Reviewerly/exHarmony huggingface dataset:
Can Industrial-Strength Static Analysis Be Used to Help Students Who Are Struggling to Complete Programming Activities?

Static analysis tools evaluate source code to identify potential problems or issues beyond typical compiler errors.Prior work has shown a statistically significant relationship between the correctness of a student's work and statically identifiable flaws or "code smells" that are likely to indicate programming errors.This paper presents a comprehensive study of this relationship in the context of small programming exercises intended for use in student skill building.We use FindBugs, a static analysis tool that identifies program features that are likely to represent actual bugs in professional software.Our goal is to identify the extent to which FindBugs warnings might help novices struggling to solve short programming exercises.In this study, we ran FindBugs against 149,054 answers submitted by 516 students on 57 drill-and-practice coding exercises.We identify the specific FindBugs warnings that are inversely correlated with correctness.We confirm that presence of these warnings is significantly associated with struggling on an exercise, as indicated by taking more time, making more submissions, and receiving lower scores.Finally, every exercise exhibited answers that trigger these warnings, and 92.4% of students would experience these warnings over a full semester.Our results indicate that static analysis with tools designed for use in industry offers an untapped opportunity to provide hints or suggestions to students who are measurably struggling.
Quantum encryption and generalized Shannon impossibility

The famous Shannon impossibility result says that any encryption scheme with perfect secrecy requires a secret key at least as long as the message. In this paper we provide its quantum analogue with imperfect secrecy and imperfect correctness. We also give a systematic study of information-theoretically secure quantum encryption with two secrecy definitions. We show that the weaker one implies the stronger but with a security loss in d, where d is the dimension of the encrypted quantum system. This is good enough if the target secrecy error is of $$o(d^{-1})$$ .
Topic Models and Fusion Methods: a Union to Improve Text Clustering and Cluster Labeling

Topic modeling algorithms are statistical methods that aim to discover the topics running through the text documents.Using topic models in machine learning and text mining is popular due to its applicability in inferring the latent topic structure of a corpus.In this paper, we represent an enriching document approach, using state-ofthe-art topic models and data fusion methods, to enrich documents of a collection with the aim of improving the quality of text clustering and cluster labeling.We propose a bi-vector space model in which every document of the corpus is represented by two vectors: one is generated based on the fusion-based topic modeling approach, and one simply is the traditional vector model.Our experiments on various datasets show that using a combination of topic modeling and fusion methods to create documents' vectors can significantly improve the quality of the results in clustering the documents.
An Interpretable and Scalable Recommendation Method Based on Network Embedding

Matrix factorization is a widely used technique in recommender systems.However, its performance is often affected by the sparsity and the scalability.To address the above-mentioned problem, we propose an interpretable and scalable recommendation method based on network embedding (ISRM_NE) in this paper.First, a novel user-item co-occurrence network is presented, which reflects both the user's preferences and the co-occurrence relationship among items.Second, the conceptions of tightness and equivalence are given to describe the structural similarity in the network, which can explore four relationships in recommender system: user's preference, item co-occurrence relationship, user's potential preference, and similarity between users.Finally, two sampling strategies are combined to traverse the network so as to get the latent vector of items and users through network representation learning.Thus, the top-N recommendation can be achieved by vector computing.The proposed method called ISRM_NE improves the performance of the recommender system in terms of interpretability and scalability.Moreover, extensive experiments on two real-world datasets demonstrate that the ISRM_NE outperforms three popular methods.
Visual Servoing Application for Inverse Kinematics of Robotic Arm Using Artificial Neural Networks

This paper presents novel approach for a visual servoing application of six axis robotic arm.Basic imageprocessing techniques were used for object recognition and position determination of robotic arm.The inverse kinematics solution of the robot arm was performed with artificial neural networks.Afterwards the robot's inverse kinematics solution was completed, the determined joint-angle values were used to control the robot arm.Performance of radial basis function network (RBF) and multilayer perceptron (MLP) were also compared.
Can Machines Learn to Comprehend Scientific Literature?

To measure the ability of a machine to understand professional-level scientific articles, we construct a scientific question answering task called PaperQA. The PaperQA task is based on more than 80 000 “fill-in-the-blank” type questions on articles from reputed scientific journals such as Nature and Science. We perform fine-grained linguistic analysis and evaluation to compare PaperQA and other conventional question and answering (QA) tasks on general literature (e.g., books, news articles, and Wikipedia texts). The results indicate that the PaperQA task is the most difficult QA task for both humans (lay people) and machines (deep-learning models). Moreover, humans generally outperform machines in conventional QA tasks, but we found that advanced deep-learning models outperform humans by 3%-13% on average in the PaperQA task. The PaperQA dataset used in this paper is publicly available at http://dmis.korea.ac.kr/downloads?id=PaperQA.
Eğitim Fakültesi Öğrencileri İle Turizm Fakültesi Öğrencilerinin Mobil Öğrenmeye Yönelik Tutumları

Mobile devices are an important representative of technological tools and the Internet development together.The importance of these devices which are called mobile due to their portability and which have shrunken hardware although their improved processing power is increasing in everyday life.The field of education is one of the areas where mobile devices are used.In this study, students' attitudes towards mobile technology in education have been investigated.In this context, 450 students from the faculties of education and tourism have been reached in 2014-2015 academic year.Students' attitudes towards mobile learning are analyzed in terms of different variables based on these two faculties, using Mobile Learning Attitude Scale.Research results indicated that the faculty of education students have more positive attitude towards mobile learning compared to the faculty of tourism students.According to the results, controlling faculty variable, although attitudes do not differentiate in terms of gender, grade level, graduated high school and the presence of Internet connection at households; attitudes towards mobile learning differentiate in terms of mobile device-computer possession.
A Practical Deep Online Ranking System in E-commerce Recommendation

User online shopping experience in modern e-commerce websites critically relies on real-time personalized recommendations. However, building a productionized recommender system still remains challenging due to a massive collection of items, a huge number of online users, and requirements for recommendations to be responsive to user actions. In this work, we present our relevant, responsive, and scalable deep online ranking system (DORS) that we developed and deployed in our company. DORS is implemented in a three-level architecture which includes (1) candidate retrieval that retrieves a board set of candidates with various business rules enforced; (2) deep neural network ranking model that takes advantage of available user and item specific features and their interactions; (3) multi-arm bandits based online re-ranking that dynamically takes user real-time feedback and re-ranks the final recommended items in scale. Given a user as a query, DORS is able to precisely capture users' real-time purchasing intents and help users reach to product purchases. Both offline and online experimental results show that DORS provides more personalized online ranking results and makes more revenue.
The Structure-Behavior Coalescence Approach for Systems Modeling

The systems modeling technique is used for developing an abstract model to help analyze and understand the functionality of a system. In this paper, we introduce a new approach for general-purpose systems modeling called structure-behavior coalescence (SBC), which supports the integration of modeling both structural and behavioral aspects of a system in a single diagram. Process algebra is utilized to describe the interactions between the components of the system. SBC combines graphical notation and process algebra to provide formalism and an intuitive representation in the development of the conceptual model. The SBC model can clearly describe the interactions between system components and precisely specify the execution order of an entire system via algebraic operations.
Three-Fast-Inter Incremental Association Markov Blanket learning algorithm

The Markov blanket is a crucial concept in Bayesian network, and a useful tool for Bayesian network structure learning and causal feature selection. Finding an efficient Markov blanket discovery method is one of the core issues in machining learning, and has been widely studied. The Markov blanket discovery methods are mainly divided into two categories: Non-topology based and topology based. Topology based method is data-efficient, but not time-efficient, while non-topology based method is time-efficient, but not data-efficient. Due to the wide existence of the high-dimensional data with limited samples in practical applications, especially in the medical and biological study with expensive sample collection, we combine the advantages of Inter Incremental Association Markov blanket (Inter-IAMB) and Fast Incremental Association Markov blanket (Fast-IAMB), and propose the Three-Fast-Inter Incremental Association Markov blanket learning (Fit-IAMB) algorithm. Experiments showed that Fit-IAMB had comparable accuracy and much better efficiency than state-of-the-art algorithms, especially for the high-dimensional data with limited samples.
Pengembangan Media Big Book Untuk Meningkatkan Kemampuan Literasi Anak Usia 5-6 Tahun

This study aimed to develop a big book media based on fieldwork, needs analysis, and the study of literature in kindergarten. This research and development is adopting the model of Borg &amp; Gall with the stages consisting of: (1) research and information collecting, (2) planning, (3) developing the preliminary form, (4) preliminary filed testing, (5) main product revision, (6) main filed testing, (7) operational product revision, (8) operational field testing, and (9) final product revision. The subjects of the preliminary field test was one class, and those for field test were five classes. The subjects of the operational testing were eleven classes. The data was collected through interviews, validation product, observation, and questionnaires. The data analysis technique used was inter-rater reliability and Cohen’s Kappa with SPSS program version 22. The result of the research was the big book media and its guide book that consisted of introduction, basic competencies and indicators, steps of using the media, and daily activity plan. The result of material expert showed that the material was in a sufficient category. The result of media expert showed that the media was excellent. The result of preliminary field test, field test, and operational test were valid according to Aiken validity. The agreement of teachers in field test with Kappa coefficient value showed 50% or moderate. The agreement of teachers in operational test with Kappa coefficient value showed 51,78% or moderate. The literacy ability of children at operational test improved well.
Improving Regional and Teleseismic Detection for Single-Trace Waveforms Using a Deep Temporal Convolutional Neural Network Trained with an Array-Beam Catalog

The detection of seismic events at regional and teleseismic distances is critical to Nuclear Treaty Monitoring. Traditionally, detecting regional and teleseismic events has required the use of an expensive multi-instrument seismic array; however in this work, we present DeepPick, a novel seismic detection algorithm capable of array-like detection performance from a single-trace. We achieve this performance through three novel steps: First, a high-fidelity dataset is constructed by pairing array-beam catalog arrival-times with single-trace waveforms from the reference instrument of the array. Second, an idealized characteristic function is created, with exponential peaks aligned to the cataloged arrival times. Third, a deep temporal convolutional neural network is employed to learn the complex non-linear filters required to transform the single-trace waveforms into corresponding idealized characteristic functions. The training data consists of all arrivals in the International Seismological Centre Database for seven seismic arrays over a five year window from 1 January 2010 to 1 January 2015, yielding a total training set of 608,362 detections. The test set consists of the same seven arrays over a one year window from 1 January 2015 to 1 January 2016. We report our results by training the algorithm on six of the arrays and testing it on the seventh, so as to demonstrate the generalization and transportability of the technique to new stations. Detection performance against this test set is outstanding, yielding significant improvements in recall over existing techniques. Fixing a type-I error rate of 0.001, the algorithm achieves an overall recall (true positive rate) of 56% against the 141,095 array-beam arrivals in the test set, yielding 78,802 correct detections. This is more than twice the 37,572 detections made by an STA/LTA detector over the same period, and represents a 35% improvement over the 58,515 detections made by a state-of-the-art kurtosis-based detector. Furthermore, DeepPick provides at least a 4 dB improvement in detector sensitivity across the board, and is more computationally efficient, with run-times an order of magnitude faster than either of the other techniques tested. These results demonstrate the potential of our algorithm to significantly enhance the effectiveness of the global treaty monitoring network.
Measure of positive and not completely positive single-qubit Pauli maps

The time evolution of an initially uncorrelated system is governed by a completely positive (CP) map. More generally, the system may contain initial (quantum) correlations with an environment, in which case the system evolves according to a not-completely positive (NCP) map. It is an interesting question what the relative measure is for these two types of maps within the set of positive maps. After indicating the scope of the full problem of computing the true volume for generic maps acting on a qubit, we study the case of Pauli channels in an abstract space whose elements represent an equivalence class of maps that are identical up to a non-Pauli unitary. In this space, we show that the volume of NCP maps is twice that of CP maps.
Improvement of quantized adaptive switching median filter for impulse noise reduction in gray-scale digital images

Digital images may suffer from fixed value impulse noise due to several causes. The noise significantly degrades the quality of the image, which may affect the subsequence image processing. Therefore, a noise reduction technique is required to restore the image. In this paper, a new method, which is called improvement of quantized adaptive switching median filter (IQASMF), has been proposed to reduce the fixed value impulse noise from gray-scale digital images. The implementation of IQASMF has five processing blocks. The first processing block is the noise detection block, where the noise pixel candidates are detected based on the intensity value. Then estimation of the local noise density is done by the second processing block. Next, the third processing block filters the corrupted pixel candidates with filters of predefined size, depending on the local noise density. After that, the noise mask is updated in the fourth processing block. Finally, the fifth processing block processes the noise residuals from the third processing block by using a size adaptive filter. Experimental results from twenty standard gray-scale images of various sizes have shown that IQASMF has the ability to restore images for up to 99 % of the impulse noise corruption. As compared with the other five median filter-based methods, from the measures of mean squared error (MSE) and structural similarity index (SSIM), it is shown that the performance of IQASMF is equivalent to the performance of other methods at low and medium levels of corruption. However, at high corruption levels, IQASMF has demonstrated the best performance in terms of MSE and SSIM. The outputs from IQASMF also have the best visual appearance.
Pixels and Panoramas: An Enhanced Cubic Mapping Scheme for Video\/Image-Based Virtual-Reality Scenes

Virtual reality (VR) is rapidly appearing in various fields, such as navigation, robotics, and documentation. Spherical panoramic video, compared to 3D modeling, provides immersive and omnidirectional views in a much more convenient way. However, state-of-the-art video or image-encoding techniques, such as high-efficiency video coding or JPEG, require rectangular input sequences. Spherical videos are traditionally projected onto a plane or a cube for convenient encoding, but mapping quality and encoding efficiency are not considered. In this article, we propose GVScube projection, a method using a cube-Snyder (Scube) projection along with a gradually varied (GV) sampling method to generate panoramic video. This method achieves better pixel uniformity and less area deviation than other methods.
Multi-Instance Convolutional Neural Network for multi-shot person re-identification

This paper tackles the challenging problem of multi-shot person re-identification with Convolutional Neural Network (CNN). As no prior information about how importance each instance plays, it is non-trivial to exploit the interaction information shared by the multi-shot images to help identification. Traditional CNN is in single-shot architecture, then how to utilize the interaction information provided by multi-shot images becomes an important problem to solve. Furthermore, as data augmentation methods are not strictly label-preserving, it increases the difficulty to select discriminative instance for CNN training. In this paper, we propose a weakly supervised CNN framework named Multi-Instance Convolutional Neural Network (MICNN) to solve the aforementioned problem. We develop two paradigms, i.e., Embedding-Space paradigm and Instance-Space paradigm, which re-formulate the person re-identification problem as a multi-instance verification problem with part-based features extracted by neural network. We respectively devise a specific bag-level loss function which incorporates the characteristics of the multi-instance problem for each paradigm. Experiments show that the proposed IS method outperforms many related state-of-the-art techniques on four benchmark datasets: CUHK03, SYSUm, RAiD and Market-1501.
Weighted Neighborhood Preserving Ensemble Embedding

Neighborhood preserving embedding (NPE) is a classical and very promising supervised dimensional reduction (DR) technique based on a linear graph, which preserves the local neighborhood relations of the data points. However, NPE uses the K nearest neighbor (KNN) criteria for constructing an adjacent graph which makes it more sensitive to neighborhood size. In this article, we propose a novel DR method called weighted neighborhood preserving ensemble embedding (WNPEE). Unlike NPE, the proposed WNPEE constructs an ensemble of adjacent graphs with the number of nearest neighbors varying. With this graph ensemble building, WNPEE can obtain the low-dimensional projections with optimal embedded graph pursuing in a joint optimization manner. WNPEE can be applied in many machine learning fields, such as object recognition, data classification, signal processing, text categorization, and various deep learning tasks. Extensive experiments on Olivetti Research Laboratory (ORL), Georgia Tech, Carnegie Mellon University-Pose and Illumination Images (CMU PIE) and Yale, four face databases demonstrate that WNPEE achieves a competitive and better recognition rate than NPE and other comparative DR methods. Additionally, the proposed WNPEE achieves much lower sensitivity to the neighborhood size parameter as compared to the traditional NPE method while preserving more of the local manifold structure of the high-dimensional data.
Depth Estimation From a Light Field Image Pair With a Generative Model

In this paper, we propose a novel method to estimate the disparity maps from a light field image pair captured by a pair of light field cameras. Our method integrates two types of critical depth cues, which are separately inferred from the epipolar plane images and binocular stereo vision into a global solution. At the same time, in order to produce highly accurate disparity maps, we adopt a generative model, which can estimate a light field image only with the central subaperture view and corresponding hypothesized disparity map. The objective function of our method is formulated to minimize two energy terms/differences. One is the difference between the two types of previously extracted disparity maps and the target disparity maps, directly optimized in the gray-scale disparity space. The other indicates the difference between the estimated light field images and the input light field images, optimized in the RGB color space. Comprehensive experiments conducted on real and virtual scene light field image pairs demonstrate the effectiveness of our method.
Digital Forensic Analysis on Android Smartphones for Handling Cybercrime Cases

As the times progressed, forensic science has developed rapidly. The science of forensics extends to new areas of technology ranging from digital forensics, computer forensics and mobile forensics. Mobile forensics in analyzing and collecting data is obtained from various resources, such as operating systems, communication lines and also various storage media. The most popular mobile operating system of the day is a smartphone based on android operating system. With android technology, criminals can use that technology as a crime medium ranging from overriding crime ideas, crime targets and crime scenarios. In this Final Project use forensic mobile application to get data residing in cell phone actors, in the form of text, sound, picture and video that have or not yet deleted in smartphone. In this study, a model for investigating the crime scene is the author using the Generic Computer Forensic Investigation Model (GCFIM). On the GCFIM model the investigator may be able to return to the previous stage because of the possibility of a changeable situation (both physical and digital), the investigation tools used, the crime tools used, and the level of investigative expertise. In this research also added weighting method of word TF-IDF, where this method can help to find keyword in digital evidence in the form of word / text.