Reviewerly/exHarmony huggingface dataset:
Latent Multi-view Semi-Supervised Classification.

To explore underlying complementary information from multiple views, in this paper, we propose a novel Latent Multi-view Semi-Supervised Classification (LMSSC) method. Unlike most existing multi-view semi-supervised classification methods that learn the graph using original features, our method seeks an underlying latent representation and performs graph learning and label propagation based on the learned latent representation. With the complementarity of multiple views, the latent representation could depict the data more comprehensively than every single view individually, accordingly making the graph more accurate and robust as well. Finally, LMSSC integrates latent representation learning, graph construction, and label propagation into a unified framework, which makes each subtask optimized. Experimental results on real-world benchmark datasets validate the effectiveness of our proposed method.
A Credential Data Privacy Preserving in web Environment using Secure Data Contribution Retrieval Algorithm

Preservation of privacy is a significant aspect of data mining and as the secrecy of sensitive information must be maintained while sharing the data among different untrusted parties.There are much application is suffering from vulnerable, data leakage, data misuse, and sensitive data disclosure issues.To protect the privacy of sensitive data without losing the usability of data, various techniques have been used in privacy-preserving data mining (PPDM).However, a system is unable to maintain the privacy during online services.Some of the approaches are available to maintain the tight privacy, but they fail to minimize the execution time and error rate.The main objective of the article is to contribute and retrieve the data with minimal classification error and execution time with enhanced privacy.To overcome the issues, the paper introduces the Secure Data Contribution Retrieval algorithm to fulfill the current issues.Proposed algorithms define a privacy policy and arrange the security based on requirements.This design applies the privacy based on the compatibility of applications.This approach computes the union of private multi-datasets that each of the interacting with attributes and actors and another that tests the inclusion of an element held by one actor in a subset of another.It displays the table with hidden attributes in multiple categories wise for a user.This approach is capable of satisfying the accuracy constraints for multiple datasets.It also considers the efficient data extraction with a good ranking of attributes in tables.Based on experimental result proposed approach performs well regarding success rate, error rate and system execution time compare than existing methods.
LibGuides: UG CamGuides: How do I find books and articles from a reading list?: Terminology

Break down a reading list into different types of resource and know how to recognise them from their reference.
Nanosys licenses quantum dot patents

Nanosys will license quantum dot technology developed by Uri Banin, a chemistry professor at Hebrew University of Jerusalem. The portfolio includes 60 issued and pending patents related to heavy-metal-free quantum dots and structures, such as nanorods for improved photoelectronic performance. Nanosys says its quantum dot materials have been used in 10 million displays worldwide.
Enhancing PIGPEN Image Steganography Method by Using Zigzag Scanning

Steganography is the art and science of writing hidden messages in such a way that no one suspects the existence of the message, a form of security through obscurity. Many different carrier file formats can be used, but digital images are the most popular because of their frequency on the internet. In this paper explains the PIGPEN image steganography technique which modifies the secret message itself not the technique of embedding. This technique represents the secret message characters by two decimal digits only not three decimal digits as ASCII encoding. So, it can save one third of the required space for embedding the message in an image. The PIGPEN technique will be enhanced by using the zigzag scanning to increase the security and achieves higher visual quality as indicated by the high peak signal-to-noise ratio (PSNR) in spite of hiding a large number of secret bits in the image.
Classroom Teaching Strategy under the Background of Situational Teaching: The Application of Jilin Li’s Situational Teaching in Basic English Teaching

Jilin Li’s concept of situational teaching creatively combines students’ activities in both emotion and cognition, greatly mobilizes students’ enthusiasm for learning, and explores an effective path for students’ happy and efficient learning. Based on Li’s theory of situational teaching, we created a real language teaching environment as much as possible allowing students to understand the information and language materials expressed by the situation in the process of experiencing the situation, focusing on how to study effectively in the English class. The situational teaching method is used to stimulate students’ interest in English learning and to cultivate students’ independent creativity, so that they can master the four basic language skills of listening, speaking, reading and writing, and finally form comprehensive language application ability.
Systems Development Life Cycle Test Driven Technique and Defect investigation

Software testing is essential to build and improve programming. Software testing is the basic order of construction software. Testing is undoubtedly expensive and involves the biggest individual time. Its supreme undertakings to conquer the costs and improve the viability of decreasing software implementation and expand the hazardous aspects of the quality level. The examination explores the convergence of test schemes with the counteractive action of imperfection and the following measurements used in Software Improvement by which the execution and viability of the value product is achieved. The research study further emphasizes pre-testing and quantifiable measurements of execution by which testing efforts can be broken down and the result can anticipate software execution within the progress process. The research study provides information of interest to understand and apply test techniques as desired. It is essential to make the correct decisions to embrace test techniques that fit the prerequisite of the venture. This study examines what works best together with SDLC and provides center information on when and how best to apply them. The measurements decide the decreased deformity rates to enhance execution by comparing each other's imperfection rates for each module or portion of usefulness. This assigns more assets and time before organization to settle deformities, resulting in a reduced rate of imperfection after sending reported by end customers.
Computation of Closures of Nonmonotonic Inference Relations Induced by Conditional Knowledge Bases

Conditionals of the form “If A, then usually B” are often used to define nonmonotonic inference relations. Many ways have been proposed to inductively complete a knowledge base consisting of a finite set of conditionals to a complete inference relation. Implementations of these semantics are usually used to answer specific queries on demand. However, for some applications it is necessary or advantageous to compute the closure of the inference relation induced by a knowledge base. In this paper, we propose an approach to computing complete inference relations using implementations of inference systems for single queries. Our approach exploits special characteristics of conditionals and inference properties like Right Weakening in order to reduce the amount of costly query answering to a minimum.
Interdisciplinary scientific communication in the content of teaching applied mathematics

Problem and goal. Today, graduates studying in the physical and mathematical areas of training in the profile of applied mathematics have high requirements [23; 24]. Such graduates should have not only fundamental knowledge in the disciplines of applied mathematics, have a scientific outlook, skills and research of applied tasks with the help of mathematical modeling, but also strive to implement applied research through environmental technologies. The achievement of such goals in teaching students applied mathematics requires the use of various pedagogical and information technologies in the educational process, the development of learning content, new forms and methods of training, the involvement of specialists in applied mathematics in teaching. Methodology. In the process of training specialists in applied mathematics, implemented the idea of developing their mathematical creativity, strengthening the motivation for the formation of deep theoretical and practical knowledge in the disciplines of applied mathematics and the foundations of humanitarian culture. The implementation of these important ideas is carried out on the basis of extensive use of interdisciplinary scientific relations in the conditions of humanitarization of university mathematical education. The formation of students’ fundamental knowledge of applied mathematics, the foundations of humanitarian culture is achieved by developing the content of such training on the basis of modern scientific achievements of applied mathematics, the implementation of scientific and educational, scientific and educational and humanitarian potential of teaching applied mathematics. Results. The obtained fundamental knowledge in applied mathematics, formed scientific worldview and humanitarian culture will allow graduates in their future professional activities to show a humane attitude to nature and the world, to apply environmental technologies in the implementation of applied research. In addition, with such a wealth of knowledge, graduates are able to be worthy members of the modern information society with a humanitarian culture. Conclusion. In the process of teaching applied mathematics, using innovative pedagogical technologies, it is advisable for students not only to give fundamental scientific knowledge, but also to instill the foundations of humanitarian culture.
Speaking materials for conversation class at Islamic senior high school

This research study aimed to develop English speaking materials to support the learning process in Conversation class of the tenth-graders at Madrasah Aliyah (Islamic senior high school) Al Ma’had An Nur, Bantul. This research was a research and development study. The development was carried out through several steps, namely the needs analysis, a course grid design, product development, product validation, product revisions, try-outs, and final product development. The research had two results. First, it produced a set of speaking materials in the form of printed books. Second, the findings showed that the materials were appropriate to be applied in the Conversation class of the tenth-graders at Madrasah Aliyah Al Ma’had An Nur, Bantul considering the result of the materials evaluation by experts and students’ try-outs. They scored 3.59 and 3.32 for the mean score of the scale 1 to 4 and were categorized as “Very Good”.
Analisis Kinerja Metode Support Vector Regression (SVR) dalam Memprediksi Indeks Harga Konsumen

The stability of commodity prices for food is very influential on the economy of a region because stable prices have a direct impact on the level of people's purchasing power. The need to maintain the stability of food commodity prices is the background of this research and this can be anticipated by forecasting the Consumer Price Index (CPI). The CPI is an index number that calculates the average change in prices of goods and services consumed by households and society. The purpose of this study is to predict the CPI of the Foodstuff Group using the Support Vector Regression (SVR) method by utilizing Linear, Polynomial, Gaussian Radial Basis Function (RBF) and SPLine Kernel Functions. Selection of this SVR method, because SVR is able to map input vectors into higher dimensions and can produce a function with a trend of bumpy data following the data path formed, resulting in more accurate predictive values Research is limited to the city of Surabaya, the period of time the study begins January 1, 2016 until December 31, 2018. The data source used is the Surabaya Food Basic Price data as an input variable with 34 input attributes and the CPI data for Surabaya city Foodstuffs group as output variables. The results of this study are expected to be able to predict CPI with an error rate below 5%, which is indicated by MSE (Mean Square Error) &lt; 0.05
Algorithms and System Architecture for Immediate Personalized News Recommendations

Personalization plays an important role in many services, just as news does. Many studies have examined news personalization algorithms, but few have considered practical environments. This paper provides algorithms and system architecture for generating immediate personalized news in a practical environment. Immediacy means changes in news trends and user interests are reflected in recommended news lists quickly. Since news trends and user interests rapidly change, immediacy is critical in news personalization applications. We develop algorithms and system architecture to realize immediacy. Our algorithms are based on collaborative filtering of user clusters and evaluate news articles using click-through rate and decay scores based on the time elapsed since the user's last access. Existing studies have not fully discussed system architecture, so a major contribution of this paper is that we demonstrate a system architecture and realize our algorithms and a configuration example implemented on top of Amazon Web Services. We evaluate the proposed method both offline and online. The offline experiments are conducted through a real-world dataset from a commercial news delivery service, and online experiments are conducted via A/B testing on production environments. We confirm the effectiveness of our proposed method and also that our system architecture can operate in large-scale production environments.
Study on Apriori Algorithm and FP-Growth Algorithm in Association Rule Mining

Data mining is a technique dedicated to data analysis and understanding and to reveal the knowledge contained in data. It has become one of the important goals of the application of information technology in the future. Association rule mining is the technique that can discover set of frequent items in a transaction. The paper highlight about apriori algorithm and fp-growth algorithm in association rule mining and compare the performance between them. Apriori algorithm discovers the itemset which is frequent and generates candidate itemset. Fp-growth discovers the frequent itemsets without candidate itemset generation.
Implementasi Metode Rough Set Dalam Memprediksi Dampak Tanah Longsor (Studi Kasus Badan Penanggulangan Bencana Daerah (BPBD) Provinsi Sumatera Utara

Landslides are displacement of slope-forming material in the form of rocks, rubble, soil or material. In the Province of North Sumatra, landslides often occur and cause loss of life and material damage. These impacts can be minimized based on predictions of the initial symptoms of landslides such as rainfall, slope, topography, landslide types and so on. To reduce the impact of landslides, it is necessary to build an understanding based on technical knowledge and skills about the prediction of landslide impacts using data from existing landslide disasters. Data mining is a series of processes to explore the added value of a data set in the form of knowledge that has not been known manually. Data mining is very helpful in extracting information about the predicted impact of landslides in North Sumatra Province. The data mining method used is the Rough Set method. The Rough Set method is used to predict the impact of a landslide based on data on factors causing landslides and the area where landslides occur. Rough set is an efficient technique for Knowledge Discovery in Database (KDD) in the process stages and Data Mining.
Generation and Detection of Down-converted Photon Pairs at $2.080\ \boldsymbol{\mu} \mathbf{m}$

We report the generation and coincidence-to-accidental ratio characterization of a pulsed spontaneous parametric down-conversion photon pair source at $2.080 \mu \mathrm{m}$ , as a basis for free-space quantum communication in an atmospheric window with low solar background.
Person Re-identification with feature Aggregation

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
ISIPTA 2019: Preface

The ISIPTA meetings are the primary forum for presenting and discussing advances in imprecise probabilities research. They are organized once every two years by SIPTA, the Society for Imprecise Probabilities: Theories and Applications. The first meeting was held in Ghent in 1999. It was followed by meetings in Ithaca, Lugano, Pittsburgh, Prague, Durham, Innsbruck, Compiegne, Pescara and Lugano. After twenty years, we return to Ghent for the 11th International Symposium on Imprecise Probabilities: Theories and Applications. This anniversary edition will be held from Wednesday 3 to Saturday 6 July 2019.
ArduCode: Predictive Framework for Automation Engineering

Automation engineering is the task of integrating, via software, various sensors, actuators, and controls for automating a real-world process. Today, automation engineering is supported by a suite of software tools including integrated development environments (IDE), hardware configurators, compilers, and runtimes. These tools focus on the automation code itself, but leave the automation engineer unassisted in their decision making. This can lead to increased time for software development because of imperfections in decision making leading to multiple iterations between software and hardware. To address this, this paper defines multiple challenges often faced in automation engineering and propose solutions using machine learning to assist engineers tackle such challenges. We show that machine learning can be leveraged to assist the automation engineer in classifying automation, finding similar code snippets, and reasoning about the hardware selection of sensors and actuators. We validate our architecture on two real datasets consisting of 2,927 Arduino projects, and 683 Programmable Logic Controller (PLC) projects. Our results show that paragraph embedding techniques can be utilized to classify automation using code snippets with precision close to human annotation, giving an F1-score of 72%. Further, we show that such embedding techniques can help us find similar code snippets with high accuracy. Finally, we use autoencoder models for hardware recommendation and achieve a p@3 of 0.79 and p@5 of 0.95.
Feedback Learning for Improving the Robustness of Neural Networks

Recent research studies revealed that neural networks are vulnerable to adversarial attacks. State-of-the-art defensive techniques add various adversarial examples in training to improve models' adversarial robustness. However, these methods are not universal and can't defend unknown or non-adversarial evasion attacks. In this paper, we analyze the model robustness in the decision space. A feedback learning method is then proposed, to understand how well a model learns and to facilitate the retraining process of remedying the defects. The evaluations according to a set of distance-based criteria show that our method can significantly improve models' accuracy and robustness against different types of evasion attacks. Moreover, we observe the existence of inter-class inequality and propose to compensate it by changing the proportions of examples generated in different classes.
Gadgets and Gaussians in Lattice-Based Cryptography

Author(s): Genise, Nicholas James | Advisor(s): Micciancio, Daniele; Kim, Young-Han | Abstract: This dissertation explores optimal algorithms employed in lattice-based cryptographic schemes. Chapter 2 focuses on optimizing discrete gaussian sampling on and algebraic lattices. These gaussian sampling algorithms are used in lattice-cryptography's most efficient trapdoor mechanism for the SIS and LWE problems: MP12 trapdoors. However, this trapdoor mechanism was previously not optimized and inefficient (or not proven to be statistically correct) for structured lattices (ring-SIS/LWE), lattice-cryptography's most efficient form, where the modulus is often a prime. The algorithms in this chapter achieve optimality in this regime and have (already) resulted in drastic efficiency improvement in independent implementations.Chapter 3 digs deeper into the gadget lattice's associated algorithms. Specifically, we explore efficiently sampling a simple subgaussian distribution on gadget lattices, and we optimize LWE decoding on gadget lattices. These subgaussian sampling algorithms correspond to a randomized bit-decomposition needed in lattice-based schemes with homomorphic properties like fully homomorphic encryption (FHE). Next, we introduce a general class of Chinese Remainder Theorem (CRT) gadgets. These gadgets allow advanced lattice-based schemes to avoid multi-precision arithmetic when the applications modulus is larger than 64 bits. The algorithms presented in the first two chapters improve the efficiency of many lattice-based cryptosystems: digital signature schemes, identity-based encryption schemes, as well as more advanced schemes like fully-homomorphic encryption and attribute-based encryption. In the final chapter, we take a closer look at the random matrices used in trapdoor lattices. First, we revisit the constants in the concentration bounds of subgaussian random matrices. Then, we provide experimental evidence for a simple heuristic regarding the singular values of matrices with entries drawn from commonly used distributions in cryptography. Though the proofs in this chapter are dense, cryptographers need a strong understanding of the singular values of these matrices since their maximum singular value determines the concrete security of the trapdoor scheme's underlying SIS problem.
Structural and semantic pattern matching analysis in Haskell

Haskell functions are defined as a series of clauses consisting of patterns that are matched against the arguments in the order of definition. In case an input is not matched by any of the clauses, an error occurs. Therefore it is desirable to statically prove that the function is defined for all well-typed inputs. Conversely, a clause that can never be matched also indicates a likely defect. Analyzing these properties is challenging due to presence of GADT and guards as well as due to Haskell's lazy evaluation. We implement a recently proposed algorithm that unifies and extends the related analyses implemented in version 7 of the Glasgow Haskell Compiler. By using an SMT solver to handle the semantic constraints arising from pattern guards, we achieve a further improvement in precision over the existing GHC 8.0.1 implementation. We present a tool that uses the analysis to give sound, more precise, and actionable warnings about program defects.