Reviewerly/exHarmony huggingface dataset:
A tree structure-based improved blockchain framework for a secure online bidding system

The BlockChain (BC) is considered as one of the most exciting developments in information and communication technology in the past decade and is widely known as a crypto intensive phenomenon. Achieving an efficient solution (e.g., decentralization, immutability, security, and transparency) using BC requires a considerable range of factors, such as: scalability, security, and privacy, which have a profound role in determining an overall solution's performance. Meanwhile, digital auctioning is one of the cornerstones of the modern Internet economy. Many of the existing auctions are open-bid auction systems where all bids are made open to either party. E-auctions rely on human resources, and often require the services of a third-party intermediary, which leads to a high cost in terms of money and time, and there is no guarantee that the third party is trustworthy. Moreover, BC's efficiency is a big challenge for large databases, particularly when the bidding system encompasses multiple options with geographically dispersed users. To address these issues, this paper proposes a BC-based framework for an open-bid auction system, for which privacy and security constraints are considered with different cryptographic primitives. The novelty of this framework derives from an enhanced approach for integrating BC structures by replacing the original chain structure with a tree structure. Throughout the online world, user privacy is a primary concern, because the electronic environment enables the collection of personal data. Hence this paper proposes a suitable cryptographic protocol for an open-bid auction atop BC. Here, the primary aim is to achieve security and privacy with greater efficiency, which largely depends on the effectiveness of the encryption algorithms used by BC. Essentially this paper considers Elliptic Curve Cryptography (ECC) and a dynamic cryptographic accumulator encryption algorithm to enhance security between auctioneer and bidder. The proposed e-bidding scheme and the findings from this study should foster further growth of BC strategies.
Consumer Choicemaking and Choicelessness in Hyperdigital Marketspaces

Technologies, especially Internet-based digital ones, are reshaping choice processes – actual considerations and actions, as well as perceptions of these – in massive, often fundamental, ways. In this paper, our goal is to explore choice processes in general, and especially choice processes in hyperdigital marketspaces (i.e., with massively, pervasively interconnected things) with examples drawn from U.S. macro consumption contexts. We start with a short review of discourses on choice and choicelessness and then shift to the emerging era of technology-shaped choice processes that are especially observable in contemporary hyperdigital marketspaces. For the increasingly large swaths of market segments that consume, indeed live, digitally, we find deft symbolic sublimations and inversions happening, wherein manipulation is perceived as autonomy enhancing.
Deep learning method for evaluating photovoltaic potential of urban land-use: A case study of Wuhan, China

To strengthen the synergy between urban photovoltaic development and urban planning, which can help to promote photovoltaic and renewable energy development in cities, a workflow based on a deep-learning method by using neural networks and urban satellite images is constructed, which is applied to study the relationship between urban rooftop photovoltaic potential and urban land use. A Chinese city, Wuhan, has been considered the subject and divided into 5184 units, which is a 1201.2 km2 area in central urban space. The result shows that, three types of urban land use type have the highest rooftop photovoltaic potential, which are Continuous Urban area, Discontinuous Dense Urban area and Industrial, commercial, public and education unit. The annual photovoltaic potential of these three land use types have reached 1818.41 GWh/year, 1957.32 GWh/year and 2022.71 GWh/year, and the average photovoltaic power generation per unit area of these three types have reached 11.23 GWh/km2·year, 9.99 GWh/km2·year and 13.07 GWh/km2·year. In addition, these three land use types contributed 71.4% of the city’s total rooftop photovoltaic potential. When considering the coordination of roof photovoltaic development and urban planning, these three types of land use should be given priority. The method and findings can provide urban planning authorities with tools and data to reference when developing urban master plan and PV development plans.
Machine Learning with Blockchain for Secure E-voting System

Voting is a central component of a country's political life cycle. Privacy, authentication and integrity of citizens' votes and their data are considered to be essential to any e-voting program. In order to resolve these concerns, we propose a stable e-voting system based on the principles of blockchain and machine learning. We use blockchain to ensure the integrity and security of votes, machine learning model to detect intrusion in voting data centers and e-voting stations. In the proposed model, we use the concepts of personal and public blockchain. The personal blockchain is used for the purposes of voter registration and voting. The public blockchain is used to maintain the integrity of the personal data of the voters by storing the root hash derived from the Merkle hash tree and revealing the results of the voting stations as soon as the voting process is completed. The proposed blockchain-based e-voting system offers transparency, treasury, confidence and prevents intrusion into the information exchange network.
Damped sliding based utility oriented pattern mining over stream data

High utility pattern mining (HUPM) discovers meaningful patterns by considering features of items and utility from non-binary data. Data called stream data is continually generated over time. Various techniques based on high utility pattern mining have been suggested for processing stream data. High utility pattern mining based on a sliding window performs pattern mining using a window. Since it uses only the data stored in a window, only the latest data can be managed. However, Stream data has the property that newly created data has a higher influence than relatively old data. It is necessary to consider the importance of the data stored in a window differently. In this paper, we propose an efficient algorithm based on a sliding window approach that mines high utility patterns considering the latest data more significantly from damped stream data where new data is constantly being inserted. In other words, our technique divides the stream data into fixed-sized multiple batch data and processes differently the importance of each batch data in a window according to the added time using the decaying factor. Moreover, we conduct experiments to compare and analyze our approach with the state-of-the-art algorithms using real and synthetic datasets. The experimental results show that our proposed method outperforms the competitors in terms of run time, memory usage, and scalability test.
Virtual Reality Racket Sports: Virtual Drills for Exercise and Training

We have developed a modular virtual reality gaming application that can be used to synthesize exercise drills for racket sports. By defining cost terms that are related to the gameplay and the mechanics of the game, as well as by allowing a user to control the parameters of the cost terms, users can easily adjust the objectives and the intensity levels of the exercise drills. Based on the user-defined exercise objectives, a Markov chain Monte Carlo optimization method called “simulated annealing” was used to optimize the exercise drill. The effectiveness of the developed virtual reality gaming application was measured in two studies by using virtual reality table tennis as the evaluation tool. The first study investigated the potential usefulness of the developed virtual reality gaming application as an exercise tool by comparing its workout effectiveness at three intensity levels (low, medium, and high) through the collection of heart rate readings. The second study explored the potential utility of the virtual reality gaming application as a training tool by exploring whether there was any improvement in participants' performance across the three conditions (no training, virtual reality training, and real-world training). The results indicate that a virtual reality gaming application, such as the examined virtual reality table tennis exergame, could indeed be used effectively as both an exercise and a training tool. Limitations and future research directions are discussed further below.
Forecasting Photovoltaic Power Generation Using Satellite Images

As the relative importance of renewable energy in electric power systems increases, the prediction of photovoltaic (PV) power generation has become a crucial technology, for improving stability in the operation of next-generation power systems, such as microgrid and virtual power plants (VPP). In order to improve the accuracy of PV power generation forecasting, a fair amount of research has been applied to weather forecast data (to a learning process). Despite these efforts, the problems of forecasting PV power generation remains challenging since existing methods show limited accuracy due to inappropriate cloud amount forecast data, which are strongly correlated with PV power generation. To address this problem, we propose a PV power forecasting model, including a cloud amount forecasting network trained with satellite images. In addition, our proposed model adopts convolutional self-attention to effectively capture historical features, and thus acquire helpful information from weather forecasts. To show the efficacy of the proposed cloud amount forecast network, we conduct extensive experiments on PV power generation forecasting with and without the cloud amount forecast network. The experimental results show that the Mean Absolute Percentage Error (MAPE) of our proposed prediction model, combined with the cloud amount forecast network, are reduced by 22.5% compared to the model without the cloud amount forecast network.
Prediction of aircraft trajectory and the associated fuel consumption using covariance bidirectional extreme learning machines

Accurate prediction of the aircraft trajectory and associated fuel consumption has become an important research topic owing to the increasing importance of air traffic management. Currently, trajectory prediction and fuel estimation are usually accomplished via complex mathematical energy-balance methods. In these methods, the prediction error could get increased due to the possible usage of global values and outdated database, resulting from that most of the information regarding aircraft operations is unavailable. In this paper, we propose a covariance bidirectional extreme learning machine (CovB-ELM) for predicting aircraft trajectories and estimating fuel consumption. The selection of randomly generated parameters for the hidden unit, such as the input weight and bias, to improve the accuracy and numerical stability of the extreme learning machine (ELM), is an open problem. The fundamental idea behind the proposed method is to maximise the covariance between the hidden unit and network errors through partially updating the hidden-unit parameters randomly generated in bidirectional ELM so that the output weight norm value is minimised and the convergence gets improved. The merits of the proposed CovB-ELM are demonstrated by the experiments involving regression problems and international airline historically flight data, which suggests that the CovB-ELM outperforms, in terms of generalisation performance, several existing methods, e.g., airline mathematical approach, backpropagation neural network, and constructive ELM methods.
End-to-end attack on text-based CAPTCHAs based on cycle-consistent generative adversarial network

As a widely deployed security scheme, text-based completely automated public Turing tests to tell computers and humans apart (CAPTCHAs) have become increasingly unable to resist machine learning-based attacks. So far, many researchers have conducted studies on approaches for attacking text-based CAPTCHAs deployed by different companies, such as Microsoft, Amazon, and Apple, and achieved specific results. However, most of these attacks have shortcomings, such as the poor portability of attack methods, which require a series of data preprocessing steps and rely on large amounts of labeled CAPTCHAs. In this study, we propose an efficient and simple end-to-end attack method based on cycle-consistent generative adversarial networks (Cycle-GANs). Compared to previous studies, our approach significantly reduces the cost of data labeling. Additionally, this method has high portability. It can attack ordinary text-based CAPTCHA schemes only by modifying a few configuration parameters, which makes the attack easier to execute. First, we train CAPTCHA synthesizers based on the Cycle-GAN to generate some fake samples. Basic recognizers based on a convolutional recurrent neural network are trained using the fake data. Subsequently, an active transfer learning method is employed to optimize the basic recognizer utilizing tiny amounts of labeled real-world CAPTCHA samples. Our approach efficiently cracked the CAPTCHA schemes deployed by 10 popular websites, indicating that our attack method may be universal. Additionally, we analyzed the current most popular anti-recognition mechanisms. The results show that the combination of more anti-recognition mechanisms can improve the security of CAPTCHAs. However, the improvement is limited. Conversely, generating more complex CAPTCHAs may cost more resources and reduce the usability of CAPTCHAs.
Modified DFS-based term weighting scheme for text classification

With the rapid growth of textual data on the Internet, text classification (TC) has attracted increasing attention. As a widely used text representation method, the vector space model (VSM) represents the content of a document as a vector composed of term frequency (TF) in the term space. Because different terms have different levels of importance in a document, designing an appropriate term weighting scheme is crucial to improve the performance of TC. In this study, we first conducted a comprehensive survey of the existing well-known term weighting schemes and found that they are not fully effective and that researchers are still focused on proposing new term weighting schemes. To further improve the performance of TC, we propose a new term weighting scheme based on the modified distinguishing feature selector (DFS), which we call TF–MDFS (modified DFS-based TF). Experimental results show that TF–MDFS is overall better than existing state-of-the-art term weighting schemes in terms of the classification accuracy of widely used base classifiers.
Trusted Detector Noise Analysis for Discrete Modulation Schemes of Continuous-Variable Quantum Key Distribution

Discrete-modulated continuous-variable quantum key distribution protocols are promising candidates for large-scale deployment due to the large technological overlap with deployed modern optical communication devices. The security of discrete modulation schemes has previously analyzed in the ideal detector scenario in the asymptotic limit. In this work, we calculate asymptotic key rates against collective attacks in the trusted detector noise scenario. Our results show that we can thus cut out most of the effect of detector noise and obtain asymptotic key rates similar to those had we access to ideal detectors.
SmallMitosis: Small Size Mitotic Cells Detection in Breast Histopathology Images

Mitotic figure count acts as a proliferative marker to measure aggressiveness of the breast cancer tumor.In this article, we have proposed a novel framework named SmallMitosis to detect mitotic cells particularly very small size mitosis from hematoxylin and eosin (H&E) stained breast histology images.SmallMitosis framework consists of an atrous fully convolution based segmentation (A-FCN) model and a deep multiscale (MS-RCNN) detector.In intended A-FCN model, the concept of atrous convolution helps to estimate mitosis mask and bounding box annotations of very small size mitotic cells.Meanwhile, the architecture of MS-RCNN internally lifts poor representations of small mitosis to ''superresolved'' ones, that are similar to real large mitosis thus more discriminative for detection of small size blurred mitotic cells, as well as a fully convolution layer at detection stage, decreases computational cost.The A-FCN model trained on fully labeled mitosis datasets (all pixels of mitosis are labeled) is applied on weakly labeled datasets (only centroid pixel is labeled) to obtain mitosis mask and bounding box annotations.Using these estimated bounding box annotations, MS-RCNN detector is trained to detect small size mitosis from weakly labeled datasets.The performance of the proposed scheme is tested on three publicly available mitosis datasets, namely ICPR 2012, ICPR 2014, and AMIDA13.On challenging ICPR 2012 dataset, we obtained F score of 0.902, outperforming all prior detection systems significantly.On ICPR 2014 and AMIDA13 datasets, we achieved a 0.495 and 0.644 F score respectively.The results demonstrated that our method impressively outperforms state-of-the-art approaches.SmallMitosis is available at https://github.com/tasleem-hello/SmallMitosis.
RETRACTED: An effective face recognition system based on Cloud based IoT with a deep learning model

As of late, the Internet of Things (IoT) innovation has been utilized in applications, for example, transportation, medical care, video observation, and so on. The quick appropriation and development of IoT in these segments are producing an enormous measure of information. For instance, IoT gadgets, for example, cameras produce various pictures when utilized in medical clinic reconnaissance sees. Here, face acknowledgement is one of the most significant instruments that can be utilized for clinic affirmations, enthusiastic discovery, and identification of patients, location of fake gadgets. patient, and test clinic models. Programmed and shrewd face acknowledgement frameworks are profoundly precise in an overseen climate; notwithstanding, they are less exact in an unmanaged climate. Additionally, frameworks must keep on running on numerous occasions in different applications, for example, insightful wellbeing. This work presents a tree-based profound framework for programmed face acknowledgement in a cloud climate. The inside and out pattern have been proposed to cost less for the PC without focusing on unwavering quality. In the model, the additional size is isolated into a few sections, and a stick is made for each part. The tree is characterized by its branch area and stature. The branches are spoken to by a leftover capacity, which comprises of a twofold layer, a stack game plan, and a non-direct capacity. The proposed technique is assessed in an assortment of generally accessible information bases. An examination of the method is likewise finished with top to bottom craftsmanship models for the eye to eye connection. The aftereffects of the tests indicated that the example was considered to have accomplished a precision of 98.65%, 99.19%, and 95.84%.
Semantic Structured Image Coding Framework for Multiple Intelligent Applications

Fast-growing intelligent media processing applications demand efficient processing throughout the processing chain from the edge to the cloud, and the complexity bottleneck usually lies in the parallel decoding of multiple-channel compressed bitstreams before analyzing. This occurs because the traditional media coding scheme generates a binary stream without a semantic structure, which is unable to be operated directly at the bitstream level to support different tasks such as classification, recognition, detection, etc. Therefore, in this article, we propose a learning-based semantically structured image coding (SSIC) framework to generate a semantically structured bitstream (SSB), where each part of the bitstream represents a specific object and can be directly used for the aforementioned intelligent tasks. Specifically, we integrate an object location extraction module into the compression framework to locate and align objects in the feature domain. Then, each object together with the background is compressed separately and reorganized to form a structured bitstream to enable the analysis or reconstruction of specific objects directly from partial bitstream. Furthermore, in contrast to existing learning-based compression schemes that train the specific model for a specific bitrate, we share most of the model parameters among various bitrates to significantly reduce the model size for variable-rate compression. The experimental results demonstrate the effectiveness of the proposed coding scheme whose compression performance is comparable to existing image coding schemes, where intelligent tasks such as classification and pose estimation can be directly performed on a partial bitstream without performance degradation, significantly reducing the complexity for analyzing tasks.
A Tensor-Based Approach Using Multilinear SVD for Hand Gesture Recognition From sEMG Signals

With the increasing number of sensors/ channels, hand gesture recognition from multiple channels has attracted the attention of the researchers in recent years. The analysis of a variety of gestures is still a challenging task in real-time applications such as in wearable devices because of the large number of channels used. This paper proposes a tensor-based approach using multilinear singular value decomposition (MLSVD) for hand gesture recognition where all available channels were used during training whereas only a single channel was used for recognition of new gestures. Tensor decompositions have very limited use in gesture recognition using surface Electromyography (sEMG) despite these signals naturally have multi-way structures. The sEMG data of different subjects were first segmented to reshape it as a fourth-order tensor of the form channels × time × gestures × subjects. The MLSVD was applied to model the tensor and extract features, which were then fed into various classifiers such as support vector machine (SVM), K-nearest neighbors (KNN), TreeBagger (TB), and dictionary learning (DL) classifiers in order to compare their performance. The proposed method was evaluated on three publicly available databases (NinaPro, CapgMyo (DB-a, DB-b and DB-c), CSL-HDEG) by performing intra-session, inter-session and inter-subject evaluations on each database. The experimental results indicated that the proposed method achieved the best accuracy (Acc) using DL, achieving Acc of 77.6% and 89.6% with CapgMyo DB-b and CSL-HDEMG databases, respectively, during inter-session evaluation, and 75.2%, 75.4%, 68.3%, and 67.7% with NinaPro, CapgMyo DB-b, CapgMyo DB-c, and CSL-HDEMG databases, respectively, during inter-subject evaluation. The proposed method performed better during both inter-session and inter-subject evaluations than the state-of-the-art methods.