Reviewerly/exHarmony huggingface dataset:
Oppositional Human Factors in Cybersecurity: A Preliminary Analysis of Affective States

The need for cyber defense research is growing as more cyber-attacks are directed at critical infrastructure and other sensitive networks. Traditionally, the focus has been on hardening system defenses. However, other techniques are being explored including cyber and psychological deception which aim to negatively impact the cognitive and emotional state of cyber attackers directly through the manipulation of network characteristics. In this study, we present a preliminary analysis of survey data collected following a controlled experiment in which over 130 professional red teamers participated in a network penetration task that included cyber deception and psychological deception manipulations [7]. Thematic and inductive analysis of previously un-analyzed open-ended survey responses revealed factors associated with affective states. These preliminary results are a first step in our analysis efforts and show that there are potentially several distinct dimensions of cyber-behavior that induce negative affective states in cyber attackers, which may serve as potential avenues for supplementing traditional cyber defense strategies.
Research on Automatic Driving Target Detection Based on YOLOv5s

Abstract Aiming at the problems of low detection accuracy, small targets and inaccurate recognition of automatic driving in natural scenes such as different lighting, different weather and different perspectives. The yolov5s network, migration learning method and varifocal loss function are used, and the hyper-parameter adjustment and network fine-tuning are completed. Experimental results show that the proposed method achieves mean Average Precision of 46.68%, and the detection speed can reach 7ms/frame. Automatic driving target detection based on YOLOv5s model network has fast training results, easy deployment, strong robustness, and high recognition accuracy in different weather conditions. It can not only meet the accuracy of automatic driving target detection, but also meet the real-time detection.
Mitigating Poisoning Attack in Federated Learning

Adversarial machine learning (AML) has emerged as one of the significant research areas in machine learning (ML) because models we train lack robustness and trustworthiness. Federated learning (FL) trains models over distributed devices and model parameters are shared instead of actual data in a privacy-preserving manner. Unfortunately, FL is also vulnerable to attacks including parameter/data poisoning attacks. In this paper, we first analyze the impact of the data poisoning attack on this training method with a label-flipping attack. We propose a poisoning attack mitigation technique based on the reputation of nodes' involved in the training process. The reputation score for each client is calculated using the beta probability distribution method. This is the first work to show the removal of malicious nodes with the poisoned dataset from the training environment based on the calculated reputation score. The improvement in model performance after filtering malicious nodes is validated using the benchmark MNIST dataset. At the same time, our work contributes to preventing denial of service attacks by considering a blockchain-based server network. Our results hold for two different attack settings with different proportions of poisoned data samples.
The Rubber Hand Illusion: Top-down attention modulates embodiment

The Rubber Hand Illusion (RHI) creates distortions of body ownership through multimodal integration of somatosensory and visual inputs. This illusion largely rests on bottom-up (automatic multisensory and perceptual integration) mechanisms. However, the relative contribution from top-down factors, such as controlled processes involving attentional regulation, remains unclear. Following previous work that highlights the putative influence of higher-order cognition in the RHI, we aimed to further examine how modulations of working memory load and task instructions-two conditions engaging top-down cognitive processes-influence the experience of the RHI, as indexed by a number of psychometric dimensions. Relying on exploratory factor analysis for assessing this phenomenology within the RHI, our results confirm the influence of higher-order, top-down mental processes. Whereas task instruction strongly modulated embodiment of the rubber hand, cognitive load altered the affective dimension of the RHI. Our findings corroborate that top-down processes shape the phenomenology of the RHI and herald new ways to improve experimental control over the RHI.
AWARE: Aspect-Based Sentiment Analysis Dataset of Apps Reviews for Requirements Elicitation

The smartphone apps market is growing rapidly which challenges apps owners to continue improving their products and to compete in the market. The analysis of users feedback is a key enabler for improvements as stakeholders can utilize it to gain a broad understanding of the successes and failures of their products as well as those of competitors. That leads to generating evidence-based requirements and enhancing the requirements elicitation activities. Aspect-Based Sentiment Analysis (ABSA) is a branch of Sentiment Analysis that identifies aspects and assigns a sentiment to each aspect. Having the aspect information adds a more accurate understanding of opinions and addresses the limited use of the overall sentiment. However, the ABSA task has not yet been investigated in the context of smartphone apps reviews and requirements elicitation. In this paper, we introduce AWARE as a benchmark dataset of 11323 apps reviews that are annotated with aspect terms, categories, and sentiment. Reviews were collected from three domains: productivity, social networking, and games. We derived the aspect categories for each domain using content analysis and validated them with domain experts in terms of importance, comprehensiveness, overlapping, and granularity level. We crowdsourced the annotations of aspect categories and sentiment polarities and performed quality control procedures. The aspect terms were annotated using a partially automated Natural Language Processing (NLP) approach and validated by annotators, which resulted in 98% correct aspect terms. Lastly, we built machine learning baselines for three tasks, namely (i) aspect term extraction using a POS tagger, (ii) aspect category classification, and (iii) aspect sentiment classification, using both Support Vector Machine (SVM) and Multi-layer Perceptron (MLP) classifiers.
Design Considerations for Supporting Mindfulness in Virtual Reality

Mindfulness practice involves bringing one’s attention to the present moment and noticing events as they unfold with a non-judgmental attitude of acceptance. Although mindfulness has been shown to reduce stress and improve mental health, it can be challenging to learn mindfulness techniques. Recent years have seen an interest in using virtual reality (VR) to help people learn mindfulness by immersing users in virtual settings that support an external focus of attention and reduce everyday environmental distraction. However, the literature currently lacks an understanding of how VR should be designed to support mindfulness. In this paper we describe the iterative design and evaluation of Place , a VR app that supports mindfulness practice by situating the user in a virtual forest environment. We present findings from our design process in which prospective users trialled Place and provided feedback on the design in focus groups. Our findings draw attention to factors that influenced the user experience and acceptance of VR for mindfulness, and we describe how the design was altered to address these factors. We end by discussing key design choices that designers should consider when creating VR for mindfulness. Our contributions include insight into the importance of following an iterative design process when creating a VR mindfulness app, and a framework that can be used to inform the design of future VR apps for mindfulness practice.
Topic-Noise Models: Modeling Topic and Noise Distributions in Social Media Post Collections

Most topic models define a document as a mixture of topics and each topic as a mixture of words. Generally, the difference in generative topic models is how these mixtures of topics are generated. We propose looking at topic models in a new way, as topic-noise models. Our topic-noise model defines a document as a mixture of topics and noise. Topic Noise Discriminator (TND) estimates both the topic and noise distributions using not only the relationships between words in documents, but also the linguistic relationships found using word embeddings. This type of model is important for short, sparse social media posts that contain both random and non-random noise. We also understand that topic quality is subjective and that researchers may have preferences. Therefore, we propose a variant of our model that combines the pre-trained noise distribution from TND in an ensemble with any generative topic model to filter noise words and produce more coherent and diverse topic sets. We present this approach using Latent Dirichlet Allocation (LDA) and show that it is effective for maintaining high quality LDA topics while removing noise within them. Finally, we show the value of using a context-specific noise list generated from TND to remove noise statically, after topics have been generated by any topic model, including non-generative ones. We demonstrate the effectiveness of all three of these approaches that explicitly model context-specific noise in document collections.
Learning disentangled representation for self-supervised video object segmentation

This paper proposes a novel self-supervised method for one-shot video object segmentation where the object annotations are only provided in the first frame. The current self-supervised video object segmentation approaches are implemented by modeling the pairwise correspondence between the target and reference frames. The pairwise correspondence only maintains spatio-temporal consistency. However, the VOS tasks not only require a spatio-temporal relationship between the two frames but also require the salient object information for each frame. In order to achieve this goal, we propose a disentangled representation strategy to disentangle the temporal correspondence into the pairwise term and unary term. The pairwise and unary terms capture inter-frame spatio-temporal and intra-frame salient object information, respectively. To demonstrate the importance of the disentangled representation, we apply the proposed approach to DAVIS-2017 and YouTube-VOS datasets. Experimental results confirm the effectiveness of the proposed solution.
Multi-exposure optimized contrast and brightness balance color image enhancement

Color image enhancement has been very challenging for researchers in imaging systems. In this paper, optimal weighted multi-exposure histogram equalization model is proposed for image contrast enhancement. The proposed method divides the input image into two sub-images, specifically low-exposed and high-exposed regions. Then, exposure information obtained from these sub-images is used to divide the input histogram into sub-histograms, and the level of excessive enhancement is controlled by clipping and the optimal weighting process before applying histogram equalization. Next, each sub-histogram is equalized individually and integrated to preserve the naturalness and fine details of the input image. Finally, dual gamma correction is adopted to improve the contrast in dark areas. In the proposed model, a metaheuristic stud krill herd optimization algorithm is employed to determine the optimal constraints to optimize the degree of enhancement based on the objective function defined as discrete entropy or mean square error. Experimental results prove that the proposed technique produces a better visual quality image with an attractive color tone and improved natural appearance without introducing artifacts.
Value Analysis and Realization of Artistic Intervention in Rural Revitalization Based on the Fuzzy Clustering Algorithm

Intervening in the revitalization and development of art villages is one of the ways to build regional brands in rural areas, fit in with contemporary aesthetics, and meet the needs of modern people for diversified life. At the same time, it is not limited by artistry itself, but a comprehensive artistic activity with modernity, industrial linkage, and sustainability. This paper uses the HPR (Hestenes–Powell–Rockafellar) multiplier method to solve and establishes a SSFCM-HPR (semisupervised fuzzy C-means clustering based on HPR) algorithm. The experimental results on datasets show that FCM-UserCF (fuzzy C-means-user collaborative filtering) algorithm effectively solves the problem of data sparsity and improves the accuracy of recommendation. Rural planning and design under the intervention of art is a kind of respect for local memory, an inheritance of folk crafts, a reshaping of history and culture, and an identification with the countryside itself. It also puts forward new development ideas and guiding methods for the sustainable development of rural areas.
Self-Attention Pooling-Based Long-Term Temporal Network for Action Recognition

With the development of Internet of Things (IoT), self-driving technology has been successful. Yet safe driving faces challenges due to such cases as pedestrians crossing roads. How to sense their movements and identify their behaviors from video data is important. Most of the existing methods fail to: 1) capture long-term temporal relationship well due to their limited temporal coverage and 2) aggregate discriminative representation effectively, such as caused by little or even no attention paid to differences among representations. To address such issues, this work presents a new architecture called a self-attention pooling-based long-term temporal network (SP-LTN), which can learn long-term temporal representations and aggregate those discriminative representations in an end-to-end manner, and on the other hand, effectively conduct long-term representation learning on a given video by capturing spatial information and mining temporal patterns. Next, it develops a self-attention pooling method to predict the importance scores of obtained representations for distinguishing them from each other and then weights them together to highlight the contributions of those discriminative representations in action recognition. Finally, it designs a new loss function that combines a standard cross-entropy loss function with a regularization term to further focus on the discriminative representations while restraining the impact of distractive ones on activity classification. Experimental results on two data sets show that our SP-LTN, fed by only red–green–blue (RGB) frames, outperforms the state-of-the-art methods.
Entropy-Based Non-Local Means Filter for Single-Look SAR Speckle Reduction

Speckle is an interference phenomenon that contaminates images captured by coherent illumination systems. Due to its multiplicative and non-Gaussian nature, it is challenging to eliminate. The non-local means approach to noise reduction has proven flexible and provided good results. We propose in this work a new non-local means filter for single-look speckled data using the Shannon and Rényi entropies under the G0 model. We obtain the necessary mathematical apparatus (the Fisher information matrix and asymptotic variance of maximum likelihood estimators). The similarity between samples of the patches relies on a parametric statistical test that verifies the evidence whether two samples have the same entropy or not. Then, we build the convolution mask by transforming the p-value into weights with a smooth activation function. The results are encouraging, as the filtered images have a better signal-to-noise ratio, they preserve the mean, and the edges are not severely blurred. The proposed algorithm is compared with three successful filters: SRAD (Speckle Reducing Anisotropic Diffusion), Lee, and FANS (Fast Adaptive Nonlocal SAR Despeckling), showing the new method’s competitiveness.
EtherProv: Provenance-Aware Detection, Analysis, and Mitigation of Ethereum Smart Contract Security Issues

The rapid adoption of blockchain technologies and particularly smart contracts has been overshadowed by numerous security concerns. Over the past few years, a number of reports exposed smart contracts vulnerabilities and exploits, which mainly stem from the immaturity of the field, and consequently a lack of knowledge and tools for automated analysis and verification of smart contracts. The restricting properties of the blockchain environment, such as the immutability of deployed contracts, encumber the analysis and mitigation of vulnerabilities and bugs in deployed contracts. To address these challenges, we propose EtherProv, a novel provenance tracking system that leverages static and dynamic analysis synergy to enable detection and mitigation of known security issues in Ethereum smart contracts. EtherProv leverages Solidity source code static and dynamic analysis data through contract bytecode instrumentation. The collected data is transformed into a unified, high-level representation, which can be queried using concise and descriptive Datalog queries. Within the provenance framework, EtherProv is able to analyze contracts' execution flow over time, to detect vulnerabilities within a single contract execution flow and across multiple interacting contracts, and to mitigate new security threats in already deployed contracts. Our evaluation shows that EtherProv can efficiently and precisely identify vulnerable contracts with an average contract instrumentation gas overhead of 18.9%.
Enhancing representation learning by exploiting effective receptive fields for object detection

Most of state-of-the-art object detectors depend on multiple anchors/reference boxes in representation learning. However, such anchor-based representation does not completely match with the visual information perceived by the sliding windows, thus degrading the overall performance of object detection. In this paper, we present an effective receptive field (eRF)-dependent region proposal network (eRPN) for proposal generation, which enhances the anchor-based representation via eRFs. Specifically, we define an eRF for each sliding window on the feature map and only encode objects within the eRF for unbiasedly representation learning. The size of eRF depends on its backbone network. An eRF-based matching rule is devised and combined with the commonly used IoU rule for pertinent sample selection. We also design an eRF filter module, which can be appended to RPN for eliminating redundant low-quality region proposals in inference time. eRPN enhances representation learning from two perspectives: input information and sample balance, to make generating region proposals more robust. We evaluate eRPN by combining with two commonly used detection heads: Faster RCNN and Faster RCNN w FPN(Faster-FPN). Experimental results on PASCAL VOC dataset and MS COCO dataset benchmarks demonstrate the effectiveness of the proposed method in learning representation for object detection.
Comparative Analysis of Five Local Search Operators on Visiting Constrained Multiple Traveling Salesmen Problem

Visiting constrained multiple traveling salesmen problem (VCMTSP) is an extension of the multiple traveling salesmen problem (MTSP). In this problem, each city is restricted to be only accessible to certain salesmen. Since the genetic algorithm (GA) has been widely utilized to solve the traveling salesman problem (TSP), this paper first adapts GA to solve VCMTSP. When solving TSP, local search (LS) methods are usually embedded into GA to refine the found solutions. Therefore, in this paper, we then embed five common LS operators, namely the single insertion method, the swap method, the two-optimization method (2-opt), the three-optimization method (3-opt), and the three-node permutation method (3-permute), into GA to investigate their influence on GA in solving VCMTSP. Extensive experiments on various VCMTSP instances show that LS operators can effectively help GA find more promising solutions. In particular, 2-opt brings the most benefit to GA to achieve promising performance in solving VCMTSP among the five LS operators. Except for 2-opt, the swap and 3-permute LS operators also bring considerable benefit to GA. With these investigations, it is expected that this paper could afford a basic guideline for new learners attempting to study VCMTSP.
ClaimChain: Secure Blockchain Platform for Handling Insurance Claims Processing

Insurance claims processing involves multi-domain entities and multi-source data, along with a number of human-agent interactions. Consequently, this processing is traditionally manually-intensive and time-consuming. Blockchain technology-based platforms for intelligent automation can significantly improve the scale and response time of claims processing. However, there is a need to secure such platforms against fraud (e.g., duplicate claims) and the loss of data integrity caused due to cyber-attacks (e.g., Sybil attack). In this paper, we propose a novel “ClaimChain”, a consortium Blockchain platform that transforms the state-of-the-art NICB/ISO database architecture approach through increased shared intelligence and participation of insurance companies. ClaimChain features include: (a) automation of insurance claim processing via implementation of a Blockchain infrastructure, (b) infrastructure-level threat modeling via attack tree formalism for data integrity attacks, and (c) application-level fraud modeling for identified prominent red flags through machine learning models and risk scoring on the basis of risk severity. We evaluate the scalability of ClaimChain by simulating realistically large number of Blockchain transactions of claim processing. Further, we show that data integrity attacks at the infrastructure-level can be mitigated (as seen in reduction of 24% probability in loss) through implementation of security design principles. We also perform fraud-detection over an open dataset in ClaimChain to show how machine learning models can detect fraudulent activity with 98% accuracy.
A design space for RDF data representations

Abstract RDF triplestores’ ability to store and query knowledge bases augmented with semantic annotations has attracted the attention of both research and industry. A multitude of systems offer varying data representation and indexing schemes. However, as recently shown for designing data structures, many design choices are biased by outdated considerations and may not result in the most efficient data representation for a given query workload. To overcome this limitation, we identify a novel three-dimensional design space. Within this design space, we map the trade-offs between different RDF data representations employed as part of an RDF triplestore and identify unexplored solutions. We complement the review with an empirical evaluation of ten standard SPARQL benchmarks to examine the prevalence of these access patterns in synthetic and real query workloads. We find some access patterns, to be both prevalent in the workloads and under-supported by existing triplestores. This shows the capabilities of our model to be used by RDF store designers to reason about different design choices and allow a (possibly artificially intelligent) designer to evaluate the fit between a given system design and a query workload.