Reviewerly/exHarmony huggingface dataset:
A Method to Search Traveling Salesman Problem Backbones Based on GA and Frequency Graph

The traveling salesman problem (TSP) is a well-known NP-hard problem in combinatorial optimization.The search space of the optimal Hamiltonian cycle (OHC) is increasing exponentially according to the scale n.In order to reduce the search space of TSP, we first convert the complete graph K n into the frequency graph (FG) with the approximate optimal n-vertex paths computed with an improved GA.The GA is improved with a four-vertex-three-line inequality to compute the approximate paths of TSP.Then, TSP backbones with 2n edges are derived from the FG.The experimental results show that the TSP backbones include at least 95% edges in the OHC.Based on the backbones, the search space of the OHC or approximations is greatly reduced.
Penerapan Knowledge Management System Menggunankan Algoritma Levenshtein

Knowledge management (KM) is an important thing to store or possess existing knowledge. The difficulty of getting knowledge that has actually been known for a long time about special planning for new information is to repair a certain position, in this case the container that contains several private universities in Palembang. The lecturer can only find out how the system discusses in the college, and many other knowledge that must be discussed by the new lecturer. Therefore the Knowledge Management System (KMS) will be built using the Inukshuk Model to become a means for existing knowledge, while the algorithm for searching knowledge stored in KMS is the Levenshtein Algorithm. The selection of the Levenshtein algorithm itself which uses this algorithm measures the relationship between strings (words to words, words to sentences and sentences to sentences) by calculating the edit distance, so that it will produce a high level of acquisition. The result is a KMS that is important for private universities to store and manage knowledge web-based services to make it easier for today's users to use many internet networks.
Perancangan dan Modelling Karakter Maskot STMIK Bumigora Mataram

STMIK Bumigora Mataram is one of the universities in NTB, which is capable of competing with other universities. In addition, in the future, STMIK Bumigora Mataram will become an international college that can reduce scientific disparity and resources. STMIK Bumigora Mataram is one of the most popular universities in Indonesia especially in the NTB region. Every year STMIK Bumigora Mataram actively promotes related to new student registration. The promotional media used at that time by STMIK Bumigora Mataram in the past 2 years in the form of brochures, advertisements, calendars, posters, websites and promotions go to scholl, then every campus needs to have a different promotional tool than before so that in the future STMIK bumigora mataram needs a promotional media in the form of mascots by reflecting on a particular company or institution that can make it a symbol that is easily remembered by the community. STMIK Bumigora Mataram is the only university that will have a mascot in the NTB region. This mascot character is in the form of 3D animation object, as a representative character representing STMIK Bumigora Mataram which is easier to recognize, for this reason it is necessary to create a mascot character as a promotional tool for STMIK Bumigora Mataram which can be a promotional activity. The design of this mascot character uses the Vilamil-Molina method, a multimedia development method that starts from the concept stage, planning, usage development until the trial stage in building application software using blender software and by utilizing Photoshop software that is used to create textures, then produce a video
Fostering Positive Affects in Software Development Environments Using Extended Reality

Recent research on affects has been aimed at establishing the hypothesis that a developer's productivity has a positive correlation with positive affects, such as her happiness quotient. Moreover, studies show that the software development environment itself has a huge role to play in how a developer feels during her day to day activities. In this paper, we present our early work regarding an Extended Reality based approach that virtually transforms a developer's extended and immediate environment, in a way that induces positive affects. This is aimed at assisting the developer to perform better during development related activities like programming and debugging. Salient features of the approach include a sense of presence, uniqueness and easy customization. As part of our initial research efforts, we showcase a couple of Augmented Reality based prototype implementations capable of augmenting virtual and interactive pets and scenic content in the real world. Finally, we also discuss some important future directions that we are investigating further as part of our research.
Classification of printed and handwritten text using hybrid techniques for gurumukhi script

Text classification is a crucial step for optical character recognition. The output of the scanner is non- editable. Though one cannot make any change in scanned text image, if required. Thus, this provides the feed for the theory of optical character recognition. Optical Character Recognition (OCR) is the process of converting scanned images of machine printed or handwritten text into a computer readable format. The process of OCR involves several steps including pre-processing after image acquisition, segmentation, feature extraction, and classification. The incorrect classification is like a garbage in and garbage out. Existing methods focuses only upon the classification of unmixed characters in Arab, English, Latin, Farsi, Bangla, and Devnagari script. The Hybrid Techniques is solving the mixed (Machine printed and handwritten) character classification problem. Classification is carried out on different kind of daily use forms like as self declaration forms, admission forms, verification forms, university forms, certificates, banking forms, dairy forms, Punjab govt forms etc. The proposed technique is capable to classify the handwritten and machine printed text written in Gurumukhi script in mixed text. The proposed technique has been tested on 150 different kinds of forms in Gurumukhi and Roman scripts. The proposed techniques achieve 93% accuracy on mixed character form and 96% accuracy achieves on unmixed character forms. The overall accuracy of the proposed technique is 94.5%.
Context-Tree Recommendation vs Matrix-Factorization: Algorithm Selection and Live Users Evaluation

We describe the selection, implementation and online evaluation of two e-commerce recommender systems developed with our partner company, Prediggo. The first one is based on the novel method of Bayesian Variable-order Markov Modeling (BVMM). The second, SSAGD, is a novel variant of the Matrix-Factorization technique (MF), which is considered state-of-the-art in the recommender literature.We discuss the offline tests we carried out to select the best MF variant, and present the results of two A/B tests performed on live ecommerce websites after the deployment of the new algorithms. Comparing the new recommenders and Prediggo’s proprietary algorithm of Ontology Filtering, we show that the BVMM significantly outperforms the two others in terms of CTR and prediction speed, and leads to a strong increase in recommendation-mediated sales. Although MF exhibits reasonably good accuracy, the BVMM is still significantly more accurate and avoids the high memory requirements of MF. This scalability is essential for its application in online businesses.
Modern MDL meets Data Mining Insights, Theory, and Practice

When considering a data set it is often unknown how complex it is, and hence it is difficult to assess how rich a model for the data should be. Often these choices are swept under the carpet, ignored, left to the domain expert, but in practice this is highly unsatisfactory; domain experts do not know how to set k, what prior to choose, or how many degrees of freedom is optimal any more than we do. The Minimum Description Length (MDL) principle can answer the model selection problem from an intuitively appealing and clear viewpoint of information theory and data compression. In a nutshell, it asserts that the best model is the one that best compresses both the data and that model. It does not only imply the best strategy for model selection, but also gives a unifying viewpoint of designing optimal data mining algorithms for a wide range of issues, and has been very successfully applied to a wide range of data mining tasks, ranging from pattern mining, clustering, classification, text mining, graph mining, anomaly detection, up to causal inference. In this tutorial we do not only give an introduction to the basics of model selection, show important properties of MDL-based modelling, successful examples as well as pitfalls for how to apply MDL to solve data mining problems, but also introduce advanced topics on important new concepts in modern MDL (e.g, normalized maximum likelihood (NML), sequential NML, decomposed NML, and MDL change statistics) and emerging applications in dynamic settings.
AVS-Net: Automatic Visual Surveillance Using Relation Network

Visual surveillance through closed circuit television (CCTV) can help to prevent crime. In this paper, we propose an automatic visual surveillance network (AVS-Net), which simultaneously performs image processing and object detection to determine the dangers of situations captured by CCTV. In addition, we add a relation module to infer the relationships of the objects in the images. Experimental results show that the relation module greatly improves classification accuracy, even if there is not enough information.
Assessing the intelligence of a student through TIC-TAC-TOE game for career guidance

In order to provide Career Guidance by an expert system to a student, apart from his/her academic record, it is necessary to assess various psychological factors of the student like Intelligence, patience and perseverance, Learning ability and Speed of solving problem, etc. The outcome of this research work is an expert system called Tic-Tac-Toe Game Playing Career Guidance System (TTT-GP-CGS) that is useful to assess the psychological factors of the student through Tic-Tac-Toe Game Playing, build the cognitive model of the student and predict the appropriate career(s) for the student. The system plays Tic-Tac-Toe game with the student several times. The average score obtained by the student reflects his/her intelligence. The average time taken by the student for playing the game reflects student's speed of solving problem. The number of attempts the student makes reflects the students patience and perseverance. When the student plays the game with the spaystem every day, if the student's score increases day by day, it reflects that the student has good learning ability. In this way the system will assess the psychological factors of the student and builds the cognitive model of the student. Internally the system converts the quantitative scores into qualitative scores. The system maintains a table of careers and the expected levels of psychological factors that are required from student's side to carry out the career successfully. The system invokes a matching process by considering the cognitive model of the student and the table of careers, and predicts the career(s) suitable to the student.
Self-Organizing Incremental Neural Networks for Continual Learning

Continual learning systems can adapt to new tasks, changes in data distributions, and new information that becomes incrementally available over time. The key challenge for such systems is how to mitigate catastrophic forgetting, i.e., how to prevent the loss of previously learned knowledge when new tasks need to be solved. In our research, we investigate self-organizing incremental neural networks (SOINN) for continual learning from both stationary and non-stationary data. We have developed a new algorithm, SOINN+, that learns to forget irrelevant nodes and edges and is robust to noise.
Exploring the Pan-Relationship Between Disease and Symptom Related to Coronary Heart Disease from Chinese Electronic Medical Records

Electronic Medical Records (EMRs) arises from the clinical treatment process, which reflects a patient's physical condition, the disease, the treatment process, etc. It has an important significance for the recovery of the patient. As the amount of electronic medical records continues to increase, people are actively looking for ways to get the valid data contained in electronic medical records. More effective methods are urgently needed for processing EMRs. In this paper, 108 real EMRs of coronary heart disease were analyzed, through named entity recognition and entity relation extraction. Pan-relationship between entities was defined in the process. Experimental results showed the analysis of pan-relation between coronary heart disease and symptom is helpful for treatment of coronary heart disease.
On the Effectiveness of Interactive Detection of Code Anomalies: An Empirical Assessment

Background: Detection of code anomalies should be performed as early as possible in order to effectively reveal refactoring opportunities in due time.Refactoring aims at improving software maintainability, but their late application is counter-productive or even prohibitive.Detection of code anomalies is traditionally supported by non-interactive detection (NID) techniques, which encourage developers to reveal anomalies in later revisions or versions of a program.The reason is that this technique does not support progressive interaction of developers with anomalous code.In addition, it reveals anomalies in the entire source code upon an eventual developer request.More recently, the notion of interactive detecti on (ID) has emerged to address NID's limitations.This technique reveals anomalies when code fragments are still being edited and without an explicit developer request, thereby encouraging early anomaly detection.Problem Statement: Recent studies suggest the use of NID might lead to: (i) a low number of correctly identified anomalies, and (ii) ineffective refactoring actions.Although ID seems promising, there is no knowledge about its impact on anomaly detection and refactoring actions.Goal: Evaluate the effectiveness of an ID technique on early anomaly detection.In addition, we analyze the aid of an ID technique in performing effective refactoring actions.Method: We conducted a controlled experiment with 14 subjects that underwent tasks related to ano maly detection and judgments of required refactoring actions.Results: Our study reveals the use of ID improves anomaly detection as developers tend to early identify more anomalies when compared to the use of NID.Conclusions: Although ID contributes to detect more anomalies than NID, the former may induce to ineffective refactoring actions.
Exploring the Impact of Environmental Sustainability on Firm Performance in the Manufacturing Industry in Jordan

Environmental sustainability is an important component of a firm’s Corporate Social Responsibility. It relates to firm practices that ensure the conservation of the environment and natural resources, such as water, land and air. This research study aims to study the concept in relation to firm performance in Jordan. It proposes that environmental sustainability practices of a company in Jordan’s manufacturing industry positively influence its financial performance. For this purpose, the study assesses the relationship between environmental sustainability score and the profitability ratios. Results reveal a significant positive impact of sustainability score on the ROA of the companies. It is therefore recommended to manufacturing firms in Jordan to focus more on environmental CSR and sustainability practices, which would result in improved efficiency and profitability.
Deep Learning Approaches for Whiteboard Image Quality Enhancement

Different whiteboard image degradations highly reduce the legibility of pen-stroke content as well as the overall quality of the images. Consequently, different researchers addressed the problem through different image enhancement techniques. Most of the state-of-the-art approaches applied common image processing techniques such as background foreground segmentation, text extraction, contrast and color enhancements and white balancing. However, such types of conventional enhancement methods are incapable of recovering severely degraded pen-stroke contents and produce artifacts in the presence of complex pen-stroke illustrations. In order to surmount such problems, the authors have proposed a deep learning based solution. They have contributed a new whiteboard image data set and adopted two deep convolutional neural network architectures for whiteboard image quality enhancement applications. Their different evaluations of the trained models demonstrated their superior performances over the conventional methods.
Strong Superadditive Deficit of Coherence and Quantum Correlations Distribution*

The definitions of strong superadditive deficit for relative entropy coherence and monogamy deficit of measurement-dependent global quantum discord are proposed. The equivalence between them is proved, which provides a useful criterion for the validity of the strong superadditive inequality of relative entropy coherence. In addition, the strong superadditive deficit of relative entropy coherence is proved to be greater than or equal to zero under the condition that bipartite measurement-dependent global quantum discord (GQD) does not increase under the discarding of subsystems. Using the Monte Carlo method, it is shown that both the strong superadditive inequality of relative entropy coherence and the monogamy inequality of measurement-dependent GQD are established under general circumstances. The bipartite measurement-dependent GQD does not increase under the discarding of subsystems. The multipartite situation is also discussed in detail .
Modulation Recognition of Digital Signals Based on Deep Belief Network

Abstract A modulation pattern recognition method for digital modulation signals, 4ASK, BPSK, QPSK, 2FSK and 4FSK digital modulation signals, which is based on deep learning model of deep belief network is proposed. The modulation signal is pre-processed and its high order cumulants are calculated as input training features. Solutions to the problem that the same high Modulation signals are generated in different SNR environments. Using the semi-supervised learning characteristics of deep confidence network, data sets are obtained to train the parameters of deep Confidence network layer by layer for feature extraction and recognition of modulation modes. The simulation results show that the recognition rate of this method is ideal.
An Effective Cell Pathology Image Detection Method Based on Deep Stacked Auto-Encoder Combined with Random Forest

Cytopathology mainly studies the causes and pathogenesis of disease occurrence, as well as the changes in the physiological functions of cells during the process of disease occurrence, providing a basis for the prevention and diagnosis of diseases. Using computational simulation technology to accurately detect cell pathology images can effectively avoid the influence of subjective factors in diagnostic operation by pathologist, and provide reliable technical means for case diagnosis. In this study, we design an effective model to detect cell pathology images based on deep stacked auto-encoder algorithm. Firstly, the cell pathology images are preprocessed by standardization. Secondly, the processed images are sent to the stacked auto-encoder algorithm to extract theirs hidden feature information. Finally, the random forest algorithm is used to quickly and accurately detect theirs category. We introduce cross-validation in our experiments to test the robustness and stability of the results. In addition, we also compared the model based on a k-nearest neighbor classifier and achieved good results. These excellent experiment results indicated that the proposed method can accurately detect cell pathology images and can be used as a reliable tool for pathologists to diagnose rapidly.
Secure Data Transfer using RSA and Steganography

Today, transferring sensitive information via secure methods and protecting information privacy are becoming critically important, the two ways for securing and transferring messages and data are cryptography and steganography.Steganography is used for hiding messages in innocuous media (carriers) such as text, image, audio, video and protocol.Amongst these different carriers, digital images are the most popular because of their frequency on the internet.To be more robust in security, steganography can be combined with cryptographic techniques.In this paper, we propose Secure Data Transfer using RSA and Steganography in order to obtain a secure system.The system will be implemented by Java programming language.
Self-adaptive Hierarchical Threshold Denoising Based on Parametric Construction of Fixed-length Tight-supported Biorthogonal Wavelets

Inspired by wavelet threshold denoising, this paper sets up the wavelet filter bank suitable for threshold denoising, following the parametric construction of fixed-length tightly-supported (FLTS) biorthogonal wavelet.Firstly, the length, symmetry and vanishing moment features of the wavelet were selected from the sequence of high-pass decomposition filters, and the proportional relationship within the sequence was configured.Next, a set of parametric filter bank expressions were derived from the said conditions and relationship.The parametric construction approach can adjust the sequence length and symmetry, and change the scale factor and sign function.The constructed wavelets were simulated on two noisy images with the global threshold denoising (GTD) and self-adaptive hierarchical threshold denoising (SAHTD).The simulation results show that the constructed wavelets can remove noises from the original image while preserving most image details.Combined with the SAHTD, the proposed wavelet construction method can greatly improve image quality and the signal-tonoise ratio (SNR).
Echo State Network with Hub Property

Reservoir computing, as an effective approach of training recurrent neural network, has drawn wide attention for its superior performance on some benchmark problems. Reservoirs which are closer to the topology of the brain have shown better performance. It has been found that some neurons in the brain act as hubs integrating and distributing information. By simulating the hub property, this paper proposed a novel topology for dynamical reservoir, called echo state network (ESN) with hub property (HESN). Different from conventional reservoir, some neurons acting as hubs are added to the reservoir of HESN. Experimental results show that our proposed approach has improved the performance of the original ESN on two benchmark datasets.
Protocol for User-Centered Website Development for the Directorate of Extension, Kerala Agricultural University - A Model Work

Any information system that is developed without considering the needs of the user fail to achieve the goals for it stands. Considering that view, the design of a User-centered bilingual website was done as per ISO 13407 guidelines for strengthening the cyber extension activities of the Directorate of Extension (DoE), Kerala Agricultural University (KAU). A user-centered design (UCD) of the website optimizes it around how the end-users want, or need to use it, rather than forcing them to accept it. The website thus developed after prototyping can be a better tool for agriculture extension. This study came up with an eight step protocol (sequential steps and procedures) for the development of a user-centered website, which can be adopted for the future works in the same line.