Reviewerly/exHarmony huggingface dataset:
PeloPartition: Improving Blockchain Resilience to Network Partitioning

Blockchain has gained considerable traction over the last few years and plays a critical role in realizing decentralized and cryptocurrency applications. A challenge that has been over-looked in prior blockchain algorithms is that they do not consider large-scale network outages and relied on the assumption of a reliable global network connectivity. In the event of a large scale network partition, forks may occur between partitioned regions. After the partition ends they will be discarded, leading to the loss of many blocks and a considerable amount of wasted work. This paper presents PeloPartition, which provides a sharding mechanism to improve blockchain's resilience to the possibility of a global internet outage. In PeloPartition we form consensus groups dynamically and consider the partitioning of the group as a hint to split the blockchain into branches and guarantee that all of them will be merged after the network is recovered. We indicate different methodologies to ensure blockchain security while partitioning occurs. Our experiments use simulations to show how this approach can improve the performance of blockchain algorithms and prevent wasted computational power during partitioning.
Scientific Study of Technological Chatbot Adoption in Customer Service

Customer service is the act of assisting and advising consumers who are considering purchasing or are already utilizing a product or service. Customers want prompt and thorough replies to their questions. Keeping consumers pleased and satisfied is critical to any company's success. As a result, a company needs to offer a variety of client services so that consumers may choose the services that best suit their needs. Telecommunication, IVR, Web Site, Email, Live Chat, and social media are the most common types of customer support. This paper presents a new way of customer service, called "chatbots". The paper discusses the benefits of adopting chatbots for customer service, an overview of chatbot technology, a few case studies about how companies are using chatbots, and challenges in adopting chatbot technology for providing customer service.
Subliminal Channels in Visual Cryptography

This paper describes three methods of creating a subliminal channel in visual cryptography that are defined for a (2,2) sharing scheme. They work by hiding additional covert information besides the original encrypted image. The first channel is revealed when the user folds the share along the specific axis. The second channel encodes subpixels on the basis of the encrypted message bits. It is designed to hide a wide range of data types. The third channel may be applied to a single share or multiple shares and is revealed when the proper parts of the shares are stacked. Fold and overlapping algorithms are adequate for printed shares, but the encryption method is only suitable for digital shares. The capacity of these methods ranges from half of the image size to the whole image size. The presented algorithms work on black-and-white images but are expandable to color visual cryptography. They may find applications in steganography and other data-hiding techniques. The created subliminal channels do not interfere with regular images that may still be revealed by stacking the shares. In short, this article introduces subliminal channels in visual cryptography, presents three algorithms for both binary and colorful images, shows examples of use with the results obtained, and discusses features of each method.
Distributed Pair Programming in Higher Education: A Systematic Literature Review

This paper presents a Systematic Literature Review (SLR) of fifty-seven studies on Distributed Pair Programming (DPP) in higher education, identifying which studies investigated factors on the effectiveness of DPP as a method for learning programming, factors related to mediating and stimulating interactions between students, the measures/instruments used for exploring these factors, as well as the tools and their features. As DPP effectiveness is very promising as regards code quality and academic performance, the findings can contribute to a better understanding of how communication, collaboration and coordination are investigated. It was found that there are very few studies concerning compatibility, pair formation, and role contribution, the majority of which did not use data derived from students’ actions. Only a small number of studies used DPP tools with logging capabilities. Even though several IDEs and Eclipse plugins have been designed for DPP and offer specialized features, there are still studies which use a combination that include video conferencing and remote sharing tools along with various IDEs and auxiliary tools. This SLR can be of interest to educators that aim to apply DPP in educational settings, researchers designing informed empirical studies, as well as designers of DPP tools.
Constructing Condition Monitoring Model of Harmonic Drive

The harmonic drive is an essential industrial component. In industry, the efficient and accurate determination of machine faults has always been a significant problem to be solved. Therefore, this research proposes an anomaly detection model which can detect whether the harmonic drive has a gear-failure problem through the sound recorded by a microphone. The factory manager can thus detect the fault at an early stage and reduce the damage loss caused by the fault in the machine. In this research, multi-layer discrete wavelet transform was used to de-noise the sound samples, the Log Mel spectrogram was used for feature extraction, and finally, these data were entered into the EfficientNetV2 network. To assess the model performance, this research used the DCASE 2022 dataset for model evaluation, and the area under the characteristic acceptance curve (AUC) was estimated to be 5% higher than the DCASE 2022 baseline model. The model achieved 0.93 AUC for harmonic drive anomaly detection.
<i>LightNAS</i>: On Lightweight and Scalable Neural Architecture Search for Embedded Platforms

Neural architecture search (NAS) is an emerging paradigm to automate the design of competitive deep neural networks (DNNs). In practice, DNNs are subject to strict latency constraints and any violation may lead to catastrophic consequences (e.g., autonomous vehicles). However, to obtain the architecture that strictly satisfies the required latency constraint, previous hardware-aware differentiable NAS methods have to repeat a plethora of search runs to tune relevant hyperparameters by trial and error, and as a result, the total design cost increases proportionally (empirically by ten times). To tackle this, we, in this article, introduce a lightweight and scalable hardware-aware NAS framework named LightNAS, which consists of two separate stages. In the first stage, we strive to search for the architecture that strictly satisfies the required latency constraint at the macro level in a differentiable manner, and more importantly, through a one-time search (i.e., you only search once). The architectures searched in the first stage are denoted as LightNets. After that, in the second stage, we introduce an efficient evolutionary scheme to further explore the micro-level channel configuration of each LightNet at low cost. To achieve this, we propose an effective yet computationally cheap proxy, namely, batchwise training estimation (BTE), as a plug-in complement to enable the channel-level exploration of LightNets on the fly such that the accuracy of LightNets can be improved without degrading the runtime latency on target hardware. Finally, extensive experiments are conducted on one popular embedded platform (i.e., Nvidia Jetson AGX Xavier) to demonstrate the efficacy of the proposed approach over previous state-of-the-art counterparts.
How Differential Privacy Reinforces Privacy of Machine Learning Models?

Image classification, facial recognition, health care and graph data analysis are just a few of the areas where machine learning (ML) models were widely applied. Recent research has revealed that ML models are subject to membership inference attacks (MIAs), which attempt to determine whether or not a data record was used to train a target model. The success of MIAs is due to the data leakage during the training phase. Differential Privacy (DP) has been applied in ML to restrict inference of training samples. That’s why in our experimentation, we try to mitigate the impact of MIAs using Differentially Private Stochastic Gradient Descent (DP-SGD) algorithm based on modifying the minibatch stochastic optimization process and adding noise in order to provide a way to get useful information about data without revealing much about any individual information. In this paper, we evaluate DP-SGD as a countermeasure against MIAs on Conventional Neural Networks (CNN) training over MNIST dataset. We consider different combinations of DP-SGD’s noise multiplier and clipping norm parameters in our evaluation. Through experimental analysis, we show that this defense strategy can mitigate the impact of MIAs on the target model while guaranteeing the accuracy of the target model. Evaluation results reveal that our suggested solution for safeguarding privacy against MIA is successful.
Exploration of Machine Learning Classification Models Used for Behavioral Biometrics Authentication

Mobile devices have been manufactured and enhanced at growing rates in the past decades. While this growth has significantly evolved the capability of these devices, their security has been falling behind. This contrast in development between capability and security of mobile devices is a significant problem with the sensitive information of the public at risk. Continuing the previous work in this field, this study identifies key Machine Learning algorithms currently being used for behavioral biometric mobile authentication schemes and aims to provide a comprehensive review of these algorithms when used with touch dynamics and phone movement. Throughout this paper the benefits, limitations, and recommendations for future work will be discussed.
Interval Type-2 Fuzzy Approach for Dynamic Parameter Adaptation in the Bird Swarm Algorithm for the Optimization of Fuzzy Medical Classifier

Optimization is essential for applications since it can improve the results provided in different areas; for this task, it is beneficial to use soft computing techniques, such as bio-inspired algorithms. In addition, it has been shown that if dynamic parameter adaptation is applied to these algorithms, they can provide a better result. For this work, the main contribution is to carry out the dynamic parameter adaptation to the bird swarm algorithm using interval type-2 fuzzy systems to realize a new fuzzy bio-inspired algorithm. The design of the proposed fuzzy system consists of two inputs corresponding to the iterations and diversity. As outputs, it takes the values of C and S, which are parameters to be adjusted by the algorithm. Once the design and the experimentation are realized, they are divided into two study cases. The first consists of a set of complex functions of the Congress of Evolutionary Competition 2017. The second case study consists of optimizing the membership functions in a fuzzy system designed to provide the nocturnal blood pressure profile, which corresponds to a neuro-fuzzy hybrid model to obtain the risk of hypertension. Analyzing the 30 experiments performed in both case studies, we can observe that the results obtained are improved when compared with the original method and other proposed methodologies, achieving good results in the complex functions. In addition, the optimized fuzzy system will reach an average of 97% correct classification. Statistically, it can be concluded that there is significant evidence to affirm that the proposed method provides good results.
Multi-Graph Convolutional Network for Fine-Grained and Personalized POI Recommendation

With the advent of the era of rapid information expansion, the massive data backlog that exists on the Internet has led to a serious information overload problem, which makes recommendation systems a crucial part of human life. In particular, the Point-Of-Interest (POI) recommendation system has been applied to many real-life scenarios, such as life services and autonomous driving. Specifically, the goal of POI recommendation is to recommend locations that match their personalized preferences to users. In existing POI recommendation methods, people tend to pay more attention to the impact of temporal and spatial factors of POI on users, which will alleviate the problems of data sparsity and cold start in POI recommendation. However, this tends to ignore the differences among individual users, and considering only temporal and spatial attributes does not support fine-grained POI recommendations. To solve this problem, we propose a new Fine-grained POI Recommendation With Multi-Graph Convolutional Network (FP-MGCN). This model focuses on the content representation of POIs, captures users’ personalized preferences using semantic information from user comments, and learns fine-grained representations of users and POIs through the relationships between content–content, content–POI, and POI–user. FP-MGCN employs multiple embedded propagation layers and adopts information propagation mechanisms to model the higher-order connections of different POI-related relations for enhanced representation. Fine-grained POI is finally recommended to users through the three types of propagation we designed: content–content information propagation, content–POI information propagation, and POI–user information propagation. We have conducted detailed experiments on two datasets, and the results show that FP-MGCN has advanced performance and can alleviate the data sparsity problem in POI recommendation tasks.
A LSTM assisted orbit determination algorithm for spacecraft executing continuous maneuver

Orbit determination (OD) for spacecraft with unknown maneuver is always a challenging task. This paper proposes a novel framework for efficiently solving continuously maneuvering spacecraft OD problems by merging the Long Short-Term Memory (LSTM) neural network and the filter algorithms. A polynomial-representation fitted the unknown continuous maneuver is first proposed. Rather than directly output the estimated maneuver, the LSTM is trained to detect the unknown maneuver and then estimate the coefficients of the polynomial-representation. Then a fusion is designed to combine the prediction of the LSTM and the estimation of the filter for a more accurate estimation. The proposed LSTM-based framework is successfully applied to solve a Low-Earth-orbit OD problem and a Middle-Earth-orbit OD problem. The continuously maneuvering target is well tracked and the unknown maneuver is accurately estimated. Numerical simulations show that the LSTM trained based on one training dataset can also be applied to other scenarios that share some common features with the training dataset.
Optical asymmetric JTC cryptosystem based on binary phase modulation and image superposition–subtraction operation

The joint transform correlator (JTC) cryptosystem is a simple and practical optical cryptosystem. But its identical key in both encryption and decryption brings security risks in the key distribution and management. To overcome these drawbacks, we first create a trapdoor one-way function based on image superposition and subtraction operation. Then combined with the one-way binary phase modulation, an optical asymmetric JTC cryptosystem is proposed in this paper. These two kinds of trapdoor one-way functions are not only effective and implementable, but also can greatly enhance the ability of our proposal to resist various attacks. In addition, we select the structured spiral phase mask (SSPM) controlled by its structural parameters as the key mask of the JTC cryptosystem to facilitate the key transmission. When the structural parameters of the SSPM are protected by the RSA algorithm during encryption and decryption, not only the security of the proposed cryptosystem can be enhanced, but also the key distribution and management will be improved. This also makes our proposal conform more closely to the basic agreement of the public key cryptosystem. Simulation analysis and initial experimental results verified the correctness and feasibility of our proposal.
Global Requirement Engineering Through Secondary Market Research: Lessons From Real Consulting Projects

Requirement Engineering (RE) for determining a product's value proposition involves a close relationship with potential customers in order to better understand their requirements. The startup community investigates market demands through a series of experiments in which assumptions are continually validated as the product evolves. Validation is typically accomplished through colocated meetings with consumers, an activity that will be hampered by the pandemic's constraints. Due to pandemic effects and resource constraints, targeting worldwide markets and taking advantage of global demand is not viable without technological support and innovation in the way RE is conducted. Secondary market research using Google technologies, for instance, Google Market Finder and Google Ad Preview tool, could be effective for worldwide market expansion for the startup community. Small- and medium-sized firms have adopted this as one of the best practices for global market expansion—lessons learnt through internal strategy management and marketing management disciplines. The ideal way to investigate global RE, RE conducted with worldwide markets, is to use Google technology's analytical support, followed by primary research. Secondary research allows resource-constrained startups to explore wide global markets with minimum effort, narrowing them down to valuable information, making primary research with focused market segments possible. The interdisciplinary research will make a significant contribution to the pandemic while also serving as a model for the new normal.
Maneuvering Extended Object Tracking with Modified Star-Convex Random Hypersurface Model Based on Minimum Cosine Distance

Maneuvering extended object tracking is a new research field due to the rapid development of modern sensor technology. Multiple measurements may be resolved from different unknown sources on an object by using a high-resolution radar. In this case, the object should be regarded as an extended one with object extension, e.g., its shape may be described by the star-convex random hypersurface model. This model is usually specified by a one-dimensional radial function. However, the divergence of the shape estimation and a high error of the kinematic state estimation are likely to occur when an extended object maneuvers. This is because the radial function may take a negative value after Fourier series expansion, which leads to unpredictable estimation results. Unfortunately, the model itself is unable to solve this problem via the subsequent iterations. In this paper, we proposed a modified shape estimation approach to track an extended object with a star-convex random hypersurface model based on minimum cosine distance. Both the extension state and kinematic state at the current time are reinitialized once the radial function takes a negative value. Moreover, a mathematical model was constructed by using the principle of minimum cosine distance, so as to obtain more reasonable weight distribution coefficients for the correction of the extension state. Simulation results in different scenarios demonstrated the effectiveness of the proposed tracking approach.
Understanding the state of criminological scholarship on cybercrimes

This review article highlights the current state of cybercrime scholarship published by criminologists in the areas of computer hacking, malware, fraud, and various economically motivated offenses. Areas for future study are identified and explored in detail.
Towards Fair Federated Recommendation Learning: Characterizing the Inter-Dependence of System and Data Heterogeneity

Federated learning (FL) is an effective mechanism for data privacy in recommender systems that runs machine learning model training on-device. While prior FL optimizations tackled the data and system heterogeneity challenges, they assume the two are independent of each other. This fundamental assumption is not reflective of real-world, large-scale recommender systems — data and system heterogeneity are tightly intertwined. This paper takes a data-driven approach to show the inter-dependence of data and system heterogeneity in real-world data and quantifies its impact on the overall model quality and fairness. We design a framework, RF2, to model the inter-dependence and evaluate its impact on state-of-the-art model optimization techniques for federated recommendation tasks. We demonstrate that the impact on fairness can be severe under realistic heterogeneity scenarios, by up to 15.8–41 × compared to a simple setup assumed in most (if not all) prior work. The result shows that modeling realistic system-induced data heterogeneity is essential to achieving fair federated recommendation learning.
Position Awareness Modeling with Knowledge Distillation for CTR Prediction

Click-through rate (CTR) Prediction is of great importance in real-world online ads systems. One challenge for the CTR prediction task is to capture the real interest of users from their clicked items, which is inherently influenced by presented positions of items, i.e., more front positions tend to obtain higher CTR values. Therefore, It is crucial to make CTR models aware of the exposed position of the items. A popular line of existing works focuses on explicitly model exposed position by result randomization which is expensive and inefficient, or by inverse propensity weighting (IPW) which relies heavily on the quality of the propensity estimation. Another common solution is modeling position as features during offline training and simply adopting fixed value or dropout tricks when serving. However, training-inference inconsistency can lead to sub-optimal performance. This work proposes a simple yet efficient knowledge distillation framework to model the impact of exposed position and leverage position information to improve CTR prediction. We demonstrate the performance of our proposed method on a real-world production dataset and online A/B tests, achieving significant improvements over competing baseline models. The proposed method has been deployed in the real world online ads systems of JD, serving main traffic of hundreds of millions of active users.