Reviewerly/exHarmony huggingface dataset:
Aggregation in Blockchain Ecosystem

Blockchain has evolved rapidly in the past decade, but it still faces challenges such as scalability, large block size, slow block verification, high communication overhead. Though multiple solutions have been proposed to overcome these challenges, a few solutions perform aggregation of blockchain data. Moreover, blockchain data can be a blockchain state, transaction, or consensus message. Therefore, the solutions involving aggregation have immense potential to solve several existing challenges in the blockchain ecosystem. In this work, we investigate and scrutinize possible aggregations in the blockchain. For that purpose, we first briefly describe cryptographic primitives with aggregation scheme and their applicability in the blockchain. These schemes can empower the blockchain with improved scalability, reduced block size, low communication overhead, and fast block verification. Then, we identify nine research problems related to these cryptographic primitives.
Towards a Digital Knowledge Base of Circular Design Examples through Product Teardowns

Design of more circular products is key to achieving Sustainable Development Goal 12, Responsible Consumption and Production. However, many designers lack the knowledge and confidence to bring aspects of circular design into their design practices. One problem is the lack of examples on how circular design is implemented in different types of products and their components. In this work, we present a framework to generate a digital knowledge base of circular design examples from product teardowns (product dissections). Leveraging teardowns, a commonly practiced activity among product designers, can allow the knowledge base to include rich and up-to-date design examples and help inspire future design. The knowledge base covers three categories of circular design aspects: reliability, RRU (repair, reuse, upgrade), and recycling. Under each aspect, we generate a comprehensive list of prompts to guide designers to analyze the product and collect circular design examples. A subset of prompts is showcased in a study of a newly released laptop. We also gathered feedback and suggestions for future developments from experienced design practitioners.
Writer identification using textural features

Writer Identification has gained increasing importance in the scientific community in recent years. In this paper, we propose an approach based on the combination of local textural descriptors and encoding methods (VLAD and Triangulation Embedding). The tests carried out in the bilingual LAMIS dataset made it possible to reach 100% in the Arabic version and 100% in the French version.
Research on Qualification Technology and Qualification System of Safety Grade Optical Fiber Cable in Nuclear Power Plant

The application range of optical fiber cable in nuclear power plant is more and more extensive, especially under the special environment conditions. However, there is no mature and uniform recognized qualification system for the safety grade optical fiber cable in nuclear power plant. According to the environmental conditions and functional requirements of safety-grade optical cables used in nuclear power plants, this paper studies the appraisal content and key appraisal technologies of safety technology optical cables. By analyzing the characteristics of the optical cable and the test sequence, the qualification requirements or specifications are determined. Finally, a appraisal system and acceptance criteria of safety-grade fiber optic cable have been established. The appraisal standard system and acceptance criteria can guide the R&D and manufacturing of safety-grade optical cables, and it can promote the standardization of fiber optic cable design, manufacturing and procurement.The identification activities of safety-grade optical cables will be standardized.
Overclocking in Immersion-Cooled Datacenters

Large cloud providers are starting to leverage liquid cooling for an increasing number of workloads. Liquid cooling enables providers to overclock server components, but they must tradeoff the potential increase in performance with higher power draw and reliability implications. We argue that two-phase immersion cooling is the most promising technology and, in that context, explore overclocking, its uses, and implications.
A Diabetes Prediction System Based on Federated Learning

In recent decades, with the rapid development of society, people’s quality of life has improved, but the incidence of diseases such as diabetes has become more and more serious. According to authoritative literature, the occurrence of diabetes may be related to a person’s age, living environment, living habits, genes, past medical history and other factors. We can get more accurate models through machine learning training on a large amount of data, but in order to protect patients' private information between different hospitals, it is impossible to conduct training directly through data transmission. In view of this situation, we propose a solution combining homomorphic encryption and federated learning, and design and implement a privacy-protected diabetes prediction system based on federated learning. The experimental results demonstrated that it broke the phenomenon of information isolation among hospitals and successfully collected patient information from different hospitals, which could not only improve the accuracy of the trained model, but also effectively protect the privacy of patients. Therefore, this work is very innovative and practical, and has strong practical and application significance under the current social background. It can provide solutions for diabetes treatment in the medical field in the future, and it is expected to provide new ideas for multi-party data combination in different fields in the future.
Towards Lightweight Provable Data Possession for Cloud Storage Using Indistinguishability Obfuscation

Cloud Computing has proved to be a boon for many individuals and organizations who cannot afford infrastructure and maintenance cost of resources. But the untrusted nature of Cloud Server (CS) brings many challenges related to security and trust. Public auditing is one process that enables users to delegate the integrity verification of outsourced data to external party such as Third-Party Auditor (TPA). Provable Data Possession (PDP) is one approach of auditing that can verify the integrity using cryptographic algorithms. Many PDP schemes are based on bilinear pairing and homomorphic authenticators that involves complex computations that leads to increased verification time. The lightweight auditing processes is a need today using modern cryptographic techniques. Indistinguishability Obfuscation (IO) is one of the modern but weaker primitive that, if used with one-way functions, provide multiple cryptographic constructs. Sahai and Waters proposed construction of cryptographic constructs using IO. Zhang <i>et al.</i> proposed lightweight public auditing scheme using these IO. But still there are many goals to achieve such as group support, collusion handling, privacy-preserving etc. using IO. In this paper, we are trying to explore these issues and lists future research directions in this field.
Training Hybrid Classical-Quantum Classifiers via Stochastic Variational Optimization

Quantum machine learning has emerged as a potential practical application of near-term quantum devices. In this work, we study a two-layer hybrid classical-quantum classifier in which a first layer of quantum stochastic neurons implementing generalized linear models (QGLMs) is followed by a second classical combining layer. The input to the first, hidden, layer is obtained via amplitude encoding in order to leverage the exponential size of the fan-in of the quantum neurons in the number of qubits per neuron. To facilitate implementation of the QGLMs, all weights and activations are binary. While the state of the art on training strategies for this class of models is limited to exhaustive search and single-neuron perceptron-like bit-flip strategies, this letter introduces a stochastic variational optimization approach that enables the joint training of quantum and classical layers via stochastic gradient descent. Experiments show the advantages of the approach for a variety of activation functions implemented by QGLM neurons.
Assessing the use of attention weights to interpret BERT-based stance classification

BERT models are currently state-of-the-art solutions for various tasks, including stance classification. However, these models are a black box for their users. Some proposals have leveraged the weights assigned by the internal attention mechanisms of these models for interpretability purposes. However, whether the attention weights help the interpretability of the model is still a matter of debate, with positions in favor and against. This work proposes an attention-based interpretability mechanism to identify the most influential words for stances predicted using BERT-based models. We target stances expressed in Twitter using the Portuguese language and assess the proposed mechanism using a case study regarding stances on COVID-19 vaccination in the Brazilian context. The interpretation mechanism traces tokens’ attentions back to words, assigning a newly proposed metric referred to as absolute word attention. Through this metric, we assess several aspects to determine if we can find important words for the classification and with meaning for the domain. We developed a broad experimental setting that involved three datasets with tweets in Brazilian Portuguese and three BERT models with support for this language. Our results are encouraging, as we were able to identify 52-82% of words with high absolute attention contributing positively to stance classification. The interpretability mechanism proved to be helpful to understand the influence of words in the classification, and they revealed intrinsic properties of the domain and representative arguments of the stances.
A Multiplayer Shooting Game Based Simulation For Defence Training

Creating environments and dangerous scenarios for physical training is very difficult and has a very high cost in terms of money and men’s power.Virtual Reality is a technology that simulates real-life experiences and allows people to don their own cyber avatars in a virtual world and interact with it like they would in the real world. The application of VR technology in the defence paradigm is to make trainees and officers better at using equipment, navigating a mode of transport, gaining experience of potential combat situations, medical training and more. One of the advantages of VR training in defence is that it offers the functionality to immerse users in a virtual yet safe world.Our immersive system provides an intuitive way for the users to interact with the VR or AR world by physically moving around the real world and aiming freely with tangible objects. This encourages physical interaction between the players as they compete or collaborate with other players. We present a new immersive multiplayer simulation game developed for defence training. We developed three game environments which are Combat situation, Bomb defusal, and Hostage rescue, and players can see their performance based on previously played games.
Game Theory-Based Proof of Stake Mining in Blockchain for Sustainable Energy Efficiency

Blockchain is termed as a ledger which is immutable and transactions will be occurred and stored in decentralized network. There are various types of application which are using blockchain technology such as financial services, supply chain, and Internet of things (IoT). However, there are still many shortcomings in blockchain technology such as scalability and fair play among the miners. This paper suggests a game theory-based effective reward distribution and punishing the bad miners who are exploiting the networks for their own profit maximization, hence to increase fair play in the blockchain which may turn in increase the participation of more loyal miners and also increase the mining rate of the blocks. The technique proof of stake-based mining approach using game theory is used for effective sustainable energy efficiency.
Ghost factors in Gauss-sum factorization with transmon qubits

A challenge in the Gauss-sum factorization scheme is the presence of ghost factors, nonfactors that behave similarly to actual factors of an integer, which might lead to the misidentification of nonfactors as factors or vice versa, especially in the presence of noise. We investigate type II ghost factors, which are the class of ghost factors that cannot be suppressed with techniques previously laid out in the literature. The presence of type II ghost factors and the coherence time of the qubit set an upper limit for the total experiment time, and hence the largest factorizable number with this scheme. Discernibility is a figure of merit introduced to characterize this behavior. We introduce preprocessing as a strategy to increase the discernibility of a system, and demonstrate the technique with a transmon qubit. This can bring the total experiment time of the system closer to its decoherence limit, and increase the largest factorizable number.
Neural Style Transfer With Adaptive Auto-Correlation Alignment Loss

The neural style transfer has achieved a significant improvement with deep learning methods. However, the existing methods are susceptible to lack the ability for handling the texture style transfer because of their less consideration of the textural structure from style images. To overcome this drawback, this letter presents a simple method to capture the textural structure by using an adaptive auto-correlation alignment loss function. Furthermore, we also introduce three metrics to quantitatively evaluate the performance. We qualitatively and quantitatively evaluate the proposed methods. The experimental results demonstrate the superiority of the proposed method and our method can synthesize the stylized images with rich texture style patterns.
Multilevel Crop Image Segmentation using Bacterial Foraging Optimization Based on Minimum Cross Entropy

Crop images have different color intensities of a pixel as well as complex backgrounds. Hence, multilevel thresholding of crop images is very significant in the field of computer vision. Entropy-based multilevel thresholding is considered a successful enhancement over the bi-level thresholding technique for image segmentation. It is a time-consuming approach for practical uses. In this paper, minimum cross entropy (MCE) has been combined with the bacterial foraging optimization (BFO) algorithm has to enhance the accuracy of the segmented image. The BFO algorithm is a newly constituted evolutionary algorithm, which offers better search capabilities. The accuracy of the proposed method is tested over 10 different crop images with complex backgrounds and compared with an efficient algorithm such as an artificial bee colony (ABC). The experimental result demonstrates that the proposed technique segments the cropped image more accurately and searches multiple thresholds value very efficiently, which are close to the optimal value. The outcome of the proposed techniques shows a high quality of segmented images.
Sentiment Analysis using Machine Learning

Twitter is one of the broadly used social networking structures with greater than 192 million each day lively customers and a 500 million Tweets. Basically, Twitter is reaching a large target audience, connects quite a few users and also gives a useful factor of view on an ongoing difficulty, based on their interest. This paper consists of sentiment evaluation on twitter statistics that is related to Airline critiques using Machine Learning, NLP. Basically customers and their rankings on the airlines they would choose for their journey are evaluated. Positive, negative and neutral opinions of the users are estimated based on their tweets in the twitter. Data visualization which may include cleaning, checking and transforming the pattern is also used. Natural Language Processing (NLP) and Machine learning are used on this data-driven model for prediction. Machine learning techniques are applied to the corresponding twitter dataset containing reviews of Airlines to find out the necessary information and inference. These algorithms are used to extract the sentiments of the user and to discover future trends based on current usage.
Perceptually Optimized Loss Function for Image Super-Resolution

Most of the learning based single image super-resolution networks employ intensity loss which measures pixel-wise difference between the estimated high resolution image and the ground truth. Since image components are different with respect to their saliency for HVS, it is desired to weight their impact on the loss functions accordingly. In this paper, a simple perceptual loss function is introduced based on the JPEG compression algorithm. In fact, the two compared images are transformed into DCT domain and then divided by the weighted quantization matrix. The difference between the resultant DCT coefficients shows the most effective components for HVS and can be considered as a perceptual loss function. The experimental results illustrate that employing the proposed loss promotes the convergence speed, and also, provides better outputs in terms of qualitative and quantitative measures.
Concept Drift and Covariate Shift Detection Ensemble with Lagged Labels

In model serving, having one fixed model during the entire often life-long inference process is usually detrimental to model performance, as data distribution evolves over time, resulting in lack of reliability of the model trained on historical data. It is important to detect changes and retrain the model in time. The existing methods generally have three weaknesses: 1) using only classification error rate as signal, 2) assuming ground truth labels are immediately available after features from samples are received and 3) unable to decide what data to use to retrain the model when change occurs. We address the first problem by utilizing six different signals to capture a wide range of characteristics of data, and we address the second problem by allowing lag of labels, where labels of corresponding features are received after a lag in time. For the third problem, our proposed method automatically decides what data to use to retrain based on the signals. Extensive experiments on structured and unstructured data for different type of data changes establish that our method consistently outperforms the state-of-the-art methods by a large margin.
Deep Reinforcement Learning Based Unmanned Aerial Vehicle (UAV) Control Using 3D Hand Gestures

The evident change in the design of the autopilot system produced massive help for the aviation industry and it required frequent upgrades.Reinforcement learning delivers appropriate outcomes when considering a continuous environment where the controlling Unmanned Aerial Vehicle (UAV) required maximum accuracy.In this paper, we designed a hybrid framework, which is based on Reinforcement Learning and Deep Learning where the traditional electronic flight controller is replaced by using 3D hand gestures.The algorithm is designed to take the input from 3D hand gestures and integrate with the Deep Deterministic Policy Gradient (DDPG) to receive the best reward and take actions according to 3D hand gestures input.The UAV consist of a Jetson Nano embedded testbed, Global Positioning System (GPS) sensor module, and Intel depth camera.The collision avoidance system based on the polar mask segmentation technique detects the obstacles and decides the best path according to the designed reward function.The analysis of the results has been observed providing best accuracy and computational time using novel design framework when compared with traditional Proportional Integral Derivatives (PID) flight controller.There are six reward functions estimated for 2500, 5000, 7500, and 10000 episodes of training, which have been normalized between 0 to -4000.The best observation has been captured on 2500 episodes where the rewards are calculated for maximum value.The achieved training accuracy of polar mask segmentation for collision avoidance is 86.36%.
Analyzing Large-Scale Blockchain Transaction Graphs for Fraudulent Activities

Abstract Early public blockchains provided low transaction throughputs in the range of 7–30 transactions per second. With the emergence of permissioned and proof-of-stake-based blockchains, transaction throughputs are expected to rise drastically to thousands per second. Blockchain transactions form directed graphs. With high transaction throughputs and growing blockchain adoption by banks, businesses and customers in general, the number of edges in transaction graphs will dynamically grow to billions. An analysis of large-scale transaction graphs is needed for tracing fraudulent activities on blockchains. This chapter will cover topics such as distributed graph data structures, the use of message passing libraries, and parallel graph algorithms in order to build a scalable transaction graph analysis system. Results from the analysis of the real Ethereum and Bitcoin public blockchain data involving cryptocurrency and ERC20 token transactions will be presented.
An Efficient Deep Learning-based Content-based Image Retrieval Framework

The use of massive image databases has increased drastically over the few years due to evolution of multimedia technology.Image retrieval has become one of the vital tools in image processing applications.Content-Based Image Retrieval (CBIR) has been widely used in varied applications.But, the results produced by the usage of a single image feature are not satisfactory.So, multiple image features are used very often for attaining better results.But, fast and effective searching for relevant images from a database becomes a challenging task.In the previous existing system, the CBIR has used the combined feature extraction technique using color auto-correlogram, Rotation-Invariant Uniform Local Binary Patterns (RULBP) and local energy.However, the existing system does not provide significant results in terms of recall and precision.Also, the computational complexity is higher for the existing CBIR systems.In order to handle the above mentioned issues, the Gray Level Co-occurrence Matrix (GLCM) with Deep Learning based Enhanced Convolution Neural Network (DLECNN) is proposed in this work.The proposed system framework includes noise reduction using histogram equalization, feature extraction using GLCM, similarity matching computation using Hierarchal and Fuzzy c-Means (HFCM) algorithm and the image retrieval using DLECNN algorithm.The histogram equalization has been used for computing the image enhancement.This enhanced image has a uniform histogram.Then, the GLCM method has been used to extract the features such as shape, texture, colour, annotations and keywords.The HFCM similarity measure is used for computing the query image vector's similarity index with every database images.For enhancing the performance of this image retrieval approach, the DLECNN algorithm is proposed to retrieve more accurate features of the image.The proposed GLCM+DLECNN algorithm provides better results associated with high accuracy, precision, recall, f-measure and lesser complexity.From the experimental results, it is clearly observed that the proposed system provides efficient image retrieval for the given query image.