Reviewerly/exHarmony huggingface dataset:
Pattern recognition method for mobile intelligent programming

Aimed at the problem that there is no efficient program learning software in the education market, leading to many programming problems cannot be solved quickly and accurately, the paper researched and proposed the recognition and analysis algorithms based on OCR (Optical Character Recognition) technology and programming knowledge base. Based on the proposed method, we developed an intelligent mobile software for programming learning. The main function of the software includes code recognition through photographing and programming exercises. After the software unit test and system integration test, it was found that the method can be excellent for code recognition and extraction. And it proved that code assistant software based on recognition analysis algorithm can solve the programming problem quickly and accurately.
Epistemic and Doxastic Contents

Abstract The case for making belief states the primary focus of our analysis and for including impossible worlds in that analysis is outlined in this chapter. This allows the reader to deny various closure principles, although this won’t help defeat worries about external-world scepticism. The issue that concerns the authors most is the problem of bounded rationality: belief states seem to be closed under ‘easy’ trivial consequence, but not under full logical consequence, and yet the former implies the latter. The solution presented here is that some trivial closure principle must fail on a given belief state, yet it is indeterminate just where this occurs. Formal models of belief states along these lines are given and it is shown that they respect the indeterminacy-of-closure intuition. Finally, the chapter discusses how we might square this approach with the fact that some people seem to believe contradictions.
Pre-grammars and Inhabitation for a Subset of Rank 2 Intersection Types

In this paper, we identify a subset of types in the rank 2 intersection type system, where types do not contain positive occurrences of intersections. We extend the notion of pre-grammar of a type and address the type-inhabitation problem for types in this subset, as well as their intersections.
Research on improved image registration algorithm based on PROSAC algorithm

In order to solve the problems of poor stability and multiple mismatching points in image registration, most scholars have used Random Sample Consensus (RANSAC) algorithm to optimize the matching algorithm. However, because of the randomness of the RANSAC algorithm itself, the matching algorithm has poor stability, low registration efficiency and poor robustness. To solve this problem, an improved SIFT (Scale-invariant feature transform) image registration optimization algorithm based on PROSAC (Progressive Sampling Consensus) was proposed. The experimental results showed that the proposed image registration optimization algorithm could effectively solve the problems of error matching and low efficiency in the process of image matching. Using the same image to test, the average correct registration rate of the traditional RANSAC improved SIFT algorithm was 82%, and the average running time was 36 seconds. The average correct registration rate of the SIFT image registration algorithm based on PROSAC improved SIFT image registration algorithm was 86.67%, the average running time was 26.51 seconds, and the running efficiency was increased by 36%. Therefore, the improved SIFT image registration algorithm based on PROSAC has higher robustness, can meet the needs of fast image mosaic, and has broad application prospects.In order to solve the problems of poor stability and multiple mismatching points in image registration, most scholars have used Random Sample Consensus (RANSAC) algorithm to optimize the matching algorithm. However, because of the randomness of the RANSAC algorithm itself, the matching algorithm has poor stability, low registration efficiency and poor robustness. To solve this problem, an improved SIFT (Scale-invariant feature transform) image registration optimization algorithm based on PROSAC (Progressive Sampling Consensus) was proposed. The experimental results showed that the proposed image registration optimization algorithm could effectively solve the problems of error matching and low efficiency in the process of image matching. Using the same image to test, the average correct registration rate of the traditional RANSAC improved SIFT algorithm was 82%, and the average running time was 36 seconds. The average correct registration rate of the SIFT image registration algorithm based on PROSAC improved SIFT image registration algorithm was 86.67%, the average running time was 26.51 seconds, and the running efficiency was increased by 36%. Therefore, the improved SIFT image registration algorithm based on PROSAC has higher robustness, can meet the needs of fast image mosaic, and has broad application prospects.
Short-Term Memory Variational Autoencoder for Collaborative Filtering

Recommender systems have been widely used by online service providers to conduct targeted marketing. However, since user behaviour and preferences are fluid and dynamic, predicting users’ online actions is indeed a very difficult task. Variational Autoencoders (VAEs) have been recently proposed to improve the prediction accuracy of user preferences. Although classic VAEs go beyond linear modeling, they suffer from underfitting problem when the underlying datasets are sparse. Moreover, we found that classic VAEs are ineffective for dynamic user preferences. To address these deficiencies, we propose Short Term Memory Variational Autoencoder (STMVAE) to overcome the underfitting issue and to better handle the dynamics. This is achieved by capturing users’ short term preferences to generate near term predictions. The validity and efficacy of our proposed model are evaluated comprehensively using three datasets in our experiments.
An Approach with Low Redundancy to Network Feature Selection Based on Multiple Order Proximity

Most models for unsupervised network feature selection use first-order proximity and reconstruction loss together as a guiding principle in the selection process. However, the first-order proximity is very sparse and insufficient in most cases. Moreover, redundant features, which can significantly hamper the performance of many machine learning algorithms, have seldom been taken into account. To address these issues, we propose an unsupervised network feature selection model called Multiple order proximity and feature Diversity guiding network Feature Selection model (MDFS), which uses multiple order proximity and feature diversity to guide the selection process. We use multi-order proximities based on the random walk model to capture linkage information between nodes. Moreover, we use an auto-encoder to capture the content information of nodes. As a last step, we design a redundancy loss to alleviate selecting highly-overlapping features. Experiment results on two real-world network datasets show the competitive ability of our model to select high-quality features among state-of-the-art models.
Development a framework for validating multi-capacity cloud resource allocation based on SimGrid

This paper contributes a framework that is used to apply the decisions of the Cloud resource algorithms to a simulated Cloud environment. This framework is developed to allow simulating any of the multi-capacity Cloud resource allocation algorithms upon SimGrid simulator. This framework is a tool for validation of the performance and robustness of the tested algorithms. The developed software facilitates testing, and validation of new scheduling algorithms. It also enables comparison of the new scheduling decision with the preliminary scheduling algorithms in a simulated Cloud. Combinatorial Ordering First-Fit Genetic Algorithm (COFFGA) was applied to the developed framework, and then compared with Multi-Capacity First Fit heuristic (MFF), Multi-Capacity First Fit Decreasing heuristic (MFFD) and, Batch job scheduling algorithm. The simulation results prove that COFFGA is applicable for real Cloud environment, as it is not associated with a big waste in time or the underlying hardware resources; COFFGA always uses the underling infrastructure with high resources utilisation and without considerable increase in the total execution time; it always yields the minimum number of servers in use in comparison with the previous heuristics like MFFD, MFF, and Batch scheduling. Importantly, COFFGA is able to reduce the number of servers in use, and it does not cause a tangible increase in the execution time. COFFGA increases the average execution time of MFF and MFFD by 5.4% and 9.9% respectively.
Prediction of the received global solar irradiance for clear days – simple approach

Abstract The prediction of the global solar radiation through a distribution function q(t), W/m2 for clear days is given where “t” is the local day time.: The distribution is based on a simple model and is expressed through well-established parameters such as the length of the solar day “t d” and the maximum value of the received solar irradiance q max , w/m 2 at time instant “t o” .q max is expressed in terms of the solar constant. Comparison between the computed values of q(t) and the corresponding published experimental data for Hong Kong (China), Valencia( Spain) and Makah (Saudi Arabia) are given as illustrative examples. A test for the degree of fitting is also clarified.
Context Aware Community Formation for MAS-Oriented Collective Adaptive System

Forming an effective collaboration community is the key to any successful collaborative process. However, community formation approaches for the closed multi-agent systems may be not fit the need of collective adaptive system comprising multi-agent in open environment. We propose a context aware community formation approach (CFAgentColla) for agent collaboration in open MAS. We employ ontology-based matching and calculation techniques to search for an optimized alternative capability of an agent or to generate a commitment according to the capabilities of two collaborative agents. Then, we define an adaptive goal model and propose an executable tree strategy to generate an optimal collaboration protocol according to the goals, capabilities and commitments. Additionally, we illustrate CFAgentColla approach with a real-world medical waste automated guided vehicle transportation scenario and evaluate the feasibility of our approach by validating the mainly executive parameters and by comparing with the planning approach.
Multi- perspective gesture recognition based on convolutional neural network

Gesture recognition is widely used in life, but single-angle gesture recognition has certain limitations. In this paper,we use the Multi-Perspective Static Gesture Database for experimental training. This database has 24 letter gestures (except j gesture and z gesture) in the international sign language alphabet. We use the self-designed convolution network to train each picture. The feature map is obtained by multiplying the feature map by the trained weights, and the information amount of each picture is obtained, and the information amount is combined with the prediction probability of each picture for each gesture, and the predicted probability after the combination can be obtained. The largest prediction probability is the predicted gesture. By contrast, the prediction accuracy of the combination of the four angles is higher than that of the single image. At the same time, the paper also expands, chooses two angles combined with three angles to compare with single angle, the prediction accuracy is also higher than the accuracy of single prediction, which shows that the method is effective, using multi-angle gesture pictures Combining can improve prediction accuracy.
Aleatoric Dynamic Epistemic Logic for Learning Agents

We propose a generalisation of dynamic epistemic logic, where propositions are aleatoric: that is, rather than having true/false values, propositions have odds of being true. Agents in such a system suppose a probability distribution of possible worlds, and based on observations are able to refine this probability distribution to match their observations. We demonstrate this logic with respect to some games of chance.
Face recognition method based on SVM and SRC

For face recognition with global features, Support Vector Machines (SVM) and Sparse Representation Classification (SRC) are two methods which are difficult to take into account the recognition effect and efficiency when used alone. Based on the study of the two methods, this paper proposes a global linear face recognition method which combines Support Vector Machines and Sparse Representation Classification. On account of the recognition error of SVM, Sparse representation model is solved by Augmented Lagrange Multiplier method and the second recognition is carried out. In addition, the results on YALE show that this method can significantly improve the accuracy and speed of recognition.
Image Restoration Models Based on Dyadic Hardy Space and Dyadic Bounded Mean Oscillation Space

Texture is widely existed in various images and plays an important role in many area such as medical image diagnosis, remote sensing, etc. However, the image in texture regions is tend to be deteriorated during restoration process. In this paper, we apply the dyadic Hardy space H1d and dyadic Bounded Mean Oscillation (BMO) space in the texture preserving image restoration model. We propose a H1d regularized minimization model to extract texture from noisy data. In this model, H1d norm is taken as regularizer to enforce the prior that the local variance of the noise is below certain level depending on the regularization parameter. We also analyze the mathematical properties of this model which indicate the mechanism of H1d regularizer to control the local variance. For the numerical solution of the model, we transform it into wavelet domain based on the wavelet characterization of dyadic Hardy space and dyadic BMO space, and solve it by the fixed iteration algorithm. Combing the total variation (TV) regularization method and frame based regularization method, a two-layers regularization model is proposed for edge and texture preserving, and then analyzed and solved in the frame of split Bregman method. Finally, we present various numerical results on images to demonstrate the potential of our methods.
Statistical mechanics of the minimum vertex cover problem in stochastic block models

The minimum vertex cover (Min-VC) problem is a well-known NP-hard problem. Earlier studies illustrate that the problem defined over the Erd\"{o}s-R\'{e}nyi random graph with a mean degree $c$ exhibits computational difficulty in searching the Min-VC set above a critical point $c = e = 2.718 \ldots$. Here, we address how this difficulty is influenced by the mesoscopic structures of graphs. For this, we evaluate the critical condition of difficulty for the stochastic block model. We perform a detailed examination of the specific cases of two equal-size communities characterized by in- and out- degrees, which are denoted by $c_{\rm in}$ and $c_{\rm out}$, respectively. Our analysis based on the cavity method indicates that the solution search becomes difficult when $c_{\rm in }+c_{\rm out} > e$, but becomes easy again when $c_{\text{out}}$ is sufficiently larger than $c_{\mathrm{in}}$ in the region $c_{\rm out}>e$. Experiments based on various search algorithms support the theoretical prediction.
Unsupervised Cross-Lingual Sentence Representation Learning via Linguistic Isomorphism

Recently, many researches on learning cross-lingual word embeddings without parallel data have achieved success by utilizing word isomorphism among languages. However, unsupervised cross-lingual sentence representation, which aims to learn a unified semantic space without parallel data, has not been well explored. Though many cross-lingual tasks can be solved by learning a unified sentence representation of different languages benefiting from cross-lingual word embeddings, the performance is not competitive with their supervised counterparts. In this paper, we propose a novel framework for unsupervised cross-lingual sentence representation learning by utilizing linguistic isomorphism in both word and sentence level. After generating pseudo-parallel sentence based on the pre-trained cross-lingual word embeddings, the framework iteratively conducts sentence modeling, word embedding tuning and parallel sentences update. Our experiments show that the proposed framework achieves state-of-the-art results in many cross-lingual tasks, as well as improves the quality of cross-lingual word embeddings. The codes and pre-trained encoders will be released upon the paper publishing.
It's a war out there: why malicious hackers are beating businesses to the punch – and what we can do about it

We see, hear and read about it every day: hackers breaching defences, stealing data and exploiting companies’ vulnerabilities. We are no longer asking ‘what if I get hacked?’ – rather, we wonder ‘when will I get hacked?’. We cannot let our fear of being phished or infected with malware prevent us from doing our jobs to the best of our ability. Instead, we need to understand the cyber security landscape today – what kinds of hackers exist, how preventative methods like ethical hacking can backfire and how our interconnected culture will continue to contribute to hackers winning the ‘cyber war’ if we don't rethink our readiness approach, argues Keenan Skelly of Circadence.
Text Classification of Network Pyramid Scheme based on Topic Model

At present, the network pyramid scheme has become a major tumor that hinders social development. In order to curb the propagation of the network pyramid scheme and effectively identify the pyramid scheme text in the network, this study proposes a joint topic model, Paragraph Vector Latent Dirichlet Allocation (PV_LDA), based on the characteristics of high-yield, high rebate, hierarchical salary and text topic diversity described in the text. The model uses the paragraph as the minimum processing unit to generate the topic distribution matrix of "high-interest rate" and "hierarchical salary" from the network pyramid scheme text. The Gibbs sampling is used to derive the "pyramid scheme" topic distribution matrix represented by the two features, which is used for classification processing by the classifier. the classification accuracy rate for the network pyramid scheme text can reach 86.25%. The conclusions show that the topic model proposed in this paper can capture the characteristics of the pyramid scheme more reasonably.
Scrambler Based AES for Countermeasure Against Power Analysis Attacks

In modern society, cryptographic algorithms that guarantee the integrity of data have been used in various fields while being secured through various research. However, since the encryption algorithms are mostly used in the computer environment, there is a difference in the amount of leakage current depending on all calculation results. A power analysis attack is a method that utilizes a difference in leakage current and is effective in attack of a cryptographic algorithm. Therefore, we propose the structure of AES encryption algorithm to cope with power analysis attack by randomizing input using scrambler so that attacker cannot input arbitrary initial value and secret key.
Adaptive edge detection via gray value differences of points

Edge detection is a fundamental tool in a wide range including image processing, machine vision and computer vision, mostly in the areas of feature detection and feature extraction. The accuracy and continuity of edges are essential indicators of edge detection and the key to affecting the final quality of them, which plays a core role in subsequent image processing. In this paper, by computing gray value differences between each point and its each neighbor, the adaptive edge detection is proposed to measure the local gray value variation around a point, which can obtain edges with high precision and continuity. Different from operators based on first-order or second-order derivatives, proposed method use the gray value difference between two adjacent points as the metric function for edge detection. Proposed method calculates the gray value difference between the current point and its neighbor, and then reserving both of them where the difference reaches most as the primary edge point. In order to reduce the number of missing edges and ensure proper continuity of the edges, the proposed method first detect the 3 &times; 3 neighbors centered on each point and get the threshold by an adaptive strategy; then expand the range of neighbors to 5 &times; 5, filtering redundant points out using threshold obtained in the previous step. Finally, an endpoint selection strategy is proposed to get the more accurate edge detection results. The experimental results show that proposed method can obtain accurate and continuous edges of good quality.
Ensemble Approach for Left Ventricle Segmentation

This paper describes a novel algorithm for segmenting the Left Ventricle (LV) from cardiac MRI. Cardiac Magnetic Resonance Imaging (CMRI) is a non invasive medical imaging technique used in radiology to form a picture of the heart. The importance of precisely identifying the LV region in a cardiac MRI is crucial as it is the key in measuring LV volumes and determining cardiac function. Hence this paper describes a new algorithm based on an improved fuzzy c means clustering. To increase the segmentation accuracy min max ant system algorithm was used to initialize the cluster centers and principal components analysis algorithm used for time optimization. The rationale is to test fuzzy c means with ant colony optimization, a studied algorithm, in the task of LV segmentation. However, till now fuzzy c means with ant colony optimization was tested in radiology problems where small amount of data needed segmentation, contrary to this case in which doctors evaluate heart condition based on many cardiac MRI. Therefore this paper demonstrate how to use principal components analysis algorithm to tackle the problem of time performance. Algorithm was evaluated on real cardiac MRI with different heart conditions dataset. Results show that this approach improves the segmentation quality compared to other methods getting the highest BDP score (fraction of the correctly classified pixels) while reserving efficiency and accuracy.
Large-scale Agent Data Partitioning Based on DensityRepel-K_medoids

Abstract Large-scale Agent data partitioning is the premise of parallel distributed computing in the process of ABMS (Agent-based Modeling and Simulation) . Based on the distance-based K_medoids clustering algorithm, this paper proposes an improved K_medoids algorithm (DensityRepel-K_medoids), which is implemented by high-performance programming language X10 and applied to large-scale Agent data partitioning simulation based on distance interaction. The DensityRepel-Kmedoids algorithm first determines the density value and the repulsion value of each Agent data in the Agent data set, and secondly pre-selects the cluster center according to the density value and the repulsion value of each Agent data, and finally uses the pre-selected cluster centers as the initial cluster centers for continuous iterative clustering until convergence. The algorithm avoids the defects of K_means clustering algorithm sensitive to outliers, and avoids the shortcomings of K_medoids clustering algorithm for large-scale data processing. By comparing and analyzing the simulation experiments of Agent data sets of different scales, the algorithm presents a better performance.