Reviewerly/exHarmony huggingface dataset:
A study on vulnerable risks in security of cloud computing and proposal of its remedies

Abstract Data innovation worldview empowers pervasive access to shared pools of configurable framework assets and more significant level administrations that can be quickly provisioned with minimal administration exertion, regularly over the Internet. Cloud computing provides dynamic asset administrations for the client on request, and it is becoming increasingly popular. Cloud administrations are conveyed from server farms all through the world (as it is otherwise called Networks of systems). Outsider associations are additionally present there, because of which some significant security dangers came and make organize helplessly so the security of cloud information is imperative to keep over Cloud architecture to offer better support, ease administrations, and so forth. Despite that there are a few undermined hazards in cloud computing security. We will be concentrating majorly on cloud computing fraught with threats and how to overcome those threats. This paper is intended to introduce information about the most current attacks on cloud computing, just as safety efforts. The seriousness and impact of these attacks are talked about alongside genuine instances of these attacks. The paper likewise proposes alleviation strategies that can be utilized to decrease or dispose of the risks of the threats talked about. Moreover, the general cloud security proposals are given.
A Fast and Unbiased Minimalistic Resampling Approach for the Particle Filter

The particle filter is an important approximation method for online state estimation in nonlinear nonGaussian scenarios. The resampling step in the particle filter is critical because it eliminates the wasteful use of particles that do not contribute to the posterior (degeneracy). The fully stochastic resamplers, despite being unbiased in approximating the posterior density, involve exhaustive and sequential communication within the particles and thus are computationally expensive. The alternate partial deterministic resamplers overcome this problem by reducing the communication within particles but this leads to approximation bias. This paper proposes a fast resampling procedure that gives an accurate approximation of the posterior and tracks as accurately as the conventional resamplers.
Simple Light-Weight Network for Human Pose Estimation

Human pose estimation has achieved significant improvement. However, most existing methods mainly consider how to improve the model performance using complex architecture or computationally expensive model, ignoring the deployment costs in practice, especially in human-robot interaction. In this paper, we investigate a highly efficient pose estimation model with comparable accuracy. We propose an adaptive convolution, which can adaptively generate one or more feature maps with desired channels. Since redundant information in the feature map is an important characteristic, to preserve the redundant information while taking only a few numbers of FLOPs and parameters, we propose a light-weight block based on adaptive convolution, which is performed with two parallel convolution operations. And then, to further reduce the FLOPs, we propose heterogeneous filters based light-weight block, which contains two different kinds of filters in each layer. Finally, three light-weight units are designed to stack light-weight block, and a simple light-weight pose estimation network (SLPE) can be easily established. Extensive evaluations demonstrate the advantages of SLPE over state-of-the-art methods in terms of model cost-effectiveness on the standard benchmark datasets, MPII and COCO dataset.
HiCOMEX: Facial Action Unit Recognition Based on Hierarchy Intensity Distribution and COMEX Relation Learning

The detection of facial action units (AUs) has been studied as it has the competition due to the wide-ranging applications thereof. In this paper, we propose a novel framework for the AU detection from a single input image by grasping the Co-Occurrence and Mutual EXclusion (COMEX) as well as the intensity distribution among AUs. Our algorithm uses facial landmarks to detect the features of local AUs. The features are input to a bidirectional long short-term memory (BiLSTM) layer for learning the intensity distribution. Afterwards, the new AU feature continuously passed through a self-attention encoding layer and a continuous-state modern Hopfield layer for learning the COMEX relationships. Our experiments on the challenging BP4D and DISFA benchmarks without any external data or pre-trained models yield F1-scores of 63.7% and 61.8% respectively, which shows our proposed networks can lead to performance improvement in the AU detection task.
You don't drink a cupboard: improving egocentric action recognition with co-occurrence of verbs and nouns

We propose a refinement module to improve action recognition by considering the semantic relevance between verbs and nouns. Existing methods recognize actions as a combination of verb and noun. However, they occasionally produce the semantically implausible combination, such as “drink a cupboard” or “open a carrot”. To tackle this problem, we propose a method that incorporates a word embedding model into an action recognition network. The word embedding model is trained to obtain co-occurrence between verbs and nouns and used to refine the initial class probabilities estimated by the network. Experimental results show that our method improves the estimation accuracy of verbs and nouns on the EPIC-KITCHENS Dataset.
Feasibility of Inferring Keystrokes on PEDs with Sensors from Mobile Devices

Sensors in smart devices can help people in their lives. But data collected by these sensors can be sensitive and cause information leakage. In this paper, when inputting keystrokes on an ATM keyboard, we consider data from the accelerometer and microphone in mobile phone and smart watch concurrently. We extract features from this sensor data, show the nature of this data and analyzed the possibility of using this data to distinguish keystrokes. Furthermore, we launched a simple experiment, trained a set of classifiers to infer keystrokes using the time difference of arrival features. The result shows the potential and feasibility of using this data to perform side-channel attack and infer keystrokes on ATM keyboard.
Detecting and Segmenting Adversarial Graphics Patterns from Images

Adversarial attacks pose a substantial threat to computer vision system security, but the social media industry constantly faces another form of "adversarial attack" in which the hackers attempt to upload inappropriate images and fool the automated screening systems by adding artificial graphics patterns. In this paper, we formulate the defense against such attacks as an artificial graphics pattern segmentation problem. We evaluate the efficacy of several segmentation algorithms and, based on observation of their performance, propose a new method tailored to this specific problem. Extensive experiments show that the proposed method outperforms the baselines and has a promising generalization capability, which is the most crucial aspect in segmenting artificial graphics patterns.
A Fully Dynamic Context Guided Reasoning and Reconsidering Network for Video Captioning

Visual reasoning and reconsidering capabilities are instinctively executed alternately as people watch a video and attempt to describe its contents with natural language. Inspired by this, a novel network that joints fully dynamic context guided reasoning and reconsidering is proposed in this paper. Specifically, an elaborate reconsidering module referred to as the reconsiderator is employed for rethinking and sharpening the preliminary results of stepwise reasoning from coarse to fine, thereby generating a higher quality description. And in turn, the reasoning capability of the network can be further boosted under the guidance of the context information summarized during reconsidering. Extensive experiments on two public benchmarks demonstrate that our approach is pretty competitive with the state-of-the-art methods.
Image Dehazing Through Dark Channel Prior and Color Attenuation Prior

With an increase in motor transportation in megacities, the pollutants in the air are rising dramatically. This increases the number of unburnt particulates, dust, and smoke being released into the air. Haze is a phenomenon that emerges from such conditions that tends to affect image quality. Two single-channel algorithms have been proposed for dehazing images: The Dark Channel Prior and the Color Attenuation Prior. The Dark Channel Prior is based on the statistic that low-intensity pixels exist in at least one-color channel in outdoor haze-free images. The color attenuation method is based on the parameters obtained via a comprehensive analysis of scene depth of hazy images modeled using a linear model. This paper does a comprehensive analysis of the two techniques to evaluate them objectively. Finally, this paper also overcomes the limitations of the FoHIS, a synthetic fog-generation technique, by integrating a novel approach to finding a depth map automatically.
A Random Multi-target Backdooring Attack on Deep Neural Networks

Deep learning has made tremendous progress in the past ten years and has been applied in various critical practical applications. However, recent studies have shown that deep learning models are vulnerable to backdoor attacks in which the target labels chosen by the attacker can be one or multiple. Conventional multi-target backdoor attack focus on applying multiple triggers to implement multi-target attack. In this paper, we propose a novel method that utilizes one trigger to correspond to multiple target labels, and the location of the trigger is not limited, which brings more flexibility. After proposing the backdoor attack, we also considered defending against this kind of attack. Therefore, to distinguish backdoor images and clean images, we propose a method to train a neural network as a detector to detect if the image has an abnormal part. Our experimental results show that our attack success rate is higher than 90% on MNIST, Cifar-10, and GTSRB. Our detection method can also successfully detect the backdoor image with a trigger at a random location of the image, and the detection success rate is 86.02%.
Ideas of Sustainable Development of Civilization in the Context of Contemporary Ecological Education: Descriptive Review

The aim of the research is to outline and interpret through comparative analysis the importance and possibilities of development of the experience in ecological education gained through theoretical and practical research in psychology and pedagogy in Russia and abroad and actualized at different stages of permanent education, primarily in higher education, for sustainable development of civilization. In connection with this, the article describes the ideas of sustainable development of civilization in the context of contemporary ecological education and conducts a descriptive overview. The research also examines the aspects of application of the ideas of sustainable development of civilization in the system of ecological education in Russia (from the tradition of ecologizing knowledge to the new technologies of ecological development). Finally, the article outlines and characterizes international trends of pedagogical (primarily, digital) technologies of ecological education for the purpose of applying ideas of sustainable development of civilization. The scientific originality of the research consists in particular results of theoretical and practical analysis of relevant theoretical and practical researches, which clarify the expected outcomes of realizing the concept of sustainable development through digital education technologies that are being implemented today in higher education in Russia and abroad. The results show that the most important outcome of realizing ideas of sustainable development of civilization today should lie in the students’ understanding of the role of ecological culture (ecological value orientations, world outlook, competences, behavior) in solving and preventing already global ecological problems.
Data Validation Through Decentralised Blockchain-Based Registries

Decentralised information systems require special attention to integrity and security, especially in the health system, because the alteration of data is a direct threat to human life. It is necessary to have mechanisms in place to ensure that the information is accurate and has not been accidentally or maliciously altered. Moreover, these mechanisms cannot compromise the performance of systems that are already under functional stress due to the current pandemic situation. In this paper, we propose a decentralised Blockchain-based registry for healthcare environments. This system is able to provide the same level of security as traditional transaction registries in distributed databases, but facilitates queries and validations through workload balancing as it is a decentralised system, thus enabling fast and efficient operations. In this way, the benefits of Blockchain can be transferred to current information systems, generating an impassable data validation mechanism.
Developing Culture Adaptation Agent for Werewolf Games

One of the AI systems introduced into our society in recent years is a system that negotiates and collaborates with users by presenting and recommending information through dialogues with multiple users. In general, users have different cultures and values. Therefore, the system must be able to estimate those values and then adapt its behavior to the user. In this study, we focus on the "werewolf game" as a benchmark for this type of technology. The werewolf game is an imperfect information game in which the game proceeds through communication. A werewolf game is a team game and has both cooperative and adversarial characteristics. In werewolf games, it is important not to cause conflicts among allies due to differences in culture and way of thinking. In this study, we first create multiple agent groups with different cultures. Then, we show that there is no specific strong strategy, and that the optimal strategy is different for each group. Then, we build an agent that can estimate the culture of the strategy that players other than itself are following, and can act in such a way that it adapts itself to that culture, and conduct an evaluation based on the winning rate. The results show that the proposed agent is able to adapt to the group and increase its winning rate under certain limitations.
Enabling the Digitalization of Claim Management in the Insurance Value Chain Through AI-Based Prototypes: The ELIS Innovation Hub Approach

(a) Situation faced: Digital transformation in the insurance value chain is fostering the adoption of artificial intelligence, namely, of deep learning methods, for enabling the improvement and the automation of two relevant tasks in the claim management process, i.e., (i) sensitive data detection and anonymization and (ii) manipulation detection on images. The proposed approach is technically feasible, lightweight, and sufficiently scalable due to the properties offered by currently available cloud platforms, and it also yields a sensible reduction in operational costs. (b) Action taken: Since well-established guidelines to address insurance digitalization use-cases requiring deep learning do not yet exist, we propose a customized data science workflow for designing and developing two prototypes that tackle: (i) sensitive data detection and anonymization and (ii) manipulation detection on claim images. We propose a six-step method that is implemented using deep convolutional neural networks in Keras and TensorFlow and is seamlessly integrable with the most frequently used cloud environments. During prototyping, different training and testing iterations were carried out, thus progressively fine-tuning detection models, up to the achievement of the desired performance. (c) Results achieved: The developed prototypes are able to (i) robustly anonymize claim images and (ii) robustly detect manipulations on claim images (robustness means that, from a statistical viewpoint, the declared performance level is preserved even in the presence of highly heterogeneous distributions of the input data). The technical realization relies on open-source software and on the availability of cloud platforms, this last both for training purposes and for scalability issues. This demonstrates the applicability of our methodology, given a reliable analysis of the available resources, including the preparation of an appropriate training dataset for the models. (d) Lessons learned: The present work demonstrates the feasibility of the proposed deep learning-based six-step methodology for image anonymization and manipulation detection purposes and discusses challenges and learnings during implementation. Specifically, key learnings include the importance of business translation, data quality, data preparation, and model training.
Single Sign-On Using Contactless Smart Cards and Fingerprint Authentication

As more services are moving onto the Internet, a user needs to maintain several online accounts, ideally each with separate username and password. Remembering a whole set of passwords for each and every web service is definitely a challenging task. There are several single Sign-on services currently available which require to be secured with a master password. The master password can be strong and still be vulnerable to theft. We propose a system which involves contactless smartcards, to store the passwords and fingerprint authorization to authenticate a user for safety and security. This system provides security and convenience at one place.
Research on Mining Sequential Association Rules Based on Conditional Confidence

Different process sequences are involved in the assembly process of aerospace complex components, among which there are many common and typical process sequences. But at present, the reusability of a large number of process files is low, and the reuse of existing process files cannot be realized. Therefore, mining out the typical process sequence can be used to guide technologists to write new process, realize the efficient reuse of historical process scheme, effectively improve the efficiency of equipment process preparation, and shorten the process design cycle. Based on the traditional Apriori algorithm and FP-Growth algorithm, this paper studies a sequence based association rule mining algorithm-Sequential Frequent Pattern Growth (SFP-Growth Algorithm). The algorithm retains the advantages of FP-Growth algorithm in constructing FP- Tree, and learns from the characteristics of Apriori algorithm in generating frequent items. The algorithm has a slight improvement in the efficiency of the algorithm. Compared with FP-Growth algorithm, it also has an improvement in the acquisition of frequent itemsets. And it can excavate the potential sequence relationship between processes, which can be better applied to the process design system and provide great convenience for the process personnel.
Intellectual System for Microwave Devices Adjustment

The paper describes a concept of the intellectual system for the microwave devices adjustment. The adjustment process involves trained personnel and special, relatively expensive equipment. The use of the intellectual adjustment system may decrease cost of the mass production of the sophisticated microwave devices (narrowband filters, diplexers etc.). The analytic model which combines lumped elements filter model and approximation of the numerically modeled tuning elements is used to synthesize data sets for the artificial neural network training. The architecture of the artificial neural network is proposed to obtain a decision making procedure for the automated filter adjustment. The results (numerical and experimental) are discussed.
RANCANG BANGUN WEBSITE WISATA GEOL DESA KABUPATEN LANGKAT

This research is conducted with the aim of designing and building a tourist website in which it provides information on Wisata Geol in Pematang Serai Village. This research is based on the basis of the needs of containers as promotional media also provides information quickly and accurately, because the delivery of information on the current Wisata Geol is still done manually which is considered still not effective. In addition, this information system makes it easy to visitors to book ticket reservations via website previosly made by hand through celluler phone calls. With this information system it will make it easier for the administration of the misata to file a visitor’s report. These web tours use Mysql Database and use PHP Programming language and employ multiple support applications such as a Balsamiq Mockup for a design and CSS display for a website page view
Speech Bandwidth Extension using DWT + DCT steganography technique

The present public switched telephone network (PSTN) transmits signals in a frequency range of 300-3400 Hz. But wideband speech lies in the region of 50-7000Hz where we are losing some information that falls under 3400-7000Hz, so we need to find a way to transmit these components using the available PSTN channel bandwidth as it is economically and practically not possible to change the complete telephone switching network. Various wideband speech coding techniques were proposed to ameliorate the intelligibility and speech quality but not in an effective way. To increase the efficiency, we exploit the steganography concept. In this, we extract the high-frequency components from the speech signal and embed these into lower frequencies using Linear Predictive model analysis and spread spectrum. At the receiver end, these embedded parameters are extracted, and speech is synthesized back so that the original input speech is retrieved without any loss of information of the speech. The main advantage of the proposed technique is that it is completely compatible with conventional end-user equipment. Experimental results show that the performance of this technique is higher in terms of speech quality in comparison to the conventional methods of bandwidth extension.
Exploring Extended Reality with Flexible Spectrum Access in Wireless Cellular Network

Extended reality (XR) technologies, including augmented reality (AR) and virtual reality (VR), is now used in a wide spectrum of applications, e.g., telemedicine, remote education, and computer gaming. However, existing experiments are predominantly deployed in the indoor environment with the help of wired connectivity or Wi-Fi networks. Recently, the global commercialization of 5G networks provides new opportunities for XR applications. By using wireless cellular network, XR is able to offer seamless user experience from anywhere at any time. Despite remarkable advantages, current cellular-enhanced XR services are still facing certain challenges. For instance, edge users may not be able to achieve a reliable XR experience due to the limitation of transmission power and free-space path loss. In this paper, a novel flexible spectrum access (FSA) approach is proposed, which allows XR users to be flexibly handed over between multiple carriers. By such means, the reliability for the edge users can be significantly improved thanks to the use of lower frequency bands. It is also shown that the end-to-end latency has been reduced due to more evenly distributed time frequency domain resources.