Reviewerly/exHarmony huggingface dataset:
An Imbalanced Deep Learning Model for Bug Localization

Debugging and locating faulty source files are tedious and time-consuming tasks. To improve the productivity and to help developers focus on crucial files, automated bug localization models have been proposed for years. These models recommend buggy source files by ranking them according to their relevance to a given bug report. There are two significant challenges in this research field: (i) narrowing the lexical gap between bug reports which are typically described using natural languages and source files written in programming languages; (ii) reducing the impact of imbalanced data distribution in model training as a far fewer of source files relate to a given bug report while the majority of them are not relevant. In this paper, we propose a deep neural network model to investigate essential information hidden within bug reports and source files through capturing not only lexical relations but also semantic details as well as domain knowledge features such as historical bug fixings, code change history. To address the skewed class distribution, we apply a focal loss function combining with a bootstrapping method to rectify samples of the minority class within iterative training batches to our proposed model. We assessed the performance of our approach over six large scale Java open-source projects. The empirical results have showed that the proposed method outperformed other state-of-the-art models by improving the Mean Average Precision (MAP) and Mean Reciprocal Rank (MRR) scores from 3% to 11% and from 2% to 14%, respectively.
HCI Design Knowledge: Critique, Challenge, and a Way Forward

This is the first of two books concerned with engineering design principles for Human-Computer Interaction-Engineering Design Principles (HCI-EDPs). The book presents the background for the companion volume. The background is divided into three parts and comprises—"HCI for EDPs," "HCI Design Knowledge for EDPs," and "HCI-EDPs—A Way Forward for HCI Design Knowledge." The companion volume reports in full the acquisition of initial HCI-EDPs in the domains of domestic energy planning and control and business-to-consumer electronic commerce (Long, Cummaford, and Stork, 2022, in press).
TV Recommendation for Multiple Users Based on Movie Ratings

Today, television is considered indispensable in the home. Connected to the Internet, television has become intelligent and offers new services to users. A large number of programs are offered every day and it is difficult for the user to make a choice, especially for parents who have to choose what their children can watch. Moreover, the choice of the program becomes even more difficult if there are several people watching television. Many research studies have proposed TV recommendation systems to meet users’ preferences. But considering only user preferences is not enough to ensure adequate content, especially for children. In this article, we want to support parents in choosing content that is safe and appropriate for their children. We propose an extension of the TV recommendation solution QSimTV by adding a keywords filter and movie rating in the recommendation process.
CMCL: Clustering-based Memory Management for Continual Learning

Continual learning (CL) is an incremental learning method to accumulate and refine knowledge over time by continually processing datasets belonging to new tasks. While learning a new task, CL may not retain essential information on previous tasks, which is known as catastrophic forgetting (CF). The CF of information about previous tasks is a challenging problem to overcome for CL. We consider a memory-based approach to combine the previous and new data for CL and propose a memory management method using unsupervised clustering to mitigate the CF. The proposed method generates a set of clusters for the combined datasets by an unsupervised clustering and stores the most representative data belonging to each cluster in memory. The number of clusters is determined by the unsupervised clustering depending on the features of the combined dataset rather than the number of tasks. Further, an experiment was performed to compare the proposed method with existing methods that store a certain amount of data for each task in the memory. The experiment results indicate that the proposed method outperformed the existing methods.
Towards a linked open data resource for direct speech acts in Greek and Latin epic

Abstract The Digital Initiative for Classics: Epic Speeches (DICES) research group reports here on preliminary work to integrate research on Greek and Latin epic speeches into the larger ecosystem of linked open data (LOD) for classical scholarship. The ability to collate speech data from different researchers and to leverage external repositories of texts and characters opens up new possibilities for interrogation of the epic corpus. We briefly survey the current state of scholarship on epic speeches and of the digital infrastructure on which we propose to build. We outline a model for harmonizing speech data across studies and aligning it with existing LOD standards. Finally, we discuss some early proof-of-concept results and the larger implications of this approach for the field. The long-term aim of the DICES project is to build a database of metadata on direct speech in Greek and Latin epic, not only covering canonical texts such as Homer and Virgil, but also including the less-studied texts of the late antique period, which will benefit greatly both from the increased accessibility and also from the diachronic perspective afforded by a corpus-based approach. The envisioned database also has the potential to include diachronic data from additional genres and languages at a later stage.
A Time Series Analysis Model for COVID-19 Prediction in India

While COVID has taken a toll globally, its imperative damage in India has been serious because of a large population base. In this scenario, daily prediction of COVID cases can help concerned authorities better brace themselves of the upcoming effect. Since cases form a time series data, their prediction remains a challenge due to inherent order in data points, which is tough to capture in statistical regressions. In addition, number of cases depends on a numerous factor in practical life, and to arrive on an exhaustive list for the purpose of modelling poses another challenge. To tackle these problems, we present a study spanning January 2020-April 2020, outlining way of using LSTMs for predicting 1-3 days in advance the number of cases in India and present a comparative analysis over inclusion of different factors in the prediction and its effect on accuracies.We achieved a R2 score of over 0.9 for short periods spanning 1-5 days, but model fails to capture long term (over 15 days) trend.Similarly, adding cases from Top 5 states as input factors increased the accuracy significantly for lookback = 4 to 0.99.
648-Hilbert space dimensionality in biphoton frequency combs for quantum-secure communications and networks

High-dimensional entanglement with larger Hilbert spaces enable an encoding of more bits per photon and thus promise increased communication capacities over quantum channels. Quantum frequency combs, which are intrinsically multimode in the temporal and frequency degrees of freedom within a single spatial mode, naturally facilitating the generation and measurement of high-dimensional entanglement. Current challenges include the extension of well-known methods for two qubits to high-dimensional quantum systems and their application in entanglement experiments with photons. More specifically, the major challenge is the certification of high-dimensional entanglement by a number of accessible experimental measurements. In this paper, we increase the Hilbert space dimensionality and provide versatile tools for quantifying and certifying high-dimensional entanglement in a biphoton frequency comb. We quantify the time binned Schmidt number up to 18 and certify entanglement of formation with 1.89 ebits. We have demonstrated a 648- dimensional Hilbert spaces with time-frequency entanglement in a biphoton frequency comb, enabling a computational space up to 13 photonic qubits, and 6.28 bits/photon classical information capacity. This high-dimensional time frequency multimode quantum states of biphoton frequency comb significantly boosting the photon information capacity that is critical for large-scale quantum information processing. Biphoton frequency comb has indeed demonstrated an attractive and powerful approach towards achieving this fundamental goal with applications in high-dimensional quantum information processing, time-frequency cluster-state quantum computation, high-dimensional encoding in quantum networks, and high-dimensional quantum simulations.
Causal Graph and Social Network Analysis for the Spread of COVID-19 from Self-reported Indicator Data

Dynamic Bayesian Network (DBN) is an useful tool to learn the causal inference and social network of random variables. In this article, we analyze the correlations between the spread of coronavirus (COVID-19) and certain self-reported COVID-19 indicators in the United States, and then adopt DBN model with search and score-based approach to analyze and interpret the causal relationships and social network between these variables by learning the structure of the Directed Acyclic Graph from the model. We explore the change of causality among fifty states during the pandemic of COVID-19 in the year of 2020 and interpret the root cause for changes and trends. We concentrate on five worst states with COVID-19 and then extended our studies to all states by comparing the causal relationships and analyzing the patterns of DAG.
Classification of Stroke Victims through Supervised Machine Learning Algorithms and Ensemble Learning

Throughout the developed and developing world, a malady has plagued humanity since antiquity: stroke. This research project explores several methodologies within machine learning that enable the classification of two distinct classes: absence or presence of a history of stroke. This paper explores several contemporary methodologies for classifying stroke victims using an aggregate of ensemble learning and a myriad of other supervised learning strategies. To this end, the research presented in this paper makes explicit use of bagging and boosting techniques to strengthen classification efficacy. Several supervised learning algorithms are utilized to perform the classification process. Python programming language with the machine learning libraries were used to design classification models and generate illustrative graphics. The experimental results, as discussed in greater detail in this paper, show that conditions such as stroke can be effectively classified with considerable high accuracy and precision.
A nonlinear feature transformation-based multi-user classification algorithm for keystroke dynamics

Multi-user authentication from keystroke data is an open problem that needs to be solved. When an online account or a common desktop is shared among multiple users, there is a need to determine who the current user is at log-in. In this paper, a non-linear feature transformation-based multi-user classification algorithm is proposed. Quantile transformation is proposed to map raw keystroke features to a uniform distribution to limit outliers. Then, dimensionality reduction techniques such as PCA, Kernel-PCA, and t-SNE are applied to the transformed features to project into reduced feature space. Unsupervised clustering algorithms such as DBSCAN and GMM are applied in the reduced feature space to identify the number of users accessing the system. Using these results, a k-nearest neighbor search algorithm is used, in conjunctions with labeled clusters to classify users. The algorithm is validated using the CMU keystroke benchmark dataset and the MobiKey touch dataset. Once we identify the number of users, we can successfully classify users with an accuracy of over 93 percent.
LCD Codes as a Counter-Measure for Relevant Security Threats: A Survey

This review article discusses linear complementary duals over finite fields as counter-measures to side-channel attacks, faulty injection attacks, hardware trojan horse attacks, etc. We first discuss some security attacks and the need for linear complementary codes. Then introduce the linear complementary dual codes and study their properties. Some constructions and bounds on the linear complementary codes over finite fields are emphasised. Linear complementary dual codes with respect to different inner products such as Hermitian, Galois, Sigma, etc., are considered in our discussion. We end the survey with some comments on codes over rings.
The Effect of Lecture's Competency, Learning Motivation, and Learning Environment on Student Achievement

The purpose of this study was to determine the effect of lecturer competence, learning motivation, and learning environment on learning achievement. The population that includes this study are students of the Office Administration Education Study Program, Faculty of Economics, Jakarta State University Class of 2019 and 2020. The number of samples taken in this study was 119 students, namely from the number of students of office administration education class 2019-2020. Data collection techniques are through questionnaires distributed online such as Whatsapp and other social media communication tools.The results showed that the variables of lecturer competence, learning motivation, and learning environment had a simultaneous positive effect on learning achievement. Furthermore, for the partial, the results show that the variable or factor of learning motivation has a partial influence on learning achievement. For variables or factors of learning competence and learning environment, there is no partial effect on learning achievement.
AICF: Attentional Item-interaction Collaborative Filtering

As of today, deep learning technology has been widely utilized in the field of recommendation algorithms. Many work utilizes the multilayer perceptrons to simulate inner product operations in matrix factorization (MF) models. Hence, those works inherit a weakness of MF, namely the ineffectiveness of identifying between a small group of closely related items. The association of items, such as high-order relations between items, endows the recommendation system with valuable information on the user’s final choices. In accordance with the above facts, this paper proposes an Attentional Item-Interaction Collaborative Filtering model (AICF). It can distinguish which items in the user’s historical interaction items play a more important role in the user’s choice of items. To examine the performance of AICF, we designed multiple experiments on two real world data sets.
Research on Shift Matching to Enhance DAM

Human&#x2019;s responses in communication depend on the context. Specifically, they are the feedback to a sentence or a word in the context. Further, external knowledge needs to be added to provide appropriate information for the human&#x2019;s answer. DAM (Deep Attention Matching Network), uses the attention mechanism of transformer to expand utterance and response into multi-level granularity representations, and then calculate the granularity similarity at the same level, which has better effects than using traditional RNN (recurrent neural network). Inspired by DAM, we propose to calculate the similarity between granularities at different levels which can explore more useful information for training and learning in this paper. We call this new matching method "shift matching", which is not limited to enhancing DAM, but can be generalized to other models. Our experiments include two parts: the first part compares the improved model with the base, and then compares the classic model to solve multi-round dialogue problem. The second part is to compare the experimental results of the different shift distances. The results are better than that of the state-of-the-art model.
A Multiple Fuzzy C-Means Ensemble Cluster Forest for Big Data

Over the recent decades, there has been an exponential growth of data streaming from various data sources, such as social networks and data centers. As data grows larger, clustering has become a challenging task. This paper proposes a new multi fuzzy ensemble cluster for big data to address this challenge. This approach is based on the use of the data reduction and the cluster forests (CF) strategy (FCFDR). It comprises two tasks. The first consists of making many clustering instances based on hybridizing feature selection (FS) with instance selection (IS), which are used to select more representative features from big data, to simultaneously reduce the high dimensional data. The selected features are then classified using the objective function. Yielding the initial fuzzy co-association matrix and regularized it. The second task, consists of aggregating the clustering instances into one final result vector, using normalized cut algorithm (Ncut). A big datasets from UCI was used to validate the effectiveness of our approach. The results prove the efficiency of the proposed approach in terms of clustering accuracy and quality.
Applying Association Rules For Summary Sheet Marketplace

Enrolling in multiple courses require a student to comprehend contents from all courses. As the examinations approach, some students got difficulty i n p reparation a s they have to manage to read the entire content so choosing what to read wisely is important. The summary sheet is an important reading source that many choose to revive their understanding. As finding a suitable summary sheet is a challenge, we apply the association rules to find t he right summary sheet f or different persons. The association rules are designed to match summary sheets with the demand of students. Our approach serves as a guideline of how to design and develop an application that delivers suitable content to the learners.
Secure Parameterized Multi-Pattern Matching in Multi-Text Owner Setting

The parameterized pattern matching (PPM) problem is seeking occurrences of one string in another, where both strings are given from two parameter and constant alphabet sets. In this matching paradigm, a consistent renaming of symbols from the parameter alphabet is allowed. The parameterized pattern matching is useful in problems in software engineering, computational biology, and other applications. In this paper, for the first time, we consider the problem of secure parameterized multi-pattern matching (SPMPM) that an owner of a set of patterns allows matching of his/her patterns against the text while pattern owner only learns matching results and the text owner learns nothing. In our setting, a pattern owner can find the matching locations in multiple texts in a privacy-preserving manner. The server only performs operations over encrypted values and cannot obtain any information about the text or the patterns. The proposed scheme is efficient for the pattern owner and every text owner. We prove the security of our scheme via the simulation-based paradigm.
A Framework of a Filtering and Classification Techniques for Enhancing the Accuracy of a Hybrid CBIR System

In recent times, content-based image retrieval (CBIR) has gained more attention. CBIR is a technology for retrieving the features of an image from a large database that closely matches the given query image. This paper proposes a novel hybrid framework for content-based image retrieval to enhance the accuracy related issues. Here, Color moment is used as a filtering metric to select some relevant images from a broad database. Then, the features of the filtered images are extracted using Color moment and Gray Level Co-occurrence Matrix (GLCM) techniques. Various distance measure functions like Euclidean, Manhattan, Cosine is used to calculate similarity compatibility. Lastly, for increasing the efficiency of the developed hybrid CBIR system, an intelligent approach, i.e., an ELM classifier has been used. The average precision obtained on the Wang database is 96.25% which is higher as compared to many other classification techniques.
A Method based on Faster RCNN Network for Object Detection

Background: Detecting occluded objects in images remains a challenging problem since the occluded objects often occlude each other or are occluded by other objects. It is hard to identify the occluded objects in an image, especially when the occlusion is significant. Methods: In this work, a two-stage object detection method has been proposed. The proposed method is based on the Faster RCNN model and uses ResNet50 as the backbone network. In addition, the method uses the feature pyramid network to reuse the higher-resolution maps of the feature hierarchy. The dilated convolution in the architecture of the proposed network has been added to expand the receptive field of the feature maps and the loss function and gradient learning rate are optimized. Result: The proposed detector is trained in an end-to-end fashion, which achieves state-of-theart results on two datasets, i.e., MAFA and WIDER FACE, particularly for WIDER FACE (with the highest mAP, 66.4%). Conclusion: In conclusion, by adding dilated convolution and optimizing gradient learning rate to the object detection model, the precision of the occlusion object detection can be improved effectively.
Multiple Noisy Private Remote Source Observations for Secure Function Computation

The problem of reliable function computation is extended by imposing privacy, secrecy, and storage constraints on a remote source whose noisy measurements are observed by multiple parties. The main additions to the classic function computation problem include 1) privacy leakage to an eavesdropper is measured with respect to the remote source rather than the transmitting terminals’ observed sequences; 2) the information leakage to a fusion center with respect to the remote source is considered as another privacy leakage metric; 3) two transmitting node observations are used to compute a function. Inner and outer bounds on the rate regions are derived for lossless single-function computation with two transmitting nodes, which recover previous results in the literature, and for special cases that consider invertible functions exact rate regions are characterized.
2D Nearest Neighbor Optimization Method Based on Constrained Programming

As quantum computing enters the era of NISQ (Noisy Intermediate-Scale Quantum), nearest neighbor constraint has become an urgent problem in qubits arrangement. In order to reduce the nearest neighbor cost, this paper proposes a 2D nearest neighbor constraint synthesis algorithm. First, in order to solve the initial arrangement problem, the nearest neighbor constraint problem is modeled as a constrained programming equation, and the optimal path method is selected to solve the optimal initial arrangement of the qubits; Then on the basis of the initial arrangement, we use a heuristic selection algorithm based on the directed acyclic graph to search for the optimal path to construct an external queue to select the optimal SWAP gate insertion method. Using a representative benchmark line for test comparison, compared with the classic algorithm, the average optimization rate of the algorithm proposed in this paper has increased by 24.1%.
Forward and Backward Warping for Optical Flow-Based Frame Interpolation

Frame interpolation methods generate intermediate frames by taking consecutive frames as inputs. This enables the generation of high frame rate videos from low frame rate videos. Recently, many deep learning-based frame interpolation methods have been proposed. One way of frame interpolation is by using the bi-directional optical flow. In many cases, these methods use backward warping to warp the input images to the desired frame. However, forward warping can also be used to warp the input frames. In this paper, we propose a frame interpolation method that utilizes both forward warping and backward warping. Experimental results show that utilizing both warping methods can enhance the performance compared to only using backward warping.