Reviewerly/exHarmony huggingface dataset:
Mapping of Security Issues and Concerns in Cloud Computing with Compromised Security Attributes

In the ongoing years with the headway of innovation, cloud computing has gotten colossal mainstream among organizations, aggressors just as people because of a few favorable circumstances however the fundamental advantages being versatility, flexibility, unwavering quality, high computation power, convenience and cost-proficient as these hugely advantage all the parties in question. Be that as it may, with this exponential increment cloud computing environments have become a high potential target for the attackers as they have witnessed some drawbacks in terms of security and data privacy. One of the fundamental reasons behind for cloud computing environments being a gold dig for attackers is the presence of unencrypted data on these platforms which are indexed and along these lines effortlessly found. This paper talks about the risks, challenges and security concerns present in a cloud computing environment that affects both the cloud service providers and the clients. This paper has talked about the issues and concerns from an end-user perspective as well as from a business viewpoint also. In this way these risks, challenges and security concerns end up being compelling and proficient in deciding the general security of a cloud domain as it covers practically all the specialized issues just as well as standardization, regulations and supervising perspectives identified with it. The challenges have likewise prompted development opportunities that must be tackled. As cloud computing environment is an extremely unpredictable and dynamic as for conventional computing environment, so the customary security practices and arrangements does not delineate in cloud situations. There are unbounded potential outcomes of development in cloud computing which cannot be inconspicuous because of security and data privacy issues so these risks, challenges, and security concerns must be tended to.
Understand Volatility of Algorithmic Stablecoin: Modeling, Verification and Empirical Analysis

An algorithmic stablecoin is a type of cryptocurrency managed by algorithms (i.e., smart contracts) to dynamically minimize the volatility of its price relative to a specific form of asset, e.g., US dollar. As algorithmic stablecoins have been growing rapidly in recent years, they become much more volatile than expected. In this paper, we took a deep dive into the core of algorithmic stablecoins and shared our answer to two fundamental research questions, i.e., Are algorithmic stablecoins volatile by design? Are they volatile in practice? Specifically, we introduced an in-depth study on three popular types of algorithmic stablecoins and developed a modeling framework to formalize their key design protocols. Through formal verification, the framework can identify critical conditions under which stablecoins might become volatile. Furthermore, we performed a systematic empirical analysis on real transaction activities of the Basis Cash stablecoin to relate theoretical possibilities to market observations. Lastly, we highlighted key design decisions for future development of algorithmic stablecoins.
Non-Markovian Dynamics of Geometric Quantum Discord in a Double Quantum Dot System

Abstract In this paper, we study the geometric quantum discord dynamics of the double quantum dot charge qubit in the non-Markovian environment. We apply the non-perturbative non-Markovian quantum state diffusion method to obtain the exact master equation of the double quantum dot system coupled to two independent non-zero temperature electronic baths. Then, we use this master equation to investigate the effects of non-Markovianity, inter-dot coupling strength and bath temperature on the dynamics of geometric quantum discord. Our studies show that the geometric quantum discord of a double quantum dot system can be modified and enhanced in some cases via these factors.
DQN Dynamic Pricing and Revenue Driven Service Federation Strategy

This paper proposes a dynamic pricing and revenue-driven service federation strategy based on a Deep Q-Network (DQN) to instantly and automatically decide federation across different service provider domains, each introduces dynamic service prices offering to its customers and towards other domains. A dynamic pricing model is considered in this work based on the analysis of real pricing data collected from public cloud provider, and upon this a dynamic arrival process as a result of the price changes is proposed for formulating the service federation problem as a Markov Decision Problem (MDP). In this work, several reinforcement learning algorithms are developed to solve the problem, and the presented results show that the DQN method reached 90% of the optimal revenue and outperformed existing state-of-the-art strategies, and it can learn the federation pricing dynamics to make optimum federation decisions according to price changes.
Single-frame super-resolution for high resolution optical remote-sensing data products

The resolution of remote-sensing images is directly related to the application value of data products. Due to differences in imaging characteristics between digital cameras and remote-sensing cameras, the existing network models cannot get the best super resolution (SR) results of satellite images. In response to the requirements of remote-sensing image production, we propose a single image super-resolution (SISR) reconstruction method for specific type of remote-sensing satellite. First, we measure and model the imaging degradation phenomenon of remote-sensing satellite, including the image blur and noise model. Then, high-quality aerial images are down-sampled and degraded to construct paired training image datasets. We chose the most popular Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN) as the basic structure and optimized the number of Residual-in-Residual Dense Block (RRDB) modules to further improve the processing efficiency. Finally, we perform a series of quantitative measurements of the SR image results, including image interpretation capability, reconstruction accuracy, ground resolution distance, and data processing efficiency, using higher resolution remote-sensing images as the benchmark. The experimental results demonstrate that our proposed method has higher interpretation capability and reconstruction accuracy for the SR processing of specific type remote-sensing satellite. Our proposed method is evaluated within a real satellite image product, that demonstrated it has the capability of pipeline super-resolution processing of remote sensing data products.
Study on Blockchain-Based Healthcare Insurance Claim System

Healthcare insurance is a vital part of today's world. With the increase in the number of health issues, not everyone can afford all kinds of treatment required and the time wasted in claims. The introduction of blockchain technology is proving to be of great benefit to lower and middle-class people. Different types of blockchain are being used to provide maximum benefits to society. Permissioned and advanced blockchain provides security and transparency to sensitive information. The attribute-based signature (MA-ABS) scheme with multiple authorities is used to provide privacy to patient's data. Smart Contracts for Ethereum Blockchain are being used to automate the process of insurance claims and payments to patients. Hyperledger Fabric is used for the implementation so that RESTful APIs can be used for easy access of data on the blockchain. The healthcare insurance system implementation on blockchain will eliminate the intermediaries, causing delay, fraud, high cost, etc., and bring about a faster process of claims and treatment while maintaining the security and privacy of all medical records such as EHRs and EMRs.
An authenticated, secure, and mutable multiple‐session‐keys protocol based on elliptic curve cryptography and text‐to‐image encryption algorithm

Summary Most of the key agreement protocols (e.g., Menezes–Qu–Vanstone [MQV] family) generate one common key per session. This leaves the session key vulnerable against various attacks. This article proposed an enhanced multiple session key (EMSK) protocol which is based on the elliptic curve Diffie–Hellman (ECDH), HMQV, and the YAK protocols. The EMSK generates multiple session keys per session. Unlike the MQV protocol, the EMSK needs only two messages to be exchanged in order to create nine session keys. However, the MQV requires 18 messages to be exchanged in order to produce these nine session keys. In EMSK, one of the session keys is used to encrypt the plaintext using the one‐time pad cipher. The encrypted message is then embedded in an RGB‐image in order to provide confidentiality service of communication. The EMSK is evaluated theoretically against various types of attacks and practically using the Scyther simulator. The results from the simulator showed that the EMSK protocol withstand various types of attacks on the MQV, HMQV, and the YAK protocols, and provided perfect forward secrecy. In addition, the EMSK provides a digital signature feature which validates the authenticity and integrity of a digital message using the zero knowledge prove.
Best practices of neurophysiological data collection for media message evaluation in social campaigns

Cognitive neuroscience techniques have become a great complement to traditional methods in marketing research. However, many difficulties have arisen, such as the collection of reliable data, processing and proper analysis. The paper presents good practices of collecting neurophysiological data for the evaluation of media messages in social campaigns. For this purpose, the most common difficulties during signal recording for devices such as EEG, GSR, HR, Eye Tracker were identified. The problems that arose were assigned to three main groups that could be affected, i.e.the examing person, the examing enviroment and the examing equipment. In the final stage, solutions were proposed to reduce or eliminate them.
Consistent Explanations by Contrastive Learning

Post-hoc explanation methods, e.g., Grad-CAM, enable humans to inspect the spatial regions responsible for a particular network decision. However, it is shown that such explanations are not always consistent with human priors, such as consistency across image transformations. Given an interpretation algorithm, e.g., Grad-CAM, we introduce a novel training method to train the model to produce more consistent explanations. Since obtaining the ground truth for a desired model interpretation is not a well-defined task, we adopt ideas from contrastive self-supervised learning, and apply them to the interpretations of the model rather than its embeddings. We show that our method, Contrastive Grad-CAM Consistency (CGC), results in Grad-CAM interpretation heatmaps that are more consistent with human annotations while still achieving comparable classification accuracy. Moreover, our method acts as a regularizer and improves the accuracy on limited-data, fine-grained classification settings. In addition, because our method does not rely on annotations, it allows for the incorporation of unlabeled data into training, which enables better generalization of the model. The code is available here: https://github.com/UCDvision/CGC
Advanced Discretisation and Visualisation Methods for Performance Profiling of Wind Turbines

Wind turbines are typically organised as a fleet in a wind park, subject to similar, but varying, environmental conditions. This makes it possible to assess and benchmark a turbine’s output performance by comparing it to the other assets in the fleet. However, such a comparison cannot be performed straightforwardly on time series production data since the performance of a wind turbine is affected by a diverse set of factors (e.g., weather conditions). All these factors also produce a continuous stream of data, which, if discretised in an appropriate fashion, might allow us to uncover relevant insights into the turbine’s operations and behaviour. In this paper, we exploit the outcome of two inherently different discretisation approaches by statistical and visual analytics. As the first discretisation method, a complex layered integration approach is used. The DNA-like outcome allows us to apply advanced visual analytics, facilitating insightful operating mode monitoring. The second discretisation approach is applying a novel circular binning approach, capitalising on the circular nature of the angular variables. The resulting bins are then used to construct circular power maps and extract prototypical profiles via non-negative matrix factorisation, enabling us to detect anomalies and perform production forecasts.
Behavioral anomaly detection system for the wellbeing assessment and lifestyle support of older people at home

The wellbeing assessment of older people is becoming crucial in today’s era of aging and home care in order to provide the best possible care. New technologies are being used to assist older people at home, which generates an extensive amount of health and wellbeing information. The application of artificial intelligence algorithms to this healthcare and wellbeing data can enhance patient care and provide support to professionals by reducing their cognitive load. These algorithms can detect anomalous physiological, physical, and cognitive conditions in older individuals, which can help to identify emergency situations, or the early detection of an emerging health condition. However, while there has been relevant research in the field of anomaly detection for various engineering applications, there is little knowledge about healthcare and wellbeing-related anomaly detection. To this end, in this article, we propose an innovative system for detecting behavioral anomalies for older people that are being monitored at home with the aim of improving their lifestyle and wellbeing as well as the early detection of any physical or cognitive condition.
Performance Analysis of Machine Learning-based Face Detection Algorithms in Face Image Transmission over AWGN and Fading Channels

Although machine learning and artificial intelligence have been widely applied, noise and interference are still major disturbances to degrade the quality of image transmission and processing efficiency in multimedia data transmission. Based on the face detection system utilizing the machine learning algorithms and artificial intelligence, this paper examines, analyzes and compares the performances of the AdaBoost machine learning algorithm and Convolutional Neural Network (CNN)-based algorithm in processing face information, which is disturbed by channel noise and fading effect encountered in the transmission of face image. The face detection system used is based on HAAR feature extraction. The extracted HAAR features are subjected to classification training and learning of the cascade classifier. Then the face detection is performed on the picture information outside the database. Results of computer simulation show that for the image data affected by the fading and AWGN, the face recognition system still marked the positions of the eyes and mouth with high accuracy. However for fading and higher AWGN, using a machine learning algorithm with a convolutional neural network is better than the ADABOOST algorithm. It can be concluded that the machine learning algorithm can effectively reduce the adverse effect of multimedia data transmission without increasing the SNR and use of higher level of modulation scheme.
Puzzles Unpuzzled

Escape rooms exist in various forms, including real-life facilities, board games, and digital implementations. The underlying idea is always the same: players have to solve many diverse puzzles to (virtually) escape from a locked room. Within the last decade, we witnessed a rapidly increasing popularity of such games, which also amplified the amount of related research. However, the respective academic landscape is mostly fragmented in its current state, lacking a common model and vocabulary that would withstand these games' variety. This manuscript aims to establish such a foundation for the analysis and construction of escape rooms. In a first step, we derive a high-level design framework from prior literature. Then, as our main contribution, we establish an atomic puzzle taxonomy that closes the gap between the analog and digital domains. The taxonomy is developed in multiple steps: we compose a basic structure based on previous literature and systematically refine it by analyzing 39 analog and digital escape room games, including recent virtual reality representatives. The final taxonomy consists of mental, physical, and emotional challenges, thereby providing a robust and approachable basis for future works across all application domains that deal with escape rooms or puzzles in general.
Image Scaling by de la Vallée-Poussin Filtered Interpolation

Abstract We present a new image scaling method both for downscaling and upscaling, running with any scale factor or desired size. The resized image is achieved by sampling a bivariate polynomial which globally interpolates the data at the new scale. The method’s particularities lay in both the sampling model and the interpolation polynomial we use. Rather than classical uniform grids, we consider an unusual sampling system based on Chebyshev zeros of the first kind. Such optimal distribution of nodes permits to consider near-best interpolation polynomials defined by a filter of de la Vallée-Poussin type. The action ray of this filter provides an additional parameter that can be suitably regulated to improve the approximation. The method has been tested on a significant number of different image datasets. The results are evaluated in qualitative and quantitative terms and compared with other available competitive methods. The perceived quality of the resulting scaled images is such that important details are preserved, and the appearance of artifacts is low. Competitive quality measurement values, good visual quality, limited computational effort, and moderate memory demand make the method suitable for real-world applications.
Towards Automatic Ethiopian Endemic Animals Detection on Android Using Deep Learning

Nowadays, deep learning became the dominant object recognition technique. Due to its outstanding feature extraction and adaptable capabilities, however, how to ensure speed and accuracy is still a significant task in the area of object detection. In this paper, we provide large-scale animals dataset for the object detection task. The animal's dataset consists six meaningful classes: chilada baboon, red fox, walia ibex, bale mountain vervet, black maned lion, and Somali wild ass. In addition, we utilize a You only look once (YOLO) v3 algorithm for animal detection. YOLOv3 is one of the most extensively used deep learning object detection algorithms; And we have developed user friendly graphical user interface (GUI) based mobile android application. Experiments are performed on our animal dataset that are collected from different positions. The results demonstrate that the proposed approach is highly efficient and effective which provides an accuracy of 97.24% on our animal dataset.
An End-to-End Traffic Light Detection Algorithm Based on Deep Learning

Traffic light detection algorithm, as an important algorithm in automatic driving and assisted driving tasks, is the focus of research. Because the traffic light is in the complex environment, it brings many difficulties to the detection algorithm. Traditional algorithms are based on color and geometric features and have many barriers. Therefore, a traffic light detection algorithm based on deep learning is proposed in paper. With YOLOv5 as the algorithm core and K-means to generate anchor, 63.3%AP and 143FPS detection speed are obtained on traffic lights in BDD100K data set respectively. The method in this paper achieves good results in speed and accuracy in an end-to-end manner.
Semi‐supervised multiple empirical kernel learning with pseudo empirical loss and similarity regularization

Multiple empirical kernel learning (MEKL) is a scalable and efficient supervised algorithm based on labeled samples. However, there is still a huge amount of unlabeled samples in the real-world application, which are not applicable for the supervised algorithm. To fully utilize the spatial distribution information of the unlabeled samples, this paper proposes a novel semi-supervised multiple empirical kernel learning (SSMEKL). SSMEKL enables multiple empirical kernel learning to achieve better classification performance with a small number of labeled samples and a large number of unlabeled samples. First, SSMEKL uses the collaborative information of multiple kernels to provide a pseudo labels to some unlabeled samples in the optimization process of the model, and SSMEKL designs pseudo-empirical loss to transform learning process of the unlabeled samples into supervised learning. Second, SSMEKL designs the similarity regularization for unlabeled samples to make full use of the spatial information of unlabeled samples. It is required that the output of unlabeled samples should be similar to the neighboring labeled samples to improve the classification performance of the model. The proposed SSMEKL can improve the performance of the classifier by using a small number of labeled samples and numerous unlabeled samples to improve the classification performance of MEKL. In the experiment, the results on four real-world data sets and two multiview data sets validate the effectiveness and superiority of the proposed SSMEKL.
Multi-Scale Speaker Embedding-Based Graph Attention Networks For Speaker Diarisation

The objective of this work is effective speaker diarisation using multi-scale speaker embeddings. Typically, there is a trade-off between the ability to recognise short speaker segments and the discriminative power of the embedding, according to the segment length used for embedding extraction. To this end, recent works have proposed the use of multi-scale embeddings where segments with varying lengths are used. However, the scores are combined using a weighted summation scheme where the weights are fixed after the training phase, whereas the importance of segment lengths can differ within a single session.To address this issue, we present three key contributions in this paper: (1) we propose graph attention networks for multi-scale speaker diarisation; (2) we design scale indicators to utilise scale information of each embedding; (3) we adapt the attention-based aggregation to utilise a pre-computed affinity matrix from multi-scale embeddings.We demonstrate the effectiveness of our method in various datasets where the speaker confusion which constitutes the primary metric drops over 10% in average relative compared to the baseline.
Evidence in Hand: Passive Vibration Response-based Continuous User Authentication

Continuous user authentication is of great importance to maintain security for a mobile system and protect user's privacy throughout a login session. In this paper, we propose HandPass, a continuous user authentication system that employs the vibration responses of concealed hand biometrics, which are passively activated by the natural user-device interactions on the touchscreen. Hand vibration responses are instantly triggered and embodied in the mechanical vibration of the force-bearing body (i.e., the mobile device and the holding hand). Therefore, a built-in accelerometer can effectively capture the intrinsic features of hand vibration responses. The hand vibration response is determined by the trigger force and the complex hand structure, which is unique to each user and is difficult (if not impossible) to counterfeit. HandPass is a passive hand vibration response-based continuous user authentication system hosted on smartphones, with advantages of non-intrusiveness, high efficiency, and user-friendliness. We prototyped HandPass on Android smartphones and comprehensively evaluated its performance by recruiting 43 volunteers. Experiment results show that HandPass can achieve 97.3 % overall authentication accuracy and only 1.8 % false acceptance rate in diverse scenarios.