Reviewerly/exHarmony huggingface dataset:
Recovering Extremely Degraded Faces by Joint Super-Resolution and Facial Composite

In the past a few years, we witnessed rapid advancement in face super-resolution from very low resolution(VLR) images. However, most of the previous studies focus on solving such problem without explicitly considering the impact of severe real-life image degradation (e.g. blur and noise). We can show that robustly recover details from VLR images is a task beyond the ability of current state-of-the-art method. In this paper, we borrow ideas from "facial composite" and propose an alternative approach to tackle this problem. We endow the degraded VLR images with additional cues by integrating existing face components from multiple reference images into a novel learning pipeline with both low level and high level semantic loss function as well as a specialized adversarial based training scheme. We show that our method is able to effectively and robustly restore relevant facial details from 16x16 images with extreme degradation. We also tested our approach against real-life images and our method performs favorably against previous methods.
Cyber-crime, Forensics and use of Data Mining in Cyber Space: A Survey

Cyberspace is a virtual computer environment or an electronic medium to ease online communication. It is a domain which facilitates interconnections between human beings through telecommunications and computers to store, modify or exchange data. Over time and increased usage of cyberspace, methods to exploit it have been developed. These methods are what is generally termed as cyber-crime. To combat these crimes, new laws have been introduced. But the law can only be called to effect with proper evidence. This gives rise to the need for cyber forensics. Data mining is a useful tool for this purpose, with varied applications and uses. In this paper, we will discuss what is defined as cyber-crime, the various types of crimes and criminals as well as some preventive measures using data mining. We shall also see the laws put in place, what cyber forensics is as well as why it is required.
Denoising Medical Images Using Hybrid Filter With Firefly Algorithm

In recent days, image processing assumes an indispensable part in numerous fields, especially in medical fields where we require a precise image details for further determination, diagnosis, research purposes etc. Medical images, for example, CT, X-ray, MRI has an imperfection that they create images with noise. These noise emerge because of camera sensor cells, poor enlightenment, transmission etc. These blended sort of noise ought to be expelled from the images keeping in mind the end goal is to deliver a quality image. So here we utilize a blend of filters to assess the noise from the images. The filtered output images are then assessed by method for firefly algorithm in light of artificial neural network systems for the exact image details. The nature of the picture is estimated by strategy for RMSE, PSNR and SSIM and so forth...
Cumulative Training and Transfer Learning for Multi-Robots Collision-Free Navigation Problems

Recently, the characteristics of robot autonomy, decentralized control, collective decision-making ability, high fault tolerance, etc. have significantly increased the applications of swarm robotics in targeted material delivery, precision farming, surveillance, defense and many other areas. In these multi-agent systems, safe collision avoidance is one of the most fundamental and important problems. Difference approaches, especially reinforcement learning, have been applied to solve this problem. This paper introduces a new cumulative learning approach which comprises of application of transfer learning with distributed multi-agent reinforcement learning techniques to solve collision-free navigation for swarm robotics. In our method, throughout the learning processes from the least complexity scenario to the most complex one, multiple agents can improve the shared policy through parameter sharing, reward shaping and multi-round multi-steps learning. We have adapted two policy gradient algorithms (TRPO and PPO) as the core of our distributed multiagent reinforcement learning method. The performance has shown that our new methodology can help reduce the training time and generate a robust navigation plan that can easily be generalized to complex in-door scenarios.
Feature Fusion and Enhancement for Single Shot Multibox Detector

Single Shot MultiBox Detector (SSD) is an effective method for multi-targets detection. However, due to the small scale and less information, small targets detection is a difficult assignment for SSD. In order to address this issue, we design an framework based on SSD, namely feature fusion and enhancement for SSD. First, considering that the feature information contained in different scale feature maps is different, especially the shallow feature maps which contains more detailed information and lacks semantic information, we propose a method of fusing different scale feature maps to introduce abundant object information. Secondly, although the feature maps has multiple channels, the importance of different channels' feature to the detected object is different. We introduce the feature enhancement module for effective feature enhancement. Training on the PASCAL VOC2007 and VOC2012, the proposed method has an mAP of 79.07%, which is 1.4% higher than SSD, and an FPS of 39 at the input size of 300 x300.
Knowledge-Based Filtering Recommender System to Propose Design Elements of Serious Game

Design elements of serious game recommender system is a web-based software to propose design elements of serious game to the system users. Design elements of serious game are components that required to design serious game such as features and game mechanics. The research about recommender system for design elements of serious game is a relatively new research in serious game domain because research about recommendation system mainly for game players not for game designers. Many things need to be considered by serious game developer to make a game which can help the players to learn something. The recommender system helped serious game developers to determine the design elements of the game from the learning content to make the game. The domain data are analyzed from the relationship of Bloom Digital Taxonomy with game genres and game mechanism. The system is developed using knowledge-based: case and constraint-based filtering. Case-based filtering is used to find similar serious game examples from the user input of learning goal, target rating, and target player. Constraint-based filtering is used to search recommendation from the knowledge base. To get recommendation from the recommender system, user need to input learning goal to the system, and the results are recommendation of serious game mechanics and features. The level of success of the recommendation system is measured by input some constraints into the system and match the constraints with the result of recommendation produced by the system. Furthermore, crosscheck of the system functionalities was done to evaluate the system. According to the evaluation result, design elements of serious game recommender system was able to provide game mechanics and game features as the base of serious game designing which suitable with the learning content. Another result of evaluation showed that the design of the recommender system could be developed to be a design elements of serious game recommender system to propose design elements of serious game that match with the learning contents.
A Dynamic Programming Approach to Secure User Image Data in Cloud Based ERP Systems

Cloud based ERP systems are now used worldwide for better organizational control and management. Centralized access structure, high degree of availability and low platform dependence are some of key reasons for which the demand of using Cloud ERP runs high in the business market. All of the cloud ERPs provides enhanced security measures which make the data safe in cloud storage. However, insider threat is one the challenges that still hovers around the cloud based ERP systems. Since there are almost no security mechanism for stopping an insider of an organization who exploits the information by knowing all the security protocol, it becomes evident that user based information security in the cloud ERPs can be enhanced if confidential information itself remains concealed in a separate security envelope, opaque to even administrators. This paper presents an approach to protect confidentiality of user image data in cloud ERPs from such threats using image realization steganography. It aims to generate a secure, selectively reversible mapping scheme between a generic cover and a specific secret image data and store the secret-cover mapping in cloud storage. The mapping itself is of no use unless supplied with the suitable user specific key and therefore ensures security through obscurity. The approach is backed by reliable and verifiable experimental results.
Probable Region Identification and segmentation in Breast Cancer using the DL-CNN

Breast Cancer in women is one of the most diagnosed diseases and it is one of the leading disease, which cause death. In past several research works have proposed various methodology to detect the cancer, however due to the Complex nature of micro calcification as well as masses it has the complex nature. Hence in this paper we have proposed a CNN based methodology named Dual layer CNN(DL-CNN), where we have used two layer Convolution Neural Network, first layer Is used for the Probable Region Identification and second layer is used for the Segmentation and false positive reduction. DL-CNN technique is robust in nature and identify the region in efficient manner. Moreover, for the evaluation we have used In Breast image dataset, other parameter considered are True Positive Rate at False positive per image. DL-CNN scores 0.9726 at 0.39706 respectively, it outperforms when compared to the other existing technique.
Determination of chalcophile elements in sulfide-bearing rocks by hydride generation inductively coupled plasma optical emission spectrometry and inductively coupled plasma mass spectrometry

Inductively coupled plasma mass spectrometry (ICP-MS) and inductively coupled plasma optical emission spectrometry with the preliminary hydride generation (HG-ICP-OES) techniques were used for the determination of chalcophile elements in sulfide-bearing rocks after different sample pretreatment. As and Se in the hydrochloric acid leaching solutions could not be determined neither by quadruple MS with correction equations, no by high resolution (HR-MS) (R = 10,000) due to high СHCl/СAs,Se ratio. Se, Sn, Ga, Bi, Te, Sb, Ge, Cd, Tl and Pb in hydrochloric acid leaching solutions can be determined by ICP-MS, and As, Se, Sn, Bi, Te and Sb by HG-ICP-OES. Main matrix elements - Co, Ni, Fe, Cu – don't affect the signal of analytes in case of ICP-MS method. The matrices of the sulfide ores and rocks were shown to significantly suppress the signals of the all elements using HG-ICP-OES techniques. To eliminate the influence of matrix components, the use of auxiliary reagents – 5% (m/V) KI and 0.5% (m/V) ascorbic acid C6H8O6 as well as 5% (m/V) KBr – has been proposed. Optimal conditions for obtaining the hydrides and for the determination of hydride-forming elements by HG-ICP-OES were established. Addition of KI and ascorbic acid mixture allows the determination of As, Sn, Bi and Sb. Six elements - Se, Te, As, Sn, Bi and Sb could be determined in the presence of 5% (m/V) KBr. The Сmin values for the determination of these elements by ICP-MS and HG-ICP-OES methods are comparable and vary between 0.01 and 0.02 μg g−1 for As, Se, Sb and Bi, and between 0.05 and 0.1 μg g−1 for Sn and Te.
A Local Patterns Weighting Approach for Optimizing Content-Based Image Retrieval Using a Differential Evolution Algorithm

In Content Based-Image Retrieval (CBIR), low-level visual characteristics like color, texture and shape are used to search for relevant images. However, the result images returned to the user are generally not satisfactory to his expectations. This is due to the gap between the low-level features of the image and the semantic (high-level) concepts given by the user to the same image. To overcome this challenge, we propose in this paper a mechanism that improves CBIR performance and consequently reduce the semantic gap. In that regard, our work involves the optimization of CBIR using a specific mechanism for weighting the extracted textural characteristics of the image. The extraction of the latter is carried out by some local patterns methods. Then, the generation of the weights associated with the local patterns, is realized using the Differential Evolution algorithm. To evaluate our approach, we tested it on Wang's database (Corel-1K). In addition, we adopted the precision as performance evaluation measure and we used Manhattan and Euclidean distances for comparing the local patterns histograms. The results of the carried-out experiments show that the obtained precisions by the weighted local patterns methods are better than those of the conventional methods.
Performance Comparison of Multiple Speech Features for Speaker Recognition using Artifical Neural Network

Human speech is the natural way of communication between humans. Speech signal contains among others, information that conveys the message being spoken by a person, speaker characteristics and language. It also carries information regarding the speaker's identity. The objective of Speaker Recognition Systems (SRS) is to automatically identify the speaker using the features extracted from their speech signals. Currently, SRS is one of the most popular biometric technique. It is used for surveillance, forensic speaker recognition, authentication, and such similar activities. In this work, Artificial Neural Network (ANN) based text-independent speaker identification is done. We compare the performance of Relative Spectral Amplitude-Perceptual Linear Prediction (RASTA-PLP), Mel-Frequency Cepstral Coefficient (MFCC) and Power Normalized Cepstral Coefficient (PNCC) features. To improve the performance accuracy of the system we extract features from the voiced frames of the signal. It was observed that MFCC outperforms the other two features namely PNCC and RASTA-PLP. TIMIT database was used for the experiments.
Experiences with Managing Data Ingestion into a Corporate Datalake

We explain our experiences in designing, building and running a large corporate Datalake. Our platform has been running for over two years and makes a wide variety of corporate data assets, such as sales, marketing, customer information, as well as data from less conventional sources such as weather, news and social media available for analytics purposes to many teams across the company. We focus on describing the management of data and in particular how it is transferred and ingested into the platform.
Rule-Based Pronunciation Models to Handle OOV Words for Indonesian Automatic Speech Recognition System

A representative pronunciation dictionary becomes a necessity to cover large vocabulary in many domains. While creating a hand designed pronunciation dictionary in an extensive corpus is expensive and time-consuming, a rule-based pronunciation generation method is considerably effective to be applied in Indonesian language due to simpler graphemetophoneme mapping. In this paper, we propose a rule based G2P translation including an efficient way to handle abbreviation and word splitting. The accuracy of the proposed G2P translation achieved 93.65% for Indonesian word entries, and 87.29% for the overall entries that contain abbreviation and words from other language. The addition of rule based G2P translated OOV words to the dictionary has comparable speech recognition system performance with the hand designed dictionary. With the same amount of OOV addition, the speech recognition system performance is reduced by only 0.27 percent when using the rule-based G2P translated dictionary compared to the hand designed dictionary. From the experiment results, generating a dictionary for the OOV words with the proposed G2P mapping is considerably efficient without a big gap in the speech recognition performance score while reducing great amount of time and resources.
Intuitionistic Fuzzy Decision Making Towards Efficient Team Selection in Global Software Development

For successful completion of any software project, an efficient team is needed. This task becomes more challenging when the project is to be completed under global software development umbrella. The manual selection of team members based on some expert judgment may lead to inappropriate selection. In reality, there are hundreds of employees in an organization and a single expert may be biased towards any member. Thus, there is a need to adopt methods which consider multiple selection criteria with multiple expert views for making appropriate selection. This article uses an intuitionistic fuzzy approach to handle uncertainty in the expert's decision in multicriteria group decision making process and ranking among the finite team members. An intuitionistic fuzzy Muirhead Mean (IFMM) is used to aggregate the intuitionistic criteria's. To gain confidence between criteria and expert score relationship, the Annova test is performed. The results are promising with p value as small as 0.02 and one-tail t-test score equals to 0.0000002.
Automated Image Splicing Detection using Texture based Feature Criterion and Fuzzy Support Vector Machine based Classifier

Nowadays manipulation in digital image becomes quite simple and convenient with the broad use of advanced image editing software and the recognition of forged region cannot be captured by naked human eyes easily. One of the most frequent alterations apply on the images is called image splicing, which consists of cropping a region from an image and paste it onto a different image in order to alter the information present in the new source. There are various methodologies which address this kind of attack, by dividing the image into blocks in an attempt to detect the inconsistencies created by the attack. Though features can be edges, corners, blobs or characteristics of noise changes, alteration in illumination acquired throughout the image. Moreover image splicing is quite harder to identify tampering in a forged image. In this paper the methodology used for splicing detection is relied on texture based features which are extracted from the tampered and actual images in the form of Grey Level Run Length Matrix calculated in four directions. The features have demonstrated the potential of proposed methodology used for identification of image forgery. Thereafter fuzzy support vector machine is utilized as a new classification technique with the high generalization capability. More effective methodology is proposed for detection of splicing forgery and produced improved classification outputs.
Identification of Co-Changed Classes in Software Applications Using Software Quality Attributes

When changes are made to software applications often, defects can occur in software applications, and eventually leads to expensive operational faults. Comprehensive testing is not feasible with the limited time and resources available. There is a need for test case selection and prioritization so that testing can be completed with maximum confidence in a minimum time. Advance knowledge of co-changed classes in software applications can be very useful during the software maintenance phase. In this article, the authors have proposed a co-change prediction model based upon the combination of structural code measures and dynamic revision history from change repository. Univariate analysis is applied to identify the useful measures in co-change identification. The proposed model is validated using eight open source software applications. The results are promising and indicate that they can be very beneficial in maintenance of software applications.
A Clustering Approach for Outliers Detection in a Big Point-of-Sales Database

Finding outliers, rare events from a collection of patterns, has become an emerging issue in the area of machine learning concerned with detecting and eventually removing anomalous objects in data. A key challenge with outliers/anomalies detection is because they are not a well-formulated issue. Outliers are defined as the extreme values that deviate from the overall patterns in data; they may indicate experimental errors, variability in measurement, or a novelty. Detecting outliers in large databases can lead to the discovery of hidden knowledge. However, identifying and removing outliers often helps to assure that the observations represent the problem correctly. Though there are several techniques for detecting outliers/anomalies in a given database, thus, no single technique is proven to be the standard universal choice. Depending on the nature of the target application, different implementations require the use of different outlier detection methods. The clustering method is a very powerful method in the field of machine learning and defines outliers in terms of their distance to the cluster centers. In this study, we propose a clustering-based approach to identifying outliers in a retail point-of-sales dataset. To select the best clustering algorithm for the purpose, two algorithms are applied, K-means for hard, crisp clustering, and (FCM) Fuzzy C-means for soft clustering. The experimental results show that the K-means algorithm outperforms the (FCM) Fuzzy C-means algorithm in terms of outlier detection efficiency, and it is an effective outlier detection solution.
Development of Competent Mathematical Speech of Students at Technical University

The development of literate speech of students, including mathematics, as one of the areas of communicative component of learning outcomes, is a requirement of higher education standards. The authors have analysed the educational standards for the content of the requirements to the general education of a graduate related to the development of literate speech, as well as the works of domestic and foreign teachers-researchers on the development of mathematical literacy. The literature review and the authors’ own experience of teaching mathematics to engineering students at technical University showed that the ability of students to use logically correct, reasoned and clear oral and written speech is developed insufficiently to apply the mathematical apparatus in their educational and professional activities. The authors have identified the criteria of competent speech and theoretical and methodological conditions of the educational process which enable the ensure the organizational and methodological support of educational process aimed at the development of competent mathematical speech of students. The main applied methods of teaching are advanced self-directed work of students, lecture-discussion, mutual dictation, interchange of tasks, interchange of themes, study of the text fragments, repetition training-game, etc. The effectiveness of educational activities is achieved through the use of active and interactive forms of teaching. The prepared organizational and methodological support of teaching, the systemic educational work allow students to improve their speech skills and skills of active use of mathematical language as a universal language of science, to develop logical, algorithmic and mathematical thinking, the ability to apply methods of mathematical analysis and modeling, theoretical and experimental research in solving professional problems.