Reviewerly/exHarmony huggingface dataset:
Automating Change-Level Self-Admitted Technical Debt Determination

Technical debt (TD) is a metaphor to describe the situation where developers introduce suboptimal solutions during software development to achieve short-term goals that may affect the long-term software quality. Prior studies proposed different techniques to identify TD, such as identifying TD through code smells or by analyzing source code comments. Technical debt identified using comments is known as Self-Admitted Technical Debt (SATD) and refers to TD that is introduced intentionally. Compared with TD identified by code metrics or code smells, SATD is more reliable since it is admitted by developers using comments. Thus far, all of the state-of-the-art approaches identify SATD at the file-level. In essence, they identify whether a file has SATD or not. However, all of the SATD is introduced through software changes. Previous studies that identify SATD at the file-level in isolation cannot describe the TD context related to multiple files. Therefore, it is beneficial to identify the SATD once a change is being made. We refer to this type of TD identification as “Change-level SATD Determination”, which determines whether or not a change introduces SATD. Identifying SATD at the change-level can help to manage and control TD by understanding the TD context through tracing the introducing changes. To build a change-level SATD Determination model, we first identify TD from source code comments in source code files of all versions. Second, we label the changes that first introduce the SATD comments as TD-introducing changes. Third, we build the determination model by extracting 25 features from software changes that are divided into three dimensions, namely diffusion, history and message, respectively. To evaluate the effectiveness of our proposed model, we perform an empirical study on 7 open source projects containing a total of 100,011 software changes. The experimental results show that our model achieves a promising and better performance than four baselines in terms of AUC and cost-effectiveness (i.e., percentage of TD-introducing changes identified when inspecting 20 percent of changed LOC). On average across the 7 experimental projects, our model achieves AUC of 0.82, cost-effectiveness of 0.80, which is a significant improvement over the comparison baselines used. In addition, we found that “Diffusion” is the most discriminative dimension among the three dimensions of features for determining TD-introducing changes.
A fitness-based multi-role particle swarm optimization

In the canonical particle swarm optimization (PSO), “self-cognitive” and “social-learning” play important roles in searching for promising solutions since the two parts can help particles efficiently share their valuable information. In this paper, a fitness-based multi-role PSO (FMPSO) is proposed, in which a new component, named as “subsocial-learning” part, is added to particles' velocity update rule. Moreover, a fitness-based multi-role PSO is proposed, in which three roles, i.e., leader, rambler, and follower, are assigned for particles at each generation based on their fitness. Accordingly, different learning weights for the three parts are introduced for different roles, the aim of which is to help the population carry out various search mechanisms. Furthermore, a particle can adjust its learning exemplar and objective dimensions for the three different parts according the particle's role. During the evolutionary process, two tuning operators are introduce to adjust particles' roles and objective dimensions. The dynamical and diverse learning models of the particles enable the population to deal with different situations. Moreover, a local searching operator based on BFGS Quasi-Newton method is proposed to refine the best solution at the later evolutionary stage. Experimental results show that FMPSO outperforms other state-of-art PSOs on a majority of functions in CEC2005 and CEC2013 test suites in terms of the global search ability, solution accuracy, and convergence speed. Two statistical tests also verify the promising performance of FMPSO. Furthermore, experiments aiming to analysis the sensitivity and effectiveness of the new proposed components in FMPSO are also conducted.
On the exact computation of the graph edit distance

The graph edit distance is a widely used distance measure for labelled graph. However, A★−GED, the standard approach for its exact computation, suffers from huge runtime and memory requirements. Recently, three better performing algorithms have been proposed: The general algorithms DF−GED and BIP−GED, and the algorithm CSI−GED, which only works for uniform edit costs. All newly proposed algorithms outperform the standard approach A★−GED. However, cross-comparisons are lacking. This paper consolidates and extends these recent advances. To this purpose, we present all existing algorithms in a unified way and show that the slightly different definitions of the graph edit distance underlying A★−GED and DF−GED, on the one side, and CSI−GED, on the other side, can be harmonised. This harmonisation allows us to develop a generalisation of CSI−GED to non-uniform edit cost. Moreover, we present a speed-up of A★−GED and DF−GED for uniform edit costs, which build upon the fact that, in the uniform case, a continuously used subroutine can be implemented to run in linear rather than cubic time. We also suggest an algorithm MIP−GED which builds upon a very compact new mixed integer linear programming formulation. Finally, we carry out a thorough empirical evaluation, which, for the first time, compares all existing exact algorithms.
Identifying Computer Generated Images Based on Quaternion Central Moments in Color Quaternion Wavelet Domain

In this paper, a novel forensics scheme for color image is proposed in color quaternion wavelet transform (CQWT) domain. Compared with discrete wavelet transform (DWT), contourlet wavelet transform, and local binary patterns, CQWT processes a color image as a unit, and so, it can provide more forensics information to identify the photograph (PG) and computer generated (CG) images by considering the quaternion magnitude and phase measures. Meanwhile, two novel quaternion central moments for color images, i.e., quaternion skewness and kurtosis, are proposed to extract forensics features. In the condition of the same statistical model as Farid's model, the CQWT can boost the performance of the existing identification models. Compared with Farid's model and Li's model in 7500 PG and 7500 CG, the quaternion statistical features show a better classification performance. Results in the comparative experiments show that the classification accuracy of the CQWT improves by 19% more than Farid's model, and the quaternion features approximately improve by 2% more than the traditional.
Single image super-resolution using multi-scale deep encoder–decoder with phase congruency edge map guidance

This paper presents an end-to-end multi-scale deep encoder (convolution) and decoder (deconvolution) network for single image super-resolution (SISR) guided by phase congruency (PC) edge map. Our system starts by a single scale symmetrical encoder–decoder structure for SISR, which is extended to a multi-scale model by integrating wavelet multi-resolution analysis into our network. The new multi-scale deep learning system allows the low resolution (LR) input and its PC edge map to be combined so as to precisely predict the multi-scale super-resolved edge details with the guidance of the high-resolution (HR) PC edge map. In this way, the proposed deep model takes both the reconstruction of image pixels’ intensities and the recovery of multi-scale edge details into consideration under the same framework. We evaluate the proposed model on benchmark datasets of different data scenarios, such as Set14 and BSD100 - natural images, Middlebury and New Tsukuba - depth images. The evaluations based on both PSNR and visual perception reveal that the proposed model is superior to the state-of-the-art methods.
Channel pruning based on mean gradient for accelerating Convolutional Neural Networks

Convolutional Neural Networks (CNNs) are getting deeper and wider to improve their performance and thus increase their computational complexity. We apply channel pruning methods to accelerate CNNs and reduce its computational consumption. A new pruning criterion is proposed based on the mean gradient for convolutional kernels. To significantly reduce Float Point Operations (FLOPs) of CNNs, a hierarchical global pruning strategy is introduced. In each pruning step, the importance of convolutional kernels is evaluated by the mean gradient criterion. Hierarchical global pruning strategy is adopted to remove less important kernels, and get a smaller CNN model. Finally we fine-tune the model to restore network performance. Experimental results show that VGG-16 network pruned by channel pruning on CIFAR-10 achieves 5.64 × reduction in FLOPs with less than 1% decrease in accuracy. Meanwhile ResNet-110 network pruned on CIFAR-10 achieves 2.48 × reduction in FLOPs and parameters with only 0.08% decrease in accuracy.
Lattice-Based Public Key Cryptosystem for Internet of Things Environment: Challenges and Solutions

Due to its widespread popularity and usage in many applications (smart transport, energy management, e-healthcare, smart ecosystem, and so on), the Internet of Things (IoT) has become popular among end users over the last few years. However, with an exponential increase in the usage of IoT technologies, we have been witnessing an increase in the number of cyber attacks on the IoT environment. An adversary can capture the private key shared between users and devices and can launch various attacks, such as IoT ransomware, Mirai botnet, man-in-the-middle, denial of service, chosen plaintext, and chosen ciphertext. To mitigate these security attacks on the IoT environment, the traditional public key cryptographic primitives are inadequate because of their high computational and communication costs. Therefore, lattice-based public-key cryptosystem (LB-PKC) is a promising technique for secure communication. We discuss the taxonomy of two major problems, namely, the shortest path and the closest path problems with respect to the applicability of lattice-based cryptographic primitives for IoT devices. Moreover, we also discuss various LB-PKC techniques, such as NTRU, learning with errors (LWEs), and ring-LWE (R-LWE) which are often used to solve shortest path and lattice NP-hard problems in a polynomial time. We further classify the R-LWE into three categories, namely identity-based encryption, homomorphic encryption, and secure authentication key exchange. We describe the operations and algorithms adopted in each of these encryption mechanisms. Finally, we discuss the challenges, open issues, and future directions for applying LB-PKC in the IoT environment.
Deep pixel‐to‐pixel network for underwater image enhancement and restoration

Turbid underwater environment poses great difficulties for the applications of vision technologies. One of the biggest challenges is the complicated noise distribution of the underwater images due to the serious scattering and absorption. To alleviate this problem, this work proposes a deep pixel-to-pixel networks model for underwater image enhancement by designing an encoding–decoding framework. It employs the convolution layers as encoding to filter the noise, while uses deconvolution layers as decoding to recover the missing details and refine the image pixel by pixel. Moreover, skip connection is introduced in the networks model in order to avoid low-level features losing while accelerating the training process. The model achieves the image enhancement in a self-adaptive data-driven way rather than considering the physical environment. Several comparison experiments are carried out with different datasets. Results show that it outperforms the state-of-the-art image restoration methods in underwater image defogging, denoising and colour enhancement.
Research on multi-camera calibration and point cloud correction method based on three-dimensional calibration object

The single camera measurement system can't obtain all the surface information of the object limited by the field of view, thus it can't achieve complete measurement of the object. Multi-camera system can overcome this difficulty. But it is difficult to unify the coordinate system of distinct cameras. In order to solve this problem, a global calibration method of multi-camera system is proposed. A three-dimensional cube calibration object is designed. Every camera is only calibrated on one surface of the cube calibration object. Because different surfaces of the cube calibration object are in a unified world coordinate, the coordinates of the feature points on different surfaces are naturally in the unified world coordinate system. Thus global calibration can be completed by calibrating multiple cameras at the same time. In addition, to minimize the error of calibration, a correction method is proposed. The correction parameters are obtained by the deviation between the world coordinates and the reconstructed coordinates of the feature points on calibration object. The parameters are applied to the measurement to improve the accuracy of the whole point cloud. Experiments are carried out on a multi-camera measurement system, and the results show that the method proposed in this paper is effective and feasible.
Generalised Differential Privacy for Text Document Processing

We address the problem of how to “obfuscate” texts by removing stylistic clues which can identify authorship, whilst preserving (as much as possible) the content of the text. In this paper we combine ideas from “generalised differential privacy” and machine learning techniques for text processing to model privacy for text documents. We define a privacy mechanism that operates at the level of text documents represented as “bags-of-words”—these representations are typical in machine learning and contain sufficient information to carry out many kinds of classification tasks including topic identification and authorship attribution (of the original documents). We show that our mechanism satisfies privacy with respect to a metric for semantic similarity, thereby providing a balance between utility, defined by the semantic content of texts, with the obfuscation of stylistic clues. We demonstrate our implementation on a “fan fiction” dataset, confirming that it is indeed possible to disguise writing style effectively whilst preserving enough information and variation for accurate content classification tasks. We refer the reader to our complete paper [15] which contains full proofs and further experimentation details.
Embedding models for episodic knowledge graphs

In recent years a number of large-scale triple-oriented knowledge graphs have been generated and various models have been proposed to perform learning in those graphs. Most knowledge graphs are static and reflect the world in its current state. In reality, of course, the state of the world is changing: a healthy person becomes diagnosed with a disease and a new president is inaugurated. In this paper, we extend models for static knowledge graphs to temporal knowledge graphs. This enables us to store episodic data and to generalize to new facts (inductive learning). We generalize leading learning models for static knowledge graphs (i.e., Tucker, RESCAL, HolE, ComplEx, DistMult) to temporal knowledge graphs. In particular, we introduce a new tensor model, ConT, with superior generalization performance. The performances of all proposed models are analyzed on two different datasets: the Global Database of Events, Language, and Tone (GDELT) and the database for Integrated Conflict Early Warning System (ICEWS). We argue that temporal knowledge graph embeddings might be models also for cognitive episodic memory (facts we remember and can recollect) and that a semantic memory (current facts we know) can be generated from episodic memory by a marginalization operation. We validate this episodic-to-semantic projection hypothesis with the ICEWS dataset.
Sparse fully convolutional network for face labeling

This paper proposes a sparse fully convolutional network (FCN) for face labeling. FCN has demonstrated strong capabilities in learning representations for semantic segmentation. However, it often suffers from heavy redundancy in parameters and connections. To ease this problem, group Lasso regularization and intra-group Lasso regularization are utilized to sparsify the convolutional layers of the FCN. Based on this framework, parameters that correspond to the same output channel are grouped into one group, and these parameters are simultaneously zeroed out during training. For the parameters in groups that are not zeroed out, intra-group Lasso provides further regularization. The essence of the regularization framework lies in its ability to offer better feature selection and higher sparsity. Moreover, a fully connected conditional random fields (CRF) model is used to refine the output of the sparse FCN. The proposed approach is evaluated on the LFW face dataset with the state-of-the-art performance. Compared with a non-regularized FCN, the sparse FCN reduces the number of parameters by 91.55% while increasing the segmentation performance by 11% relative error reduction.
A label-specific multi-label feature selection algorithm based on the Pareto dominance concept

In multi-label data, each instance is associated with a set of labels, instead of one label. Similar to single-label data, feature selection plays an important role in improving classification performance. In multi-label classification, each class label might be specified by some particular characteristics of its own which are called label-specific features. In this paper, a fast accurate filter-based feature selection method is exclusively designed for multi-label datasets to find label-specific features. It maps the features to a multi-dimensional space based on a filter method, and selects the most salient features with the help of Pareto-dominance concepts from multi-objective optimization domain. Our proposed method can be used as online feature selection that deals with problems in which features arrive sequentially while the number of data samples is fixed. In this method, the number of features to be selected is specified during the process of feature selection. However, sometimes it is desired to predefine the number of features. For this reason, an extension of the proposed method is presented to solve this problem. To prove the performance of the proposed methods, several experiments are conducted on some multi-label datasets and the results are compared to five well-established multi-label feature selection methods. The results show the superiority of the proposed methods in terms of different multi-label classification criteria and execution time.
Meta heuristic-based task deployment mechanism for load balancing in IaaS cloud

An Infrastructure-as-a-service (IaaS) cloud is a new paradigm which offers computing and storage services in form of virtual machines to the cloud users. Due to increasing demand and diversity of the applications, most of the servers are become overloaded or imbalanced and affects the performance of the cloud system. Most of the existing strategies have developed a series of algorithms to pick out the optimal target server to achieve the immediate load balancing. However, it is proved in the literature that immediate load balancing does not meet the high execution efficiency and resource utilization of the servers. In this paper, we propose a new load balancing mechanism for a long-term process referred as LB-RC (load balancing resource clustering). The meta-heuristic Bat-algorithm is applied to obtain optimal resource clustering and their cluster centers for faster convergence. We also propose a new dynamic task assignment policy to achieve the minimum makespan and execution cost within the given constraints. The proposed algorithm is tested and compared with the existing algorithms over various synthetic datasets and performance matrices to prove its superiority.
Accelerating genetic algorithms with GPU computing: A selective overview

The emergence of GPU-CPU heterogeneous architectures has led to a fundamental paradigm shift in parallel programming. Accelerating Genetic Algorithms (GAs) on these architectures has received significant attention from both practitioners and researchers ever since GPUs emerged. In the past decade we have witnessed many progresses on migrating parallel GAs from CPU to GPU (Graphical Processing Unit) architecture, which makes this research field truly enter into the world of High Performance Computing (HPC), and demonstrates a great potential to many research disciplines and industrial worlds that can benefit from the power of GPU accelerated stochastic global search to explore large and complex search spaces for better solutions. Designing a parallel algorithm on GPU is quite different from designing one on CPU. On CPU architecture, we typically consider how to distribute data across tens of CPU threads, while on GPU architecture, we have more than hundreds of thousands of GPU threads running simultaneously. Therefore, we should rethink the design approaches and implementation strategies of parallel algorithms to fully utilize the computing power of GPUs to accelerate the computation of GAs. The intention of this paper is to give an overview on selective works of parallel GAs designed for GPU architecture. In this survey paper, we first reexamine the concept of granularity of parallelism for GAs on GPU architecture, discuss how the aspect of data layout affect the kernel design to maximize memory bandwidth, and explain how to organize threads in grid and blocks to expose sufficient parallelism to GPU. The comprehensive overview on selective works since 2010 then follows. The focus is mainly on the perspective of GPU architecture: how to accelerate GAs with GPU computing. Performance issues are not touched in this review, because most of these works are conducted on very early GPU cards, which are out of date already. We finally discuss some future research suggestions in the last section, especially about how to build up an efficient implementation of parallel GAs for hyper-scale computing. Many industrial and academic disciplines will be benefited from the GPU accelerated parallel GAs, one of the promising area is to evolve better deep neural networks.