Reviewerly/exHarmony huggingface dataset:
A Hybrid Cryptosystem Using Vigenère Cipher and Rabin-p Algorithm in Securing BMP Files

Vigenère cipher is a classical cryptography algorithm and similar to other classical algorithms, it produces smaller but less secure ciphertexts than a public key cryptography algorithm. Meanwhile, Rabin-p is a public key cryptography algorithm with a stronger encryption than Vigenère cipher. Nevertheless, as a public key algorithm, Rabin-p is inefficient to encrypt vast amounts of messages such as BMP image files, since the size of the cipherimages will increase manyfold and this would lead to a problem in storing and sending the cipherimages. To overcome these problems, in this study, we combined the Vigenère cipher and the Rabin-p algorithm in a hybrid cryptosystem scheme. In the experiment, the Vigenère cipher was used to encrypt the BMP files and the Rabin-p algorithm was used to encrypt the Vigenère keys. The result showed that the size of the cipherimages did not increase and the decryption procedure could recover the original BMP files while maintaining their integrity.
Personal privacy preserving data publication: Ɛ- Differential privacy perspective

Personal privacy preservation has been very important topic in Internet of Things (IoT) and big data world from the recent years. Though, many research studiescontain lot of restrictions about the opponent awareness that protect personal privacy of individual’s information. When the opponenthas a prior idea about the sensitive attributes then only it is possible to restrict attacks on the personal data. Today Differential Privacy (DP) is a fresh shift in personal privacy preservation with conventional assumptions about the opponent knowledge. Differential privacy is a good proposal in gaining the interest in many fields of information technology which gives more insights to work on the modern differential privacy analysis motivates to implement algorithms from that one can get optimal solutions to the personal privacy problems. This menu script proposes an algorithm based on the idea of differential privacy along with the Game theory mechanism to address the data publication of continuous data generation of medical records.KEYWORDS: PERSONAL PRIVACY, EQUIVALENCE CLASS, GENERALIZATION
Construction and Update of an Online Ensemble Score Involving Linear Discriminant Analysis and Logistic Regression

The present aim is to update, upon arrival of new learning data, the parameters of a score constructed with an ensemble method involving linear discriminant analysis and logistic regression in an online setting, without the need to store all of the previously obtained data.Poisson bootstrap and stochastic approximation processes were used with online standardized data to avoid numerical explosions, the convergence of which has been established theoretically.This empirical convergence of online ensemble scores to a reference "batch" score was studied on five different datasets from which data streams were simulated, comparing six different processes to construct the online scores.For each score, 50 replications using a total of 10N observations (N being the size of the dataset) were performed to assess the convergence and the stability of the method, computing the mean and standard deviation of a convergence criterion.A complementary study using 100N observations was also performed.All tested processes on all datasets converged after N iterations, except for one process on one dataset.The best processes were averaged processes using online standardized data and a piecewise constant step-size.
Consciousness and Understanding in Autonomous Systems.

This position paper highlights the importance of having a formal notion of understanding as one of the cornerstones in the construction of conscious AIs. It will show that the capability of understanding both the perceptual and the action flows is critical for the correct operation of situated autonomous systems. An assessment is also made on the contribution of the machine learning domain towards this direction.
Code-Switching and Code-Mixing in WhatsApp Group Chats by FEB UMY Lecturers

Communication is compulsory in everyday life of society.In the majority, people in today's society are multilingual; they use more than one language in their daily conversation.Moreover, in the current 4.0 industrial era, linguistic phenomena are increasingly varied, especially when it comes to the various media people use in conducting conversations.One of the familiar media used by society nowadays is WhatsApp (WA).The existence of a multilingual society supported by the presence of a media like WA will allow the occurrence of code-switching and code-mixing.This research was descriptive qualitative research with a documentation method.The study's data were the chats of the lecturers on the WhatsApp Group (WAG) of FEB (Faculty of Economics and Business) UMY lecturers.It aimed to describe the types of code-switching and code-mixing, the factors causing it, and its functions.It found that the types of code-switching in WAG of FEB UMY lecturers were internal and external code-switching.Meanwhile, the caused factors were the speakers, the speech partners, and the changing topic in the conversation.Furthermore, this research also uncovered that the types of code-mixing in the WAG were insertion (word phrase), alternation, and congruent lexicalization.This code-mixing was caused by the limitation in the use of the code, the use of popular terms, speech partners, topics to evoke a sense of humor, and the last was to be merely prestigious.Besides, the social functions of both code-switching and code-mixing were to assert power, declare solidarity, and convey the affective meaning of information.
THE EFFICACY OF L1 REGULARIZATION IN NEURAL NETWORKS

A crucial problem in neural networks is to select the most appropriate number of hidden neurons and obtain tight statistical risk bounds. In this work, we present a new perspective towards the bias-variance tradeoff in neural networks. As an alternative to selecting the number of neurons, we theoretically show that L1 regularization can control the generalization error and sparsify the input dimension. In particular, with an appropriate L1 regularization on the output layer, the network can produce a statistical risk that is near minimax optimal. Moreover, an appropriate L1 regularization on the input layer leads to a risk bound that does not involve the input data dimension. Our analysis is based on a new amalgamation of dimension-based and norm-based complexity analysis to bound the generalization error. A consequent observation from our results is that an excessively large number of neurons do not necessarily inflate generalization errors under a suitable regularization.
Improving vehicle re‐identification using CNN latent spaces: Metrics comparison and track‐to‐track extension

Herein, the problem of vehicle re-identification using distance comparison of images in CNN latent spaces is addressed. First, the impact of the distance metrics, comparing performances obtained with different metrics is studied: the minimal Euclidean distance (MED), the minimal cosine distance (MCD) and the residue of the sparse coding reconstruction (RSCR). These metrics are applied using features extracted from five different CNN architectures, namely ResNet18, AlexNet, VGG16, InceptionV3 and DenseNet201. We use the specific vehicle re-identification dataset VeRi to fine-tune these CNNs and evaluate results. Overall, independently of the CNN used, MCD outperforms MED, commonly used in the literature. These results are confirmed on other vehicle retrieval datasets. Second, the state-of-the-art image-to-track process (I2TP) is extended to a track-to-track process (T2TP). The three distance metrics are extended to measure distance between tracks, enabling T2TP. T2TP and I2TP are compared using the same CNN models. Results show that T2TP outperforms I2TP for MCD and RSCR. T2TP combining DenseNet201 and MCD-based metrics exhibits the best performances, outperforming the state-of-the-art I2TP-based models. Finally, experiments highlight two main results: i) the impact of metric choice in vehicle re-identification, and ii) T2TP improves the performances compared with I2TP, especially when coupled with MCD-based metrics.
An Empirical Analysis of Threshold Techniques for identifying Faulty Classes.

The experimental validation of the proficiency of the proposed techniques is a mandatory task in the research regime. The existing literature has proved the presence of extensive work attained the statistical validation of software metrics by utilizing them in the development of fault prediction models, where, both statistical and machine learning techniques were engaged into the construction of the models being capable of identifying faulty and non-faulty classes. On the contrary, the research area involving the investigation of threshold concept has not gained sufficient maturity. An effective threshold technique can assist in the identification of optimal cut-off value in software metric which can discriminate the faulty from non-faulty classes with minimal misclassification rate. The idea of threshold calculation can make the applicability of the existing metrics in software industries, a much easier task. As the developers only need to know the cut-off values which can help them to concentrate on the specific classes that exceeds the computed thresholds. Also, the presence of peculiarity in the software metric index can alert the testers and in turn helps them to disburse the resources systematically. The current study empirically validated and compared the discriminating strength of two threshold techniques, i.e., ROC curve and Alves Rankings, on the public dataset. This study selected twenty Object Oriented (OO) measures for the process of threshold calculation. Besides, the widely addressed metric suite proposed by Chidamber and Kemerer, this study also considered other fourteen OO measures for the experiment. Furthermore, Wilcoxon signed ranks test was used to enquire the classification difference between the aforementioned threshold techniques. The outcome from the statistical analysis revealed the better predictive capability of ROC curve than the Alves Rankings.
A Comprehensive Survey of Feature Extraction and Feature Selection Techniques of Face Recognition System

Among various types of biometric techniques it is the face recognition that gains huge popularity in the last few decades due to its vast applicationsand more user friendly nature.Face Recognition is the procedure of identification and verification of a person's identity using his face.Main steps involved in a FR System are Face Detection, Feature Extraction, Feature Selection and Recognition.Feature Extraction and Feature Selection are the two main phases to be focused on in order to get a good FR System.In this paper, we have carried out anextensive literature review on face recognition focusing on feature extraction and feature selection phases a bit more.
Real-time detection of uncalibrated sensors using neural networks

Abstract Nowadays, sensors play a major role in several fields, such as science, industry and everyday technology. Therefore, the information received from the sensors must be reliable. If the sensors present any anomalies, serious problems can arise, such as publishing wrong theories in scientific papers, or causing production delays in industry. One of the most common anomalies are uncalibrations. An uncalibration occurs when the sensor is not adjusted or standardized by calibration according to a ground truth value. In this work, an online machine-learning based uncalibration detector for temperature, humidity and pressure sensors is presented. This development integrates an artificial neural network as the main component which learns from the behavior of the sensors under calibrated conditions. Then, after being trained and deployed, it detects uncalibrations once they take place. The obtained results show that the proposed system is able to detect the 100% of the presented uncalibration events, although the time response in the detection depends on the resolution of the model for the specific location, i.e., the minimum statistically significant variation in the sensor behavior that the system is able to detect. This architecture can be adapted to different contexts by applying transfer learning, such as adding new sensors or having different environments by re-training the model with minimum amount of data.
LR-Net: A Multi-task Model Using Relationship-based Contour Information to Enhance the Semantic Segmentation of Cancer Regions

The segmentation of cancer regions is a key step in pathological image analysis. Although traditional methods (such as U-Net) have achieved good results in general medical image segmentation, the segmentation performance of the tumor region is still unsatisfactory because the boundary of the tumor is too blurred. Moreover, most tumor region segmentation methods focus on the learning of image content features while ignoring learning relationship among pixels on tumor contours. In this paper, we developed a multi-task learning technique to enhance the importance of contours and increase the weight of pixels relationship learning for the tumor segmentation. Different from the traditional single-decoder network, a parallel contour decoder with LRLM (location relationship learning module) is introduced as an auxiliary decoder to learn the relationship-based features of tumor contours, which forms a two-decoder network. To promote the information fusion of the two tasks, the two decoders share a same encoder with bidirectional skip connections between the auxiliary contour decoder and the main content decoder. Experimental results show that LR-Net is superior to many popular approaches, such as CE-Net and U-Net.
Dropout at university level in Serbia: Analysis of measurement, research findings, services and prevention measures

Dropout in higher education attracts great attention due to serious effects on an individual and on society. This is an exploratory study aimed at: analysing drop?out measurement and tracking in the Serbian higher education, reviewing studies on dropout causes, and identifying available services and prevention measures. Various techniques are used to achieve these goals (statistics analysis, literature review, qualitative content analysis). Our results show that: 1. There are no accepted dropout definitions nor official statistics, but some rate estimate could be given; 2. Research is not systematic, but covers three major dropout factors (individual, socio-demographic, studying conditions); 3. Services are sporadic and not visible enough; 4. Legislation reveals state interest in promoting higher education, but implemented measures are not adequate enough. We see establishment of the Unified Education Information System as an important step in precise dropout measurement. There is a need for flexible studying routes and increased financial support orientated more towards vulnerable student groups. International studies highlight the importance of intensification of teacher-student and peer interaction built in modern student-centred teaching practice. At the end, we advocate creating a new educational policy by combining two major frameworks in this field and relying on inclusive research.
Digital Governance: How Blockchain Technologies Revolutionize the Governance of Interorganizational Relationships

Digital technologies are changing the business world at a rapid pace, bringing new challenges and opportunities for companies. This is especially true for blockchain technologies, which are increasingly being used to manage interorganizational transactions. Blockchains are distributed ledger technologies where data is replicated, shared, and synchronized over a network of computers without central servers. The main advantages of blockchains over standard contracts are that they are particularly secure, easily scalable, and comparatively inexpensive. At the same time, blockchains also present managers with a number of challenges in terms of network participation rules, control mechanisms, technology choices, and legal affairs. In this book chapter, we discuss these aspects and provide practical guidance for decision-makers. This discussion makes it easier for managers to determine whether and under what conditions blockchains offer an advantage for their business.
Bootstrap Analysis of Compression Algorithms

Compression algorithms have been proposed with the technology advance. However, there are not objective analysis procedures to guide a future choice of an algorithm directed for the type of data in the system they are intended for. This paper introduces a statistical framework, based on the bootstrap method, to execute the analysis of compression algorithms using an objective comparison parameter as a criterion. A case study using the compression ratio as the parameter and file samples of 4 different types was analyzed. The proposed scheme allowed us to infer which algorithm is better to be used for each data type. RLE has proven more suitable to image, audio and video files with Huffman obtaining comparable performance. For text files, LZW has remarkably outperformed all other algorithms.
PSYCHOLOGICAL AND PEDAGOGICAL FEATURES OF TEENAGERS IN THE CONTEXT OF AESTHETIC EDUCATION

The aim of this paper is to prove: to determine the main periods of individual mental development of the individual and their criteria, which include characteristic for each age social situation of development, leading activities in its relationship with other activities. In our study – this is an aesthetic activity provided by effective aesthetic education of adolescents. The mental development of the body of adolescents occurs within a certain function of the psyche and is based on the mechanisms of maturation of the relevant physiological systems. This suggests that teenager is a transition between childhood and adulthood and it determines the uniqueness of the social situation of its development. In adolescence, there is a change in reference orientations. As a «significant teenager», he is looking for a reference group – a real or conditional community in which he can carry out acts of self-affirmation. There is a need for self-improvement, self-esteem, the need to «understand yourself». The teenager motives become more conscious, more fully and deeply experienced. The socialization occurs without any problems. We create a situation of acme success, which under the conditions of effective aesthetic activity provides aesthetic education of teenagers. The teenagers, performing any aesthetic activity and receiving certain information, socialize; develop in various ways and harmoniously.
Modeling blockchain e-health systems

E-health can be defined as a set of health-related computing solutions that uses the Internet to provide services. In these systems, managing the sharing of the various types of generated data is a challenge, making it difficult to develop and maintain. A software intensive system is a system in which software essentially influences the design, construction, deployment, and evolution of the system as a whole to encompass individual applications, subsystems, systems of systems, and aggregations of interest. Blockchain has emerged as a solution to provide privacy and data security, providing interesting solutions for E-health applications. The objective of this work is to present architectural and behavioral aspects of E-health systems, using Blockchain. In addition, some research opportunities are also presented.
CLUE: Personalized Hospital Readmission Prediction Against Data Insufficiency under Imbalanced-Data Environment

Hospital readmission prediction employs reliable predictive models to evaluate the readmission risk of patients upon discharge. Identifying patients with high readmission risk and paying additional attention to them can ease the burden on both patients and society. Recently, considerable attention has been paid to personalized readmission predictions, i.e., building an independent model for each target patient. However, existing personalized predictive models can be easily affected by data insufficiency and provide poor generalization capabilities. To address these challenges, in this paper, we propose the CLusterbased mUlti-task lEarning model (CLUE) to achieve personalized hospital readmission prediction. CLUE groups patients into different clusters based on a multi-angle similarity metric to preserve the interrelated information of patients with highly similar clinical behaviors. Due to different group characteristics of patients, the clusters of patients are imbalanced. Given that, CLUE treats the hospital readmission prediction for each cluster of patients as one task and learns multiple tasks jointly by parameter sharing mechanisms. In this way, not only can the data insufficiency problem be alleviated by supplementing individual models with the shared information from other clusters, but also the specific information of each cluster can be preserved for personalization. We conduct extensive experiments on a real-world dataset of electronic health records, and show that CLUE significantly outperforms competitive comparative methods.
The Technical Debt Density Over Multiple Releases and the Refactoring Story

Do developers postpone fixing Technical Debt (TD) in software systems? TD is a metaphor that refers to short-term decisions in software development that may affect the cost of the software development life cycle. The bad smell is an imperfect solution in the software system that negatively impacts the internal software quality and maintainability. In this paper, we will study five open-source software projects (OSSPs) that have several releases and also estimate the numbers of architecture smells (ASs), design smells (DSs), and code smells (CSs) for every release. Designite will be used to detect smells. We describe a case study conducted to explore the following: (1) What is the average smells density for architecture, design, and code smells in an OSSP? (2) Does the density of each smell type increase over multiple releases? (3) What percentage of each smell-type density is eliminated by refactoring? We collected around 2 million LOC from five OSSPs that have multiple releases from the GitHub repository to statistically analyze the software concerning the smells as indicators of TD. We find 36% of Architecture Technical Debt (ATD) is Cyclic Dependency, while 33% of Design Debt (DD) is Cyclically-dependent Modularization. More than 70% of Code Debt (CD) is Magic Number. Even though the developers do refactoring between releases, the TD density in general increases. On average, by refactoring, developers remove around 48%, 16%, and 22% from the introduced ATD, DD, and CD from their next release, respectively.
Klasifikasi Jenis Pisang Menggunakan Support Vector Machine dengan Fitur GLCM dan HOG

Banana fruit supplies not only the domestic market, but also the international market. In the process of introducing a variety of bananas are generally done in two ways, firstly done manually by humans to introduce bananas and secondly to use destructive methods by taking samples. The problem that occurs in this second process is having relatively large and greater costs. This requires a system that can classify bananas using digital image processing and the Vector Support Machine (SVM) implemented in this study. The image of a banana is taken with a Xiaomi Note 4x cellphone camera and is processed using Matlab software. Digital images are used to extract the shape and texture features of bananas, while SVM is used for banana classification. This study uses 420 images of bananas divided into 7 classes, namely Ambon banana class, Barangan banana class, golden banana class, Kepok banana class, Raja banana class, milk banana class and banana horn class. Where in the test using cross validation for 7 classes of bananas. SVL is able to classify the types of bananas in the image with GLCM and HOG features in iteration 1 with an overall accuracy of 74.28% in the type of milk bananas