Reviewerly/exHarmony huggingface dataset:
A comparison of refinement techniques for the backtracking algorithms for the subgraph isomorphism problem

A well-known NP-hard problem from graph theory is the subgraph isomorphism problem where the goal is for given pattern and target graphs to determine whether the pattern is a subgraph of the target graph. A plethora of algorithms for solving the problem exist in literature and most of them are based on the backtracking approach. In this paper, we explore several such algorithms. In particular, we focus on algorithmic techniques as well as code tuning and optimization approaches with the goal of refining basic backtracking algorithms. We also experimentally compare the performance of algorithms in order to evaluate and demonstrate their usefulness in practice.
Efficient and Accurate Classification Enabled by a Lightweight CNN

With the rapid development of cloud computing technology, many applications such as image recognition and fault diagnosis are applied in the power grid, and the data is collected and uploaded to the cloud for processing. However, the amount of data and the amount of calculation required by the model are too large, so that the cloud computing model cannot solve the current problem well. Edge computing refers to processing data at the edge of the network, which can reduce request response time, improve battery life, and reduce network bandwidth while ensuring data security and privacy. However, existing edge devices are difficult to meet complex models' demands. In order to solve the above problems, this paper proposes a lightweight CNN model that can be operated on the edge device. Experiments prove that the model is a reliable method for fault diagnosis at the edge.
A Study on the Path Planning Algorithm of the Unmanned Aerial Vehicle

The path planning of the unmanned aerial vehicle refers to the computation of the optimum or suboptimum executable flight path of the unmanned aerial vehicle from the starting position to the final position under the condition of meeting corresponding performance indexes and their dynamic constraints. Since the actual flight environment of the unmanned aerial vehicle, there are more constraint conditions that need to be met, and the key factors of solving the path planning of the unmanned aerial vehicle are how to conduct accurate modeling on environment and design the effective path planning algorithm. The path planning of the unmanned aerial vehicle is studied in this paper. A PSO-SA algorithm is proposed against the defect of the particle swarm optimization being easily trapped into local best, in which the particle state is updated through the simulated annealing probabilistic leap strategy. The simulation result indicates that the global search capability of the path can be enhanced, and the path planning time can be reduced through the algorithm, thereby optimizing the path.
A <scp>2‐Phase</scp> Merge Filter Approach to <scp>Computer‐Aided</scp> Detection of Breast Tumors on <scp>3‐Dimensional</scp> Ultrasound Imaging

Objectives The role of image analysis in 3‐dimensional (3D) automated breast ultrasound (ABUS) images is increasingly important because of its widespread use as a screening tool in whole‐breast examinations. However, reviewing a large number of images acquired from ABUS is time‐consuming and sometimes error prone. The aim of this study, therefore, was to develop an efficient computer‐aided detection (CADe) algorithm to assist the review process. Methods The proposed CADe algorithm consisted of 4 major steps. First, initial tumor candidates were formed by extracting and merging hypoechoic square cells on 2‐dimensional (2D) transverse images. Second, a feature‐based classifier was then constructed using 2D features to filter out nontumor candidates. Third, the remaining 2D candidates were merged longitudinally into 3D masses. Finally, a 3D feature‐based classifier was used to further filter out nontumor masses to obtain the final detected masses. The proposed method was validated with 176 passes of breast images acquired by an Acuson S2000 automated breast volume scanner (Siemens Medical Solutions USA, Inc., Malvern, PA), including 44 normal passes and 132 abnormal passes containing 162 proven lesions (79 benign and 83 malignant). Results The proposed CADe system could achieve overall sensitivity of 100% and 90% with 6.71 and 5.14 false‐positives (FPs) per pass, respectively. Our results also showed that the average number of FPs per normal pass (7.16) was more than the number of FPs per abnormal pass (6.56) at 100% sensitivity. Conclusions The proposed CADe system has a great potential for becoming a good companion tool with ABUS imaging by ensuring high sensitivity with a relatively small number of FPs.
Adoption of cloud and containerization technologies in Mongolia

Over the last few years, cloud-based virtualization and containerization are getting momentum day by day. Many countries have been using these novel technologies because of various benefits, such as low overhead, scalability, portability, stability, and high performance. The largest companies have already replaced the traditional architecture with cloud and containerization technologies for their daily operations. However, are developing countries able to keep abreast with this new technological development? How can they adopt these advanced technologies and utilize them for their own benefit? In order to get an answer to these questions, we took a survey from 33 organizations in Mongolia. These organizations are the country's largest representatives of the public and private sectors. The aim of this survey is to evaluate the current state of the system architecture of Mongolian organizations. Moreover, we investigate their readiness to transition to cloud and container technology. Based on the survey result, we identify challenges and make suggestions for improvements.
Distributed ledger technologies building trust in value chains?

The following topics are dealt with: production engineering computing; industrial robots; industrial manipulators; robot programming; computerised numerical control; control engineering computing; intelligent robots; human-robot interaction; factory automation; maximum power point trackers.
Quantum Router for Qutrit Networks

Networks of quantum circuits or, more generally, networks transmitting quantum information will need, just like classical networks (e.g. internet), a mechanism for directing data to adequate nodes. Routing, understood as packet switching, is one of the most important processes in classical networks. The issue of routing is also present in quantum networks and an appropriate construction of a quantum router is required to transfer data to specific points in the network. We describe an implementation of a router for qutrits in this chapter. The router is four-qutrit quantum circuit (with one controlling unit). The efficiency and the accuracy of router's work is tested by the Fidelity measure. The circuit's dynamics is expressed by a Hamiltonian where the role of generalized Pauli operators is played by the Gell-Mann operators.
Detecting the Most Insightful Parts of Documents Using a Regularized Attention-Based Model

Every individual text or document is generated for specific purpose(s). Sometime, the text is deployed to convey a specific message about an event or a product. Other occasions, it may be communicating a scientific breakthrough, development or new model and so on. Given any specific objective, the creators and the users of documents may like to know which part(s) of the documents are more influential in conveying their specific messages or achieving their objectives. Understanding which parts of a document has more impact on the viewer’s perception would allow the content creators to design more effective content. Detecting the more impactful parts of a content would help content users, such as advertisers, to concentrate their efforts more on those parts of the content and thus to avoid spending resources on the rest of the document. This work uses a regularized attention-based method to detect the most influential part(s) of any given document or text. The model uses an encoder-decoder architecture based on attention-based decoder with regularization applied to the corresponding weights.
Hashing Based Prediction for Large-Scale Kernel Machine

Kernel Machines, such as Kernel Ridge Regression, provide an effective way to construct non-linear, nonparametric models by projecting data into high-dimensional space and play an important role in machine learning. However, when dealing with large-scale problems, high computational cost in the prediction stage limits their use in real-world applications. In this paper, we propose hashing based prediction, a fast kernel prediction algorithm leveraging hash technique. The algorithm samples a small subset from the input dataset through the locality-sensitive hashing method and computes prediction value approximately using the subset. Hashing based prediction has the minimum time complexity compared to the state-of-art kernel machine prediction approaches. We further present a theoretical analysis of the proposed algorithm showing that it can keep comparable accuracy. Experiment results on most commonly used large-scale datasets, even with million-level data points, show that the proposed algorithm outperforms the state-of-art kernel prediction methods in time cost while maintaining satisfactory accuracy.
The Impact of Thinking-Aloud on Usability Inspection

This study compared the results of a usability inspection conducted under two separate conditions: An explicit concurrent think-aloud that required explanations and silent working. 12 student analysts inspected two travel websites thinking-aloud and working in silence to produce a set of problem predictions. Overall, the silent working condition produced more initial predictions, but the think-aloud condition yielded a greater proportion of accurate predictions as revealed by falsification testing. The analysts used a range of problem discovery methods with system searching being favoured by the silent working condition and the more active, goal playing discovery method in the think-aloud condition. Thinking-aloud was also associated with a broader spread of knowledge resources.
Analysis of the ratio expansion watermarking scheme and its improvement

Abstract Ratio expansion is a recently proposed, remarkable method for reversible data hiding in 3D meshes. The capacity and distortion are the two most common performance measures for data hiding. This paper analyses the ratio expansion method, and a novel mathematical framework is introduced and described, which proves that the magnitude of the distortion depends on three parameters. By appropriately selecting two of these parameters and a factor called “reference value”, the proposed method achieves reduction of the distortion. Another novel coding and decoding phrase is also proposed to get higher capacity. The experimental results show that this method improves the embedding capacity significantly without affecting the visual quality of 3D mesh model.
Simulations of Quantum Finite Automata

This paper presents a Python library to simulate different kinds of quantum finite automata on a classical computer. The library also provides tools for language generation and visual representation of simulation results. We have conducted experiments to measure the time complexity of the simulation in a function of the automaton size, alphabet size and word length. Examples of library usage are also provided.
Ensemble of online sequential extreme learning machine based on cross-validation

Abstract The online sequential extreme learning machine (OS-ELM) was proposed as an evolution of the Extreme learning machine (ELM) in 2006. The learning algorithm can learn data by fixing or changing the size of the chunk (a block of data). Compared with other online algorithms, it has faster training speed, and the generalization performance is better. The proposed ensemble of neural networks further demonstrates that the use of the neural network sets with multiple consensus schemes has a greater improvement over the stability of a single network. The basic idea of cross-validation is to group the original data sets, part of which is to train as the training set and the other part of which is to verify as the test set, so the algorithm can reduce the over-fitting problem of the data to a certain extent, and extract more valid information in the limited data set. In this paper, a new ensemble of online sequential extreme learning machine based on cross-validation (ENOS-ELM) is proposed. Through application of cross-validation and integration to the training stage, the accuracy of the algorithm in classification is further improved.
A proposal for service design based on user’s action history using machine learning

With the development of IoT techniques, it become easier to collect users' action data. By analyzing and using those data, consumers and producers will mutually exchange their intelligence and better customize product development processes. This study examines a proposed system using sensor shoes with several sensor devices embedded in the insoles, collecting action data of users, extracting their action features, and then issuing some advice to help users train more efficiently. As described herein, a service model uses a backpropagation (BP) network to distinguish users' actions and to extract their action features using using Self-organization Map from the presented sensing data. Finally, we review their performance via experimentation.
Micro Distortion Image Compression Based on Huffman Encoding

Image quality has been improved continuously with the help of the fast development of hardware technology. However the corresponding high volume image information raises a high demand on the transport method and compression. In communication model, source coding mainly guarantee the validity of the information. This requires a certain compression method to improve transmission efficiency. Existing theories of image encoding are basically divided into no distortion and limited distortion two categories, but the one with micro distortion has been hardly mentioned. In this paper, an image compression method with micro distortion is introduced, which creates very little distortion comparing to the traditional limited distortion compression. Besides that the new method can largely enhance the encoding efficiency comparing to no distortion compression. The method focuses on the problem of how to shift an individual pixel to make all characters' probability suit to Huffman encoding's feature and to reach the theoretical maximum encoding efficiency. This paper covers the pixel transformation method in detail. Simulations and comparisons are made to prove that this micro distortion image compression method has raised almost no distortion while improve the efficiency by a certain degree.
Retraining a Pruned Network: A Unified Theory of Time Complexity

Fine-tuning of neural network parameters is an essential step that is involved in model compression via pruning, which let the network relearn using the training data. The time needed to relearn a compressed neural network model is crucial in identifying a hardware-friendly architecture. This paper analyzes the fine-tuning or retraining step after pruning the network layer-wise and derives lower and upper bounds for the number of iterations; the network will take for retraining the pruned network till the required error of convergence. The bounds are defined with respect to the desired convergence error, optimizer parameters, amount of pruning and the number of iterations for the initial training the network. Experiments on LeNet-300-100 and LeNet-5 networks validate the bounds defined in the paper for pruning using the random connection pruning and clustered pruning approaches.
Single I mage Haze Removal Based on Concentration Scale Prior

Haze is a major degradation factor in outdoor images. Removing haze from a single image is an ill-posed problem and the performance of existing prior-based image dehazing methods is limited by the effectiveness of hand-designed features. In this paper, new dehazing method is introduced which is refined using gamma transformation and does not utilize the traditional atmospheric scattering model. The proposed method restores haze-free images without reference to corresponding clear image or estimating a depth-dependent transmission map. A novel, simple and powerful Concentration Scale Prior (CSP) is then utilized for haze removal in a single haze image to enhance gamma transformation, and its performance is verified. Experimental results show that the proposed approach achieves superior dehazing performance compared to current state-of-the-art methods.
Unpacking Editorial Agreements in Collaborative Video Production

Video production is a collaborative process involving creative, artistic and technical elements that require a multitude of specialised skill sets. This open-ended work is often marked by uncertainty and interpretive flexibility in terms of what the product is and should be. At the same time, most current video production tools are designed for single users. There is a growing interest, both in industry and academia, to design features that support key collaborative processes in editing, such as commenting on videos. We add to current research by unpacking specific forms of collaboration, in particular the social mechanisms and strategies employed to reduce interpretive flexibility and uncertainty in achieving agreements between editors and other collaborators. The findings contribute to the emerging design interest by identifying general design paths for how to support collaboration in video editing through scaffolding, iconic referencing, and suggestive editing.
Development of a Shape Aware Path Planning Algorithm for a Mobile Robot

A shape aware path planning algorithm is necessary for real time execution of a task by a mobile robot. Current work proposes a shape-aware A* path planning approach to facilitate accurate path finding in a given environment to accommodate the shape of mobile robot for differential wheeled mobile robot. The real-time map allows to assign a favorable cost value for each grid location of the map which later used to develop a shape aware global path planning strategy by using the well-known A* algorithm. For implementation and validation, an overhead camera is used to capture the task space and the obstacles which work in Robot operating software platform. The proposed method was tested in a real-time environment and proved the algorithm is capable of moving in a path that minimizes the distractions to static obstacles.
Research on Alteration Information Extraction Based on Sentinel-2A Satellite Data : Taking the Beiya Gold Mine as an example

Sentinel-2A satellite data has more advantages in spatial resolution and spectral resolution than Landsat series images. In order to explore more accurate prospecting methods, Sentinel-2A satellite data was used in this paper to conduct research experiments on extraction of mineralization alteration information in Beiya gold mine area of Heqing county, Yunnan province. Through the analysis of different alteration types in the spectrum of Sentinel-2A satellite data model, selecting the best scheme of principal component analysis to extract alteration information of the band combination, based on Sentinel-2A satellite data mask threshold denoising, principal component analysis, cutting, smooth filtering, density slicing, grading extraction system of mineralized alteration information extraction technology. On this basis, combined with the regional geological background data, the characteristics of mineralization alteration information distribution in the study area are analyzed. The verification of known ore sites shows that the alteration information extraction technology system proposed in this paper is reliable and has an effective auxiliary significance for metallogenic prediction in the region.
Building Knowledge Bases for Timestamp Changes Detection Mechanisms in MFT Windows OS

File timestamps do not receive much attention from information security specialists and computer forensic scientists. It is believed that timestamps are extremely easy to fake, and the system time of a computer can be changed. However, operating system for synchronizing processes and working with file objects needs accurate time readings. The authors estimate that several million timestamps can be stored on the logical partition of a hard disk with the NTFS. The MFT stores four timestamps for each file object in $STANDARD_INFORMATION and $FILE_NAME attributes. Furthermore, each directory in the $INDEX_ROOT or $INDEX_ALLOCATION attributes contains four more timestamps for each file within it. File timestamps are set and changed as a result of file operations. At the same time, some file operations differently affect changes in timestamps. This article presents the results of the tool-based observation over the creation and update of timestamps in the MFT resulting from the basic file operations. Analysis of the results is of interest with regard to computer forensic science.
DISSECT: Dynamic Skew-and-Split Tree for Memory Authentication

Memory integrity trees are widely-used to protect external memories in embedded systems against replay, splicing and spoofing attacks. However, existing methods often result in high-performance overhead that is proportional to the height of the tree. Reducing the height of the integrity tree by increasing its arity, however, leads to frequent overflowing of the counters that are used for encryption in the tree. We will show that increasing the tree arity of a widely-use integrity tree from 2 to 8 can result in over 200% increase in memory authentication overhead for some benchmark applications, despite the reduction in tree height. In this paper, we propose DISSECT, a memory authentication framework which utilizes a dynamic memory integrity tree that can adapt to the memory access patterns of the application by progressively adjusting the tree height and arity in order to significantly reduce performance overhead. This is achieved by 1) initializing an integrity tree structure with the largest arity possible to meet the security requirements, 2) dynamically skewing the tree such that the more frequently accessed memory locations are positioned closer to the tree root (overcomes the tree height problem), and 3) dynamically splitting the tree at nodes with counters that are about to overflow (overcomes the counter overflow problem). Experimental results undertaken using Multi2Sim on benchmarks from SPEC-CPU2006, SPLASH-2, and PARSEC demonstrate the performance benefits of our proposed memory integrity tree.
On The Auxiliary Role of Computer and Network in Japanese Translation

Abstract In recent years, with the rapid development of information technology, computer-aided translation system has gradually entered people’s vision. With its advantages of high efficiency and low cost, it has attracted much attention in many linguistic fields. With the development of the international era, people are no longer satisfied with artificial Japanese translation, they prefer to use computer-aided translation system. If we can’t know the culture of Japan, artificial translation can’t make people understand the real meaning of Japanese sentences. However, the computer with huge information storage can solve this problem. This paper includes systematic summary of the computer-aided system and the analysis of the affect of computer-aided in the process of Japanese translation.