Reviewerly/exHarmony huggingface dataset:
Bag-of-Words Baselines for Semantic Code Search

The task of semantic code search is to retrieve code snippets from a source code corpus based on an information need expressed in natural language.The semantic gap between natural language and programming languages has for long been regarded as one of the most significant obstacles to the effectiveness of keyword-based information retrieval (IR) methods.It is a common assumption that "traditional" bag-of-words IR methods are poorly suited for semantic code search: our work empirically investigates this assumption.Specifically, we examine the effectiveness of two traditional IR methods, namely BM25 and RM3, on the CodeSearchNet Corpus, which consists of natural language queries paired with relevant code snippets.We find that the two keyword-based methods outperform several pre-BERT neural models.We also compare several code-specific data pre-processing strategies and find that specialized tokenization improves effectiveness.Code for reproducing our experiments is available at https:
Liver Segmentation in MRI Images using an Adaptive Water Flow Model

Identification and precise localization of the liver surface and its segments are essential for any surgical treatment. An algorithm of accurate liver segmentation simplifies the treatment planning for different types of liver diseases. Although liver segmentation turns researcher's attention, it still has some challenging problems in computer-aided diagnosis.This study aimed to extract the potential liver regions by an adaptive water flow model and perform the final segmentation by the classification algorithm.In this experimental study, an automatic liver segmentation algorithm was introduced. The proposed method designed the image by a transfer function based on the probability distribution function of the liver pixels to enhance the liver area. The enhanced image is then segmented using an adaptive water flow model in which the rainfall process is controlled by the liver location in the training images and the gray levels of pixels. The candidate liver segments are classified by a Multi-Layer Perception (MLP) neural network considering some texture, area, and gray level features.The proposed algorithm efficiently distinguishes the liver region from its surrounding organs, resulting in perfect liver segmentation over 250 Magnetic Resonance Imaging (MRI) test images. The accuracy of 97% was obtained by quantitative evaluation over test images, which revealed the superiority of the proposed algorithm compared to some evaluated algorithms.Liver segmentation using an adaptive water flow algorithm and classifying the segmented area in MRI images yields more robust and reliable results in comparison with the classification of pixels.
Research Progress of Deep Optical Flow Estimation

Combining the deep learning model to compute end-to-end optical flow is a hot topic in current computer vision field.The optical flow estimation methods based on deep learning are summarized and reviewed.Firstly, the origin and concept of optical flow is introduced.Secondly, the optical flow datasets and evaluation metrics are summarized.Most importantly, classical methods are introduced and the deep optical flow estimation methods are reviewed in three aspects, including supervised deep optical flow estimation methods, unsupervised deep optical flow estimation methods and the performance of these methods.The analysis shows designing compact and generalized deep optical flow model is the future research direction.On this basis, the joint learning of optical flow estimation and specific video analysis tasks are introduced.It is pointed out that the design of task-driven deep optical flow network is a valuable research direction in practice application.Finally, the problems and challenges of deep optical flow estimation are summarized and the future work of this field is prospected.
Investigating the Accuracy of Google Translate in Translating Urdu Linguistic Elements

This study aims to investigate the accuracy of Google Translate regarding the translation of idioms and proverbs from and to English language. The shortcomings of Google Translate and the limitations of Neural Machine Translation have also been pointed out after in-depth analysis of carefully chosen examples from authentic lexicons. An experiment has been conducted by running Urdu and English idioms, proverbs and other linguistic expressions through Google Translate. The theoretical framework of Antoine Berman's twelve deforming tendencies has been employed to analyse these examples and Mixed Methods design has been applied for the evaluation in the study. The error analysis highlighted all the linguistic mistakes and errors committed by the software during Google machine translation. Major contribution of this study is the provision of the accurate alternatives for the wrong translation. This study plays a diagnostic role in identifying and classifying those errors. Furthermore, it contributes in improving the quality of Machine Translation from Urdu to English and English to Urdu. In the end, suggestions for the effective advancements in the software have also been provided.
Unsupervised Paradigm Clustering Using Transformation Rules

This paper describes the submission of the CU-UBC team for the SIGMORPHON 2021 Shared Task 2: Unsupervised morphological paradigm clustering.Our system generates paradigms using morphological transformation rules which are discovered from raw data.We experiment with two methods for discovering rules.Our first approach generates prefix and suffix transformations between similar strings.Secondly, we experiment with more general rules which can apply transformations inside the input strings in addition to prefix and suffix transformations.We find that the best overall performance is delivered by prefix and suffix rules but more general transformation rules perform better for languages with templatic morphology and very high morpheme-to-word ratios.
Online teaching and learning: a year later what has changed?

The world is currently experiencing the covid-19 pandemic that has been going on for almost a year and which has transformed the way of carrying out the economic activities that support contemporary societies. The education sector is precisely an example of this abrupt change. The face-to-face teaching-learning process was converted to an online format, so students, teachers, schools and families had to adapt immediately to this transformation. There was no time to provide schools and persons with digital equipment and tools, nor with educational agents for training oriented to the practice of online teaching. Everyone had to do their best, adapting, learning autonomously and with their peers and solving their own problems. This research was developed with the support of the case study methodology in a Portuguese educational institution and aims to understand, through the Dynamic Digital Competence Reference Framework (DigComp), the evolution of students in terms of digital skills in the period of the covid-19 pandemic. The results show that students acquired a set of digital skills that allowed them to participate and interact with stakeholders in the online teaching-learning process. In addition, a set of suggestions is also presented for the acquisition or improvement of other students' skills.
An Evaluation of Primary School Year 6 (KSSR) English Textbook

This study investigates the effectiveness of Malaysian Primary School Year 6 (KSSR) English textbook to determine the extent to which the textbook conforms to the universal characteristics of ESL/EFL textbooks. A modified version of Alamri’s (2008) English as Foreign Language (EFL) textbook evaluation checklist developed by Akef and Moosavi (2014) was distributed along with the textbook to 12 TESL postgraduate students and 18 primary school English Language teachers currently studying at University Technology Malaysia (UTM) for evaluation. The data collected from this study was analysed using descriptive statistical technique which is a quantitative method. The findings highlight some of the weaknesses of the textbook in compliance to the universal characteristics of ELT textbooks. These include lack of supplementary materials and teachers’ dissatisfaction towards language contents and language skills elements. The findings from this study can provide significant feedback for teachers, textbook designers, curriculum developers and policy makers to place more emphasis on textbook evaluation when developing textbooks.
Meta Analisi Pengaruh Model Pembelajaran Numbered Head Together Terhadap Hasil Belajar Matematika Siswa Kelas V SD

This study aims to analyze the influence of the Numbered Head Together (NHT) learning model in improving elementary mathematics learning outcomes. The method used in this study is a meta-analysis method. This research begins with formulating a research topic, then from that research topic it is developed again into a research problem formulation, and at the final stage is tracing relevant articles for data collection. The data collection technique in this study is by searching for articles contained in online journals that have been indexed in Google Scholar, and. From the results of further searches, 10 articles were then taken published in online journals that will be used as the basis for this research with the search keywords "Numbered Heads Together (NHT)", "mathematical learning outcomes", and "Mathematics Learning for Grade V Elementary School". Based on the results of the analysis, it turns out that the Numbered Head Together Model can improve student learning outcomes, from the previous 49.91%, to 64.24%.
Feature Selection Based on Divergence Functions: A Comparative Classiffication Study

Due to the extensive use of high-dimensional data and its application in a wide range of scientifc felds of research, dimensionality reduction has become a major part of the preprocessing step in machine learning. Feature selection is one procedure for reducing dimensionality. In this process, instead of using the whole set of features, a subset is selected to be used in the learning model. Feature selection (FS) methods are divided into three main categories: flters, wrappers, and embedded approaches. Filter methods only depend on the characteristics of the data, and do not rely on the learning model at hand. Divergence functions as measures of evaluating the differences between probability distribution functions can be used as flter methods of feature selection. In this paper, the performances of a few divergence functions such as Jensen-Shannon (JS) divergence and Exponential divergence (EXP) are compared with those of some of the most-known flter feature selection methods such as Information Gain (IG) and Chi-Squared (CHI). This comparison was made through accuracy rate and F1-score of classifcation models after implementing these feature selection methods.
Leveraging Product Adopter Information from Online Reviews for Product Recommendation

The availability of the sheer volume of online product reviews makes it possible to derive implicit demographic information of product adopters from review documents. This paper proposes a novel approach to the extraction of product adopter mentions from online reviews. The extracted product adopters are then categorise into a number of different demographic user groups. The aggregated demographic information of many product adopters can be used to characterise both products and users, which can be incorporated into a recommendation method using weighted regularised matrix factorisation. Our experimental results on over 15 million reviews crawled from JINGDONG, the largest B2C e-commerce website in China, show the feasibility and effectiveness of our proposed framework for product recommendation.
Hierarchical Portfolio Search: Prismata's Robust AI Architecture for Games with Large Search Spaces

Online strategy video games offer several unique challenges to the field of AI research. Due to their large state and action spaces, existing search algorithms have difficulties in making strategically strong decisions. Additionally, the nature of competitive on-line video games adds the requirement that game designers be able to tweak game properties regularly when strategic imbalances are found. This means that an AI system for a game like this needs to be robust to such changes and less reliant on expert knowledge. This paper makes two main contributions to advancing the state of the art for AI in modern strategy video games which have large state and action spaces. The first is a novel method for performing hierarchical search using a portfolio of algorithms to reduce the search space while maintaining strong action candidates. The second contribution is an overall AI architecture for strategy video games using this portfolio search method. The proposed methods are used as the AI system for Prismata, an online turn-based strategy game by Lunarch Studios. This system is evaluated using three experiments: on-line play vs.~human players, off-line AI tournaments to test the relative strengths of the AI bots, and a survey to determine user satisfaction of the system so far. Our result show that this system achieves a skill level in the top 25% of human players on the ranked ladder, can be modified quickly to create different difficulty settings, is robust to changes in game unit properties, and creates an overall AI experience which is user rated more enjoyable than those currently found in similar video games.
Providing Performance Guarantees for Cloud-Deployed Applications

Applications with a dynamic workload demand need access to a flexible infrastructure to meet performance guarantees and minimize resource costs. While cloud computing provides the elasticity to scale the infrastructure on demand, cloud service providers lack control and visibility of user space applications, making it difficult to accurately scale the infrastructure. Thus, the burden of scaling falls on the user. That is, the user must determine when to trigger scaling and how much to scale. Scaling becomes even more challenging when applications exhibit dynamic changes in their behavior. In this paper, we propose a new cloud service, Dependable Compute Cloud (DC2), that automatically scales the infrastructure to meet the user-specified performance requirements, even when multiple user requests execute concurrently. DC2 employs Kalman filtering to automatically learn the (possibly changing) system parameters for each application, allowing it to proactively scale the infrastructure to meet performance guarantees. Importantly, DC2 is designed for the cloud - it is application-agnostic and does not require any offline application profiling or benchmarking, training data, or expert knowledge about the application. We evaluate DC2 via implementation on OpenStack using a multi-tier application under a range of workload mixes and arrival traces. Our experimental results demonstrate the robustness and superiority of DC2 over existing rule-based approaches with respect to avoiding SLA violations and minimizing resource consumption.
Comparative Evaluations of Selected Tracking-by-Detection Approaches

In this paper, we present a comparative evaluation of various multi-person tracking-by-detection approaches on public data sets. This paper investigates five popular trackers coupled with six relevant visual people detectors evaluated on seven public data sets. The evaluation emphasizes on exhibited performance variation depending on tracker-detector choices. Our experimental results show that the overall performance depends on how challenging the data set is, the performance of the detector on the specific data set, and the tracker-detector combination. Some trackers are more sensitive to the choice of a detector and some detectors to the choice of a tracker than others. Based on our results, two of the trackers demonstrate the best performances consistently across different data sets, whereas the best performing detectors vary per data set. This underscores the need for careful application context specific evaluation when choosing a detector.
Learning From Experts’ Experience: Toward Automated Cyber Security Data Triage

Security operations centers (SOCs) employ various cyber defend measures to monitor network events. Apart from these measures, SOCs also have to resort to human analysts to make sense of the collected data for incident detection and response. However, with the oncoming network data collected and accumulated at a rapid speed, analysts are usually overwhelmed by tedious and repeated data triage tasks so that they can hardly concentrate on in-depth analysis to create timely and quality incident reports. This paper aims to reduce the analysts' workloads by developing data triage automatons. We have developed a computer-aided tracing method for capturing analysts' operations while they are performing a task. This paper proposes a graph-based trace mining approach for constructing useful patterns for data triage from the operation traces. Finite state machines can be constructed based on the rules to automate data triage. A human-in-the-loop case study is conducted to evaluate our approach, in which 30 professional analysts were recruited and asked to complete a cyber-analysis task with their operations being traced. State machines were constructed based on the traces and then the effectiveness of developing state machines and the performance of state machines are evaluated. The result shows that it is feasible to conduct automated data triage by leveraging analysts' traces. The state machines are able to complete processing a large amount of data within minutes. Comparing the performance of automated data triage with the ground truth, we found that a satisfactory false positive rate can be achieved.
Continuity of the quantum Fisher information

In estimating an unknown parameter of a quantum state the quantum Fisher information (QFI) is a pivotal quantity, which depends on the state and its derivate with respect to the unknown parameter. We prove the continuity property for the QFI in the sense that two close states with close first derivatives have close QFIs. This property is completely general and irrespective of dynamics or how states acquire their parameter dependence and also the form of parameter dependence---indeed this continuity is basically a feature of the classical Fisher information that in the case of the QFI naturally carries over from the manifold of probability distributions onto the manifold of density matrices. We demonstrate that, in the special case where the dependence of the states on the unknown parameter comes from one dynamical map (quantum channel), the continuity holds in its reduced form with respect to the initial states. In addition, we show that, when one initial state evolves through two different quantum channels, the continuity relation applies in its general form. A situation in which such a scenario can occur is an open-system metrology where one of the maps represents the ideal dynamics, whereas the other map represents the real (noisy) dynamics. In the making of our main result, we also introduce a regularized representation for the symmetric logarithmic derivative which works for general states even with incomplete rank, and it features continuity similar to the QFI.
Induction of Emotional States in Educational Video Games Through a Fuzzy Control System

It has been shown that the emotional state of students plays an important role towards learning; for instance, engaged concentration is positively correlated with learning. This paper proposes Inductive Control (IC) for educational games. Unlike conventional approaches that only modify the game level, the proposed technique also induces emotions in the player for supporting the learning process. This paper explores a fuzzy system that analyzes the players' performance and their emotional state for controlling the level and aesthetic content of an educational video game. The emotional state of the player is recognized through voice analysis. A total of 40 subjects played a video game designed to practice basic math skills; for each trial, a student plays twice in a row the same game but each time the game was controlled by one of the two approaches-Dynamic Difficulty Adjustment (DDA) and IC, the playing order was assigned randomly. Results show that when the proposed approach is used the participants changed faster from unpleasant-low to pleasant or high emotions. These experiments demonstrate that the inductive control technique improves the learning effectiveness through detection and stimulation of positive emotions.
Indefinite Kernel Logistic Regression With Concave-Inexact-Convex Procedure

In kernel methods, the kernels are often required to be positive definitethat restricts the use of many indefinite kernels. To consider those nonpositive definite kernels, in this paper, we aim to build an indefinite kernel learning framework for kernel logistic regression (KLR). The proposed indefinite KLR (IKLR) model is analyzed in the reproducing kernel Kreĭn spaces and then becomes nonconvex. Using the positive decomposition of a nonpositive definite kernel, the derived IKLR model can be decomposed into the difference of two convex functions. Accordingly, a concave-convex procedure (CCCP) is introduced to solve the nonconvex optimization problem. Since the CCCP has to solve a subproblem in each iteration, we propose a concave-inexact-convex procedure (CCICP) algorithm with an inexact solving scheme to accelerate the solving process. Besides, we propose a stochastic variant of CCICP to efficiently obtain a proximal solution, which achieves the similar purpose with the inexact solving scheme in CCICP. The convergence analyses of the above-mentioned two variants of CCCP are conducted. By doing so, our method works effectively not only in a deterministic setting but also in a stochastic setting. Experimental results on several benchmarks suggest that the proposed IKLR model performs favorably against the standard (positive definite) KLR and other competitive indefinite learning-based algorithms.
Real-time geometric fitting and pose estimation for surface of revolution

This paper presents a novel ellipse fitting method to simultaneously estimate the Euclidean pose and structure of a surface of revolution (SOR) by minimizing the geometric reprojection error of the visible cross sections in image space. This geometric error function and its Jacobian matrix are explicitly derived to enable Levenberg-Marquardt (LM) optimization. With the obtained pose and structure, the Euclidean shape of a SOR can be reconstructed by generating the ellipse tangency to the apparent contour of the SOR. Given the real size of several visible cross sections, this approach can be extended to perform a real-time 3D tracking of the SOR. Additionally, this technique can be also generalized to fitting for imaged parallel circles. Sufficient experiments validate the accuracy and the real-time performance of the proposed method.
Markov Boundary-Based Outlier Mining

It is a grand challenge to identify the outliers existing in subspaces from a high-dimensional data set. A brute-force method is computationally prohibitive since it requires examining an exponential number of subspaces. Current state-of-the-art methods explore various heuristics to significantly prune subspaces, facing the tradeoff between the subspace completeness and search efficiency. In this brief, we discuss a principal type of subspace outliers whose behaviors are different from the others on individual attributes. We formulate such outliers by a novel notion of the Markov boundary-based (MBB) outliers. The central idea is that for each attribute T in a data set, we consider only the subspace representing the knowledge needed to predict the behavior on T , which is captured by the MB of T . Then, the outliers whose behavior is different from others on T can be detected in the subspace of the MB, and thus, our approach reduces the number of possible subspaces from exponential to linear with respect to dimensionality. Using both synthetic and real data sets, we validate the effectiveness and efficiency of our method.