Reviewerly/exHarmony huggingface dataset:
Scalable anomaly detection in blockchain using graphics processing unit

In blockchain, approved transactions, including illegal ones, cannot be modified unlike existing bank transactions. To prevent the damage caused by illegal transactions, rapid anomaly detection of transactions is required because transactions can be modified before approval. However, existing anomaly detection methods must process all transactions in blockchain, and the processing time is longer than the interval of each approval. In this paper, we propose a subgraph-based anomaly detection method to perform the detection using a part of the blockchain data. The proposed structure of the subgraph is suitable for graphics processing units (GPUs) to accelerate detection by using parallel processing. In an evaluation using real Bitcoin transaction data, when the number of targeted transactions was one hundred, the proposed method was 11.1x faster than an existing GPU-based method without lowering the detection accuracy.
A Block Chain based Management System for Detecting Counterfeit Product in Supply Chain

In recent years, Counterfeit goods play a vital role in product manufacturing industries. This Phenomenon affects the sales and profit of the companies. To ensure the identification of real products throughout the supply chain, a functional block chain technology used for preventing product counterfeiting. By using a block chain technology, consumers do not need to rely on the trusted third parties to know the source of the purchased product safely. Any application that uses block chain technology as a basic framework ensures that the data content is “tamper-resistant”. In view of the fact that a block chain is the decentralized, distributed and digital ledger that stores transactional records known as blocks of the public in several databases known as chain across many networks. Therefore, any involved block cannot be changed in advance, without changing all subsequent block. In this paper, counterfeit products are detected using barcode reader, where a barcode of the product linked to a Block Chain Based Management (BCBM) system. So the proposed system may be used to store product details and unique code of that product as blocks in database. It collects the unique code from the customer and compares the code against entries in block chain database. If the code matches, it will give notification to the customer, otherwise it gets information from the customer about where they bought the product to detect counterfeit product manufacturer.
How Far Are We from Robust Voice Conversion: A Survey

Voice conversion technologies have been greatly improved in recent years with the help of deep learning, but their capabilities of producing natural sounding utterances in different conditions remain unclear. In this paper, we gave a thorough study of the robustness of known VC models. We also modified these models, such as the replacement of speaker embeddings, to further improve their performances. We found that the sampling rate and audio duration greatly influence voice conversion. All the VC models suffer from unseen data, but AdaIN-VC is relatively more robust. Also, the speaker embedding jointly trained is more suitable for voice conversion than those trained on speaker identification.
An efficient Group Skip-Connecting Network for image super-resolution

This paper proposes an efficient Group Skip-Connecting Network (GSCN) for image super-resolution to increase the reconstruction performance and reduce the running time. Different from ResNet and DenseNet, which are two typical representative networks based on the skip connection, the proposed network designs a group skip connection layer to fuse the features between different separated groups. Hence GSCN can enjoy the merits of the lightweight from the group convolution and the high-efficiency from the skip connection. Considering the difficulty of restoring the high-frequency details, we further propose an Enhanced-GSCN (E-GSCN), which uses an enhanced attention module (EAM) to adaptively enhance the high-frequency details of the intermediate layer features. In addition, to better exploit the hierarchical information, we also design a hierarchical feature fusion framework to adaptively learn the hierarchical information at both the local and global levels. Experiments on the benchmark test sets show that the proposed models are more efficient than most of the state-of-the-art methods.
Conditional sliding windows: An approach for handling data limitation in colorectal histopathology image classification

Large amounts of data are required for the training process with a convolutional neural network (CNN) because small datasets with low variation will cause over-fitting, and the model cannot predict new data with high accuracy. Additionally, the non-availability of histopathological medical data presents an issue because without ethical permission, such data cannot be obtained easily. Therefore, this study proposes a conditional sliding window algorithm to obtain sub-sample data on images of histopathology. Two sets of original data were used, one from the Warwick dataset with dimensions of 775 × 522 pixels and the other from the Department of Pathology and Anatomy, Faculty of Medicine Universitas Indonesia. The algorithm used was inspired by the conventional sliding window method, but implemented with added conditions, such as sliding the window algorithm from the left on (x,y) pixel coordinates, thereby moving from left to right, then up to down until the entire image was covered. Consequently, the new image was produced with two dimensions: 200 × 200 and 300 × 300 pixels. However, to avoid loss of information, the 25 and 50 pixels overlap were used. In this study, CNN 7-5-7 was designed and proposed to perform the process. The conditional sliding window algorithm can produce various sub-samples depending on the image and window size. Furthermore, the images produced were used to develop a CNN and were proven to accurately predict benign and malignant tissues compared to the model from the original dataset. Moreover, the sensitivity values of the Warwick public dataset and the one generated in this study are above 0.80, which shows that the proposed CNN architecture is more stable compared to the existing methods such as AlexNet and DenseNet121. This study succeeded in solving the limitations of colorectal histopathological training data by developing a conditional sliding window algorithm. This algorithm can be applied to generate other histopathological data. Moreover, our proposed CNN 7-5-7 is the fastest architecture for training, comparable to state-of-the-art methodologies. Furthermore, the dataset was used to develop the model for colorectal cancer identification and integrated on the web-based application for further implementation.
3-D strain estimation in sandstone using improved digital volumetric speckle photography algorithm

Accurately and stably estimating the strain in rocks is important to understand their failure mechanism. Digital volumetric speckle photography (DVSP) combined with X-ray computed tomography (CT) has been used to investigate the strain in rocks. This study introduced the inverse compositional Gauss–Newton algorithm into DVSP to improve the accuracy and precision of displacement and strain measurements. Granules with different particle sizes were used to simulate the mesostructure of sandstone, and a series of baseline experiments were conducted to evaluate the accuracy of the improved algorithm, which had a performance that was approximately three times better than DVSP alone. Subsequently, the algorithm was applied to red sandstone subjected to the Brazilian test and an indentation experiment. The interior deformation characteristics of the specimen were clearly depicted, which clarified the failure mechanism of the rock material.
Hybrid prediction-based pixel-value-ordering method for reversible data hiding

Pixel-value-ordering (PVO) is an effective and promising method of reversible data hiding (RDH) and has received much attention in recent years. To improve performance, a pixel-based PVO (PPVO) method was recently introduced to predict the pixels to be embedded in a pixel-wise manner instead of the block-wise manner used by PVO. However, for PPVO, the surrounding neighbors of the predicted pixels are underutilized; moreover, its embedding does not adapt to the local complexity of the image to be embedded. To overcome the shortcomings of PPVO, this paper proposes a novel PVO method based on hybrid prediction for RDH. First, the surrounding neighbors of the pixel to be predicted are fully utilized by a hybrid prediction method, which combines rhombus prediction and pixel-wise prediction. Second, a modified embedding scheme based on multiple histograms is presented for adaptive embedding. Experimental results show the superior performance of the proposed method by comparing it with state-of-the-art RDH methods.
An Accelerated Second-Order Method for Distributed Stochastic Optimization

We consider centralized distributed algorithms for general stochastic convex optimization problems which we approximate by a finite-sum minimization problem with summands distributed among computational nodes. We exploit statistical similarity between the summands and the whole sum to construct a distributed accelerated cubic-regularized Newton’s method that achieves lower communication complexity bound for this setting and improves upon existing upper bound. Further, we use this algorithm to obtain convergence rate bounds for the original stochastic optimization problem and compare our bounds with the existing ones in several regimes when the goal is to minimize the number of communication rounds and improve the parallelization when increasing the number of workers.
Exploring Classic and Neural Lexical Translation Models for Information Retrieval: Interpretability, Effectiveness, and Efficiency Benefits

We study the utility of the lexical translation model (IBM Model 1) for English text retrieval, in particular, its neural variants that are trained end-to-end. We use the neural Model1 as an aggregator layer applied to context-free or contextualized query/document embeddings. This new approach to design a neural ranking system has benefits for effectiveness, efficiency, and interpretability. Specifically, we show that adding an interpretable neural Model 1 layer on top of BERT-based contextualized embeddings (1) does not decrease accuracy and/or efficiency; and (2) may overcome the limitation on the maximum sequence length of existing BERT models. The context-free neural Model 1 is less effective than a BERT-based ranking model, but it can run efficiently on a CPU (without expensive index-time precomputation or query-time operations on large tensors). Using Model 1 we produced best neural and non-neural runs on the MS MARCO document ranking leaderboard in late 2020.
Meteorology-Aware Path Planning for the UAV Based on the Improved Intelligent Water Drops Algorithm

A wide range of applications of the unmanned aerial vehicle (UAV) have been observed in the past few years, and path planning is one of the most critical issues that require to be resolved. UAVs are still prone to meteorological impediments such as thunderstorms, ice accumulation, and severe convective weather for the safety of flights. This paper proposes a meteorology-aware path planning method based on the improved intelligent water drops (IIWD) algorithm. The algorithm consists of both static and dynamic path planning. In the static path planning phase, ice accumulation and the Richardson number are considered when determining the trajectory with low risk. In the dynamic path planning phase, the latest forecast products are adopted to modify the planned path, and the virtual potential force is applied to adjust the flight direction of the UAV when encountered with severe convective weather. The validation and efficiency of the proposed algorithm are verified via simulations in comparison with the ant colony optimization, genetic algorithm, and Q-learning algorithm, where the quality of our algorithm's performance in terms of flight time and risk degree is determined. Meanwhile, the risk degree of the UAV flight path at different altitudes is analyzed. The simulation results show that the average flight speed decreases, and the risk degree increases along with the descending of the flight altitude, respectively, which is found to be in consensus with the theory of meteorology.
User behavior modeling for Web search evaluation

Search engines are widely used in our daily life. Batch evaluation of the performance of search systems to their users has always been an essential issue in the field of information retrieval. However, batch evaluation, which usually compares different search systems based on offline collections, cannot directly take the perception of users to the systems into consideration. Recently, substantial studies have focused on proposing effective evaluation metrics that model user behavior to bring human factors in the loop of Web search evaluation. In this survey, we comprehensively review the development of user behavior modeling for Web search evaluation and related works of different model-based evaluation metrics. From the overview of these metrics, we can see how the assumptions and modeling methods of user behavior have evolved with time. We also show the methods to compare the performances of model-based evaluation metrics in terms of modeling user behavior and measuring user satisfaction. Finally, we briefly discuss some potential future research directions in this field.
Automatic Generation of Descriptive Titles for Video Clips Using Deep Learning

Over the last decade, the use of Deep Learning in many applications produced results that are comparable to and in some cases surpassing human expert performance. The application domains include diagnosing diseases, finance, agriculture, search engines, robot vision, and many others. In this paper, we are proposing an architecture that utilizes image/video captioning methods and Natural Language Processing systems to generate a title and a concise abstract for a video. Such a system can potentially be utilized in many application domains, including, the cinema industry, video search engines, security surveillance, video databases/warehouses, data centers, and others. The proposed system functions and operates as followed: it reads a video; representative image frames are identified and selected; the image frames are captioned; NLP is applied to all generated captions together with text summarization; and finally, a title and an abstract are generated for the video. All functions are performed automatically. Preliminary results are provided in this paper using publicly available datasets. This paper is not concerned about the efficiency of the system at the execution time. We hope to be able to address execution efficiency issues in our subsequent publications.
Knowledge Management in Startups and SMEs: A Systematic Review

Even though startups have limited human and financial resources, they can utilize these resources for success and overcome the obstacles against knowledge management (KM) usage. This paper conducts a systematic literature review of KM in startups and small and medium enterprises (SMEs). This systematic review answers three research questions including the critical success factors and barriers affecting the usage of KM in startups/SMEs, the various KM tools/practices/systems adopted for usage by startups/SMEs and how the performance of startups/SMEs are affected by the use of KM. We aims to provide a thorough analysis of 28 research papers (out of 2123) published in reputable journals and conference proceedings throughout the past 10 years, from 2010 to 2020. The impact of KM usage on the performance of startups/SMEs demonstrated across all collected studies included looking at different domains such as market, organizational, economic, technological, financial and human performance. The review investigates the different KM techniques and technologies gathered from the reviewed literature and recommends the most suitable KM solutions. The paper also accentuates how the use of KM approaches plays a vital role in contributing to the overall growth of startups/SMEs, while also enhancing their efficiency, performance and scalability.
Blockchain-as-a-Utility for Next-Generation Healthcare Internet of Things

The scope of the Internet of Things (IoT) applications varies from strategic applications, such as smart grids, smart transportation, smart security, and smart healthcare, to industrial applications such as smart manufacturi... | Find, read and cite all the research you need on Tech Science Press
A video streaming vehicle detection algorithm based on YOLOv4

To achieve safe driving of vehicles, it is necessary to perceive information about the vehicle's surroundings, and computer vision is one of the key technologies to solve this problem. The YOLO series and SSD, RetinaNet algorithm are representative of one-stage target detection algorithms, which have high accuracy and high speed. YOLOv4 is the latest algorithm of YOLO series, which has improved the speed and accuracy of vehicle target detection than before, but there is still a distance from the real real-time in vehicle detection. This paper proposes an improved YOLOv4-based video stream vehicle target detection algorithm to solve the problem in the detection speed which is not fast enough. This paper first introduces the YOLOv4 algorithm theoretically, then proposes an algorithmic process to speed up the detection speed, and finally conducts practical road experiments. From the experimental results, the algorithm of this paper can improve the detection speed of the algorithm without losing accuracy, which can provide a basis for decision making for safe vehicle driving.
Regime switches and commonalities of the cryptocurrencies asset class

In this paper we test for regime changes and possible regime commonalities in the price dynamics of Bitcoin, Ethereum, Litecoin and Monero, as representatives of the cryptocurrencies asset class. Several parametric models are considered for the joint dynamics of the basket price where parameters are modulated through a Hidden Markov Chain with finite state space. Best specifications within Gaussian and Autoregressive models for price differences are selected by means of the AIC and BIC information criteria and through an out-of-sample forecasting performance. The empirical results, within the period January 2016 to October 2019, suggest that three or four states may be relevant to describe the dynamics of each individual cryptocurrency, depending on the selection criteria, while the entire basket displays at most three common states. Finally, we show how the identification of appropriate models may be exploited in order to build profitable investment strategies on the considered cryptocurrencies.
Enhancing relationships between criminology and cybersecurity

‘Cybercrime’ is an umbrella concept used by criminologists to refer to traditional crimes that are enhanced via the use of networked technologies (i.e. cyber-enabled crimes) and newer forms of crime that would not exist without networked technologies (i.e. cyber-dependent crimes). Cybersecurity is similarly a very broad concept and diverse field of practice. For computer scientists, the term ‘cybersecurity’ typically refers to policies, processes and practices undertaken to protect data, networks and systems from unauthorised access. Cybersecurity is used in subnational, national and transnational contexts to capture an increasingly diverse array of threats. Increasingly, cybercrimes are presented as threats to cybersecurity, which explains why national security institutions are gradually becoming involved in cybercrime control and prevention activities. This paper argues that the fields of cyber-criminology and cybersecurity, which are segregated at the moment, are in much need of greater engagement and cross-fertilisation. We draw on concepts of ‘high’ and ‘low’ policing ( Brodeur, 2010 ) to suggest it would be useful to consider ‘crime’ and ‘security’ on the same continuum. This continuum has cybercrime at one end and cybersecurity at the other, with crime being more the domain of ‘low’ policing while security, as conceptualised in the context of specific cybersecurity projects, falls under the responsibility of ‘high’ policing institutions. This unifying approach helps us to explore the fuzzy relationship between cyber- crime and cyber- security and to call for more fruitful alliances between cybercrime and cybersecurity researchers.
Query-based targeted action-space adversarial policies on deep reinforcement learning agents

Advances in computing resources have resulted in the increasing complexity of cyber-physical systems (CPS). As the complexity of CPS evolved, the focus has shifted to deep reinforcement learning-based (DRL) methods for control of these systems. This is in part due to: 1) difficulty of obtaining accurate models of complex CPS for traditional control 2) DRL algorithms' capability of learning control policies from data which can be adapted and scaled to real, complex CPS. To securely deploy DRL in production, it is essential to examine the weaknesses of DRL-based controllers (policies) towards malicious attacks from all angles. This work investigates targeted attacks in the action-space domain (actuation attacks), which perturbs the outputs of a controller. We show that a black-box attack model that generates perturbations with respect to an adversarial goal can be formulated as another reinforcement learning problem. Thus, an adversarial policy can be trained using conventional DRL methods. Experimental results showed that adversarial policies which only observe the nominal policy's output generate stronger attacks than adversarial policies that observe the nominal policy's input and output. Further analysis revealed that nominal policies whose outputs are frequently at the boundaries of the action space are naturally more robust towards adversarial policies. Lastly, we propose the use of adversarial training with transfer learning to induce robust behaviors into the nominal policy, which decreases the rate of successful targeted attacks by approximately 50%.
On the constructions of n-cycle permutations

Any permutation polynomial is an n-cycle permutation. When n is a specific small positive integer, one can obtain efficient permutations, such as involutions, triple-cycle permutations and quadruple-cycle permutations. These permutations have important applications in cryptography and coding theory. Inspired by the AGW Criterion, we propose criteria for n-cycle permutations, which mainly are of the form xrh(xs). We then propose unified constructing methods including recursive ways and a cyclotomic way for n-cycle permutations of such form. We demonstrate our approaches by constructing three classes of explicit triple-cycle permutations with high index and two classes of n-cycle permutations with low index, many of which are new both at levels of permutation property and cycle property.