Reviewerly/exHarmony huggingface dataset:
Encrypted Classification Using Secure K-Nearest Neighbour Computation

Machine learning (ML) is one of the growing areas of engineering with sweeping applications. Executing machine learning algorithms on vast amount of data raises demand of huge resources and large data set handling. Thus, machine learning was too costly for many enterprise budgets. However, cloud service suppliers are making this technology reasonable to enterprises by offering massive shared resources. Machine learning as a service (MlaaS) is a category of cloud computing services that provides machine learning tools to allow customers to run, develop and manage applications in cloud without the complexity of building and maintaining. However, ascent of machine learning as a service procreates scenarios where one faces concealment dilemma, where the model must be revealed to the outsourced platform. Hence, cloud data security is an important issue where users can fancy the ability of executing applications by outsourcing sensitive data. Fully Homomorphic Encryption (FHE) offers a refined way to accommodate these conflicting interests in the cloud scenario by preserving data confidentiality as well as applying Mlaas in secure domain. However, processing on FHE data can not be directly performed on traditional instruction execution flow, but requires special circuit based representation of algorithms. In this paper, we focus on realizing K-Nearest Neighbour (KNN) computation on encrypted data, where data is stored using a generalized encrypted representation. Such representation will be suitable for easily extending to encrypted ensemble learning framework supporting multiple encrypted learners for higher accuracy. Extensive performance studies are carried out to evaluate the timing overhead of the encrypted KNN computation.
Hole filling algorithm for image array of one-dimensional integrated imaging

Compared to traditional integrated imaging, the one-dimensional integrated imaging system abandons the stereoscopic effect in the vertical direction, leaving only the stereoscopic effect in the horizontal direction. While satisfying the stereoscopic perception, it greatly reduces the storage space of data and alleviates the large attenuation of resolution caused by integrated imaging. However, at present, there is no 3D film source suitable for a one-dimensional integrated imaging system. In order to address this problem, we propose an array image generation and padding algorithm based on a one-dimensional integrated imaging system. First, based on the theory of geometric optics and DIBR(Depth-image Based Rendering), we use depth maps to simulate viewpoint images at arbitrary locations. In the process, we classify the points mapped to the viewpoint image to make the generated viewpoint image more accurate. Secondly, when conducting hole filling, we first use the optical flow method to fill the large holes inside the image. We extract the edge of the hole, compare the depth values on both sides of the edge, and estimate the depth value of the hole by taking the optical flow value on the larger side, so as to calculate the mapping block of the hole on the reference frame. Finally, the other holes are filled using the Criminisi image restoration algorithm.
Bayesian Neural Networks of Probabilistic Back Propagation for Scalable Learning on Hyper-Parameters

Extensive multilayer neural systems prepared with back proliferation have as of late accomplished best in class results in some of issues. This portrays and examines Bayesian Neural Network (BNN). The work shows a couple of various uses of them for grouping and relapse issues. BNNs are included a Probabilistic Model and a Neural Network. The plan of such a plan is to join the qualities of Neural Networks and stochastic demonstrating. Neural Networks display ceaseless capacity approximates abilities. Be that as it may, utilizing back drop for neural networks adapting still has a few disservices, e.g., tuning a substantial figure of hyper-parameters to the information, absence of aligned probabilistic forecasts, and a propensity to over fit the preparation information. The Bayesian way to deal with learning neural systems does not have these issues. Nonetheless, existing Bayesian systems need versatility to expansive dataset and system sizes. In this work we present a novel versatile strategy for learning Bayesian neural systems, got back to probabilistic engendering (PBP). Like traditional back spread, PBP works by figuring a forward engendering of probabilities through the system and afterward completing a retrogressive calculation of inclinations. A progression of analyses on ten true datasets demonstrates that PBP is essentially quicker than different methods, while offering aggressive prescient capacities. Our examination additionally demonstrates that PBP-BNN gives precise appraisals of the back change on the system weights.
E-Transaction Services for Retail Business Process in IoT using Object Analysis and Design

Abstract The transaction process in business is an important factor that must be maintained for a business increase. This activity can be maintained by computerized. Application of computer which help process business transactions is an important thing that should be provided. This research aims to provide a system that helps routine transaction in daily food sales. Transaction in this system has included activities such as ordering, fulfil orders, payment, recording, financing, and reporting to management. Implementation of the system, that have used a prototype system development method. At each stage of the prototype is assisted by object-oriented programming, namely UML (Unified Modelling Language). Use of UML diagrams includes use case diagrams, activity diagrams, sequence diagrams, class diagrams, and deployment diagrams. The results of this research are web technology-based transaction systems. The system has included order, delivery of an order, payment, order recording, and financial reporting for management. The system is made for the implementation of five main business processes that help the transaction system. The main business processes, that have created, is consisted of services, order, fulfil orders, financial services, and report for management.
Proof of the method of paired zeroing of numbers in a residue system

Abstract The paper studies the problem of determining the lower boundary of number correction speed in computing systems operating in the basis of non-positional arithmetic in residual classes. The topicality of the problem is necessitated by the search of methods allowing reducing the time for digital processing of signals in non-positional neuroprocessors. The work considers the variants for error correction with single and multiple control bases of the residue system. The proof is provided that allows justifying the adequacy of the modified method that implements paired zeroing of numbers in the system of residual classes. It was shown that the suggested solutions allow considerably reducing the time expenses for digital processing of signals in neuroprocessors specialized in summation and multiplication operations. The proof is elaborated by the method of mathematical induction.
PolyVis: Cross-Device Framework for Collaborative Visual Data Analysis

Multi device environments present new opportunities for collaborative visual data analysis and sense making by utilizing each device's strengths and capabilities. However, one of the associated challenges with visual data analysis in multi device environments is the sharing of visual components across devices. We present a framework developed on top of SAGE2 platform for cross-device collaborative visual data exploration. As part of our framework, we contribute the concept of rapid development and assembling of visualizations that can span multiple devices of different modalities. It provides the users with an environment for visualization compositions that delegate the rendering to the target device, allowing them to augment their large display workspace with portable devices for further exploration territories. Facilitated by its intuitive visualization composition pipeline, users with no programming skills such as data analysts can enhance their analytical scope with no coding barriers. We describe the framework, its implementation with a use case, and the rationale behind its design.
Migration Pool Technique for Parallel Particle Swarm Optimization Algorithm

Particle swarm optimization algorithm (PSO) is used to solve optimization problems. In this algorithm, communication between particles is an important factor that enables us to find an optimum or near optimum solution. In parallel version of this algorithm, communication is ensured by the migration of particles between sub-population. In this study, we present a new migration pool technique when selecting the particle to migrate. According to the number of processors, we set three migration pool size. Then, we apply the proposed migration pool technique on 9 different test functions. The results show what the migration pool size should be for each function to reach the optimal solution.
RCorp: a Resource for Chemical Disease Semantic Extration in Chinese

To identify knowledge of combination therapy for chronic diseases, high-quality manually curated corpora are required. In this study, we describe the construction work of a corpus for chemical disease semantic extraction in Chinese (RCorp) from a collection of Chinese biomedical abstracts. During the RCorp corpus construction, we used domain-existing guidelines as references, and incorporated annotation tools to the standard annotation process. The resulting corpus consists of 339 Chinese biomedical articles with 2,367 annotated chemicals, 2,113 diseases, 237 symptoms, 164 chemical-induce-disease relations, 163 chemical-induce-symptom relations, and 805 chemical-treat-disease relations. RCorp gets an inter-annotator agreement score of 0.883 for chemical entities, 0.791 for disease entities which are measured by F score. And the F score for chemical-treat-disease relations gets 0.788 after unifying the entity mentions. The result analysis of the corpus proves the quality of the corpus for the combination therapy of chronic diseases knowledge discovery task.
Replica analysis of Bayesian data clustering

We use statistical mechanics to study model-based Bayesian data clustering. In this approach, each partition of the data into clusters is regarded as a microscopic system state, the negative data log-likelihood gives the energy of each state, and the data set realisation acts as disorder. Optimal clustering corresponds to the ground state of the system, and is hence obtained from the free energy via a low 'temperature' limit. We assume that for large sample sizes the free energy density is self-averaging, and we use the replica method to compute the asymptotic free energy density. The main order parameter in the resulting (replica symmetric) theory, the distribution of the data over the clusters, satisfies a self-consistent equation which can be solved by a population dynamics algorithm. From this order parameter one computes the average free energy, and all relevant macroscopic characteristics of the problem. The theory describes numerical experiments perfectly, and gives a significant improvement over the mean-field theory that was used to study this model in past.
Convergence characteristics at stochastic estimation of image inter-frame deformations

Several approaches to the numerical description of image inter-frame geometric deformations parameters estimates behavior at iterations of non-identification relay stochastic gradient estimation are considered. The probability density of the Euclidean mismatch distance of estimates vector is chosen as an argument of the characteristics forming the numerical values. It made it possible to ensure invariance of research to the set of parameters of the used inter-frame geometric deformations model. The mathematical expectation, the probability of exceeding a given threshold value of the convergence rate and the confidence interval of the Euclidean mismatch distance were investigated as characteristics. Probabilistic mathematical modeling is applied to calculate the probability density of the Euclidean mismatch distance.
Learning a minimum similarity projection and lowest correlation representation for image classification

The projection and representation learning is an attractive tool for image classification problem due to its effectiveness and efficiency of extracting interior structure for data. However, the complexity and diversity of real data lead to the decline of classification performance. A novel image classification method is proposed by learning a minimum similarity projection and lowest correlation representation. This method attempts to produce a discriminative representation on a low-dimensional space for the data, which takes two steps: feature projection and feature representation. By learning a projection matrix, the feature projection aims to map the samples into a low-dimensional space which jointly minimises the similar within-class difference and maximises the dissimilar cross-class difference. A discriminative representation for the data on the new space is generated by using the de-correlated effect to the representation results of all classes. Therefore, the learned projection and representation simultaneously demonstrate discriminative properties in the learning of both steps. The extensive experiments conducted on different visual classification tasks consist of face recognition, object categorisation, and scene classification that the proposed method performs superior performance for image classification.
Efek Variasi Substrat Terhadap Sifat Listrik Boron Nitrida

Abstrak Tugas akhir ini mempelajari sebaran lapisan BN dan sifat lstrik BN (Boron Nitrida) yang dideposisi di atas substrat Polyethylene Terephthalate (PET), Indium Tin Oxide/Polyethylene Terephthalate (ITO/PET), dan SiO 2. Lapisan BN dibuat dengan cara memodifikasi 1 mg/mL BN menggunakan metode eksfoliasi fasa cair yang ditambahkan interkalator 2 mg/mL NaOH dan diikuti dengan drop casting di atas substrat. Sebaran lapisan BN pada PET dan ITO/PET terlihat merata, sedangkan pada SiO 2 terlihat daerah tipis dan sebagian tebal. Perilaku semikonduktor terlihat pada BN/PET yang menghasilkan arus -315 nA sampai 154 nA saat tegangan divariasi dari -10 v sampai 10 v dengan Vth = 2V dan memiliki resistivitas 0,29x10 4 I©cm. Perilaku yang sama ditunjukan oleh BN/SiO 2 yang menghasilkan arus -0,7 mA sampai 0,1 mA saat tegangan divariasi dari -10V sampai 10V dengan V th = 4V dan mempunyai resistivitas 1,1 I©cm. Hal ini berbeda pada BN pada ITO/PET yang menunjukkan perilaku gabungan insulator dan semikonduktor serta menghasilkan arus berkisar antara -4,3 mA sampai 3,14 mA saat tegangan divariasi dari -5V sampai 5V dan memiliki resistivitas 0,02 I©cm. Dapat disimpulkan bahwa substrat mempengaruhi sebaran BN, ketebalan lapisan BN, dan sifat listrik BN. Namun tidak ada pengaruh substrat terhadap serapan dan emisi spektrum cahaya. Kata Kunci: BN, PET, ITO, SiO 2 , Eksfoliasi, Sifat Listrik Abstract This final project aims to understand the distribution of Boron Nitride (BN) layer and the electrical properties of BN deposited on Polyethylene Terephthalate (PET), Indium Tin Oxide/Polyethylene Terephthalate (ITO/PET), and SiO 2 substrate. The BN layer was created by modifying 1 mg/mL BN using liquid phasse exfoliation method which was added by intercalator 2 mg/mL NaOH followed by drop casting deposition on top of the substrates. BN layers were distributed more uniform on PET and less uniform on ITO/PET, while BN layers on SiO 2 consisted of thick and thin layers. Electrical characterization was conducted by varying bias voltage and observing output current. Typical semiconductor characteristic I-V curve was observed on BN/PET. The current varies between -315 nA to 154 nA with bias voltage ranging from -10 v to 10 v and the Vth = 2 v. It has resistivity of 0,29x10 I©cm. The same characteristic is showed by BN/SiO2 which produces currents -0.7 mA to 0.1 mA with bias voltage ranging from -10 to 10v and the Vth = 4 v. It has resistivity of 1.1 I©cm. This is different with BN deposited on ITO/PET showing both insulators and semiconductors which produces currents ranging from -4.3 mA to 3.14 mA with bias voltage ranging from -5 v to 5 v. It has resistivity of 0.02 I©cm. It can be concluded that substrates influence the BN layer distribution, BN layer thickness, and electrical properties. However, there is no effect on optical spectrum measurement. Keywords: BN, PET, ITO, SiO 2 , Exfoliation, Electrical Properties, Mechanical Properties.
Taxonomy of an Application Model

With the advent of advanced computing systems beyond personal computing, such as mobile computing, cloud computing or recently, vehicular ad-hoc network, it is crucial that we understand the application development process of each type of these systems. Better understanding of how applications are built in different environment allows us to design better application models and system supports for developers. This paper studies the taxonomy of application models and defines its consisting aspects, namely application scope, application abstraction level, application structure, communication model and programming model. With the better understanding of the application models in general, we lay out the requirements for developing a class of large scale connected vehicle applications.
Training System for Interdisciplinary Relationships

The article examines a learning system that allows the creation of electronic textbooks. In addition, when using a learning environment, it is often necessary to repeat a topic from previously studied basic disciplines, which form interdisciplinary relationships. In the course of learning, students often have to use a variety of information and reference materials, the essence of which is determined by the field of study. These can be regulations, standards, technical references of materials used, guides on the choice of certain parameters, regulatory — legal documents, financial advice or court practice, etc. Instrumental technologies based on a non-linear way of presenting information and contributing to the unity of disparate knowledge bases (interdisciplinary communication in learning) can improve the quality of education and become a significant motivation for studying related disciplines. Examples are given of working with a training system for interdisciplinary relationships.
Design and Analysis of an Isotropic Wavelet Features-Based Classification Algorithm for Adenocarcinoma and Squamous Cell Carcinoma of Lung Histological Images

One of the most prevailing types of lung cancer is non-small cell lung cancer (NSCLC). Differential diagnosis of NSCLC into adenocarcinoma (ADC) and squamous cell carcinoma (SCC) is important because of prognosis. Histological images are taken from a database consisting of 72 lung tissue samples collected indigenously with a core needle biopsy. In this work, a novel method has been developed where the features of ADC and SCC for a histological image are taken from various statistical and mathematical models implemented on the coefficients of the wavelet transform of an image. The method provides a precision of 95.1% and 96.2% in classifying malignant and non-malignant tissue type respectively. This methodology of classifying ADC and SCC without coding clinical diagnostic features into the system is a necessary step forward towards an autonomous decision system.
Semantic Document Classification Based on Semantic Similarity Computation and Correlation Analysis

Document (text) classification is a common method in e-business, facilitating users in tasks such as document collection, analysis, categorization and storage. However, few previous methods consider the classification tasks from the perspective of semantic analysis. This paper proposes two novel semantic document classification strategies to resolve two types of semantic problems: (1) polysemy problem, by using a novel semantic similarity computing strategy (SSC) and (2) synonym problem, by proposing a novel strong correlation analysis method (SCM). Experiments show that the proposed strategies improve the performance of document classification compared with that of traditional approaches.
Nonlinear analysis of the degree of order and chaos of morphology of porous silicon nanostructures

This work has been done to identify quantitative criteria the degree of order and chaos morphology of porous layers consisting of silicon nanowire arrays. In order to fulfill the work, a method of using metal-assisted chemical etching has been utilized to produce nanowires. There has been done a work of digital processing of porous film images which were extracted by scanning electron microscope. Informational-entropic and Fourier analysis have been applied to quantitatively describe the degree of order and chaos in nanostructure distribution in the layers. Self-similarity of the layer morphology has been quantitatively described via its fractal dimensions by correlation method. The applied approach for image processing allows us to distinguish the morphological features of as-called "black" (more ordered) and "white" (less ordered) silicon layers, which are characterized by minimal and maximal optical reflection, respectively. From all of the methods of digital techniques that we have used the method for determining the conditional information of a chaotic set was proved to be the most informative.
Investigating DNS Manipulation by Open DNS Resolvers

Open DNS resolvers are resolvers that perform recursive resolution on behalf of any user. They can be exploited by adversaries because they are open to the public and require no authorization to use. Therefore, it is important to understand the state of open resolvers to gauge their potentially negative impact on the security and stability of the Internet. In this study, we conducted a comprehensive probing over the entire IPv4 address space and found that more than 3 million open resolvers still exist in the wild. More importantly, we found that many open resolvers answer queries with the incorrect, even malicious, responses. Contrasting to results obtained in 2013, we found that the number of open resolvers has decreased significantly, while the number of open resolvers providing malicious responses has increased.
The Implicit Effect of Items Rating on Recommendation System

Currently, most of the recommendation systems use user’s feedbacks to suggest an item for the user. However, the current recommendation system used only explicit rating information, which not fully evaluating the factors that directly affect the feeling of users in rating. To achieve more accurate results, this paper proposes a solution to add the implicit effect of items rating to the recommendation system based on the TrustSVD model and matrix factorization (MF) techniques. The experimental results showed our proposed solution achieve better than 18% the matrix factorization method and 15% the Multi-Relational Matrix Factorization method.