Reviewerly/exHarmony huggingface dataset:
Physical Violence Detection in Video Streaming Using Partitioned Skeleton Analysis

We propose a skeleton-based method for identifying hostile behavior in this paper. The method does not require a lot of powerful hardware, but it is very quick to implement. Our approach consists of two stages: feature extraction from video frames to assess a person&#x0027;s posture, followed by action classification using a neural network to identify whether the frames include bullying situations. We also generated a dataset of 400 minutes of video data comprising one person&#x0027;s activities and 20 hours of video data including physical bullying and aggressive acts, as well as 13 classifications for distinguishing aggressor and victim behavior. On the gathered dataset, the method was put to the test. The results indicate that identifying aggressive conduct in video sequences is more than 97 percent accurate.
DNSonChain: Delegating Privacy-Preserved DNS Resolution to Blockchain

Domain Name System (DNS) is known to present privacy concerns. To this end, decentralized blockchains have been used to host DNS records, so that users can synchronize with the blockchain to maintain a local DNS database and resolve domain names locally. However, existing blockchain-based solutions either do not guarantee a domain name is controlled by its "true" owner; or have to resort to DNSSEC, a not yet widely adopted protocol, for verifying ownership. In this paper, we present DNSonChain, a new blockchain-based naming service compatible with DNS. It allows domain owners to claim their domain ownership on the blockchain where DNS records are hosted. The core function of DNSonChain is to validate the domain ownership in a decentralized manner. We propose a majority vote mechanism that randomly selects multiple participants (i.e., voters) in the system to vote for the authority of domain ownership. To provide resistance to attacks from fraudulent voters, DNSonChain requires two rounds of voting processes. Our security analysis shows that DNSonChain is robust against several types of security failures, able to recover from various attacks. We implemented a prototype of DNSonChain as an Ethereum decentralized application and evaluate it on an Ethereum Testnet.
PG-RRT: A Gaussian Mixture Model Driven, Kinematically Constrained Bi-directional RRT for Robot Path Planning

Path planning and smooth trajectory generation are critical capabilities for efficient navigation of mobile robots operating in challenging and cluttered environments. For real time and autonomous operations of mobile robots, intelligent algorithms, efficient and light-weight compute, and smooth trajectory are key components. In this work, we propose an intelligent, probabilistic Gaussian mixture model driven Bi-RRT (PG-RRT) algorithm which generates nodes in the most probable regions for faster convergence. The proposed algorithm is tested in various simulated environments including highly cluttered obstacles. The experimental results of PG-RRT are compared with state-of-the-art path planning algorithms. The results show significant improvement in the number of iterations (up to 26X) and runtime (up to 17.5X) demonstrating the superiority of the proposed PG-RRT algorithm.
SAR Ship Target Detection Based on Improved YOLOv5s

Synthetic Aperture Radar (SAR) image ship target detection is of great significance to the field of marine monitoring. At present, many mainstream deep learning SAR image ship target detection networks have a large network model size, which is difficult to meet the requirements of equipment miniaturization. Therefore, in order to solve this problem, this paper proposes an improved YOLOv5s SAR image ship target detection network, which is based on the lightweight ideas of GhostNet and DWConv, and redesigns the structure of the YOLOv5s network model. The network performance is verified on the SAR Ship Detection Dataset (SSDD). Experimental results show that the model size of the improved YOLOv5s network proposed in this paper is only one-half of the original YOLOv5s, but its maen Average Precision(mAP) and Recall do not have much loss compared with the original YOLOv5s model.
Comprehensive Analysis of Various Cyber Attacks

Cyber attacks are malevolent undertakings to damage, take or destroy fundamental corporate data, compromise locales, and upset practical establishments. The aggressor misuses shortcomings in the structure, acquainting a harmful code with adjust PC code, reasoning, or data inciting cybercrimes, similar to information and discount misrepresentation. As associations and the clients, they serve have come to depend upon locales and electronic applications to make, eat up, and cooperate, the insurance and security threats to which they are revealed every day are growing significantly. Cyber attacks have gotten logically refined and risky. They are as of now not put something aside for high profile targets and can impact any affiliation that relies upon orchestrated applications, devices, and systems. Government associations and monetary firms stay the focal point of numerous cyber attacks, especially those completed for the sake of hacktivism. Be that as it may, because of the open foundation of the Internet and the expanded accessibility of simple to-execute assault devices, nearly anybody with the essential abilities can do a cyber attack. This paper focuses on comprehensive analysis of various cyber-attacks. The analysis of cyber-attack is done using kali Linux. Many tools that are present in the kali Linux are being explored in this paper.
Image Aesthetics Classification using Deep Features and Image Category

Selection of aesthetic images is important in any application where a visual presentation is required, including industries such as e-commerce, tourism, and real-estate. With large datasets of pictures typically available, manual selection of an appropriate image is a difficult task. Automated aesthetic classification aims to address this problem. In our approach, we constrain the problem to determine aesthetics of an image based on knowing what category of image it is, as different image categories have different associated aesthetics. For example, different considerations for camera placement, lighting, etc. are made when capturing a human portrait, compared to a photo of a landscape to produce an aesthetic result. Our approach integrates an individual classifier for each image category type. Image features used by the classifier are extracted from three off-the-shelf pre-trained convolutional deep neural networks. Our approach achieves a higher classification accuracy compared to classifiers that do not take category into account. Further, our off-the-shelf deep feature-based classifiers show improved accuracy over results reported in the literature.
Subcarrier wave quantum key distribution system with gaussian modulation

Abstract The aim of the paper is to describe a well-known quantum key distribution GG02 protocol using multimode coherent states generated on subcarrier frequencies of the optical spectrum. In order to detect signal states, we use a method of coherent detection without the participation of a local oscillator directly but where power from a carrier wave is used as such. Within the framework of the modern GG02 protocol description and the secutity proof against collective attacks, we introduce the necessary amendments to reduce our model to a model of the common system.
STEM Educational Outreach and Indigenous Culture: (Re)Centering for Design Scholarship

Integrating Indigenous culture into STEM education is a critical process in building pathways to justice and diversifying design. This process serves to (re)center our conceptions of STEM education by challenging strictly Western notions of STEM, representing an opportunity for learning not just in curricular design, but in technological design as well. Postcolonial computing scholars have critically examined design processes, highlighting the dominance of Western knowledge undergirding cross-cultural design. However, such efforts have yet to fully leverage insights from national curricular (re)centering initiatives. We take up this opportunity through a qualitative case study of an educational outreach organization in British Columbia, Canada, a subsidiary of a nation-wide effort in curricular integration of Indigenous and Western STEM material. Applying postcolonial computing thought, we offer enrichments to theory by providing an empirical basis for a) integrating resiliency, b) politicization in design, and c) arguments for (re)centering epistemological authority in computing. These contributions both enrich theory and enhance the practice of cross-cultural design by encouraging and exploring an Indigenous (re)centering of our understanding of both curricular and technological design.
A New Metaheuristic Approach to Solving Benchmark Problems: Hybrid Salp Swarm Jaya Algorithm

Metaheuristic algorithms are one of the methods used to solve optimization problems and find global or close to optimal solutions at a reasonable computational cost.As with other types of algorithms, in metaheuristic algorithms, one of the methods used to improve performance and achieve results closer to the target result is the hybridization of algorithms.In this study, a hybrid algorithm (HSSJAYA) consisting of salp swarm algorithm (SSA) and jaya algorithm (JAYA) is designed.The speed of achieving the global optimum of SSA, its simplicity, easy hybridization and JAYA's success in achieving the best solution have given us the idea of creating a powerful hybrid algorithm from these two algorithms.The hybrid algorithm is based on SSA's leader and follower salp system and JAYA's best and worst solution part.HSSJAYA works according to the best and worst food source positions.In this way, it is thought that the leader-follower salps will find the best solution to reach the food source.The hybrid algorithm has been tested in 14 unimodal and 21 multimodal benchmark functions.The results were compared with SSA, JAYA, cuckoo search algorithm (CS), firefly algorithm (FFA) and genetic algorithm (GA).As a result, a hybrid algorithm that provided results closer to the desired fitness value in benchmark functions was obtained.In addition, these results were statistically compared using wilcoxon rank sum test with other algorithms.According to the statistical results obtained from the results of the benchmark functions, it was determined that HSSJAYA creates a statistically significant difference in most of the problems compared to other algorithms.
Aesthetic-guided outward image cropping

Image cropping is a commonly used post-processing operation for adjusting the scene composition of an input photography, therefore improving its aesthetics. Existing automatic image cropping methods are all bounded by the image border, thus have very limited freedom for aesthetics improvement if the original scene composition is far from ideal, e.g. the main object is too close to the image border. In this paper, we propose a novel, aesthetic-guided outward image cropping method. It can go beyond the image border to create a desirable composition that is unachievable using previous cropping methods. Our method first evaluates the input image to determine how much the content of the image should be extrapolated by a field of view (FOV) evaluation model. We then synthesize the image content in the extrapolated region, and seek an optimal aesthetic crop within the expanded FOV, by jointly considering the aesthetics of the cropped view, and the local image quality of the extrapolated image content. Experimental results show that our method can generate more visually pleasing image composition in cases that are difficult for previous image cropping tools due to the border constraint, and can also automatically degrade to an inward method when high quality image extrapolation is infeasible.
Anomaly Detection in Semiconductor Cleanroom Using Isolation Forest

Wafer fabrication in semiconductor companies is frequently afflicted by anomalies that can have a negative impact on the cleanroom environment, leading to wafer defects. This study describes an anomaly detection system for edge devices in a semiconductor cleanroom that uses the Isolation Forest method. The multidimensional dataset, which comprises various sizes of ultrafine PM1 particles, is extremely harmful to wafers and can be detected very effectively using Isolation Forest, with an F <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> -score of 0.9899795 and an AUC of 0.99.
Learning Context Restrained Correlation Tracking Filters via Adversarial Negative Instance Generation

The tracking performance of discriminative correlation filters (DCFs) is often subject to unwanted boundary effects. Many attempts have already been made to address the above issue by enlarging searching regions over the last years. However, introducing excessive background information makes the discriminative filter prone to learn from the surrounding context rather than the target. In this article, we propose a novel context restrained correlation tracking filter (CRCTF) that can effectively suppress background interference via incorporating high-quality adversarial generative negative instances. Concretely, we first construct an adversarial context generation network to simulate the central target area with surrounding background information at the initial frame. Then, we suggest a coarse background estimation network to accelerate the background generation in subsequent frames. By introducing a suppression convolution term, we utilize generative background patches to reformulate the original ridge regression objective through circulant property of correlation and a cropping operator. Finally, our tracking filter is efficiently solved by the alternating direction method of multipliers (ADMM). CRCTF demonstrates the accuracy performance on par with several well-established and highly optimized baselines on multiple challenging tracking datasets, verifying the effectiveness of our proposed approach.
Risk Assessment of Alpine Skiing Events Based on Knowledge Graph: A Focus on Meteorological Conditions

The alpine skiing event is particularly vulnerable to changes in meteorological conditions as a winter sport held outdoors. The commonly used risk assessment methods cannot be inflexible and cannot be dynamically adjusted to combine multiple risk factors and actual conditions. A knowledge graph can organize data resources in the risk domain as structured knowledge systems. This paper combines a knowledge graph and risk assessment to effectively assess the risk status. First of all, we introduce the relevant literature review of sports event risk assessment, combining the characteristics of alpine skiing events. Then, we summarize the risk types of alpine skiing events and related risk knowledge. Secondly, a model is proposed to introduce an event risk assessment model based on the RippleNet framework combined with the characteristics of large-scale sports events. Moreover, the validity of the model is verified. The results show that the RippleNet-based event risk assessment model can be used to assess the risk of alpine skiing events. In order to effectively deal with the large-scale sports events that occur with a variety of risks, the smooth implementation of large-scale sports events provides a strong guarantee.
Salient object detection based on global to local visual search guidance

The human eye has the behavior of fixation shift, which is represented by visual search mechanism. Human visual search mechanism has the characteristics of visual guidance from global to local. In order to imitate this behavior, this paper proposes a novel salient object detection model which integrates global and local perception based on visual search guidance. First, by using multi-level feature complementarity, a coarse global perceptual map can be obtained by encoding VGG network and decoding deconvolution network. Then, through the Maximally Stable External Regions (MSER) algorithm, the local hotspot sub-regions in the coarse global perceptual map are intercepted, and generating a visual guidance search path based on the maximum response principle, and the local fine perceptual map is successively merged through the Squeeze-and-Excitation Networks (SENet) step by step. Finally, the salient map integrating the coarse global and fine local perception is achieved. Because only one local area needs to be integrated at a time, which is equivalent to solving a relatively simple problem at a time, it is easy to get more accurate results. The experimental results show that the time-consuming of our model is relatively low and compared with the classical and more recent SOTA methods, our model also shows competitive results.
Simultaneously Improve Transferability and Discriminability for Adversarial Domain Adaptation

Although adversarial domain adaptation enhances feature transferability, the feature discriminability will be degraded in the process of adversarial learning. Moreover, most domain adaptation methods only focus on distribution matching in the feature space; however, shifts in the joint distributions of input features and output labels linger in the network, and thus, the transferability is not fully exploited. In this paper, we propose a matrix rank embedding (MRE) method to enhance feature discriminability and transferability simultaneously. MRE restores a low-rank structure for data in the same class and enforces a maximum separation structure for data in different classes. In this manner, the variations within the subspace are reduced, and the separation between the subspaces is increased, resulting in improved discriminability. In addition to statistically aligning the class-conditional distribution in the feature space, MRE forces the data of the same class in different domains to exhibit an approximate low-rank structure, thereby aligning the class-conditional distribution in the label space, resulting in improved transferability. MRE is computationally efficient and can be used as a plug-and-play term for other adversarial domain adaptation networks. Comprehensive experiments demonstrate that MRE can advance state-of-the-art domain adaptation methods.
Intelligent weight generation algorithm based on binary isolation tree

In this paper, in order to make the statistical information of the scoring system more reasonable, a new questionnaire weighting algorithm is proposed to intelligently identify the credibility of survey in this paper. Based on the core idea of the Isolation Forest (iForest), the weights of questionnaire are computationally identified by considering depth of nodes and relative mass between nodes. Using Swee Chuan Tan's approach of constructing Half-Space Trees, the problem of non-unique weights with repeated calculations is solved and the lower randomness of the result is obtained. In addition, the impact of multiplicity of point on weight is considered by introducing a tiny deviation, and a weight updating method is proposed to deal with the dynamic change of data. The final simulation results demonstrate that the proposed method is defective and accurate, and that the reliability of the questionnaire can be more intelligently recognized in the scoring and evaluation system.
Discriminative distribution alignment for domain adaptive object detection

Domain adaptive object detection has achieved appealing performance by constructing an effective transferable model for unlabeled target images, which takes advantage of the well-labeled source images with different distributions. However, two crucial factors are overlooked by most current methods: 1) different areas of an image should not be equally aligned since some areas may contribute more to distribution alignment if they contain more discriminative information for classifying the objects; and 2) the objectives of feature alignment and classification should not be independently optimized since it will fail to capture the discriminative information of data. To address these issues, we propose a new domain adaptive object detection model, referred to as discriminative distribution alignment domain adaptive detector. To be specific, the proposed method first makes the model focus on the areas that are quantified with high localization probability at the image level to enhance discrimination between foregrounds and backgrounds. Then the source and target images are aligned at the category level to learn class-invariant features by two adversarial regions-of-interest classifiers. Comprehensive experiments on several visual tasks verify that the proposed method outperforms the competitive domain adaptive object detection methods significantly in unsupervised domain adaptation setting.
A Text-Based Hybrid Approach for Multiple Emotion Detection Using Contextual and Semantic Analysis

With the growing importance of textual data processing, sentiment analysis which is a field of text mining, has been widely researched. But it is insufficient for the detection of human emotions. Emotion detection, an extension of sentiment analysis, has proven to be one of the most important areas in text mining, especially in the field of human-computer interactions. The recent works on emotion detection primarily focus on facial expressions, voice, audio and gestures. However, the content on the web is mostly text-based and it becomes difficult to capture the human emotions in the absence of facial and audio aspects in the data. Therefore, there is a need to design efficient mining techniques for processing textual data. Traditional approaches overlook disambiguation and ignore the presence of multiple emotions in text. In this paper, we propose a hybrid model which uses rules, sentiments and context for the disambiguation of words by using sentence transformers which recognize the various emotions involved by using natural language processing, sentence embeddings, BERT and similarity techniques so as to overcome such shortcomings. Our work ensures that Ekman's emotions along with neutral emotion are identified such that multiple emotions are tagged precisely based on the context. This hybrid method has proven to be far superior than existing approaches for the detection of multiple emotions.
Attacking Websites Using HTTP Request Smuggling: Empirical Testing of Servers and Proxies

Securing web servers and proxies is critical for enterprise networks. Such Internet-facing systems make up a significant portion of the remote attack surface and, thus, serve as prime targets. HTTP Request Smuggling (HRS) is a vulnerability that arises when web servers and proxies interpret the length of a single HTTP request differently. In this study, empirical testing was used to find parsing behaviors that could lead to HRS in six popular proxies and six servers. A literature study was conducted to compile a corpus containing requests adopting all known HRS techniques and different variations. A test harness was built to enable the automatic sending of requests and recording of responses. The responses were then manually analyzed to identify behaviors vulnerable to HRS. In total, 19 vulnerable behaviors were found, and by combining the proxies with the servers, two almost full and four full attacks could be performed. At least one behavior that went against the HTTP specification was found in every system tested. However, not all of these behaviors enabled HRS. In conclusion, most proxies had strict parsing and did not accept requests that could lead to HRS. The servers, however, were not so strict.