Reviewerly/exHarmony huggingface dataset:
Facilitating program comprehension with call graph multilevel hierarchical abstractions

Program comprehension is a fundamental prerequisite for software maintenance and evolution. In order to understand a software structure, developers often read its codebase or documentation—if available and not outdated. Both approaches are tedious, time-consuming, and inefficient. Recent methods and tools have emerged to facilitate program comprehension, such as static call graphs, which depict the structure of the software system as a directed graph. However, the usage of call graphs still faces two main challenges: (1) large call graphs can be difficult to understand, and (2) they are limited to a single level of granularity, such as function calls. In this paper, we introduce a coarsening technique to create multi-level, hierarchical representations of the call graph. Specifically, we propose a hierarchical clustering approach of the execution paths to visualize the call graph at different granularity levels and for different software units, including packages, classes, and functions. Our overarching goal is to assist software developers in understanding the software system from a high-level of abstraction to the low-level of implementation with the ability to focus on particular parts of the system individually. To validate our approach and tool support, we conducted a user study of 18 software engineers from more than 11 industries who carried out several tasks using our system and then answered a survey. The results demonstrate that our approach is feasible to automatically construct multi-level abstractions of the call graph and hierarchically cluster them into meaningful abstractions. A video demo of the tool is available at https://rakanalanazi.github.io/CodEx/.
Artificial Intelligence Techniques for Prostate Cancer Detection through Dual-Channel Tissue Feature Engineering

The optimal diagnostic and treatment strategies for prostate cancer (PCa) are constantly changing. Given the importance of accurate diagnosis, texture analysis of stained prostate tissues is important for automatic PCa detection. We used artificial intelligence (AI) techniques to classify dual-channel tissue features extracted from Hematoxylin and Eosin (H&amp;E) tissue images, respectively. Tissue feature engineering was performed to extract first-order statistic (FOS)-based textural features from each stained channel, and cancer classification between benign and malignant was carried out based on important features. Recursive feature elimination (RFE) and one-way analysis of variance (ANOVA) methods were used to identify significant features, which provided the best five features out of the extracted six features. The AI techniques used in this study for binary classification (benign vs. malignant and low-grade vs. high-grade) were support vector machine (SVM), logistic regression (LR), bagging tree, boosting tree, and dual-channel bidirectional long short-term memory (DC-BiLSTM) network. Further, a comparative analysis was carried out between the AI algorithms. Two different datasets were used for PCa classification. Out of these, the first dataset (private) was used for training and testing the AI models and the second dataset (public) was used only for testing to evaluate model performance. The automatic AI classification system performed well and showed satisfactory results according to the hypothesis of this study.
Incremental Evaluation in Genetic Programming

Often GP evolves side effect free trees. These pure functional expressions can be evaluated in any order. In particular they can be interpreted from the genetic modification point outwards. Incremental evaluation exploits the fact that: in highly evolved children the semantic difference between child and parent falls with distance from the syntactic disruption (e.g. crossover point) and can reach zero before the whole child has been interpreted. If so, its fitness is identical to its parent (mum). Considerable savings in bloated binary tree GP runs are given by exploiting population convergence with existing GPquick data structures, leading to near linear O(gens) runtime. With multi-threading and SIMD AVX parallel computing a 16 core desktop can deliver the equivalent of 571 billion GP operations per second, 571 giga GPop/s. GP convergence is viewed via information theory as evolving a smooth landscape and software plasticity. Which gives rise to functional resilience to source code changes. On average a mixture of 100 +, −, $$\times $$ and (protected) $$\div $$ tree nodes remove test case effectiveness at exposing changes and so fail to propagate crossover infected errors.
Deep Collocative Learning for Immunofixation Electrophoresis Image Analysis

Immunofixation Electrophoresis (IFE) analysis is of great importance to the diagnosis of Multiple Myeloma, which is among the top-9 cancer killers in the United States, but has rarely been studied in the context of deep learning. Two possible reasons are: 1) the recognition of IFE patterns is dependent on the co-location of bands that forms a binary relation, different from the unary relation (visual features to label) that deep learning is good at modeling; 2) deep classification models may perform with high accuracy for IFE recognition but is not able to provide firm evidence (where the co-location patterns are) for its predictions, rendering difficulty for technicians to validate the results. We propose to address these issues with collocative learning, in which a collocative tensor has been constructed to transform the binary relations into unary relations that are compatible with conventional deep networks, and a location-label-free method that utilizes the Grad-CAM saliency map for evidence backtracking has been proposed for accurate localization. In addition, we have proposed Coached Attention Gates that can regulate the inference of the learning to be more consistent with human logic and thus support the evidence backtracking. The experimental results show that the proposed method has obtained a performance gain over its base model ResNet18 by 741.30% in IoU and also outperformed popular deep networks of DenseNet, CBAM, and Inception-v3.
Features to Text: A Comprehensive Survey of Deep Learning on Semantic Segmentation and Image Captioning

With the emergence of deep learning, computer vision has witnessed extensive advancement and has seen immense applications in multiple domains. Specifically, image captioning has become an attractive focal direction for most machine learning experts, which includes the prerequisite of object identification, location, and semantic understanding. In this paper, semantic segmentation and image captioning are comprehensively investigated based on traditional and state-of-the-art methodologies. In this survey, we deliberate on the use of deep learning techniques on the segmentation analysis of both 2D and 3D images using a fully convolutional network and other high-level hierarchical feature extraction methods. First, each domain’s preliminaries and concept are described, and then semantic segmentation is discussed alongside its relevant features, available datasets, and evaluation criteria. Also, the semantic information capturing of objects and their attributes is presented in relation to their annotation generation. Finally, analysis of the existing methods, their contributions, and relevance are highlighted, informing the importance of these methods and illuminating a possible research continuation for the application of semantic image segmentation and image captioning approaches.
Embedding Refinement Framework for Targeted Aspect-Based Sentiment Analysis

The state-of-the-art approaches to targeted aspect-based sentiment analysis (TABSA) are mostly built on deep neural networks with attention mechanisms. One problem is that embeddings of targets and aspects are either pre-trained from large external corpora or randomly initialized. We argue that affective commonsense knowledge and words indicative of sentiment could be used to learn better target and aspect embeddings. We therefore propose an embedding refinement framework called <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RAEC</b> ( <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</b> efining <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</b> ffective <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">E</b> mbedding from <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</b> ontext), in which sentiment concepts extracted from affective commonsense knowledge and word relative location information are incorporated to derive context-affective embeddings. Furthermore, a sparse coefficient vector is exploited in refining the embeddings of targets and aspects separately. In this way, embeddings of targets and aspects can capture the highly relevant affective words. Experimental results on two benchmark datasets show that our framework can be easily integrated with existing embedding-based TABSA models and achieves state-of-the-art results compared to models relying on pre-trained word embeddings or built on other embedding refinement methods.
An Inter-Comparative Survey on State-of-the-Art Detectors—R-CNN, YOLO, and SSD

In recent years, breakthrough enhancements in computer hardware and supercomputers made object detection a significant topic of research. Accurate object detection models are computationally expensive and are inefficient on simpler and limited configuration settings while faster models achieve real-time speed, work well on simpler configurations but fail to be accurate. There is always a trade-off between speed and accuracy. There is no clear-cut answer on which detector performs the best. The user will have to make a choice based on the requirement. This paper aims at analyzing numerous CNN-based object detection algorithms—R-CNN, Fast R-CNN, Faster R-CNN, You Only Look Once (YOLO), and Single Shot MutliBox Detector (SSD)—and make comparisons concerning performance, precision and speed and state as to which algorithm performs better under certain constraints. This enables the user to pick an object detector of his/her choice that better addresses the demands of an application.
Deep joint learning for language recognition

Deep learning methods for language recognition have achieved promising performance. However, most of the studies focus on frameworks for single types of acoustic features and single tasks. In this paper, we propose the deep joint learning strategies based on the Multi-Feature (MF) and Multi-Task (MT) models. First, we investigate the efficiency of integrating multiple acoustic features and explore two kinds of training constraints, one is introducing auxiliary classification constraints with adaptive weights for loss functions in feature encoder sub-networks, and the other option is introducing the Canonical Correlation Analysis (CCA) constraint to maximize the correlation of different feature representations. Correlated speech tasks, such as phoneme recognition, are applied as auxiliary tasks in order to learn related information to enhance the performance of language recognition. We analyze phoneme-aware information from different learning strategies, like joint learning on the frame-level, adversarial learning on the segment-level, and the combination mode. In addition, we present the Language-Phoneme embedding extraction structure to learn and extract language and phoneme embedding representations simultaneously. We demonstrate the effectiveness of the proposed approaches with experiments on the Oriental Language Recognition (OLR) data sets. Experimental results indicate that joint learning on the multi-feature and multi-task models extracts instinct feature representations for language identities and improves the performance, especially in complex challenges, such as cross-channel or open-set conditions.
Development of a Wearable Glove System with Multiple Sensors for Hand Kinematics Assessment

In traditional hand function assessment, patients and physicians always need to accomplish complex activities and rating tasks. This paper proposes a novel wearable glove system for hand function assessment. A sensing system consisting of 12 nine-axis inertial and magnetic unit (IMMU) sensors is used to obtain the acceleration, angular velocity, and geomagnetic orientation of human hand movements. A complementary filter algorithm is applied to calculate the angles of joints after sensor calibration. A virtual hand model is also developed to map with the glove system in the Unity platform. The experimental results show that this glove system can capture and reproduce human hand motions with high accuracy. This smart glove system is expected to reduce the complexity and time consumption of hand kinematics assessment.
Election Prediction on Twitter: A Systematic Mapping Study

Context. Social media platforms such as Facebook and Twitter carry a big load of people’s opinions about politics and leaders, which makes them a good source of information for researchers to exploit different tasks that include election predictions. Objective. Identify, categorize, and present a comprehensive overview of the approaches, techniques, and tools used in election predictions on Twitter. Method. Conducted a systematic mapping study (SMS) on election predictions on Twitter and provided empirical evidence for the work published between January 2010 and January 2021. Results. This research identified 787 studies related to election predictions on Twitter. 98 primary studies were selected after defining and implementing several inclusion/exclusion criteria. The results show that most of the studies implemented sentiment analysis (SA) followed by volume-based and social network analysis (SNA) approaches. The majority of the studies employed supervised learning techniques, subsequently, lexicon-based approach SA, volume-based, and unsupervised learning. Besides this, 18 types of dictionaries were identified. Elections of 28 countries were analyzed, mainly USA (28%) and Indian (25%) elections. Furthermore, the results revealed that 50% of the primary studies used English tweets. The demographic data showed that academic organizations and conference venues are the most active. Conclusion. The evolution of the work published in the past 11 years shows that most of the studies employed SA. The implementation of SNA techniques is lower as compared to SA. Appropriate political labelled datasets are not available, especially in languages other than English. Deep learning needs to be employed in this domain to get better predictions.
A Simple Nearly Optimal Restart Scheme For Speeding Up First-Order Methods

We present a simple scheme for restarting first-order methods for convex optimization problems. Restarts are made based only on achieving specified decreases in objective values, the specified amounts being the same for all optimization problems. Unlike existing restart schemes, the scheme makes no attempt to learn parameter values characterizing the structure of an optimization problem, nor does it require any special information that would not be available in practice (unless the first-order method chosen to be employed in the scheme itself requires special information). As immediate corollaries to the main theorems, we show that when some well-known first-order methods are employed in the scheme, the resulting complexity bounds are nearly optimal for particular—yet quite general—classes of problems.
A Robust Quadruplet and Faster Region-Based CNN for UAV Video-Based Multiple Object Tracking in Crowded Environment

Multiple object tracking (MOT) from unmanned aerial vehicle (UAV) videos has faced several challenges such as motion capture and appearance, clustering, object variation, high altitudes, and abrupt motion. Consequently, the volume of objects captured by the UAV is usually quite small, and the target object appearance information is not always reliable. To solve these issues, a new technique is presented to track objects based on a deep learning technique that attains state-of-the-art performance on standard datasets, such as Stanford Drone and Unmanned Aerial Vehicle Benchmark: Object Detection and Tracking (UAVDT) datasets. The proposed faster RCNN (region-based convolutional neural network) framework was enhanced by integrating a series of activities, including the proper calibration of key parameters, multi-scale training, hard negative mining, and feature collection to improve the region-based CNN baseline. Furthermore, a deep quadruplet network (DQN) was applied to track the movement of the captured objects from the crowded environment, and it was modelled to utilize new quadruplet loss function in order to study the feature space. A deep 6 Rectified linear units (ReLU) convolution was used in the faster RCNN to mine spatial–spectral features. The experimental results on the standard datasets demonstrated a high performance accuracy. Thus, the proposed method can be used to detect multiple objects and track their trajectories with a high accuracy.
Interactive Data Visualization in Jupyter Notebooks

Interactive visualizations are at the core of the exploratory data analysis process, enabling users to directly manipulate and gain insights from data. In this article, we present three different ways in which interactive visualizations can be included in Jupyter Notebooks: 1) matplotlib callbacks; 2) visualization toolkits; and 3) embedding HTML visualizations. We hope that this article will help developers to select the best tools to build their interactive charts in Jupyter Notebooks.
Mapping the Psychosocialcultural Aspects of Healthcare Professionals’ Information Security Practices: Systematic Mapping Study

Background Data breaches in health care are on the rise, emphasizing the need for a holistic approach to mitigation efforts. Objective The purpose of this study was to develop a comprehensive framework for modeling and analyzing health care professionals’ information security practices related to their individual characteristics, such as their psychological, social, and cultural traits. Methods The study area was a hospital setting under an ongoing project called the Healthcare Security Practice Analysis, Modeling, and Incentivization (HSPAMI) project. A literature review was conducted for relevant theories and information security practices. The theories and security practices were used to develop an ontology and a comprehensive framework consisting of psychological, social, cultural, and demographic variables. Results In the review, a number of psychological, social, and cultural theories were identified, including the health belief model, protection motivation theory, theory of planned behavior, and social control theory, in addition to some social demographic variables, to form a comprehensive set of health care professionals’ characteristics. Furthermore, an ontology was developed from these theories to systematically organize the concepts. The framework, called the psychosociocultural (PSC) framework, was then developed from the various combined psychological and sociocultural attributes of the ontology. The Human Aspect of Information Security Questionnaire was adopted as a comprehensive tool for gathering staff security practices as mediating variables in the framework. Conclusions Data breaches occur often in health care today. This frequency has been attributed to the lack of experience of health care professionals in information security, the lack of development of conscious care security practices, and the lack of motivation to incentivize health care professionals. The frequent data breaches in health care threaten the mutual trust between health care professionals and patients, which implicitly impacts the quality of the health care service. The modeling and analysis of health care professionals’ security practices can be conducted with the PSC framework by combining methods of statistical survey, observations, and interviews in relation to PSC variables, such as perceptions (perceived benefits, perceived threats, and perceived barriers) or psychological traits, social factors, cultural factors, and social demographics.
A Comparison of Question Rewriting Methods for Conversational Passage Retrieval

Conversational passage retrieval relies on question rewriting to modify the original question so that it no longer depends on the conversation history. Several methods for question rewriting have recently been proposed, but they were compared under different retrieval pipelines. We bridge this gap by thoroughly evaluating those question rewriting methods on the TREC CAsT 2019 and 2020 datasets under the same retrieval pipeline. We analyze the effect of different types of question rewriting methods on retrieval performance and show that by combining question rewriting methods of different types we can achieve state-of-the-art performance on both datasets (Resources can be found at https://github.com/svakulenk0/cast_evaluation .)
Improved Density-Based Learning to Cluster for User Web Log in Data Mining

The improvements in tuning the website and improving the visitors’ retention are done by deploying the efficient weblog mining and navigational pattern prediction model. This crucial application initially performs data clearing and initialization procedures until the hidden knowledge is extracted as output. To obtain good results, the quality of the input data has to be promisingly good, and hence, more focus should be given to pre-processing and data cleaning operations. Other than this, the major challenge faced is the poor scalability during navigational pattern prediction. In this paper, the scalability of weblog mining is improved by using suitable pre-processing and data cleaning operations. This method uses a tree-based clustering algorithm to mine the relevant items from the datasets and to predict the navigational behavior of the users. The algorithm focus will be mainly on density-based learning to cluster and predict future requests. The proposed method is evaluated over BUS log data, where the data is of greater significance since it contains the log data of all the students in the university. The conducted experiments prove the effectiveness and applicability of weblog mining by using the proposed algorithm.
Designing Interpretable Recurrent Neural Networks for Video Reconstruction via Deep Unfolding

Deep unfolding methods design deep neural networks as learned variations of optimization algorithms through the unrolling of their iterations. These networks have been shown to achieve faster convergence and higher accuracy than the original optimization methods. In this line of research, this paper presents novel interpretable deep recurrent neural networks (RNNs), designed by the unfolding of iterative algorithms that solve the task of sequential signal reconstruction (in particular, video reconstruction). The proposed networks are designed by accounting that video frames' patches have a sparse representation and the temporal difference between consecutive representations is also sparse. Specifically, we design an interpretable deep RNN (coined reweighted-RNN) by unrolling the iterations of a proximal method that solves a reweighted version of the l1 - l1 minimization problem. Due to the underlying minimization model, our reweighted-RNN has a different thresholding function (alias, different activation function) for each hidden unit in each layer. In this way, it has higher network expressivity than existing deep unfolding RNN models. We also present the derivative l1 - l1 -RNN model, which is obtained by unfolding a proximal method for the l1 - l1 minimization problem. We apply the proposed interpretable RNNs to the task of video frame reconstruction from low-dimensional measurements, that is, sequential video frame reconstruction. The experimental results on various datasets demonstrate that the proposed deep RNNs outperform various RNN models.