Reviewerly/exHarmony huggingface dataset:
Developers’ need for the rationale of code commits: An in-breadth and in-depth study

Communicating the rationale behind decisions is essential for the success of software engineering projects. In particular, understanding the rationale of code commits is an important and often difficult task. Although the software engineering community recognizes rationale need and importance, there is a lack of in-depth study of rationale for commits. To bridge this gap, we apply a mixed-methods approach, interviewing software developers and distributing two surveys, to study their perspective of rationale for code commits. We found that software developers need to investigate code commits to understand their rationale when working on diverse tasks. We also found that developers decompose the rationale of code commits into 15 components, each is differently needed, found, and recorded. Furthermore, we explored software developers' experiences with rationale need, finding, and recording. We discovered factors leading software developers to give up their search for rationale of code commits. Our findings provide a better understanding of the need for rationale of code commits. In light of our findings, we discuss and present our vision about rationale of code commits practitioners' documentation, tools support, and documentation automation. In addition, we discuss the benefits of analyzes that could arise from good documentation of rationale for code commits.
Construction of Personalized Learning Platform Based on Collaborative Filtering Algorithm

On the network service platform for vocational education, there are currently over 10,000 online courses. Learners face a challenge in selecting interesting courses from the vast resources available. Learners’ urgent need for personalized learning is becoming more apparent as educational informatization progresses. Personalized recommendation (PR) technology can aid personalized learning and increase learners’ learning efficiency significantly. This paper constructs a smart classroom model based on AI (artificial intelligence) by studying the connotation and characteristics of smart classroom in light of the current research status and trend of smart classroom at home and abroad. The merits of the recommendation system are determined by the recommendation algorithm used by PR system. This paper primarily focuses on developing a personalized learning platform based on the CF (collaborative filtering) algorithm, as well as conducting system requirements analysis, database design, functional module design, implementation, and testing on this foundation. Experiments are carried out to see if the optimized PR algorithm in the network learning platform is effective.
Transfer learning model for false positive reduction in lymph node detection via sparse coding and deep learning

Transfer learning technique is popularly employed for a lot of medical image classification tasks. Here based on convolutional neural network (CNN) and sparse coding process, we present a new deep transfer learning architecture for false positive reduction in lymph node detection task. We first convert the linear combination of the deep transferred features to the pre-trained filter banks. Next, a new point-wise filter based CNN branch is introduced to automatically integrate transfer features for the false and positive image classification purpose. To lower the scale of the proposed architecture, we bring sparse coding process to the fixed transferred convolution filter banks. On this basis, a two-stage training strategy with grouped sparse connection is presented to train the model efficiently. The model validity is tested on lymph node dataset for false positive reduction and our approach indicates encouraging performances compared to prior approaches. Our method reaches sensitivities of 71% /85% at 3 FP/vol. and 82% /91% at 6 FP/vol. in abdomen and mediastinum respectively, which compare competitively to previous approaches.
A Temporal Convolutional Neural Network Based Activity Recognition Model using a Real-Time Two-Dimensional Single Pose Estimation Framework

Human pose estimation and human activity recognition are two of the most researched domains in computer vision applications. Surveillance, human-computer interaction and video retrieval, all benefit from robust solutions of this domain. Due to disparities in inter-personal parameters, mobility efficiency, and recording configurations, the process is challenging. In this paper, deep learning algorithms are used for both pose estimation and activity recognition models. The pose estimation model generates an array of objects for each keypoint with their coordinated over a specific interval. This time series data is then consumed by the activity recognition model which was trained using a temporal convolutional neural network. The attained accuracy of the model was found to be 92.7&#x0025; with a loss of 0.19.
Improving the performance of computer-aided classification of breast lesions using a new feature fusion method

Computer-Aided Diagnosis (CAD) schemes used to classify suspicious breast lesions typically include machine learning classifiers that are trained using features computed from either the segmented lesions or fixed regions of interest (ROIs) covering the lesions. Both methods have advantages and disadvantages. In this study, we investigate a new approach to train a machine learning classifier that fuses image features computed from both the segmented lesions and the fixed ROIs. We assembled a dataset with 2,000 mammograms. Based on lesion center, a ROI is extracted from each image. Among them, 1,000 ROIs depict verified malignant lesions and rest include benign lesions. An adaptive multilayer region growing algorithm is applied to segment suspicious lesions. Several sets of statistical features, texture features based on GLRLM, GLDM and GLCM, Wavelet transformed features and shape-based features are computed from the original ROI and segmented lesion, respectively. Three support vector machines (SVM) are trained using features computed from original ROIs, segmented lesions, and fusion of both, respectively, using a 10-fold cross-validation method embedded with a feature reduction method, namely a random projection algorithm. By applying the area under the ROC curve (AUC) as an evaluation index, our study results reveal no significant difference between AUC values computed using classification scores generated by two SVMs trained with features computed from original ROIs or segmented lesions. However, utilizing the fused features, AUC of SVM increases more than 10% (p&lt;0.05). This study demonstrates that image features computed using the segmented lesions and the fixed ROIs contain complementary discriminatory information. Thus, fusing these features can significantly improve CAD performance.
Learning Hierarchical Variational Autoencoders With Mutual Information Maximization for Autoregressive Sequence Modeling

Variational autoencoders (VAEs) are a class of effective deep generative models, with the objective to approximate the true, but unknown data distribution. VAEs make use of latent variables to capture high-level semantics so as to reconstruct the data well with the help of informative latent variables. Yet, training VAEs tends to suffer from posterior collapse, when the decoder is parameterized by an autoregressive model for sequence generation. VAEs can be further enhanced by introducing multiple layers of latent variables, but the posterior collapse issue hinders the adoption of such hierarchical VAEs in real-world applications. In this paper, we introduce InfoMaxHVAE, which integrates mutual information estimated via neural networks into hierarchical VAEs to alleviate posterior collapse, when powerful autoregressive models are used for modeling sequences. Experimental results on a number of text and image datasets show that InfoMaxHVAE can outperform the state-of-the-art baselines and exhibits less posterior collapse. We further show that InfoMaxHVAE can shape a coarse-to-fine hierarchical organization of the latent space.
Exploring Neuroscience Literature and Understanding Relations Between Brain-Related Topics - Using Augmented Reality

Neuroscience researchers are interested in understanding relation between anatomical regions of the brain and disorders that affect them, for example. Using the topics themselves, rather than individual articles, to examine relation in a large body of literature, can provide a higher-level approach. I investigate the use of 3D representations in Augmented Reality to aid neuroscientists to explore literature and understand relations between brain-related topics, given the three-dimensional nature of the brain. Distant reading refers to comprehending the results of studies of a large number of articles, as opposed to the more common "close reading" of individual publications. For distant reading of neuroscience literature, I identify visualization and interaction design requirements. My assumption is that by providing overviews of the correlations among topics through the use of literature, these will allow neuroscientists to better understand the gaps in the literature and more quickly identify the most suitable experiments to carry out. The DatAR team at Utrecht University has created a prototype 3D AR implementation using which I have carried out two studies of a literature exploration interface. These studies showed that visualizing topics and their relation in an immersive AR environment is clear, understandable and helpful for exploring neuroscience literature. In the following, I will carry out a study that participants can make parallel query and compare the results. I will further investigate in finding indirect relations between brain regions and brain diseases. The last study will support neuroscience students to understand course material. Interface improvements are considering where necessary.
On the IND-CCA1 Security of FHE Schemes

Fully homomorphic encryption (FHE) is a powerful tool in cryptography that allows one to perform arbitrary computations on encrypted material without having to decrypt it first. There are numerous FHE schemes, all of which are expanded from somewhat homomorphic encryption (SHE) schemes, and some of which are considered viable in practice. However, while these FHE schemes are semantically (IND-CPA) secure, the question of their IND-CCA1 security is much less studied, and we therefore provide an overview of the IND-CCA1 security of all acknowledged FHE schemes in this paper. To give this overview, we grouped the SHE schemes into broad categories based on their similarities and underlying hardness problems. For each category, we show that the SHE schemes are susceptible to either known adaptive key recovery attacks, a natural extension of known attacks, or our proposed attacks. Finally, we discuss the known techniques to achieve IND-CCA1-secure FHE and SHE schemes. We concluded that none of the proposed schemes were IND-CCA1-secure and that the known general constructions all had their shortcomings.
Bringing Semantics into Historical Archives with Computer-aided Rich Metadata Generation

This article relies on the idea that a semantically rich metadata layer is required in order to provide an effective, intelligent, and engaging access to historical archives. However, building such a semantic layer represents a well-known bottleneck that can be overcome only by a hybrid strategy, integrating user-generated content and automatic techniques. The PRiSMHA project provides a contribution in this direction with the design and development of the prototype of an ontology-driven platform supporting users in semantic metadata generation. In particular, the main contribution of this article is to show how automatic information extraction techniques (namely, Named Entity and Temporal Expression Recognition) and information retrieved from external datasets in the LOD cloud can support users in the identification and characterization of new entities to annotate documents with.
Justificatory explanations in machine learning: for increased transparency through documenting how key concepts drive and underpin design and engineering decisions

Given the pervasiveness of AI systems and their potential negative effects on people's lives (especially among already marginalised groups), it becomes imperative to comprehend what goes on when an AI system generates a result, and based on what reasons, it is achieved. There are consistent technical efforts for making systems more "explainable" by reducing their opaqueness and increasing their interpretability and explainability. In this paper, we explore an alternative non-technical approach towards explainability that complement existing ones. Leaving aside technical, statistical, or data-related issues, we focus on the very conceptual underpinnings of the design decisions made by developers and other stakeholders during the lifecycle of a machine learning project. For instance, the design and development of an app to track snoring to detect possible health risks presuppose some picture or another of "health", which is a key notion that conceptually underpins the project. We take it as a premise that these key concepts are necessarily present during design and development, albeit perhaps tacitly. We argue that by providing "justificatory explanations" about how the team understands the relevant key concepts behind its design decisions, interested parties could gain valuable insights and make better sense of the workings and outcomes of systems. Using the concept of "health", we illustrate how a particular understanding of it might influence decisions during the design and development stages of a machine learning project, and how making this explicit by incorporating it into ex-post explanations might increase the explanatory and justificatory power of these explanations. We posit that a greater conceptual awareness of the key concepts that underpin design and development decisions may be beneficial to any attempt to develop explainability methods. We recommend that "justificatory explanations" are provided as technical documentation. These are declarative statements that contain at its simplest: (1) a high-level account of the understanding of the relevant key concepts a team possess related to a project's main domain, (2) how these understandings drive decision-making during the life-cycle stages, and (3) it gives reasons (which could be implicit in the account) that the person or persons doing the explanation consider to have plausible justificatory power for the decisions that were made during the project.
Model Predictive Collision Avoidance for Non-Convex Environment Using Projected C-Space

This paper presents a path generation method for the autonomous vehicle to obtain a collision-free path for the parking task in a narrow parking space. The path planning based on model predictive control (MPC) considers the kinematics of the vehicle and the collision avoidance constraints to obtain a collision-free path. In order to achieve the fast computation of the optimization problem, a novel shape-based collision avoidance constraint in projected C-space using coordinate transform is proposed and used for the MPC. Since the proposed method can express the collision avoidance constraints between a rectangle and parallelograms and line segments, the proposed path planning method can handle not only the rectangular-shaped objects but also the objects expressed as arbitrary polygons while the conventional method is limited to convex polygons. The usefulness of the proposed method was verified and its computation time was examined by assuming a narrow parking space in the simulation.
Type-2 Fuzzy Adaptive PID Controller for Differential Drive Mobile Robot: A Mechatronics Approach

This paper studies the effect of a type-2 fuzzy adaptive PID (type-2 FAPID) controller on the performance of a mechatronics system such as differential mobile robot. In the proposed adaptive controller, the PID controller gains are automatically tuned using type-2 fuzzy systems. The performance of controllers is compared by simulating the robot traveling an undisturbed straight route and a circular route. Torque load disturbance is later added to the DC motors to draw better comparisons between the type-1 and type-2 FAPID controllers. The findings of this paper showed that the designed type-2 fuzzy adaptive PID controllers have enhanced the performance of differential mobile robot.
Gesture and Voice Controlled Robot for Industrial Applications

The subject of this study is a robot that can be controlled via hand gestures and vocal instructions. The robots can now be controlled via speech and gestures, despite the fact that they were previously operated manually. This gesture and speech recognition technology is defined by the interaction of the computer with human body language. To build voice and gesture recognizer using speech and image processing along with machine leaning models such that accuracy of the recognition is increased was primary goal. The project&#x0027;s primary purpose is to develop a robotic vehicle using Arduino and to control the vehicle using voice and gesture-based information. Further to improve the robot&#x0027;s overall security while also simplifying the control mechanism. The robot can perform various operations such as start/stop, left/right, forward/backward movement. Also, many industries are working on programming and operating robots using gestures and speech for various applications, such as industrial uses (assembly robots), transportation (mobile robots), and household usage (cleaning robots).
Data Mining Technology-Based English Listening Prediction Strategy and Its Training Approach

The big data era of “data-driven schools, analysis, and change education” has arrived, and the technology of data mining was born in the education industry. Based on data mining technology, this study explores English listening prediction strategies and training approaches. Prediction is an effective learning strategy in listening comprehension. Cultivating students to use predictive strategies is helpful to improve their listening comprehension, mining and analyzing the listening data generated by English skill training system, selecting the data related to students’ listening as features, aiming at students’ test listening scores, carrying out model training, and realizing the prediction of students’ listening. After evaluation and analysis, it is found that data mining technology can be used to predict students’ listening more accurately.
Effective Bots’ Detection for Online Smartphone Game Using Multilayer Perceptron Neural Networks

Online smartphone game bots can cause unfair behaviors and even shorten the game’s life cycle. The random forest algorithm in machine learning is a widely used solution to identify game bots through behavioral features. Although the random forest algorithm can exactly detect more definite game bot players, some players belonging to the gray area cannot be detected accurately. Therefore, this study collects players’ data and extracts the features to build the multilayer perceptron, neural network model, for effectively detecting online smartphone game bots. This approach calculates each player’s abnormal rate to judge game bots and is evaluated on the famous mobile online game. Based on these abnormal rates, we then use K means to cluster players and further define the gray area. In the experimental evaluation, the results demonstrate the proposed learning model has better performance, not only increasing the accuracy but also reducing the error rate as compared with the random forest model in the same players’ dataset. Accordingly, the proposed learning model can detect bot players more accurately and is feasible for real online smartphone games.
Second-degree branch structure blockchain expansion model

The blockchain runs in a complex topological network which is affected by the principle of consensus, and data storage between nodes needs to maintain global consistency in the entire network, which causes the data storage inefficient. At the same time, the information exchange between large-scale communication node groups leads to the problems of bandwidth expropriation and excessive network load. In response to these problems, this article proposes a second-degree branch structure blockchain expansion model. First, a ternary storage structure is established. Data use the way of fully integrated storage, multi-cell storage, and fully split storage, and data are classified and stored in parallel between the structures. Second, a second-degree branch chain model is constructed. The main chain forks into multiple sub-chains, and a free competition chain structure and a Z-type chain structure are defined; a two-way rotation mechanism is introduced to realize the integration and transition between chain structures. Finally, a set of malicious attacks is simulated to realize the security constraints of the blockchain, to verify the security of the second-degree branch chain model. Experiment shows that the second-degree branch structure expansion model proposed in this article has great advantages in data storage efficiency and network load.
Face Detection from Blurred Images based on Convolutional Neural Networks

This paper proposes face detection from blurry and noisy images robustly and efficiently using convolutional neural networks. It has also been demonstrated that the method of face detection from blurred images utilizing convolutional neural networks is superior to other methods under consideration concerning precision-recall and discontinuity and continuity scores. Face detection is the infrastructure of face recognition; also, it includes but is not limited to the following topics: traffic surveillance, stereo videos, finding a criminal from large crowds in terrorist accidents, calibrated stereo images, face alignment of images from sensors with heterologous wavelengths, driving license photos, and animations. Some difficulties of this topic include the lack of joint datasets, movements (displacements), changing expression, intensive illumination, the likelihood of overfitting in the case of employing high-dimensional data, and the presence of numerous blurs and various aspects. There are multiple face detection methods from blurry images, such as blur kernel estimation and complex Fourier coefficients of a trained neural network, exerting metrics that define arduous patches. This paper executes face detection in noisy and blurry images applying convolutional neural networks that make face detection more applicable. This is due to exploiting two techniques for blur elimination and face detection on the foundation of convolutional neural networks.
Evading generated-image detectors: A deep dithering approach

In this paper, an end-to-end and black-box deep dithering model is proposed as an anti-forensic measure against general generated-image detectors. Firstly, we show the limitations of conventional generated-image simulator, and propose to use VQ-VAE as an effective alternative to simulate the image generation process. The general end-to-end anti-forensic framework using deep dithering model for generated images is then presented, which generates imperceptible spatial additive dithering to evade the fake-image detectors for various image generation models. The contrastive learning strategy is employed to train the deep dithering model, as we use real images and their VQ-VAE simulated fake images as positive and negative samples respectively, which could greatly eliminate the generative patterns on various generated images. In addition, the dithering amplitude constraint is also adopted to maintain the visual fidelity of dithered images and prevent them from degradation in visual quality. The experimental results show that our proposed deep dithering approach could effectively eliminate the generative artifacts on various GAN based generated images, and outperforms other state-of-the-art generated-image anti-forensic methods.