digitalisation" of society, there are several risks, ranging from identity theft, scam emails or phone calls, online fraud, offensive material and child pornography, material promoting racial hatred or religious extremism, access to online services, email account hacking, online banking fraud, cyber extortion or malicious software. In order to determine the impact that cyber attacks have on society it is necessary to understand how people and companies use ICTs, such as social networks, the information they share, their privacy concerns, or the use of electronic services such as online payments or the cloud. This study becomes central not only to try to prevent/minimise risks, showing what has been done in this area, but more importantly, the way forward to try to prevent or minimise possible risks in the future.
Absynthe: Abstract Interpretation-Guided Synthesis

Synthesis tools have seen significant success in recent times. However, past approaches often require a complete and accurate embedding of the source language in the logic of the underlying solver, an approach difficult for industrial-grade languages. Other approaches couple the semantics of the source language with purpose-built synthesizers, necessarily tying the synthesis engine to a particular language model. In this paper, we propose Absynthe, an alternative approach based on user-defined abstract semantics that aims to be both lightweight and language agnostic, yet effective in guiding the search for programs. A synthesis goal in Absynthe is specified as an abstract specification in a lightweight user-defined abstract domain and concrete test cases. The synthesis engine is parameterized by the abstract semantics and independent of the source language. Absynthe validates candidate programs against test cases using the actual concrete language implementation to ensure correctness. We formalize the synthesis rules for Absynthe and describe how the key ideas are scaled-up in our implementation in Ruby. We evaluated Absynthe on SyGuS strings benchmark and found it competitive with other enumerative search solvers. Moreover, Absynthe's ability to combine abstract domains allows the user to move along a cost spectrum, i.e., expressive domains prune more programs but require more time. Finally, to verify Absynthe can act as a general purpose synthesis tool, we use Absynthe to synthesize Pandas data frame manipulating programs in Python using simple abstractions like types and column labels of a data frame. Absynthe reaches parity with AutoPandas, a deep learning based tool for the same benchmark suite. In summary, our results demonstrate Absynthe is a promising step forward towards a general-purpose approach to synthesis that may broaden the applicability of synthesis to more $\ldots$