Reviewerly/exHarmony huggingface dataset:
Towards Multiparty Computation Withstanding Coercion of All Parties

Incoercible multi-party computation [Canetti-Gennaro’96] allows parties to engage in secure computation with the additional guarantee that the public transcript of the computation cannot be used by a coercive external entity to verify representations made by the parties regarding their inputs to and outputs from the computation. That is, any deductions regarding the truthfulness of such representations made by the parties could be made even without access to the public transcript. To date, all incoercible secure computation protocols withstand coercion of only a fraction of the parties, or else assume that all parties use an execution environment that makes some crucial parts of their local states physically inaccessible even to themselves. We consider, for the first time, the setting where all parties are coerced, and the coercer expects to see the entire history of the computation. In this setting we construct: Finally, we show that protocols with certain communication pattern cannot be incoercible, even in a weaker setting where only some parties are coerced.
A Preliminary Study of the Current Situation and Analysis of Fencing Teaching in Harbin Colleges and Universities

Harbin; Colleges and universities; Physical education; Fencing teaching
Query Optimization Algorithm of Replication Join Based on Sampling Partition

Abstract Aiming at the low efficiency of join query in MapReduce traditional partition join algorithm when data skew, a replication join optimization algorithm based on sampling partition is proposed. According to the sampled statistics of connection attribute data, the algorithm divides the datasets in connection relationship into skewed data subset and non skewed data subset. In order to optimize the query performance, join query processing is carried out on them respectively. For the join queries of non skewed data subsets, the improved consistency hash function is used to partition these subsets, so that the load of data connection query processing of each node is balanced. For the skewed data subset join query, the smaller skewed data subsets are distributed to each node, and the larger skewed data subsets are partitioned according to the non skewed fields. In the Reduce stage, these skewed data subsets are join queried. Experiments show that the algorithm can optimize the join query performance under different data skew rates, and achieve efficient join query processing of large datasets.
Single Image Super-Resolution Neural Network Using Frequency-Domain Information

Singe image super-resolution (SISR) has been widely researched because of its application value. Despite the remarkable performance of the super-resolution methods based on deep convolutional neural networks, the lack of high-frequency information in the recovered images remains a core problem. In this work, we proposed a new loss function based on the spectral information of the image and discussed the effectiveness of this loss function in guiding the recovery of image details. Our experiment results on extensive benchmark datasets reveal the superiority of our proposed loss function, and the visual effects of the networks trained with the proposed loss function were significantly improved compared with those without this loss function.
EVALUASI SISTEM INFORMASI AKUNTANSI ATAS PENJUALAN KREDIT PT. XYZ (DEALER RESMI MITSUBISHI CABANG SEMARANG)

This study to evaluate the application of accounting information system of credit sales in an effort to improve internal control that has been carried out by PT. XYZ (Mitsubishi Official Dealer Semarang Branch) is a company engaged in the trading business. The method used in this research is descriptive qualitative method using data collection techniques such as observation, interviews, and documentation. The results showed that the accounting information system of credit sales implemented by PT. XYZ (Mitsubishi Official Dealer Semarang Branch) has presented the information needed by the company precisely and accurately although there are still weaknesses that need to be addressed, like the separation of cash receipts and recording functions, and the separation of sales admin and stock admin functions. But overall internal control at PT. XYZ (Mitsubishi Official Dealer Semarang Branch) is adequate because it meets the elements of internal control.
Improved Double-tree RRT* Algorithm for Efficient Path Planning of Mobile Robots

With a modified form of the rapidly- exploring random tree (RRT), RRT* algorithm is an important and effective tool for sampling-based path planning. However, the partial extension and low efficiency of the traditional RRT* make it very difficult to satisfy specific constraints or real-time requirements of mobile robotic scenarios. Based on the double tree structure expansion, a double-tree RRT* (D-RRT*) algorithm is proposed in this paper with the ability to improve space collision detection and search the feasible connection area using constrained nodes. The proposed method can effectively utilize the fast preprocessing ability of a double-tree structure and reduce the implementation of iterations. Meanwhile, node filtering and regression are designed to reduce the node numbers and prevent over space searching. Through the validation examples of mobile robots, it is shown that the proposed D-RRT* method can search for a global safe path with enhanced efficiency and convergence as compared with conventional methods.
Formation of information culture of the future teacher in the practice of studying professional disciplines at the university

The relevance of the problem of forming an information culture of the individual is due to the creation of a new information educational space, the use of global network technologies at all levels of study, which implies the formation of the ability of modern students to develop in a modern educational environment, to use information and computer technologies. It is important to include in the training of specialists in the university technologies that form the information culture of the individual, develop the awareness and readiness of students to use information technologies in the process of mastering the profession. The purpose of the work is to justify the relevance of the formation of an information culture in the professional training of a future primary school teacher. We presented components of the teacher's information culture, justified our position in the organization of the process of forming components of the future teacher's information culture. It illustrates the use of a professionally oriented method of organizing the activities of students, aimed at the use of information technologies when future teachers master mathematical and methodological content in order to form components of information culture.
Machine Learning-based Analysis of correlation between Energy Consumption data of the Company and its Sales

This paper has researched about the correlation between company data, and its annual sales data. Using the identified correlation, we propose a new method of predicting the company's annual sales data with energy consumption data. For this work, gradient boosting was applied to the predictive learning model for effective and better performance of prediction. To implement this method, district address-based energy consumption data is merged into company survey data with pre-processing. Then to predict the sales of each company, the gradient boosting based machine learning model has applied. Our approach to utilizing the energy consumption data can contribute to a new method of predicting the status of companies.
Path Planning of Composite Trackless AGV Considering Map Preprocessing

With mounted robot arm, the compound trackless automatic guided vehicle (AGV) has strong flexibility and adaptability in industrial environments. However, due to the space constraints of the robot arm, the path planning in complex large scenes is hard to achieve high efficiency, and it is easy for falling into local minimum and stagnation. In this paper, a novel AGV path planning algorithm is proposed on the basis of map preprocessing to improve the planning efficiency and guarantee operating safety. First, by integrating the multiple constraints including the obstacles and robotic position/posture, the preprocessing method of the environmental map is proposed utilizing obstacle expansion and Delaunay triangulation. Then, to achieve better convergence and global optimization capacity, the global path searching is modified by (1) constructing the OpenList with priority queues; (2) exploring the adaptive rules for step size and dynamic weighted heuristic function. Finally, combining linearization and cubic Hermite interpolation method, the planned path is smoothed to enhance the movement stability and energy consumption rate. Simulation analysis and experimental results verify the feasibility, efficiency and superiority of the proposed path planning method.
Enhanced Small Object Detection Neural Network

Faster-RCNN is an vital deep learning object detection algorithm. Nevertheless, the small object detection effect of Faster-RCNN, which does not use multi-layer feature map, is not good enough. In this paper, a new network architecture called enhanced small object detection neural network (ESOD) is proposed. Two advancements are proposed in ESOD. Firstly, a innovative multilayer combining network which integrating multi-layer feature map is designed to leverage multi-layer convolutional information. Thereupon, the ability of small object detection can be improved. Secondly, the scale-transfer layer (STL) is introduced in the fusion of features which are in high layer and features which are in low layer, which extends the size of the features by reducing the quantity of aisles. In this way, the semantic features of distinct size are acquired without additional parameters. Extensive experimental results on PASCAL VOC 2007 and 2012 datasets indicate that, compared with several commonly used algorithms, ESOD brings better small object detection ability than existing methods for object detection.
Persian OCR with Cascaded Convolutional Neural Networks Supported by Language Model

Persian <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> OCR is a difficult task because of some specific features of Persian writing style, like different styles of letters in different places of the word and similarity of letters to each other. Recognizing sub-words instead of individual letters can reduce these difficulties. In this manner sub-word segmentation is the critical task of pre-process step. In this paper, a cascaded Convolutional Neural Network is utilized to convert sub-word images into text. A large dictionary of Persian sub-word images with different font styles is used as training data and an Auto-Encoder enriches the features needed for constructing the cascade classifier structure. The initial classifier learns the overall structure of sub-word images that its training data is the result of applying k-means clustering on the huge sub-word image dataset. The later classifier finds the exact text equivalent of the sub-word image. A word segmentation method forms the words based on extracted sub-words. This method use contour distances as a measure for distinguishing words from sub-words. The initial OCR result is improved using Natural Language Processing techniques. Two fast search structures in word dictionaries with the help of a language model build the post-processing module and substitute the misspelled extracted words with the best alternative. Comparison results with Tesseract OCR engine shows the superiority of the algorithm.
Implementation of Lashkari et al Scheme and Convergent Encryption on Cloud-Based Secure Deduplication Application

Abstract The development and growth of data is rapidly increasing and it triggers development on storage technology namely cloud storage. In this research, implementation of Lashkari et al scheme and convergent encryption was conducted on cloud application to provide data confidentiality service and cloud storage space efficiency. Tests were conducted through comparing application that implemented this scheme with application that did not implement scheme within the same condition. Based on statistically significant comparison results, application that implements the scheme reduce 5% of storage space and takes less time for files uploading. Moreover, it can prevent attacker to obtain data illegally by implementing the convergent encryption.
A Zone Routing Algorithm for Service Network

The increasing number of available open services demands distributed architectures to promote scalability to enable their precise and efficient retrieval. As web services proliferate, it is more and more difficult to find suitable services and further compose them into complex applications automatically. In this paper, a novel service infrastructure named Service Network(ServNet), which is constructed on the basis of service routers and service switches, is proposed to deploy and manage large-scale services. Furthermore, the zone routing algorithm (ZRA) combines the advantages of the proactive and reactive approaches by maintaining an up-to-date topological map of a zone centered on each service node. Experimental evaluations confirmed the efficiency in terms of accuracy of query and network traffic.
Reasoning for Resolving Customer Complaints

We report on a novel approach to modeling a dynamic domain with limited knowledge. A domain may include participating agents where we are uncertain about the motivations and decision-making principles of some of these agents. Our reasoning setting for such domains includes deductive, inductive and abductive components. The deductive component is based on situation calculus and describes the behavior of agents with complete information. The machine learning-based inductive and abductive components involve the previous experience with the agents, whose actions are uncertain to the system. Suggested reasoning machinery is applied to the problem of processing customer complaints in the form of textual messages that contain a multiagent conflict. The task is to predict the future actions of an opponent agent to determine the required course of action to resolve a multiagent conflict. This chapter demonstrates that the hybrid reasoning approach outperforms both stand-alone deductive and inductive components. The suggested methodology reflects the general situation of reasoning in dynamic domains in the conditions of uncertainty, merging analytical (rule-based) and analogy-based reasoning.
Adaptive Subspace Sampling for Class Imbalance Processing-Some clarifications, algorithm, and further investigation including applications to Brain Computer Interface

Kohonen's Adaptive Subspace Self-Organizing Map (ASSOM) learns several subspaces of the data where each subspace represents some invariant characteristics of the data. To deal with the imbalance classification problem, earlier we have proposed a method for oversampling the minority class using Kohonen's ASSOM. This investigation extends that study, clarifies some issues related to our earlier work, provides the algorithm for generation of the oversamples, applies the method on several benchmark data sets, and makes an application to a Brain Computer Interface (BCI) problem. First we compare the performance of our method using some benchmark data sets with several state-of-the-art methods. Finally, we apply the ASSOM-based technique to analyze a BCI based application using electroencephalogram (EEG) datasets. Our results demonstrate the effectiveness of the ASSOM-based method in dealing with imbalance classification problem.
A Systematic Literature Review of Studies Related to Mental Activities of Software Developers

In this paper we have performed a systematic review of studies related to the mental states of people engaged in software development, particularly programmers. This review underscores consensus relating to some more thoroughly studied areas, such as the relationship between emotional state and productivity. It also highlights emerging trends such as the use of biosensors, and many areas of limited understanding that might be fruitful topics for the application of new emerging techniques.
ASANet: Adaptive Scale Aggregation Network for Semantic Segmentation

Scale variance of different objects is one of essential problem in per-pixel classification tasks, e.g. semantic segmentation. Existing works mainly focus on extracting multiscale information on the predefined and fixed grid, such as dilated convolutions and polling grids, which maybe a suboptimal solution for scale variance problems due to its fixed sampling strategy. To mitigate this issue, we propose an adaptive scale aggregation module (ASAM) to dynamically extract multiscale feature according to the input image during inference. Specifically, ASAM employs multiple branches to construct multi-scale representation in parallel ways and each branch utilizes a scale adaptive feature extraction module to collect scale-specific visual information for different pixels. Moreover, we incorporate our plug-and-play module ASAM into Adaptive Scale Aggregation Network (ASANet) and demonstrate the efficacy of our approach through extensive experiments, verifying that the proposed method can effectively extract pixel-specific multi-scale information and achieves impressive performance on semantic segmentation dataset.
An approach for Text Recognition from Document Images

In the recent scenario, it is important to extract text from scanned documents and text images. Analyzing of the text within the images utilizes the optical character recognition (OCR). The proposed system deals with the utilization of the Otsu segmentation algorithm and Hough transforming method of bias detection within the kind of input and separating the content from the photographs into a word format. In this system, the alphabets plus the numerals of English were known. In order to recognize the characters, OCR method has been applied. The screenshots of the scanned documents to the photographs from the searched document on the web resources performed validation checks. Experimental results demonstrate that the alphabets written in Verdana fonts of 14 size is recognized by the proposed system and even have got great outcomes together with pivoted images. The efficiency for correctly determining the rotatory angle was calculated at 90% and also the total accuracy of the device was calculated at 93 percentage.
COSINE:a software development model integrating collective intelligence, service and ecosystem

With the development of the internet technology, a large amount of softwares have emerged to meet users' increasing needs. At the mean time, software systems have been faced with a problem that they must adapt to the dynamic network environment. It is obvious that a variety of software development models have been proposed in the past few decades. However, the majority of these methods are gradually unadaptable to new circumstances. In this paper, we proposed a new software development model integrating collective intelligence, service and ecosystem. On the one hand, we have introduced the model in detail. On the other hand, We took a practical example to demonstrate the effectiveness of the proposed model.
CFGAT: A Coarse-to-Fine Graph Attention Network for Semi-supervised Node Classification

In this paper, we propose a novel semi-supervised graph node classification algorithm called Coarse-to-Fine Graph Attention Network (CFGAT), which can hierarchically enhance node representation ability in a coarse to fine manner. Specifically, CFGAT consists of two subnets: CoarseNet and FineNet. For the CoarseNet, we present a simple-yet-nontrivial node information coarsening strategy, which can generate coarse-grained features for all nodes on the graph by performing average on the structure-similar neighborhood information within densely-connected subgraphs. For the FineNet, the coarse-grained features obtained from the CoarseNet can be refined level by level using multiple reformulated graph attention layers. In addition, we also propose a Node-wise Receptive Field Selection Module which performs an adaptive receptive field selection for each node on the graph by assigning different attentions to different-scale node features extracted from multiple layers of the network. All proposed sub-algorithms can be integrated into an overall framework and trained in an end-to-end manner. Experimental results on three commonly-used datasets demonstrate the effectiveness and superiority of the proposed framework.
Bidirectional Consistency Constrained Template Update Learning for Siamese Trackers

This paper presents an online template update method with bidirectional consistency constraint for Siamese trackers. Due to continuously applying cross-correlation mechanism between template and the search region, the performance of Siamese trackers highly relies on the fidelity of template. Therefore, besides standard linear update, learning the template update methods attract attention. Inspired by this, in this paper we adopt a learning to update model called UpdateNet as our baseline. Different from it, we further propose a novel bi-directional consistency loss as a constraint to learn the template update more smoothly and stably. Our method considers both forward and backward information for each medium frame, thus introducing a multi-stage bidirectional simulated tracking training mechanism. We apply our model to a Siamese tracker, SiamRPN and demonstrate the effectiveness and robustness of our proposed method compared with traditional UpdateNet in the Large-scale Single Object Tracking (LaSOT) dataset.
Diagnosis of Feline Skin Disease Using C4.5 Algorithm

Cats are one type of animal that is very popular with many people, and there is even a community of cat fans known as cat lovers. The health indicator in cats lies in the condition of their skin, so it needs special care to maintain their skin condition. Many cat owners are not aware of the skin diseases suffered by their cats. This is due to the owner's limited knowledge of the diseases experienced by cats and the difficulty in identifying the similar symptoms experienced by cats. To overcome this problem, we need a method to diagnose skin diseases that occur in cats. Diagnosis of symptoms of cat skin disease can be done by a classification method in data mining. In this study, the classification method used to diagnose skin diseases in cats is the C4.5 algorithm. The dataset used was obtained from the animal clinic “Purple Shop” in Malang. The algorithm testing process is carried out using k-fold cross-validation. Algorithm performance evaluation is measured by using a confusion matrix, namely by measuring the value of accuracy, precision, and recall. The results of this study indicate that the resulting accuracy value is 95.42%, the average precision is 96.93%, and the average recall is 97.19%. These results indicate that the C4.5 algorithm shows a very high level of performance and can be applied to diagnose symptoms of skin disease in cats.