Reviewerly/exHarmony huggingface dataset:
Anonymous scheme for blockchain atomic swap based on zero-knowledge proof

The blockchain's cross-chain atomic exchange uses smart contracts to replace trusted third parties, but atomic exchange cannot guarantee the anonymity of transactions, and it will inevitably increase the risk of privacy leakage. Therefore, this paper proposes an atom based on zero-knowledge proof. Improved methods of exchange to ensure the privacy of both parties in a transaction. The anonymous improvement scheme in this article uses the UTXO unconsumed model to add a new anonymous list in the blockchain. When sending assets to smart contracts, zero-knowledge proof is used to provide self-certification of ownership of the asset, and then the transaction is broken down. Only the hash value of the transaction is sent to the node, and the discarded list is used to verify the validity of the transaction, which achieves the effect of storing assets anonymously in the smart contract. At the same time, a smart contract is added when the two parties in the transaction communicate to exchange the contract address of the newly set smart contract between the two parties in the transaction. This can prevent the smart contract address information from being stolen when the two parties in the transaction communicate directly.
A fuzzy clustering algorithm for developing predictive models in construction applications

Fuzzy inference systems (FISs) are a predictive modeling technique based on fuzzy sets that utilize approximate reasoning to mimic the decision-making process of human experts. There are several expert- and data-driven methods for developing FISs, among which fuzzy clustering algorithms are the most frequently used data-driven methods. This paper introduces a new fuzzy clustering algorithm for developing FISs in construction applications that addresses two limitations of existing fuzzy clustering algorithms: the lack of capacity to determine the number of clusters automatically from the characteristics of the data, and the poor performance in predictive modeling of highly dimensional problems. Existing fuzzy clustering algorithms are limited in construction applications since determining the number of clusters based on subjective expert judgment reduces the accuracy of the resulting FIS, and construction systems are often highly dimensional with a large number of inputs affecting the system outputs. The fuzzy clustering algorithm proposed in this paper determines the number of clusters automatically based on the characteristics of the data, specifically the non-linearity observed within clusters, and assigns weights to the rules of FISs to improve their accuracy in highly dimensional problems. This paper advances the state-of-the-art of fuzzy clustering and contributes to construction modeling by providing a new data-driven technique for developing FISs that suits the characteristics of construction problems.
Implementation of Virtual Assistant with Sign Language using Deep Learning and TensorFlow

The paper is all about the system and interface developed, that allows deaf mutes to make use of various voice automated virtual assistants with help of Sign Language. Majority of Virtual Assistants work on basis of audio inputs and produces audio outputs which in turn makes it impossible to be used by people with hearing and speaking disabilities. The project makes various voice controlled virtual assistants respond to hand gestures and also produces results in form of text outputs. It makes use of concepts like Deep Learning, Convolutional Neural Network, Tensor Flow, Python Audio Modules. A webcam first captures the hand gestures, then Convolutional Neural Network interprets the images produced and produces rational languages. These languages are then mapped to pre-defined datasets using Deep learning. For this purpose, Neural Networks are linked with Tensor flow library. The designed system will then produce audio input for the Digital Assistant, using one of the Python text to speech module. The final audio output of the Digital Assistant will be converted into text format using one of the Python speech to text module which will be displayed on the viewing screen.
DroTrack: High-speed Drone-based Object Tracking Under Uncertainty

We present DroTrack, a high-speed visual single-object tracking framework for drone-captured video sequences. Most of the existing object tracking methods are designed to tackle well-known challenges, such as occlusion and cluttered backgrounds. The complex motion of drones, i.e., multiple degrees of freedom in three-dimensional space, causes high uncertainty. The uncertainty problem leads to inaccurate location predictions and fuzziness in scale estimations. DroTrack solves such issues by discovering the dependency between object representation and motion geometry. We implement an effective object segmentation based on Fuzzy C Means (FCM). We incorporate the spatial information into the membership function to cluster the most discriminative segments. We then enhance the object segmentation by using a pre-trained Convolution Neural Network (CNN) model. DroTrack also leverages the geometrical angular motion to estimate a reliable object scale. We discuss the experimental results and performance evaluation using two datasets of 51,462 drone-captured frames. The combination of the FCM segmentation and the angular scaling increased DroTrack precision by up to 9% and decreased the centre location error by 162 pixels on average. DroTrack outperforms all the high-speed trackers and achieves comparable results in comparison to deep learning trackers. DroTrack offers high frame rates up to 1000 frame per second (fps) with the best location precision, more than a set of state-of-the-art real-time trackers.
Quantum Cryptanalysis on Contracting Feistel Structures and Observation on Related-Key Settings

In this paper we show several quantum chosen-plaintext attacks (qCPAs) on contracting Feistel structures. In the classical setting, a d-branch r-round contracting Feistel structure can be shown to be PRP-secure when d is even and $$r \ge 2d-1$$ , meaning it is secure against polynomial-time chosen-plaintext attacks. We propose a polynomial-time qCPA distinguisher on the d-branch $$(2d-1)$$ -round contracting Feistel structure, which solves an open problem by Dong et al. In addition, we show a polynomial-time qCPA that recovers the keys of the d-branch r-round contracting Feistel structure when each round function $$F^{(i)}_{k_i}$$ has the form $$F^{(i)}_{k_i}(x) = F_i(x \oplus k_i)$$ for a public random function $$F_i$$ . This is applicable to the Chinese block cipher standard SM4, which is a special case where $$d=4$$ . Finally, in addition to quantum attacks under single-key setting, we also show related-key quantum attacks on balanced Feistel structures in the model that adversaries can only control part of the key difference in quantum superposition. Our related-key attacks on balanced Feistel structures can easily be extended to ones on contracting Feistel structures.
Measuring Students' Motivation towards Virtual Reality Game-Like Learning Environments

Learning concepts of science is very challenging, especially when the concepts used are abstract. Teachers have recourse to many different types of teaching methods which are however limited when it comes to explaining students about three dimensionalities (3-D) concepts. There are various technologies like AR, VR, and MR that have come into play and are already been used in the field of education. Virtual Reality (VR) technologies are rapidly becoming more popular in the field of education. VR based Learning Environments (VRLEs) create a real-world environment with the help of 3-D models that brings immersion, interaction and triggers the imagination power of the student. This paper shows that VR technology has a positive impact on students' motivation. The development and deployment of a game “Magnex” has been done to explain the concepts of magnetism and its current effects. The Instructional Materials Motivation Survey (IMMS) which is based upon the ARCS model as deployed for collecting information; it consists of four factors: satisfaction, attention, confidence and relevance. When the effect of VR application was analyzed, confidence and attention factors were best rated among all.
Modified Moth-Flame Optimization Algorithm-Based Multilevel Minimum Cross Entropy Thresholding for Image Segmentation

Multilevel thresholding is a widely used image segmentation technique. However, multilevel thresholding becomes more and more computationally expensive as the number of thresholds increase. Therefore, it is essential to incorporate some suitable optimization technique to make it practical. In this article, a modification is proposed to the Moth-Flame Optimization (MFO) algorithm and then it is applied to multilevel thresholding for image segmentation. Cross entropy is used as the objective function to select the optimal thresholds. A set of benchmark test images are used to evaluate the proposed technique. The Mean Structural SIMilarity (MSSIM) index is used to measure the quality of the segmented images. The results of the proposed technique are compared with the original MFO, PSO, BFO, and WOA. Experimental results and analysis suggest that the proposed technique outperforms other techniques in terms of segmentation quality images and stability. Moreover, computation time required for multilevel thresholding is also reduced to a manageable level.
Constructing Knowledge Economy Composite Indicators using an MCA-DEA approach.

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Side-Channel Analysis and Countermeasure Design on ARM-Based Quantum-Resistant SIKE

The implementations of post-quantum cryptographic algorithms have been newly explored, whereas, the protection against side-channel attacks shall be considered upfront, since it can have a non-negligible impact on security and performance. In this article, the security of supersingular isogeny key encapsulation (SIKE), a second-round candidate of NIST's on-going post-quantum standardization process, is thoroughly evaluated under side-channel analysis. First, the vulnerabilities of reference and optimized implementations of SIKE are thoroughly analyzed in terms of both horizontal and vertical side-channel leakage. After the optimized SIKE, which is based on Three-point Montgomery Differential Ladder algorithm, is proved to be constant-time and there is no horizontal leakage, a vertical vulnerability is analyzed based on the source code at the algorithmic level, and a theoretical differential power analysis (DPA) attack is proposed. In order to exploit this vulnerability, the differential electromagnetic attack (DEMA) is put into practice to extract the private key of SIKE based on a 32-bit ARM platform. To the best of our knowledge, this is the first practical side-channel attack at SIKE implemented on real ARM-based devices. Our experiments show that the DEMA needs only hundreds of electromagnetic traces to carry out the attack. More importantly, an efficient window-based countermeasure is proposed to eliminate the vertical leakage and prevent side-channel attacks with only a little overhead. The security of our countermeasure is carefully evaluated against most of well-known power analysis attacks. Through careful evaluation and comparison with other countermeasures, this method can lead to higher security at a very small cost in terms of time and memory.
Sentiment Analysis of E-commerce Text Reviews Based on Sentiment Dictionary

With the rapid development of the Internet, the advantages of e-commerce in marketing have become prominent. However, it is difficult for consumers to choose a variety of products of the same type. Consumers give feedback on the purchase process in the form of comments, and in this way influence other users' purchase decisions. This paper mainly divides the text comments of e-commerce from the three aspects of keywords, evaluation objects and emotional resources, and classifies them according to the constructed emotional dictionary. Specific experimental steps: Firstly, the tf - idf algorithm to extract of subjects of keywords. Secondly, by using the method of part of speech combining keywords similarity evaluation object extraction, and based on the characteristics of parts of speech and location rules set to extraction of emotional resources. Finally, based on the emotional resources to build a dictionary, and for the same emotional words to different evaluation objects with different polarity, constructing reverse dictionary. The experimental results verify that the emotion dictionary constructed in this paper has a good effect on the emotion classification of e-commerce text reviews.
A Machine Learning Approach for the Automated Interpretation of Plasma Amino Acid Profiles

Abstract BACKGROUND Plasma amino acid (PAA) profiles are used in routine clinical practice for the diagnosis and monitoring of inherited disorders of amino acid metabolism, organic acidemias, and urea cycle defects. Interpretation of PAA profiles is complex and requires substantial training and expertise to perform. Given previous demonstrations of the ability of machine learning (ML) algorithms to interpret complex clinical biochemistry data, we sought to determine if ML-derived classifiers could interpret PAA profiles with high predictive performance. METHODS We collected PAA profiling data routinely performed within a clinical biochemistry laboratory (2084 profiles) and developed decision support classifiers with several ML algorithms. We tested the generalization performance of each classifier using a nested cross-validation (CV) procedure and examined the effect of various subsampling, feature selection, and ensemble learning strategies. RESULTS The classifiers demonstrated excellent predictive performance, with the 3 ML algorithms tested producing comparable results. The best-performing ensemble binary classifier achieved a mean precision-recall (PR) AUC of 0.957 (95% CI 0.952, 0.962) and the best-performing ensemble multiclass classifier achieved a mean F4 score of 0.788 (0.773, 0.803). CONCLUSIONS This work builds upon previous demonstrations of the utility of ML-derived decision support tools in clinical biochemistry laboratories. Our findings suggest that, pending additional validation studies, such tools could potentially be used in routine clinical practice to streamline and aid the interpretation of PAA profiles. This would be particularly useful in laboratories with limited resources and large workloads. We provide the necessary code for other laboratories to develop their own decision support tools.
A Survey on Serverless Computing and Its Implications for JointCloud Computing

Serverless computing is known as an appealing alternative cloud computing paradigm with its auto-scaling nature and pay-as-you-go charging model. Mainstream cloud vendors have proposed their own serverless platforms, while various kinds of applications have been refactored in a serverless manner for execution. However, the serverless computing model still entails refinement as it introduces performance and security issues. In this paper, we conduct a comprehensive survey on the serverless computing, mainly in three aspects: the type of applications suitable for serverless, the performance issues, and the security issues. We specifically elaborate previous efforts on resolving issues in serverless and shed light on the unresolved issues. We also discuss the opportunities and challenges in integrating serverless computing in the jointcloud infrastructure.
Copying Machine Learning Classifiers

We study copying of machine learning classifiers, an agnostic technique to replicate the decision behavior of any classifier. We develop the theory behind the problem of copying, highlighting its properties, and propose a framework to copy the decision behavior of any classifier using no prior knowledge of its parameters or training data distribution. We validate this framework through extensive experiments using data from a series of well-known problems. To further validate this concept, we use three different use cases where desiderata such as interpretability, fairness or productivization constrains need to be addressed. Results show that copies can be exploited to enhance existing solutions and improve them adding new features and characteristics.
Dimensionality Reduction based on SHAP Analysis: A Simple and Trustworthy Approach

In this 21st century the world is driven by data, analysis, and predictions based on this data is substantial. However, these predictions that have an immense impact on our daily life comes with an overhead of complex data mining and large datasets. With this paper, we will suggest a way to reduce the dimensionality of the dataset without a great loss of accuracy and reduce the necessity for complex data mining, by analyzing the features based on their SHAP - SHapley Additive explanation, values we prioritize the features and discard the features of unsubstantial relevance to the accuracy of the model.
Quantitative phase imaging in dual-wavelength interferometry using a single wavelength illumination and deep learning

In this manuscript, we propose a quantitative phase imaging method based on deep learning, using a single wavelength illumination to realize dual-wavelength phase-shifting phase recovery. By using the conditional generative adversarial network (CGAN), from one interferogram recorded at a single wavelength, we obtain interferograms at other wavelengths, the corresponding wrapped phases and then the phases at synthetic wavelengths. The feasibility of the proposed method is verified by simulation and experiments. The results demonstrate that the measurement range of single-wavelength interferometry (SWI) is improved by keeping a simple setup, avoiding the difficulty caused by using two wavelengths simultaneously. This will provide an effective solution for the problem of phase unwrapping and the measurement range limitation in phase-shifting interferometry.
Exploring the benefits and limitations of augmented reality for palletization

Superimposing virtual visual information within the real world is referred to as augmented reality (AR). This technique becomes increasingly popular and due to technical advances can be applied to various purposes ranging from consumer entertainment to industrial use cases. Workplaces equipped with AR assistance-systems promise fast and accurate performance. Within the field of intralogistics, palletization constitutes a field of application which may benefit from guidance by AR. In the present investigation, participants performed a palletization task while they received instructions via three different devices (paper list, tablet, AR glasses). Performance data and subjective ratings were obtained. Participants subjectively preferred to work with the AR device. Number of errors was reduced while at the same time handling times were prolonged when the AR device was used. In all experimental conditions, performance improved in the course of the experiment. Least improvement was observed in the AR condition, which emphasizes that such applications allow decent performance without prior experience. Overall, the present results indicate that the utility of AR applications is limited when task demands are low. Such applications might be more useful in unclear and complex situations. Furthermore, AR possesses high hedonic qualities which may ease the integration of novel work processes.
Detection of Fake and Clone accounts in Twitter using Classification and Distance Measure Algorithms

Online Social Network (OSN) is a network hub where people with similar interests or real world relationships interact. As the popularity of OSN is increasing, the security and privacy issues related to it are also rising. Fake and Clone profiles are creating dangerous security problems to social network users. Cloning of user profiles is one serious threat, where already existing user's details are stolen to create duplicate profiles and then it is misused for damaging the identity of original profile owner. They can even launch threats like phishing, stalking, spamming etc. Fake profile is the creation of profile in the name of a person or a company which does not really exist in social media, to carry out malicious activities. In this paper, a detection method has been proposed which can detect Fake and Clone profiles in Twitter. Fake profiles are detected based on set of rules that can effectively classify fake and genuine profiles. For Profile Cloning detection two methods are used. One using Similarity Measures and the other using C4.5 decision tree algorithm. In Similarity Measures, two types of similarities are considered - Similarity of Attributes and Similarity of Network relationships. C4.5 detects clones by building decision tree by taking information gain into consideration. A comparison is made to check how well these two methods help in detecting clone profiles.
Reliable Architectures for Composite-Field-Oriented Constructions of McEliece Post-Quantum Cryptography on FPGA

Code-based cryptography based on binary Goppa codes is a promising solution for thwarting attacks based on quantum computers. The McEliece cryptosystem is a code-based public-key cryptosystem which is believed to be resistant against quantum attacks. In fact, it is successfully advanced to the second round of the post-quantum cryptography standardization competition early 2019. Due to its very large key size, different variants of binary Goppa codes have been proposed. Nevertheless, research has shown that such codes can be thwarted through the injection of faults, causing erroneous outputs. In this work, we present countermeasures for the implementation of different composite field arithmetic units used in the McEliece cryptosystem. The proposed architectures use overhead-aware and tailored signatures. We apply these error detection signatures to the McEliece cryptosystem and perform field-programmable gate array (FPGA) implementations to show the feasibility of adopting the proposed schemes. We benchmark the overhead and performance degradation of the proposed approaches and show their suitability for constrained embedded systems.
Trajectory Generation and Tracking Control of an Autonomous Vehicle Based on Artificial Potential Field and optimized Backstepping Controller

This paper presents a global trajectory generation and tracking control algorithms for a tracked unmanned ground vehicle (UGV) in cluttered environment. First, it is assumed that the surrendering environment is fully known. Then, the UGV path is planned based on a modified artificial potential field (APF), for the vehicle to move from the start location to the desired destination while avoiding the collision with the surrounding obstacles. Next, an optimized back-stepping controller is developed to achieve the trajectory tracking control. In order to find the optimum controller's gains, the trajectory tracking problem is solved as an optimization problem where the objective is to minimize the error between the UGV actual and desired positions. The optimization problem is formulated as a sequential quadratic problem (SQP) considering the UGV kinematic and dynamic constraints. Finally, numerical simulations are conducted in order to show the effectiveness of the proposed algorithms.