Reviewerly/exHarmony huggingface dataset:
Traveling Salesman Problem Solution Using Plate Tectonics Based Neighborhood Search Optimization

Traveling salesman problem (TSP) is one of the most fundamental constraint satisfaction and optimization problem in computer science and engineering. In this paper, we have adapted the plate tectonics based neighborhood search optimization technique on this problem. This theory explains and describes the large scale motions of Earth’s lithosphere. This actually motivates the application of plate tectonics and create a new paradigm, i.e., plate tectonics based neighborhood search optimization. PBO search technique find solutions for the traveling salesman problem (TSP) which depend very much on the way the initialization part is handled. We have used biogeography based optimization BBO for it and also tried many crossover operators in it. As the movements along the paths in solving TSP problem are continual and happen to be very stable, it naturally induces optimized solution of a function by choosing the most stable position. The proposed algorithm can exploit the local information available from the gradients of the function, while also allowing for exploration to scout for better solutions throughout the search space. This allows the algorithm to improve the known solution in each iteration and locate the global minimum efficient. Our results are comparable to ACO and PSO. For the dataset Att48, the number of cities are 48, and the optimal cost is 33522. The cost using ACO and PSO are 33,514.1 and 33,534.2, respectively, whereas the cost using PBO is 33, 537.1. For this case, the cost by PBO lies between ACO and PSO. The cost is better than ACO. Comparable results of many datasets are discussed in the paper.
Investigating the Effect of Organizational Structure Dimensions on Knowledge Management in Public Hospitals in Qom Province

Investigating the Effect of Organizational Structure Dimensions on Knowledge Management in Public Hospitals in Qom Province
Recorrect Net: Visual Guidance for Image Captioning

Most image caption methods directly learn the mapping relationship from image to text. In practice, however, paying attention to both sentence structure and visual content at the same time can be difficult. In this paper, we propose a model, called Re-correct Net, which aims to use the existing caption information by other captioners, to guide the visual content in the generation of new caption. In addition, to obtain the more accurate caption, our method uses the existing textured entity as additional prior knowledge. Experiments show that our model can be used as re-correct block after all captioner training, which is beneficial to improve the quality of caption and is also flexible.
Speaker Verification with Disentangled Self-attention

In prior works of speaker verification, self-attention networks attract remarkable interests among end-to-end models and achieve great results. In this paper, we have proposed an integrated framework which is used for speaker verification (SV), disentangled self-attention network (DSAN), which focuses on the self-attention in depth. Based on Transformer, attention computation in DSAN is divided into two parts, a pairwise part which learns the relationship between two frames and a unary part which learns the importance of each frame. The original self-attention mechanism trains these two parts together which hinders the learning of each part. We show the effectiveness of this modification on speaker verification task. The proposed model trained on TIMIT, AISHELL-1 and VoxCeleb shows significant performance improvement over LSTM and traditional self-attention network. And we improve the interpretability of the model. Our best result yields an equal error rate (EER) result of 0.91% on TIMIT and 2.11% on VoxCeleb-E.
The Implementation of Failure Mode and Effects Analysis (FMEA) of the Information System Security on the Government Electronic Procurement Service (LPSE) System

This research presents solving the problem of information system security and risk management using the failure mode and effects analysis (FMEA) method. Although popular in the field of industrial engineering, the FMEA method is still rarely used in research on information systems objects. So, it becomes interesting to be explored further to investigate further exploring the use of FMEA methods in information system security. The object of the research that reported on this paper is the government’s electronic procurement services (LPSE) Web site which has been be suspected vulnerableb to hacking. The variables measured in this study were occurrence, severity (impact), and detection (detection or prevention) of each failure mode. The research data were taken mostly based on direct observations. The results of data processing indicate a high level of vulnerability. During the data collection period, there were at least four security holes that could potentially cause a minimum of seven potential information system failures. The highest security priority focus is on system availability and system display consistency with risk priority numbers 576 and 400. Some security tips for the government electronic procurement services (LPSE) Web site have also been mentioned in this study.
The Comparison of Steepest Ascent Hill Climbing and A-star for Classic Game

Tic-tac-toe is a classic game that remains popular today. This game requires two players. When applied to software, of course this game requires an AI-integrated bot as an opponent to play. Several algorithms can be used as artificial intelligence in tic-tac-toe games such as Steepest Ascent Hill Climbing and A-star. The purpose of this research is to find a more effective and efficient algorithm for this game. The components used to compare the two algorithms are the elapsed time and also the performance of the two algorithms. SAHC recorded a shorter elapsed time with 0.0448 ms compared to the A-star which recorded an average of 1.192 ms. However, based on other tests case, A-star performs better than SAHC.
Private-Share: A Secure and Privacy-Preserving De-Centralized Framework for Large Scale Data Sharing

The various data and privacy regulations introduced around the globe, require data to be stored in a secure and privacy-preserving fashion. Non-compliance with these regulations come with major consequences. This has led to the formation of huge data silos within organizations leading to difficult data analysis along with an increased risk of a data breach. Isolating data also prevents collaborative research. To address this, we present Private-Share, a framework that would enable secure sharing of large scale data. In order to achieve this goal, Private-Share leverages the recent advances in blockchain technology specifically the InterPlanetary File System and Ethereum.
Research on the Impact of the Integration of Two Styles of Icon Design and Task Characteristic on UI Visual Search Performance

From the perspective of icon search paradigm, this study analyzes the impact of skeuomorphism style and flat style design on icon visual search performance. In this study，Using the eye movement experiment method, Obtain the relevant data of the icon’s cognitive performance and perform relevant analysis. The purpose is to explore the advantages and disadvantages of visual search performance under the mechanism of controlling factor variables. The research results and content are as follows. In the task characteristics, the search performance of the two types of style icons(color) in the form of the target template picture is higher than that of the text form， Under the condition of delay time. the search performance of the two types of style icons (color) under the shorter delay time are both higher than the longer delay time. When the icon is (gray and color), there is a significant difference in search performance between the skeuomorphism style and the flat style icon. Based on these research results, it is recommended that in interface design, from the perspective of search efficiency, skeuomorphism style and flat style icons are equally important in adoption and design.
MGSAN: A Multi-granularity Self-attention Network for Next POI Recommendation

Next Point-of-Interest (POI) recommendation has become a vital research trend, helping people find interesting and attractive locations. Existing methods usually exploit the individual-level POI sequences but failed to utilize the information of collective-level POI sequences. Since collective-level POIs, like shopping malls or plazas, are common in the real world, we argue that only the individual-level POI sequences cannot represent more semantic features and cannot express complete transition patterns. To this end, we propose a novel Multi-Granularity Self-Attention Network (MGSAN) for next POI recommendation, which utilizes the multi-granularity representation and the self-attention mechanism to capture the transition patterns of individual-level and collective-level POI sequences on two different levels of granularities. Specifically, individual-level and collective-level POI sequences are first constructed and embeddings of each check-in tuple are normalized. Then, MGSAN incorporates spatio-temporal features by introducing two temporal-aware encoders and two spatial-aware encoders and learns sequential patterns with the self-attention network for two granularities. Finally, we recommended a user’s next POI with the help of two sub-tasks, i.e., the activity task to predict the next category and the auxiliary task to predict the next POI type. Extensive experiments on three real-world datasets show that MGSAN outperforms state-of-the-art methods consistently.
Impossible Differential Cryptanalysis and Integral Cryptanalysis of the ACE-Class Permutation

ACE is a block cipher proposal that entered the 2nd round of the NIST Lightweight Cryptography Standardization process. So far, not much cryptanalysis has been devoted to the ACE permutation and specifically impossible differential distinguishers covering more than 10-steps have not been specified yet. In this article, a MILP (Mixed Integer Linear Programming) model that describes the propagation of word-oriented trails is employed (also considered in []), which serves as a basis for a new automatic cryptanalytic tool for finding impossible differential distinguishers. As an application, the cryptographic security of the ACE permutation can be evaluated using this new tool. Specifically, we show that impossible differential distinguishers for the ACE permutation can be built for up to 12 steps, essentially covering the largest number of steps so far by an impossible differential distinguisher. Due to the high efficiency of our method, we can explore all possible linear permutations to modify the original one. The security of these modified ACE permutations against integral and impossible differential attacks can then be evaluated by using our MILP model of word-oriented trails. In particular, the modified ACE that uses a linear permutation (4, 2, 3, 0, 1) instead of its original version offers better security against integral and impossible differential cryptanalysis. At the same time, ACE with the modified linear permutation preserves the same resistance against differential and linear cryptanalysis.
Natural language processing analysis method of neural network model

With the continuous development of information technology, neural network algorithms have also been applied. Aiming at the problems of neural networks, this research has improved the BP algorithm, that is, using the GA proposed in this paper to optimize the BP neural network to ensure that the neural network training reaches the global best At the same time, it also helps to overcome the tendency of BP algorithm to fall into local and small points, and BP neural network has the right to learn. The main problem of value selection. This paper also selects a natural language data set for experimental verification. It can be seen from the experimental results that this method has achieved good results in natural language processing.
Hierarchical Advantage for Reinforcement Learning in Parameterized Action Space

We propose a hierarchical architecture for the advantage function to improve the performance of reinforcement learning in parameterized action space, which consists of a set of discrete actions and a set of continuous parameters corresponding to each discrete action. The hierarchical architecture extends the actor-critic architecture with two specialized advantage functions, one for discrete actions and the other for continuous parameters, to estimate a better baseline. We incorporate this architecture into proximal policy optimization, which is referred to as HA-PPO. We evaluated all of our methods on the Half Field Offense domain, and found that the hierarchical architecture of the advantage function, which is referred to as the hierarchical advantage, helps to stabilize the learning and leads to a better performance.
Segmented Region based Feature Extraction for Image Classification

Reliability and accuracy is the key concern of an automated image classification process. However, the impact of background or surrounding area is very less in compared to object features, which create ambiguity while assigning the appropriate class label and reduce the classification accuracy. This paper presents a new model to address this issue which select the relevant features from the segmented images based on the inner and outer regions. The key idea of this model is that the texture features inside the objects are more relevant than the surrounding or outside region of the objects. The proposed model applying a segmentation method for automated segment the image. These segmented images are further partition into two parts (i.e. inner and outer). The 463 shape and texture features are extracted from the inner, outer parts of the segmented images and also from the whole image. Next, these extracted features are used to train the classifier using support vector machine (SVM). A database of 644 images that consists of 8 classes is used to verify the efficacy of the proposed model. The result proves the efficacy of the proposed model which achieves classification accuracy up to 97.79 % from the inner part of the image. The classification accuracy of inner features is increased by 9.58% from surroundings features.
CAESR: Conditional Autoencoder and Super-Resolution for Learned Spatial Scalability

In this paper, we present CAESR, an hybrid learning-based coding approach for spatial scalability based on the versatile video coding (VVC) standard.Our framework considers a low-resolution signal encoded with VVC intra-mode as a base-layer (BL), and a deep conditional autoencoder with hyperprior (AE-HP) as an enhancement-layer (EL) model.The EL encoder takes as inputs both the upscaled BL reconstruction and the original image.Our approach relies on conditional coding that learns the optimal mixture of the source and the upscaled BL image, enabling better performance than residual coding.On the decoder side, a super-resolution (SR) module is used to recover high-resolution details and invert the conditional coding process.Experimental results have shown that our solution is competitive with the VVC full-resolution intra coding while being scalable.
Spline-based Path Tracking for VTOL Vehicles

In order to fulfill their tasks in the context of future air transportation, VTOL vehicles are intended to navigate through urban environments autonomously. Lacking a pilot on-board, an approved 3-dimensional reference flight path needs to be available that can be tracked and followed by the vehicle's guidance system. This work presents a path tracking algorithm tailored for VTOL vehicle capabilities that builds upon a spline-based path planning algorithm. The approach obtains feed-forward references from the spline data and integrates seamlessly into an existing velocity-based manual control scheme resulting in reduced mode-switching complexity. It can be shown that the presented guidance algorithm results in sufficiently good tracking precision.
A Survey on Hybrid Recommendation Systems

As India is moving fast towards digital economy, E-commerce industry has been on rise. Many platforms such as Amazon and Flipkart provide their customers with a shopping experience better than actual physical stores. Several E-commerce websites use different methods to improve the customer engagement and revenue. One such technique is the use of personalized recommendation systems which uses customer’s data like interests, purchase history, ratings to suggest new products which they may like. Recommendation systems are used by E-commerce websites to suggest new products to their users. The products can be suggested based on the top merchants on the website, based on the interests of the user or based the past purchase pattern of the customer. Recommender systems are machine learning based systems that help users discover new products. Due to the recent pandemic situation of 2020 and 2021, many of the local retail stores have been trying to shift their business to online platforms such as dedicated websites or social media. The proposed methodology based on Machine Learning aims to enable local online retail business owners to enhance their customer engagement and revenue by providing users with personalized recommendations using past data using methods such as Collaborative Filtering and Content-Based Filtering.
Structural Properties of Self-dual Monomial Codes with Application to Code-Based Cryptography

This article focuses on the self-dual monomial codes that have an underlying structure of decreasing/weakly decreasing monomial codes. Having such a property permits an in-depth analysis of their structure: The permutation group of a subclass is (significantly) bigger than the affine group. Upon looking at higher powers of the code, we see that its third power is the entire space, but the dual of the square code gives information helpful for decoding. Using operations such as shortening, puncturing and taking the discrete derivative, we extract the subcode generated by the multiples of a certain variable. Recently, self-dual monomial codes have been proposed for a McEliece public key encryption scheme. They seem to possess strong security features - they have a large permutation group, they are self-dual, there are exponentially many of them by counting the possible monomial bases used in their construction. A more detailed analysis allows us to identify subclasses where the square code and shortening methods yield non-trivial results; in these cases, the security is dominated by the complexity of the Information Set Decoding, which is exponential in the square root of the length of the code. This is a solid argument for the security of the McEliece variant based on self-dual monomial codes.
Epilogue

This epilogue argues that the information security challenges of today will remain intractable until the past is better understood. Indeed, the three stigmata — data breaches, nation-state hacking, and epistemic closure — became manifest precisely because the past is so easy to overlook. Given these challenges, there is the question of what should be done. There must be a special effort to better understand how complexity affects information security and how that complexity can be managed. Modern programming languages that do not fall prey to certain kinds of security vulnerabilities are an example of a means by which accidental complexity can be reduced. A second example is the removal of unnecessary security decisions from both end users and technology practitioners.
Gaussian Mixture Probability Hypothesis Density Filter with State-Dependent Probabilities

The Gaussian mixture probability hypothesis density (GM-PHD) filter has been successfully applied to various multiple target tracking (MTT) applications due to its ability to estimate both the number of targets and target states from noisy measurements effectively and efficiently. However, since the GM-PHD filter assumed the target detection and survival probabilities and the birth rate to be constant, its performance could be degraded when the targets are temporarily occluded or when measurements are missing. To address this, we propose the state-dependent GM-PHD filter which explicitly considers the state-dependent probability of detection and survival as well as the state-dependent birth rate. In addition, in order to consider the different kinds of maneuvers of the target, we use a hybrid system model for a target. The performance of the proposed algorithm is illustrated with an example in a road traffic simulation, which includes target occlusion, and compared with that of the original GM-PHD filter.
Features of Physical Preparation of Students for Passing the GTO Standards

The article deals with the problems of motor activity, the development of physical qualities and speed abilities of students. The importance of physical culture and physical fitness is revealed. The search for effective means and methods of physical culture in preparation for passing the GTO standards is analyzed. The features of physical training of high-speed orientation of students to pass the GTO standards for speed are considered. The effectiveness of the use of speed-oriented exercises, which are necessary for students to achieve the norms of the GTO complex for speed, within the framework of physical culture classes, is revealed. Research methods. In the course of the study, a comparative analysis of the results was carried out. The method of control tests was used. The overall assessment of the effectiveness of the above tools and methods is expressed in positive dynamics. The results of the study. According to the results of the study, there is a positive trend. The analysis of the results according to the table of GTO standards in the framework of physical culture classes allows us to note the growth of indicators for all established tests. Systematic classes using high-speed, high-speed and power-oriented exercises with a gradual change in the load, improving the technique of performing exercises, contribute to the development of the physical quality «speed». Conclusions. Regular physical education classes with the use of speed exercises contribute to the development and improvement of physical qualities, the achievement of growth rates of GTO standards. The level of readiness of students to fulfill the norms and requirements of the GTO complex at the average level. Affordable, effective means and methods of physical training make it possible to actively use the scientific and educational potential of the university. The purposeful organization of the educational process contributes to achieving the proper level of physical fitness necessary for the successful implementation of the regulatory requirements of the AR PCS Complex GTO.
Model Checking, Synthesis, and Learning

Reliability is a central concern of software system development. It can be approached in three ways, in a post-mortem fashion via verification of an unknown artefact, by construction applying correctness preserving steps, and via testing of the final product. In this paper, we introduce the nine contributions to the Festschrift dedicated to Bengt Jonsson on the occasion of his 60th birthday. Verification is addressed here from the model checking perspective, correctness by construction via synthesis, and testing as both a means and a as by-product of active automata learning while reflecting on the impact Bengt had on these developments.