Reviewerly/exHarmony huggingface dataset:
Two staged data preprocessing ensemble model for software fault prediction

Software fault prediction is an essential task for the researchers and software testers to determine the faulty modules in the software in early stages. This early identification of faulty modules improves the software quality and thus the software produced will be of higher quality and cost effective. The use of imbalanced dataset hinders in the performance of the software fault prediction model. The model gets biased towards the majority class and thus the worthy results may not be produced. Moreover, the class overlap problem in the data results in the incorrect prediction. This class overlap problem needs to be addressed as the available datasets are highly imbalanced and overlapped. Many fault predictions models have been proposed in the literature using machine learning classifiers but there is always a room for improvement. In this study, the main objective is to utilize the balanced and non-overlapping data in the training of our model, thus improving the prediction capability of the model. In this study, we have used the two staged preprocessing of the dataset before training of our model. Firstly, class overlap problem is addressed using neighborhood cleaning method and then secondly, data is balanced using random oversampling technique. Five publicly available datasets from PROMISE repository are utilized in this study. The four base learners are used and then the results of these base learners are ensembled using the model averaging method. The results are then compared with the use of overlapping method only and using the resampling technique only, to determine the usefulness of the proposed approach. Moreover, the results of the proposed approach are also compared with the existing approach of handling imbalanced data. Through experiments it is seen that the proposed technique has outperformed the prediction capability. For evaluation purpose, the performance measure used is area under the curve (AUC). To avoid the randomness and biasness, results are cross validated using k-fold (k = 10) cross validation.
Frequent Subgraph Mining Algorithms in Static and Temporal Graph-Transaction Settings: A Survey

Networks are known as perfect tools for modeling various types of systems. In the literature of network mining, frequent subgraph mining is considered as the essence of mining network data. In this problem, the dataset is composed of networks representing multiple independent systems or one system at multiple time stamps. The cores of mining frequent subgraphs are graph and subgraph isomorphism. Due to the complexities of these problems, the frequent subgraph mining algorithms proposed in the literature employ various heuristics for candidate generation, duplicate subgraphs pruning, and support computation. In this survey, we provide a classification of proposed algorithms in the literature. The algorithms for static networks have found numerous applications. Therefore, these algorithms will be reviewed in detail. Besides, it is discussed that consideration of temporality of data can impact the derived insight and attracted substantial attention in recent years. However, prior surveys have not comprehensively examined the algorithms of frequent subgraph mining in a database of temporal networks represented as network snapshots. Therefore, the algorithms proposed for mining frequent subgraphs in temporal networks are reviewed. Moreover, most of the surveys have focused on main-memory algorithms. Here, we review disk-based, parallel, and distributed algorithms proposed for mining frequent subgraphs.
Domain Adaptation Preconceived Hashing for Unconstrained Visual Retrieval

Learning to hash has been widely applied for image retrieval due to the low storage and high retrieval efficiency. Existing hashing methods assume that the distributions of the retrieval pool (i.e., the data sets being retrieved) and the query data are similar, which, however, cannot truly reflect the real-world condition due to the unconstrained visual cues, such as illumination, pose, background, and so on. Due to the large distribution gap between the retrieval pool and the query set, the performances of traditional hashing methods are seriously degraded. Therefore, we first propose a new efficient but transferable hashing model for unconstrained cross-domain visual retrieval, in which the retrieval pool and the query sample are drawn from different but semantic relevant domains. Specifically, we propose a simple yet effective unsupervised hashing method, domain adaptation preconceived hashing (DAPH), toward learning domain-invariant hashing representation. Three merits of DAPH are observed: 1) to the best of our knowledge, we first propose unconstrained visual retrieval by introducing DA into hashing for learning transferable hashing codes; 2) a domain-invariant feature transformation with marginal discrepancy distance minimization and feature reconstruction constraint is learned, such that the hashing code is not only domain adaptive but content preserved; and 3) a DA preconceived quantization loss is proposed, which further guarantees the discrimination of the learned hashing code for sample retrieval. Extensive experiments on various benchmark data sets verify that our DAPH outperforms many state-of-the-art hashing methods toward unconstrained (unrestricted) instance retrieval in both single- and cross-domain scenarios.
An joint end-to-end framework for learning with noisy labels

Deep neural networks (DNNs) have achieved excellent performance in image classification research, part of which is due to the large-scale training data with accurate annotations. However, it is expensive and time-consuming to collect such clean data. In contrast, when collecting a dataset by crawling from websites, noisy labels are ubiquitous, which makes it easy for deep neural networks (DNNs) to overfit noisy labels and cause performance degradation. Most recent efforts have been focused on defending noisy labels by roughly ignoring some samples with high losses, which are treated as noise or reweight the training data in the loss function. Both strategies inevitably have priori conditions, such as a clean validation set or a ground-truth noise transition matrix, which are impractical in real-world datasets. In this paper, we propose a novel end-to-end framework for noise correction, called End-to-end Correction with Mixup and Balance terms (ECMB). ECMB can completely correct noisy labels to true labels and keep the number of each class more balanced. This framework uses a backbone network that is pre-trained by using an improved Mixup entropy instead of the traditional cross entropy, and does not need any extra conditions. In addition, we introduce a new balance term that can update noisy labels more accurately. Compared with other state-of-the-art methods, the experimental results on publicly available CIFAR-10, CIFAR-100 and Clothing1M datasets demonstrate that our method has superior performance. • A novel framework named ECMB is proposed for learning from noisy labels. • The backbone network pre-trained by robust loss function can update noisy labels more accurately. • We introduce a new balance term, which is useful for label correction. • We propose an improved mixup learning principle for label correction. • ECMB can be embedded into many existing methods and achieve better performance.
A Pattern Driven Graph Ranking Approach to Attribute Extraction for Knowledge Graph

Attribution extraction refers to find the attributes for the instances of a given semantic class, which is essential to enhance the schema of a knowledge graph. To facilitate the attribution extraction from the query log, this article proposes a pattern driven graph ranking approach to jointly employ the pattern and context distribution information. First, a simple pattern on query text is applied to automatically acquire seed attributes. Then, a graph-based weight propagation is designed to rank the patterns by context distribution algorithm information. Experimental results show that, on a Chinese query log collected by Baidu, the automatically acquired seeds are more representative than the classical manually assembled seeds, achieving an improvement of 11.6% in MAP as compared to the baseline approach. And the graph-based ranking algorithm manipulates the two types of evidence more effectively, outperforming both the distributional similarity based baseline and the HITS algorithm by 29.2% and 11.3%, respectively.
A simple classification of discrete system interactions and some consequences for the solution of the interoperability puzzle

In this article, we introduce a classification of system interactions to guide the discourse on their interfaces and interoperability. It is based on a simple, but nevertheless complete classification of system interactions with respect to information transport and processing. Information transport can only be uni- or bidirectional and information processing is subclassified along the binary dimensions of state, determinism and synchronicity. For interactions with bidirectional information flow we are able to define a criterion for a layered structure of systems: we name a bidirectional interaction ”horizontal” if all interacting systems behave the same with respect to state, determinism and synchronicity and we name it “vertical” — providing a semantic direction — if there is a behavioral asymmetry between the interacting systems with respect to these properties. It is shown that horizontal interactions are essentially stateful, asynchronous and nondeterministic and are described by protocols. Vertical interactions are essentially top-down-usage, described by object models or operations, and bottom-up-observation, described by anonymous events. The interaction classification thereby helps to better understand the significant relationships that are created between interacting discrete systems by their interactions and guides us on how to talk about discrete systems, their interfaces and interoperability. To show its conceptual power, we apply the interaction classification to assess several other architectural models, communication technologies and so called software design or architectural styles like SOA and REST.
Robot Navigation among External Autonomous Agents through Deep Reinforcement Learning using Graph Attention Network

Finding collision-free and efficient paths in an uncertain dynamic environment is a challenge for robot navigation tasks, especially when there are external autonomous agents that also have decision-making abilities in the same environment. This paper develops a novel method based on DRL with graph attention network (GAT) to solve the problem of robot navigation among external autonomous agents (other agents). Specifically, GAT is adopted to describe the robot and other agents as a specific graph, and extract the spatial structural influence features of other agents on the robot from the graph. Multi-head attention mechanism is utilized to calculate the weights of interactions between the robot and other agents. This GAT uses observations of an arbitrary number of other agents in dynamic environments. Furthermore, the proposed method is combined with optimal reciprocal collision avoidance to improve its safety in new environments. Various simulations demonstrate that our method has good performance and robustness in different environments.
LChecker: Detecting Loose Comparison Bugs in PHP

Weakly-typed languages such as PHP support loosely comparing two operands by implicitly converting their types and values. Such a language feature is widely used but can also pose severe security threats. In certain conditions, loose comparisons can cause unexpected results, leading to authentication bypass and other functionality problems.
Classification of Breast Cancer from Mammogram images using Deep Convolution Neural Networks

Breast cancer is intrusive form of cancer which affects every 1 woman out of 9 in Pakistan. To detect breast cancer at early stage, mammography technique is used which is a manual process and is susceptible to radiologist error. Therefore, this paper proposes a new CAD technique, which relies on customized deep convolutional neural network to detect and classify breast cancer into malignant and benign. Mammogram images from digital database for screening mammography dataset are used to train proposed model. First, region of interest is extracted using region based segmentation technique which is further enhanced using contrast limited adaptive histogram equalization. Later, a customized deep convolution neural network is used to learn features from mammograms. Support vector machine classifier is used to classify breast masses into benign and malignant. 88.7% accuracy is achieved with 0.885 area under the curve. Other parameters like System specificity, sensitivity, precision, F1 score and AUC are recorded as 0.93, 0.841, 0.917, 0.877 and 0.885 respectively.
Sources of uncertainty in field gamma-ray spectrometry: Implications for exploration in the Lower-Middle Jurassic sedimentary succession of the Lusitanian Basin (Portugal)

Uncertainties related to in situ gamma-ray spectrometry measurements have been widely acknowledged. Despite being commonly used in exploration, the evaluation of the uncertainty range of K, U and Th gamma spectrometry field measurements is generally limited due to the lack of corresponding laboratory measurements. Estimating uncertainty, however, is essential to ensure reliable and accurate results. In this paper, we establish a set of procedures to acquire gamma-ray spectrometry data while minimizing uncertainty, based on high-resolution lithostratigraphy and composite gamma-ray logging techniques. We compare the concentration of radionuclides measured in situ in 250 selected sites in outcrops of the Lower and Middle Jurassic sedimentary successions of the Lusitanian Basin to the concentration of radionuclides in representative samples collected from the same outcrops and analysed in the laboratory. The global radiometric dataset obtained for K, U and Th is similar between field and laboratory gamma spectrometry measurements, which are correlated. The root-mean-square deviation was determined for K, U and Th, and the error of the field measurements was estimated at 0.6%, 1.4 and 2.5 ppm, respectively. We calculated the relative error (RE) between laboratory and field data. If we use the interquartile range of the RE to distinguish outliers in each radionuclide dataset, approximately 8%, 6% and 10% of the K, U and Th field measurements correspond to outliers. Conversely, 92%, 94% and 90% of the samples offer reliable field data for K, U and Th, respectively. The RE is similar between K and U, with a mean of 0.73 and 0.69, respectively, and is higher for Th (with a mean of 1.29). Higher RE values are also observed in dolostones (D), dolomitic limestones (DL), limestones (L) and marly limestones (ML). Lower RE values are linked to sandstones (S) and fine-grained siliciclastic (F-g.S) lithologies, with a mean value of 0.19, 0.33 and 0.25 for K, U and Th, and 100% of reliable data. As the accuracy of field gamma-ray measurements varies with lithology, field procedures must be adjusted for lithological variability to reduce uncertainty. Given that the RE decreases with an increase in layer thickness, thicker sedimentary layers should be favoured when measuring K, U and Th concentration in field campaigns.
The PRM-A* Path Planning Algorithm For UUVs: An Application To Navy Mission Planning

This paper presents a novel path planning algorithm for unmanned underwater vehicles (UUVs) that arises by combining two well-known algorithms in such a way as to exploit their respective advantages while mitigating their deficiencies. The two algorithms are A-star (A*) and Probabilistic Road Map (PRM). The operational environment is given as a portion of open sea interspersed with various obstacles. The free space is the portion not occupied by obstacles. A* employs heuristic search to explore the free space for a path from the start to the goal. This algorithm always returns the shortest (optimal cost) path. A drawback is that the path sometimes runs dangerously close to an obstacle. In addition, the computed path can have sharp turns that are difficult for the UUV to negotiate. PRM starts with a random sampling of the points in the free space. Once some predetermined number of such points are chosen, each point is paired with some of it's free space neighbors, and these pairs are recorded in what is called a road map. Then Dijkstra's algorithm is applied to the road map using nearest neighbor information to find a path from the start node to the goal. This has the advantage that the resulting path is smooth (no sharp turns) and stays safely away from the obstacles. It has the drawback that the path is not optimal; it can be much longer than necessary. The idea presented in this paper is to first run both A* and PRM on the given operational environment. Then take the region bounded by these two lines as a new (smaller) operational environment, and rerun PRM. This produces a new path that more closely approximates the A* path while at the same time is smooth and, with proper monitoring, is assured to keep a safe distance from the obstacles. This procedure can be repeated as many times as desired, each time producing a better approximation of the optimal A* path. While the focus here has been on UUVs, the same approach can be applied for unmanned surface vehicles (USVs),
End-to-End Image-to-Speech Generation for Untranscribed Unknown Languages

Describing orally what we are seeing is a simple task we do in our daily life. However, in the natural language processing field, this simple task needs to be bridged by a textual modality that helps the system to generalize various objects in the image and various pronunciations in speech utterances. In this study, we propose an end-to-end Image2Speech system that does not need any textual information in its training. We use a vector-quantized variational autoencoder (VQ-VAE) model to learn the discrete representation of a speech caption in an unsupervised manner, where discrete labels are used by an image-captioning model. This self-supervised speech representation enables the Image2Speech model to be trained with the minimum amount of paired image-speech data while still maintaining the quality of the speech caption. Our experimental results with a multi-speaker natural speech dataset demonstrate our proposed text-free Image2Speech system's performance close to the one with textual information. Furthermore, our approach also successfully outperforms the most recent existing frameworks with phoneme-based and grounding-based Image2Speech systems.
Minimally-Supervised Structure-Rich Text Categorization via Learning on Text-Rich Networks

Text categorization is an essential task in Web content analysis. Considering the ever-evolving Web data and new emerging categories, instead of the laborious supervised setting, in this paper, we focus on the minimally-supervised setting that aims to categorize documents effectively, with a couple of seed documents annotated per category. We recognize that texts collected from the Web are often structure-rich, i.e., accompanied by various metadata. One can easily organize the corpus into a text-rich network, joining raw text documents with document attributes, high-quality phrases, label surface names as nodes, and their associations as edges. Such a network provides a holistic view of the corpus’ heterogeneous data sources and enables a joint optimization for network-based analysis and deep textual model training. We therefore propose a novel framework for minimally supervised categorization by learning from the text-rich network. Specifically, we jointly train two modules with different inductive biases – a text analysis module for text understanding and a network learning module for class-discriminative, scalable network learning. Each module generates pseudo training labels from the unlabeled document set, and both modules mutually enhance each other by co-training using pooled pseudo labels. We test our model on two real-world datasets. On the challenging e-commerce product categorization dataset with 683 categories, our experiments show that given only three seed documents per category, our framework can achieve an accuracy of about 92%, significantly outperforming all compared methods; our accuracy is only less than 2% away from the supervised BERT model trained on about 50K labeled documents.
An Exploration of Tester-based Evaluation of User Simulators for Comparing Interactive Retrieval Systems.

User simulation is needed for evaluating Interactive Information Retrieval (IIR) Systems. However, for any user simulator to be useful, it must be reliable. In this paper, we propose a novel Tester-based evaluation approach to evaluating the reliability of user simulators, in which we would construct a Tester based on a set of IR systems with an expected performance pattern and apply such a Tester to a user simulator to see if the user simulator would generate the expected performance pattern. We construct multiple Testers and apply them to a set of representative user simulators to empirically study the feasibility and effectiveness of the proposed Tester-based evaluation method. The results show that Tester-based evaluation is a feasible and effective method for evaluating user simulators and selecting reliable ones for evaluating IIR systems.
Design and implementation of neural network computing framework on Zynq SoC embedded platform

Limited resources and low computing power of embedded platform make it difficult to apply neural network technology. To overcome this problem, a new neural network computing framework “Zynq-Darknet” was proposed. The framework is based on Darknet, which constructs depthwise separable convolution and a lightweight classiﬁcation model MobileNetV2 and was deployed to Xilinx Zynq-7000 System-on-Chip (SoC) with Linux operating system (OS). In order to verify the performance of the framework and model, experiments were conducted on imagenet-1k dataset using different network structures. The results show that the MobileNetV2 network model based on Zynq-Darknet can effectively perform image classification, and ensure a certain real-time and accuracy while reducing the computational complexity and storage overhead, assuming promising application prospects.