Reviewerly/exHarmony huggingface dataset:
Employee trust repair: A systematic review of 20 years of empirical research and future research directions

Employee trust, and increasingly its absence, is a critical topic for researchers and practitioners interested in social relations in the context of work and organizing. Employee trust repair is particularly important in the current disrupted work environment, due to unpredictable changes such as the Covid-19 pandemic and the uncertainty those bring to our lives. It is not surprising that employee trust is attracting increasing interest among researchers and practitioners alike. In this article, we systematically review and take stock of the research on trust repair conducted in the past two decades to provide comprehensive insights and future research directions for researchers and managers. In our review, we propose that early use of trust repair strategies in response to small violations, prevents these violations escalating into larger violations, and hence, enhances the efficiency and effectiveness of trust repair with employees. We conclude by describing future directions.
Dealing with the Class Imbalance Problem in the Detection of Fake Job Descriptions

In recent years, the detection of fake job descriptions has become increasingly necessary because social networking has changed the way people access burgeoning information in the internet age. Identifying fraud in job descri... | Find, read and cite all the research you need on Tech Science Press
Model for optimizing quantum key distribution with continuous-wave pumped entangled-photon sources

Quantum key distribution (QKD) allows unconditionally secure communication based on the laws of quantum mechanics rather than assumptions about computational hardness. Optimizing the operation parameters of a given QKD implementation is indispensable in order to achieve high secure key rates. So far, there exists no model that accurately describes entanglement-based QKD with continuous-wave pump lasers. We analyze the underlying mechanisms for QKD with temporally uniform pair-creation probabilities and develop a simple but accurate model to calculate optimal tradeoffs for maximal secure key rates. In particular, we find an optimization strategy of the source brightness for given losses and detection-time resolution. All experimental parameters utilized by the model can be inferred directly in standard QKD implementations, and no additional assessment of device performance is required. Comparison with experimental data shows the validity of our model. Our results yield a tool to determine optimal operation parameters for already existing QKD systems, to plan a full QKD implementation from scratch, and to determine fundamental key rate and distance limits of given connections.
Blockchain technology and modern slavery: Reducing deceptive recruitment in migrant worker populations

Modern slavery, including forced labour, is a practice that impacts more than 40 million people, 20 million of whom are trapped in corporate supply chains. Migrant workers are disproportionately affected by this practice, who are often from developing countries and are vulnerable to exploitation through deceptive recruitment practices. With questions raised as to the efficacy of social audit and increasing pressure on governments and large businesses to identify and reduce the incidence of modern slavery in corporate activities, new approaches and tools are needed. The purpose of this paper is to offer a conceptual discussion as to how a relatively new technology, the blockchain, might be used to reduce vulnerability and risk in migrant worker populations. Limitations and directions for future research are also considered.
Artificial Intelligence based Smart Door with Face Mask Detection

In this pandemic situation, health plays an important role in everyone's life. Most of the people are not aware of preventing themselves and their surroundings from this pandemic. Face mask is essential to prevent ourselves and others. So, people are in need to wear face mask regularly. People who visit home won't wear mask due to their unawareness which may affect people. People may not know if someone visits their home when they are not there. AI based smart device (Raspberry pi with AI model with camera) is proposed in this project which identifies whether a person is wearing face mask and gives us an alert message (via mobile app). This device is integrated with a mobile app. Mobile app identifies if someone enters home when people are not physically present in their home. This smart device automatically opens the door only if people wear face mask. This device works both day and night. It can be used in multiple places like malls, shops, hospitals and temples.
OntoZSL: Ontology-enhanced Zero-shot Learning

Zero-shot Learning (ZSL), which aims to predict for those classes that have never appeared in the training data, has arisen hot research interests. The key of implementing ZSL is to leverage the prior knowledge of classes which builds the semantic relationship between classes and enables the transfer of the learned models (e.g., features) from training classes (i.e., seen classes) to unseen classes. However, the priors adopted by the existing methods are relatively limited with incomplete semantics. In this paper, we explore richer and more competitive prior knowledge to model the inter-class relationship for ZSL via ontology-based knowledge representation and semantic embedding. Meanwhile, to address the data imbalance between seen classes and unseen classes, we developed a generative ZSL framework with Generative Adversarial Networks (GANs).
Neural probabilistic logic programming in DeepProbLog

We introduce DeepProbLog, a neural probabilistic logic programming language that incorporates deep learning by means of neural predicates. We show how existing inference and learning techniques of the underlying probabilistic logic programming language ProbLog can be adapted for the new language. We theoretically and experimentally demonstrate that DeepProbLog supports (i) both symbolic and subsymbolic representations and inference, (ii) program induction, (iii) probabilistic (logic) programming, and (iv) (deep) learning from examples. To the best of our knowledge, this work is the first to propose a framework where general-purpose neural networks and expressive probabilistic-logical modeling and reasoning are integrated in a way that exploits the full expressiveness and strengths of both worlds and can be trained end-to-end based on examples.
Adversary Instantiation: Lower Bounds for Differentially Private Machine Learning

Differentially private (DP) machine learning allows us to train models on private data while limiting data leakage. DP formalizes this data leakage through a cryptographic game, where an adversary must predict if a model was trained on a dataset D, or a dataset D′ that differs in just one example. If observing the training algorithm does not meaningfully increase the adversary's odds of successfully guessing which dataset the model was trained on, then the algorithm is said to be differentially private. Hence, the purpose of privacy analysis is to upper bound the probability that any adversary could successfully guess which dataset the model was trained on.In our paper, we instantiate this hypothetical adversary in order to establish lower bounds on the probability that this distinguishing game can be won. We use this adversary to evaluate the importance of the adversary capabilities allowed in the privacy analysis of DP training algorithms.For DP-SGD, the most common method for training neural networks with differential privacy, our lower bounds are tight and match the theoretical upper bound. This implies that in order to prove better upper bounds, it will be necessary to make use of additional assumptions. Fortunately, we find that our attacks are significantly weaker when additional (realistic) restrictions are put in place on the adversary's capabilities. Thus, in the practical setting common to many real-world deployments, there is a gap between our lower bounds and the upper bounds provided by the analysis: differential privacy is conservative and adversaries may not be able to leak as much information as suggested by the theoretical bound.
Tracing Knowledge State with Individual Cognition and Acquisition Estimation

Knowledge tracing, which dynamically estimates students' learning states by predicting their performance on answering questions, is an essential task in online education. One typical solution for knowledge tracing is based on Recurrent Neural Networks (RNNs), which represent students' knowledge states with the hidden states of RNNs. Such type of methods normally assumes that students have the same cognition level and knowledge acquisition sensitivity on the same question. Thus, they (i) predict students' responses by referring to their knowledge states and question representations, and (ii) update the knowledge states according to the question representations and students' responses. No explicit cognition level or knowledge acquisition sensitivity is considered in the above two processes. However, in real-world scenarios, students have different understandings on a question and have various knowledge acquisition after they finish the same question. In this paper, we propose a novel model called Individual Estimation Knowledge Tracing (IEKT), which estimates the students' cognition on the question before response prediction and assesses their knowledge acquisition sensitivity on the questions before updating the knowledge state. In the experiments, we compare IEKT with 11 knowledge tracing baselines on four benchmark datasets, and the results show IEKT achieves the state-of-the-art performance.
GR-RNN: Global-context residual recurrent neural networks for writer identification

This paper presents an end-to-end neural network system to identify writers through handwritten word images, which jointly integrates global-context information and a sequence of local fragment-based features.The global-context information is extracted from the tail of the neural network by a global average pooling step.The sequence of local and fragment-based features is extracted from a low-level deep feature map which contains subtle information about the handwriting style.The spatial relationship between the sequence of fragments is modeled by the recurrent neural network (RNN) to strengthen the discriminative ability of the local fragment features.We leverage the complementary information between the global-context and local fragments, resulting in the proposed global-context residual recurrent neural network (GR-RNN) method.The proposed method is evaluated on four public data sets and experimental results demonstrate that it can provide state-of-the-art performance.In addition, the neural networks trained on gray-scale images provide better results than neural networks trained on binarized and contour images, indicating that texture information plays an important role for writer identification.
A First Look at Zoombombing

Online meeting tools like Zoom and Google Meet have become central to our professional, educational, and personal lives. This has opened up new opportunities for large scale harassment. In particular, a phenomenon known as zoombombing has emerged, in which aggressors join online meetings with the goal of disrupting them and harassing their participants. In this paper, we conduct the first data-driven analysis of calls for zoombombing attacks on social media. We identify ten popular online meeting tools and extract posts containing meeting invitations to these platforms on a mainstream social network, Twitter, and on a fringe community known for organizing coordinated attacks against online users, 4chan. We then perform manual annotation to identify posts that are calling for zoombombing attacks, and apply thematic analysis to develop a codebook to better characterize the discussion surrounding calls for zoombombing. During the first seven months of 2020, we identify over 200 calls for zoombombing between Twitter and 4chan, and analyze these calls both quantitatively and qualitatively. Our findings indicate that the vast majority of calls for zoombombing are not made by attackers stumbling upon meeting invitations or bruteforcing their meeting ID, but rather by insiders who have legitimate access to these meetings, particularly students in high school and college classes. This has important security implications because it makes common protections against zoombombing, e.g., password protection, ineffective. We also find instances of insiders instructing attackers to adopt the names of legitimate participants in the class to avoid detection, making countermeasures like setting up a waiting room and vetting participants less effective. Based on these observations, we argue that the only effective defense against zoombombing is creating unique join links for each participant.
Enhancing social recommendation via two-level graph attentional networks

As an effective deep representation learning technique for graph data, graph convolutional network (GCN) has recently been widely applied to obtain better embedding of vertex. The existing studies have successfully explored user-item interaction via GCN for recommendation task and proved its effectiveness. We argue that a significant limitation of these methods is that social relation, which has been proven to impose positive effects for recommendation in many loss optimization models, has received relatively little scrutiny in GCN. Thus, the resultant embeddings are insufficient to model the potential social propagation effect. In our work, we propose to obtain embeddings by using the neighborhood propagation mechanism on two coupled graphs, i.e. user-item interaction graph and social relation graph, which is capable of capturing the interplay between the user’s item taste and user’s friend relationship to integrate social effect into the embeddings. In particular, to address the challenge that different factors on neighborhood propagation process make different contributions for the embedding, we develop Attentional Social Recommendation system (ASR), a new social recommendation framework with hierarchical attention(i.e., neighbor-level and graph-level attention). This allows much flexibility for adaptively acquiring relative importance for different factors. Extensive experiment on three real-world datasets not only show the superior performance of our proposed model over the baselines, but also demonstrate the effectiveness of simultaneous neighborhood propagation on two graphs.
Noise-Resistant Image Encryption Scheme for Medical Images in the Chaos and Wavelet Domain

In this paper, a noise-resistant image encryption scheme is proposed. We have used a cubic-logistic map, Discrete Wavelet Transform (DWT), and bit-plane extraction method to encrypt the medical images at the bit-level rather than pixel-level. The proposed work is divided into three sections; In the first and the last section, the image is encrypted in the spatial domain. While the middle section of the proposed algorithm is devoted to the frequency domain encryption in which DWT is incorporated. As the frequency domain encryption section is a sandwich between the two spatial domain encryption sections, we called it a ”sandwich encryption.” The proposed algorithm is lossless because it can decrypt the exact pixel values of an image. Along with this, we have also gauge the proposed scheme's performance using statistical analysis such as entropy, correlation, and contrast. The entropy values of the cipher images generated from the proposed encryption scheme are more remarkable than 7.99, while correlation values are very close to zero. Furthermore, the number of pixel change rate (NPCR) and unified average change intensity (UACI) for the proposed encryption scheme is higher than 99.4% and 33, respectively. We have also tested the proposed algorithm by performing attacks such as cropping and noise attacks on enciphered images, and we found that the proposed algorithm can decrypt the plaintext image with little loss of information, but the content of the original image is visible.
Hierarchical Personalized Federated Learning for User Modeling

User modeling aims to capture the latent characteristics of users from their behaviors, and is widely applied in numerous applications. Usually, centralized user modeling suffers from the risk of privacy leakage. Instead, federated user modeling expects to provide a secure multi-client collaboration for user modeling through federated learning. Existing federated learning methods are mainly designed for consistent clients, which cannot be directly applied to practical scenarios, where different clients usually store inconsistent user data. Therefore, it is a crucial demand to design an appropriate federated solution that can better adapt to user modeling tasks, and however, meets following critical challenges: 1) Statistical heterogeneity. The distributions of user data in different clients are not always independently identically distributed which leads to personalized clients; 2) Privacy heterogeneity. User data contains both public and private information, which have different levels of privacy. It means we should balance different information to be shared and protected; 3) Model heterogeneity. The local user models trained with client records are heterogeneous which need flexible aggregation in the server. In this paper, we propose a novel client-server architecture framework, namely Hierarchical Personalized Federated Learning (HPFL) to serve federated learning in user modeling with inconsistent clients. In the framework, we first define hierarchical information to finely partition the data with privacy heterogeneity. On this basis, the client trains a user model which contains different components designed for hierarchical information. Moreover, client processes a fine-grained personalized update strategy to update personalized user model for statistical heterogeneity. Correspondingly, the server completes a differentiated component aggregation strategy to flexibly aggregate heterogeneous user models in the case of privacy and model heterogeneity. Finally, we conduct extensive experiments on real-world datasets, which demonstrate the effectiveness of the HPFL framework.
Learning-Based Prediction, Rendering and Association Optimization for MEC-Enabled Wireless Virtual Reality (VR) Networks

Wireless-connected Virtual Reality (VR) provides immersive experience for VR users from anywhere at anytime. However, providing wireless VR users with seamless connectivity and real-time VR video with high quality is challenging due to its requirements in high Quality of Experience (QoE) and low VR interaction latency under limited computation capability of VR device. To address these issues, we propose a MEC-enabled wireless VR network, where the field of view (FoV) of each VR user can be real-time predicted using Recurrent Neural Network (RNN), and the rendering of VR content is moved from VR device to MEC server with rendering model migration capability. Taking into account the geographical and FoV request correlation, we propose centralized and distributed decoupled Deep Reinforcement Learning (DRL) strategies to maximize the long-term QoE of VR users under the VR interaction latency constraint. Simulation results show that our proposed MEC rendering schemes and DRL algorithms substantially improve the long-term QoE of VR users and reduce the VR interaction latency compared to rendering at VR devices.
Neural network laundering: Removing black-box backdoor watermarks from deep neural networks

Creating a state-of-the-art deep-learning system requires vast amounts of data, expertise, and hardware, yet research into copyright protection for neural networks has been limited. One of the main methods for achieving such protection involves relying on the susceptibility of neural networks to backdoor attacks in order to inject a watermark into the network, but the robustness of these tactics has been primarily evaluated against pruning, fine-tuning, and model inversion attacks. In this work, we propose an offensive neural network “laundering” algorithm to remove these backdoor watermarks from neural networks even when the adversary has no prior knowledge of the structure of the watermark. We can effectively remove watermarks used for recent defense or copyright protection mechanisms while retaining test accuracies on the target task above 97% and 80% for both MNIST and CIFAR-10, respectively. For all watermarking methods addressed in this paper, we find that the robustness of the watermark is significantly weaker than the original claims. We also demonstrate the feasibility of our algorithm in more complex tasks as well as in more realistic scenarios where the adversary can carry out efficient laundering attacks using less than 1% of the original training set size, demonstrating that existing watermark-embedding procedures are not sufficient to reach their claims.
SCAB - IoTA: Secure communication and authentication for IoT applications using blockchain

The Internet of Things (IoT) becomes a leading field of computer science, which simplifies the life of human beings. The IoT, devices can interconnect and communicate with each other, in a fully autonomous mode. This autonomy has made the IoT network vulnerable to various security threats. However, due to this autonomy, there is a strong need for reliable security and storage mechanism for authentication to exchange data between IoT devices. To solve this problem, we propose an efficient method known as SCAB-IoTA, which ensures identification and authentication of IoT devices and also provides secure communication in the open environment. Along with authentication and secure communication, the scheme also ensures data integrity. SCAB-IoTA used the blockchain and hybrid cryptosystem (the combination of Advanced Encryption Standard (AES) and Elliptic Curve Digital Signature Algorithm (ECDSA) cryptographic techniques) to enhance the security of IoT applications to prevent various attacks along with less computational, and storage overhead. Furthermore, we have a secure cluster of IoT devices based on angular distance (AD), so that devices can securely communicate without any interruption. A secure and robust trust mechanism has employed to build these clusters, where each IoT device has to authenticate itself before joining the cluster. The obtained results satisfy the IoT security requirements with 60% reduced computation, storage and communication cost compared with state-of-the-art methods. The security analysis illustrates that SCAB-IoTA withstands against various cyberattacks such as impersonation, message replay, man-in-the-middle, and botnet attacks.