Reviewerly/exHarmony huggingface dataset:
Super-resolution Reconstruction of Infrared Images of Internal Defective Metal Plates Based on Generative Adversarial Networks

Infrared thermal imaging non-destructive testing technology has made great progress and has been used in the field of defect detection. However, due to the internal noise of the infrared imaging equipment and the influence of the surrounding environment interference, the infrared images used for defect recognition have the disadvantages of low contrast, low resolution, and low signal-to-noise ratio. Our article first briefly introduces the basic principle of infrared thermal imaging detection technology and the development status at home and abroad, then we builds an infrared image acquisition system that uses long-pulsed thermal method to collect internal defects of a metal plate. In order to solve the problem of noises, our paper first adopts Gaussian blur and homomorphic filtering on the acquired infrared images. Then we use Laplacian operator to process the filtered images and obtained second-order differential images. Finally, we use GAN for super-resolution reconstruction of the filtered second-order differential images. The results show that the super-resolution reconstructed images have higher PSNR and SSIM. What's more, it retains detailed information about defects in the original infrared images.
Amur Tiger Detection for Wildlife Monitoring and Security

In our ecosystem, wildlife plays a key role in sustaining different natural processes in nature. So, protection and conservation of wildlife become vital, especially those which are on verge of being extinct. One such species is the Amur Tiger, which is categorized as endangered. The traditional method of Amur Tiger recognition is volunteer intensive and hence was difficult and time-consuming as well. Therefore in our project, we attempt to provide a more efficient and reliable method for detecting Amur tiger with the help of the recent advancements of neural networks and deep learning algorithms. However, deep learning algorithms require ample amounts of data for the training, in which ATRW (Amur Tiger Re-identification in the wild) datasets is the most suitable one with an adequate number of variations. This dataset contains 2485 images for training and 277 images for validation with their annotation in xml format for each instance of the tiger. To detect the Amur tiger, we have applied various state-of-the-art object detection algorithms on this dataset. Out of all the models applied on this dataset, SSDlite model achieves 0.955 mean Average Precision values, which is an outstanding performance of any deep learning models applied for detection tasks. In addition, out of all the models applied and available in the literature, SSDlite is one of the faster models which inturn have least inference time comparatively.
The Role of Cultural Intelligence in the Localization of Computer Games

In this paper, we are trying to understand the role of cultural intelligence and intercultural competence for developers and specialists in localization of computer games. We indicate the parameters that will form the basis of the developed cultural intelligence of specialists working in this field.
Minimizing Driving Risk of Mobile Robots by Combining a Goal Guidance Vector Algorithm with Reactive Navigation

In the navigation of mobile robots, the driving risk can be minimized by increasing the probability of success. The algorithm, which is currently commonly known as the shortest path algorithm, performs efficiently, but does not exhibit a good probability of success for achieving the final goal. In this paper, we develop a new reactive navigation algorithm, known as the goal guidance vector (G2V), which can minimize the driving risk within the sensing range. The G2V is designed to improve the performance of the reactive navigation algorithm using a hazard cost function (HCF) that accounts for the scale and locations of the obstacles within the sensing range. We also adopt real-time fuzzy reactive control to determine the weighting factors of the HCF in an unknown environment to determine the optimal G2V. Simulations are conducted to validate the use of this approach for various environments.
CWAI-CNER: Chinese entity recognition based on adaptive incorporation of characters and words

Chinese Named Entity Recognition (CNER) is an important sub topic in the field of Chinese Natural Language Processing, which plays an important role in multi tasks. However, it's difficult to determine the boundaries of entities in Chinese texts because the Chinese words are not naturally separated, which further causes the task of CNER much more difficult. In addition, the mainstream Named Entity Recognition (NER) is based on sequence tagging, which causes the cost of training set labeling very high, so many NER tasks are limited by training sets' deficiency. In this work, we propose a new CNER method based on adaptive incorporation of characters and words-CWAI to solve the problem of words information loss caused by lacking of words boundaries, which uses convolution neural network (CNN) to capture the local semantics for every character, and then adaptively calculates the weights of potential words that match a lexicon for each character based on attention mechanism between characters and words. And for the problem of limited model effects due to insufficient training set, we combined our model with pre-trained models to solve that.
Path planning in autonomous ground vehicles

Path planning is an important task in unmanned vehicle systems. This chapter examines different path planning algorithms for unmanned ground vehicles such as grid, sampling, and computational-intelligence based algorithms.
Analysis of Vulnerability Webserver Office Management of Information And Documentation Diskominfo using OWASP Scanner

Cases of lost scripts/programs have occurred, but the server crashed due to electrical problems for problems related to attacks whose impact is felt, namely the type of defacing attack. However, his hopes of this research outside of the attack could secure it before another attack case. There are various kinds of attacks, for example, such as cross-site scripting (XSS) attacks. This case has not yet happened, and the web server admin hopes to secure it early, so there are no data/information loss cases. Data/information that is more important on this website than other data is data/information from the Information Management and Documentation Officer. Vulnerability is a loophole that people often use not for getting the data and information they need in any way. This research type of research uses action research methods, the nature of this research uses quasi-experimental research, and the approach conducted by researchers used a quantitative approach. The research was carried out using the OWASP scanner approach on the Diskominfo web server, with the hope of providing solutions or recommendations to the webserver admin in implementing web server security as needed.
Improving History Literacy among Adolescences: Towards Hoax Prevention through KCBS (Kumpulan Cerita Buku Sejarah)

Abstract Teenagers tend to find difficulties in filtering hoax due to a lack of literacy skills. They view that learning history through a book is boring. Instead, teenagers prefer to read information online. This study aims to improve history literacy among adolescences in order to prevent the spread out of hoax through KCBS (Kumpulan Buku Cerita Sejarah/the collection of historical narratives). The method used is qualitative with a descriptive approach. The result of this study is improved history literacy of the adolescences which enabling them to filter information gathered from blogs and websites as well as the use of Facebook as a source of information and the development of positive characters. One of many ways to improve history literacy is through literacy movement in which students are imperatively encouraged to read history books that are followed by summarizing a certain topic into the KCBS (Kumpulan Cerita Buku Sejarah). Rewriting historical narratives is hoped to improve the students’ literacy skills so that in the near future they will understand the danger of spreading out a hoax and will better respond to prevent the widespread.
Search and Explore Strategies for Interactive Analysis of Real-Life Image Collections with Unknown and Unique Categories

Many real-life image collections contain image categories that are unique to that specific image collection and have not been seen before by any human expert analyst nor by a machine. This prevents supervised machine learning to be effective and makes evaluation of such an image collection inefficient. Real-life collections ask for a multimedia analytics solution where the expert performs search and explores the image collection, supported by machine learning algorithms. We propose a method that covers both exploration and search strategies for such complex image collections. Several strategies are evaluated through an artificial user model. Two user studies were performed with experts and students respectively to validate the proposed method. As evaluation of such a method can only be done properly in a real-life application, the proposed method is applied on the MH17 airplane crash photo database on which we have expert knowledge. To show that the proposed method also helps with other image collections an image collection created with the Open Image Database is used. We show that by combining image features extracted with a convolutional neural network pretrained on ImageNet 1k, intelligent use of clustering, a well chosen strategy and expert knowledge, an image collection such as the MH17 airplane crash photo database can be interactively structured into relevant dynamically generated categories, allowing the user to analyse an image collection efficiently.
Security Analysis Using Subjective Attack Trees

Subjective attack trees are an extension to traditional attack trees, proposed so to take uncertainty about likelihoods of security events into account during the modelling of security risk scenarios, using subjective opinions. This paper extends the work of subjective attack trees by allowing for the modelling of countermeasures, as well as conducting a comprehensive security and security investment analysis, such as risk measuring and analysis of profitable security investments. Our approach is evaluated against traditional attack trees. The results demonstrate the importance and advantage of taking uncertainty about probabilities into account. In terms of security investment, our approach seems to be more inclined to protect systems in presence of uncertainty (or lack of knowledge) about security events evaluations.
Application of bioinspired algorithms for solving transcomputational tasks

Abstract The paper investigates the pattern recognition task as one of the most important transcomputational problems. Pattern recognition is applied for statistical data analysis, signal processing, image analysis, bioinformatics, machine learning, and many other fields. The area of application is related to automated pattern detection in the data using computer algorithms and data classification in terms of different categories. The development of new ideas in the processing of large volumes of information creates a new trend in the pattern recognition area. A specific field is formed by the bioinspired algorithms as mathematical reorganization, that transform the initial information into the results based on simulating the evolution methods, natural analogies, and statistic approach. In terms of computer modelling based on the bioinspired algorithms, we can create and develop difficult concepts, for which we have no analytical description. The authors present the optimal bioinspired algorithms based on the ant, monkey, and bat behavior in nature and develop software based on the principle of pattern recognition using the uploaded photo and downloading the results into the file. The experiments demonstrate the effectiveness of the proposed approach.
Federating digital contact tracing using structured overlay networks

In this paper, we present a comprehensive, yet simple, extension to the existing systems used for Digital Contact Tracing in Covid-19 pandemic. The extension, called BubbleAntiCovid19 (BAC19), enables those systems, regardless of their underlying protocol, to enhance their sets of traced contacts and to improve the global fight against pandemic during the phase of opening borders and enabling more traveling. BAC19 is a Structured Overlay Network. Its protocol is inspired by the Chord and Synapse Structured Overlay Networks. We design the architecture of the Overlay Network Federation. We show that the federation can be used as a formal model of Forward Contact Tracing. BAC19 provides a fully exhaustive retrieving procedure thanks to avoiding search during peer churn. Furthermore, we give simulation results for the BAC19 system, the simulator written in Python.
Supervised Feature Learning for Music Recommendation

The advent of music streaming services coupled with the drastic increase in the rate of music consumption has made automatic music recommendation a relevant and active area of research. Most modern recommendation systems utilize a combination of one or more techniques. Collaborative filtering and content-based recommendation are popularly used techniques. The former works well on large datasets but suffers from the well documented cold start problem. The latter tackles this issue, but generally requires intensive deep neural networks and large datasets to achieve high recommendation accuracy. In this paper, we propose a content based recommendation system that attempts to address the cold start problem using a relatively shallow neural network. We train a convolutional neural network to indirectly learn feature vectors for users and songs through a classification task. We show that our model generates sensible, diverse and personalized recommendations and is effective even on small datasets. We compare our results quantitatively against that of the popular latent factor models for music recommendation and show that our song to vector model outperforms traditional recommendation methods.
Face-Image Source Generator Identification

Recent advances in deep networks and specifically, Generative Adversarial Networks, have introduced new ways of manipulating and synthesizing “fake” images. Concerns have been raised as to the sinister use of these images, and accordingly challenges have been raised to detect “fake” from “real” images. In this study we address a slightly different problem in image forensics. Rather than discriminating real from fake, we attempt to perform “Source Generator Identification”, i.e. determine the source generator of the synthesized image. In this study we focus on face images. We exploit the specific characteristics associated with each fake face image generator and introduce a face generator representation space (the profile space) which allows a study of the distribution of face generators, their distinctions as well as allows estimating probability of images arising from the same generator.
Transmission Map Estimation Function to Prevent Over-Saturation in Single Image Dehazing

This paper proposes a transmission map estimation function with the objective to prevent over-saturation in fine haze and haze-free regions. The conventional transmission map estimation function often results in over-saturation and over-dehazing due to its lack of variance with the corresponding scene depth. To overcome this, the proposed transmission map estimation function considers dark channel along with saturation and value (or brightness) channels’ information of the input hazy image. Furthermore, the proposed method is independent of any heuristics such as the scattering coefficient of the atmosphere, and depends only on the input image. The performance of the proposed method in this paper has been analyzed quantitatively and qualitatively on various standard images frequently used in literature. The experimental results demonstrate the effectiveness of the proposed method with respect to the state-of-the-art.
Histological Image Segmentation With Fuzzy Clustering Method

In this study, a method for nuclei image segmentation in histopathological images is proposed.This method is based on a fuzzy clustering method, which is pre-trained on an supplementary domain with very large labeled images, and coupled with an additional network composed of fully connected layers.In this study, Fuzzy Clustering Mean (FCM) was used for clustering and segmentation and the effective ways for breast cancer nuclei detection were obtained.Wherefore, fuzzy clustering means have been used to detect the centers of breast cancer nuclei, then the extracted centers were compared with the ground truth samples.It is worth mentioning, that this work passes through many experimental stages, of detection and segmentation by applying a combination of more than one effective method.
Sistem Pakar Diagnosis Penyakit Hipertensi Menggunakan Metode Dempster Shafer

AbstractHypertension or high blood pressure. This disease occupies the highest prevalence in Indonesia, which is more than a quarter, or 25.8%. Riskesdas 2013 data states, Hypertension is found in 3 (three) provinces in Indonesia, namely Bangka Belitung 30.9%, South Kalimantan 30.8%, and East Kalimantan 29.6%, , hypertension causes heart attack, stroke and failure kidney, blindness due to hypertension retinopathy, and also peripheral arterial disease, there are still many people who do not know the symptoms of hypertension and how to diagnose with a high certainty value. To be able to know the certainty of this bacterial infection the researchers used the Dempster-Shafer method. This method was chosen because this method is considered capable of providing a high level of certainty. The Dempster-Shafer method is the representation, combination and propogation of uncertainty, where this theory has several characteristics that are instutive in accordance with the way of thinking of an expert. In this system I will use several variables related to hypertension, such as age, heredity, being overweight or obese, lack of smoking, etc. After that the client will be asked to fill in the list of symptoms that are felt later, the system will detect whether the client is affected by hypertension or the like that is infected on that patient.Keywords: Expert System, Dempster Shafer method, hypertensi
Dynamic resource allocation for service in mobile cloud computing with Markov modulated arrivals

Mobile Cloud Computing (MCC) is a modern architecture that brings together cloud computing, mobile computing and wireless networks to assist mobile devices in storage, computing and communication. A cloud environment is developed to support a wide range of users that request the cloud resources in a dynamic environment with possible constraints. Burstiness in users service requests causes drastic and unpredictable increases in the resource requests that have a crucial impact on policies of resource allocation. How can the cloud system efficiently handle such burstiness when the cloud resources are limited? This problem becomes a hot issue in the MCC research area. In this paper, we develop a system model for the resource allocation based on the Semi-Markovian Decision Process (SMDP), able of dynamically assigning the mobile service requests to a set of cloud resources, to optimize the usage of cloud resources and maximize the total long-term expected system reward when the arrival process is a finite-state Markov-Modulated Poisson Process (MMPP). Numerical results show that our proposed model performs much better than the Greedy algorithm in terms of achieving higher system rewards and lower service requests blocking probabilities, especially when the burstiness degree is high, and the cloud resources are limited.
Maximum a posteriori path for open quantum systems

Abstract In this work, the Stratonovich stochastic differential equation is considered in the derivation of the maximum a posteriori path (MAP) for an open quantum system. We have derived the modified Onsager-Machlup (OM) function for a stochastic process, which plays an analogous role of the Lagrangian in the conventional classical mechanics. Variational method is applied to obtain the Euler-Lagrange equations. We show that the MAP trajectory is close to the quantum most-likely path obtained by Chantasri et al. [1], but in general not exactly the same. For an open quantum system subject to a no-knowledge measurement, both methods produce the same most-likely path. Numerical simulations for both the modified OM and the quantum most-likely path methods agree very well with theoretical predictions, regardless of the evolution time and different post-selected states.
Improving Recommendation Accuracy using Cross-domain Similarity

The accuracy of collaborative filtering based recommendation system depends on sufficient rating information of the target user and his/her neighbors for similar items. In a real scenario where a huge number of users and items exist, there may be a possibility of very less (high sparsity) or no (cold start problem) rating information in the rating dataset, which degrades the recommendation accuracy significantly. This opens up a scope for improvement in the prediction accuracy of the collaborative filtering based recommender system. In this paper, if the target user does not find k similar neighbors in a particular domain, the proposed algorithm utilizes the rating information of that user from other domains, if available. This not only reduces the sparsity in the rating dataset but also solves the cold start problem. Additionally, the modified similarity measure of the proposed approach not only considers the high ratings but also the low ratings which makes the recommendation more personalised. Overall, the proposed approach improves the recommendation accuracy to a great extent, which has been been evident from the accuracy measures (e.g., MAE and RMSE) of the comparative analysis of the proposed algorithm and other prevalent collaborative filtering methods, investigated on the Amazon dataset.
Efficient Comparison Of Independence Structures Of Log-Linear Models

Abstract Log-linear models are a family of probability distributions which capture relationships between variables. They have been proven useful in a wide variety of fields such as epidemiology, economics and sociology. The interest in using these models is that they are able to capture context-specific independencies, relationships that provide richer structure to the model. Many approaches exist for automatic learning of the independence structure of log-linear models from data. The methods for evaluating these approaches, however, are limited, and are mostly based on indirect measures of the complete density of the probability distribution. Such computation requires additional learning of the numerical parameters of the distribution, which introduces distortions when used for comparing structures. This work addresses this issue by presenting the first measure for the direct and efficient comparison of independence structures of log-linear models. Our method relies only on the independence structure of the models, which is useful when the interest lies in obtaining knowledge from said structure, or when comparing the performance of structure learning algorithms, among other possible uses. We present proof that the measure is a metric, and a method for its computation that is efficient in the number of variables of the domain.