Miracle-dz/UniFAR-FTUs huggingface dataset:
Mutation-Based Harmony Search Algorithm for Hybrid Testing of Web Service Composition

Web service composition is a method of developing a new web service from an existing one based on business goals. Web services selected for composition should provide accurate operational results and reliable applications. However, most alternative service providers have not yet fulfilled users' needs in terms of services and processes. Service providers, in fact, have focused on enhancing nonfunctional attributes, such as efficiencies of time, cost, and availability, which still face limitations. Furthermore, it remains advantageous to compose services and suitably plan them around business plans. Thus, this study introduces hybrid testing using a combination of the functional and nonfunctional testing approaches. The former was used to design a test case through the equivalence class partitioning technique, and the latter was used to select suitable services for the test results. We find defects and appropriate solutions for combining services based on business requirements. The mutation-based harmony search (MBHS) algorithm is proposed to select web services and to compose with minimum defects. The results of this study reveal that MBHS can support a combination of various services more efficiently and dramatically than other metaheuristic methodologies. Additionally, it helps find appropriate solutions to compose services based on business plans.
Mitigating Planner Overfitting in Model-Based Reinforcement Learning

An agent with an inaccurate model of its environment faces a difficult choice: it can ignore the errors in its model and act in the real world in whatever way it determines is optimal with respect to its model. Alternatively, it can take a more conservative stance and eschew its model in favor of optimizing its behavior solely via real-world interaction. This latter approach can be exceedingly slow to learn from experience, while the former can lead to "planner overfitting" - aspects of the agent's behavior are optimized to exploit errors in its model. This paper explores an intermediate position in which the planner seeks to avoid overfitting through a kind of regularization of the plans it considers. We present three different approaches that demonstrably mitigate planner overfitting in reinforcement-learning environments.
Large dataset summarization with automatic parameter optimization and parallel processing for outlier detection

As one of the most important research problems of data analytics and data mining, outlier detection from large datasets has drawn many research attentions in recent years. In this paper, we investigate the interesting research problem of summarizing large datasets for supporting efficient local outlier detection. To summarize large datasets, efficient summarization algorithms are proposed which produce a highly compact summary of the original dataset which can be applied to detect local outliers from future similar datasets. A novel automatic parameter optimization method is proposed to produce the optimal setup of the key parameters used in the summarization algorithm. Parallel processing methods are also proposed to accelerate significantly the summarization process. The experimental evaluation results demonstrate that our proposed algorithms are highly scalable for large datasets and effective in producing dataset summary for local outlier detection.
Bach2Bach: Generating Music Using A Deep Reinforcement Learning Approach

A model of music needs to have the ability to recall past details and have a clear, coherent understanding of musical structure. Detailed in the paper is a deep reinforcement learning architecture that predicts and generates polyphonic music aligned with musical rules. The probabilistic model presented is a Bi-axial LSTM trained with a pseudo-kernel reminiscent of a convolutional kernel. To encourage exploration and impose greater global coherence on the generated music, a deep reinforcement learning approach DQN is adopted. When analyzed quantitatively and qualitatively, this approach performs well in composing polyphonic music.
Small Object Detection with Multiscale Features

The existing object detection algorithm based on the deep convolution neural network needs to carry out multilevel convolution and pooling operations to the entire image in order to extract a deep semantic features of the image. The detection models can get better results for big object. However, those models fail to detect small objects that have low resolution and are greatly influenced by noise because the features after repeated convolution operations of existing models do not fully represent the essential characteristics of the small objects. In this paper, we can achieve good detection accuracy by extracting the features at different convolution levels of the object and using the multiscale features to detect small objects. For our detection model, we extract the features of the image from their third, fourth, and 5th convolutions, respectively, and then these three scales features are concatenated into a one-dimensional vector. The vector is used to classify objects by classifiers and locate position information of objects by regression of bounding box. Through testing, the detection accuracy of our model for small objects is 11% higher than the state-of-the-art models. In addition, we also used the model to detect aircraft in remote sensing images and achieved good results.
TextField: Learning a Deep Direction Field for Irregular Scene Text Detection

Scene text detection is an important step in the scene text reading system. The main challenges lie in significantly varied sizes and aspect ratios, arbitrary orientations, and shapes. Driven by the recent progress in deep learning, impressive performances have been achieved for multi-oriented text detection. Yet, the performance drops dramatically in detecting the curved texts due to the limited text representation (e.g., horizontal bounding boxes, rotated rectangles, or quadrilaterals). It is of great interest to detect the curved texts, which are actually very common in natural scenes. In this paper, we present a novel text detector named TextField for detecting irregular scene texts. Specifically, we learn a direction field pointing away from the nearest text boundary to each text point. This direction field is represented by an image of 2D vectors and learned via a fully convolutional neural network. It encodes both binary text mask and direction information used to separate adjacent text instances, which is challenging for the classical segmentation-based approaches. Based on the learned direction field, we apply a simple yet effective morphological-based post-processing to achieve the final detection. The experimental results show that the proposed TextField outperforms the state-of-the-art methods by a large margin (28% and 8%) on two curved text datasets: Total-Text and SCUT-CTW1500, respectively; TextField also achieves very competitive performance on multi-oriented datasets: ICDAR 2015 and MSRA-TD500. Furthermore, TextField is robust in generalizing unseen datasets.
A Two-Step Learning and Interpolation Method for Location-Based Channel Database Construction

Timely and accurate knowledge of channel state information (CSI) is necessary to support scheduling operations at both physical and network layers. In order to support pilot-free channel estimation in cell sleeping scenarios, we propose to adopt a channel database that stores the CSI as a function of geographic locations. Such a channel database is generated from historical user records, which usually can not cover all the locations in the cell. Therefore, we develop a two-step interpolation method to infer the channels at the uncovered locations. The method firstly applies the K-nearest-neighbor method to form a coarse database and then refines it with a deep convolutional neural network. When applied to the channel data generated by ray tracing software, our method shows a great advantage in performance over the conventional interpolation methods.
Polymeric vanadyl species determine the low-temperature activity of V-based catalysts for the SCR of NOx with NH3

The coupling effect of polymeric vanadyl species greatly promotes the NH3-SCR de-NOx process over vanadia-based catalysts. The structure of dispersed vanadyl species plays a crucial role in the selective catalytic reduction (SCR) of NO with NH3 over vanadia-based catalysts. Here, we demonstrate that the polymeric vanadyl species have a markedly higher NH3-SCR activity than the monomeric vanadyl species. The coupling effect of the polymeric structure not only shortens the reaction pathway for the regeneration of redox sites but also substantially reduces the overall reaction barrier of the catalytic cycle. Therefore, it is the polymeric vanadyl species, rather than the monomeric vanadyl species, that determine the NH3-SCR activity of vanadia-based catalysts, especially under low-temperature conditions. The polymeric vanadia-based SCR mechanism reported here advances the understanding of the working principle of vanadia-based catalysts and paves the way toward the development of low vanadium–loading SCR catalysts with excellent low-temperature activity.
Short time Fourier transformation and deep neural networks for motor imagery brain computer interface recognition

Motor imagery (MI) is an important control paradigm in the field of brain‐computer interface (BCI), which enables the recognition of personal intention. So far, numerous methods have been designed to classify EEG signal features for MI task. However, deep neural networks have been seldom applied to analyze EEG signals. In this study, two novel kinds of deep learning schemes based on convolutional neural networks (CNN) and Long Short‐Term Memory (LSTM) were proposed for MI‐classification. The frequency domain representations of EEG signals were obtained using short time Fourier transform (STFT) to train models. Classification results were compared between conventional algorithm, CNN, and LSTM models. Compared with two other methods, CNN algorithms had shown better performance. These conclusions verified that CNN method was promising for MI‐based BCIs.
A novel sentence similarity model with word embedding based on convolutional neural network

In this paper, we propose an effective model for the similarity metrics of English sentences. In the model, we first make use of word embedding and convolutional neural network (CNN) to produce a sentence vector and then leverage the information of the sentence vector pair to calculate the score of sentence similarity. Considering the case of long‐range semantic dependencies between words, we propose a novel method transforming word embeddings to construct the three‐dimensional sentence feature tensor. In addition, we incorporate the k‐max pooling into the convolutional neural network to adapt to variable lengths of input sentences. The proposed model requires no external resource such as WordNet and parse tree. Meanwhile, it consumes very little time for training. Finally, we carried out extensive simulations to evaluate the performance of our model compared with other state‐of‐the‐art works. Experimental results on SemEval 2014 task (SICK test corpus) indicated that our model can achieve a good performance in the terms of Pearson correlation coefficient, Spearman correlation coefficient, and mean squared errors. Furthermore, experimental results on Microsoft research paraphrase identification (MSRP) indicated that our model can achieve an excellent performance in the terms of F1 and Accuracy.
Batch Selection for Parallelisation of Bayesian Quadrature

Integration over non-negative integrands is a central problem in machine learning (e.g. for model averaging, (hyper-)parameter marginalisation, and computing posterior predictive distributions). Bayesian Quadrature is a probabilistic numerical integration technique that performs promisingly when compared to traditional Markov Chain Monte Carlo methods. However, in contrast to easily-parallelised MCMC methods, Bayesian Quadrature methods have, thus far, been essentially serial in nature, selecting a single point to sample at each step of the algorithm. We deliver methods to select batches of points at each step, based upon those recently presented in the Batch Bayesian Optimisation literature. Such parallelisation significantly reduces computation time, especially when the integrand is expensive to sample.
Finding Dependencies between Cyber-Physical Domains for Security Testing of Industrial Control Systems

In modern societies, critical services such as transportation, power supply, water treatment and distribution are strongly dependent on Industrial Control Systems (ICS). As technology moves along, new features improve services provided by such ICS. On the other hand, this progress also introduces new risks of cyber attacks due to the multiple direct and indirect dependencies between cyber and physical components of such systems. Performing rigorous security tests and risk analysis in these critical systems is thus a challenging task, because of the non-trivial interactions between digital and physical assets and the domain-specific knowledge necessary to analyse a particular system. In this work, we propose a methodology to model and analyse a System Under Test (SUT) as a data flow graph that highlights interactions among internal entities throughout the SUT. This model is automatically extracted from production code available in Programmable Logic Controllers (PLCs). We also propose a reachability algorithm and an attack diagram that will emphasize the dependencies between cyber and physical domains, thus enabling a human analyst to gauge various attack vectors that arise from subtle dependencies in data and information propagation. We test our methodology in a functional water treatment testbed and demonstrate how an analyst could make use of our designed attack diagrams to reason on possible threats to various targets of the SUT.
Rapid hierarchical clustering of biological sequences

Genomic sequences can be viewed as special types of documents. These are typically organised and stored as collections of text documents. The collections are vast; a single metagenomics sequencing experiment can produce as much text data as the entire contents of Wikipedia. The increasing scale of the datasets obtained from environmental and clinical metagenomic studies has posed great difficulties to researchers attempting to organise this data with traditional clustering techniques. In this work we introduce a clustering approach that combines the highly effective strategy of using binary signature representations of sequence data -- allowing extremely fast pairwise comparison through hardware level operations -- and couples these representations with an O(N log N) hierarchical clustering approach based on the k-tree data structure. The k-tree approach has previously been applied to the clustering of English-language documents from the ClueWeb collection. Here we extend its use to the clustering of genomic sequence text files. We demonstrate the success of our approach on the largest real biological datasets available from the SILVA project, showing that our methods provide clustering quality comparable to those of a number of standard methods while offering speed performance about an order of magnitude greater than these alternatives. Through the use of synthetic data, we show that this new approach is able to handle even extreme scale data sets in convenient timeframes, allowing rapid analyses to be performed over collections of tens of millions of reads (sequences) that are typically produced in a single sequencing run.
SHIELD: A Framework for Efficient and Secure Machine Learning Classification in Constrained Environments

Machine learning classification has enabled many innovative services, e.g., in medicine, biometrics, and finance. Current practices of sharing sensitive input data or classification models, however, causes privacy concerns among the users and business risk among the providers. In this work, we resolve the conflict between privacy and business interests using Secure Two-Party Computation. Concretely, we propose SHIELD, a framework for efficient, and accurate machine learning classification with security in the semi-honest model. Building on SHIELD, we realize several widely used classifiers and real-world use cases that compare favorably against related work. Departing definitively from prior works, all of SHIELD's protocols are designed from the ground up to enable secure outsourcing to untrusted computation clouds enabling even constrained devices to handle our most complex use cases in (milli)seconds.
A Deep Learning Framework for Semi-Supervised Cross-Modal Retrieval with Label Prediction

Due to abundance of data from multiple modalities, cross-modal retrieval tasks with image-text, audio-image, etc. are gaining increasing importance. Of the different approaches proposed, supervised methods usually give significant improvement over their unsupervised counterparts at the additional cost of labeling or annotation of the training data. Semi-supervised methods are recently becoming popular as they provide an elegant framework to balance the conflicting requirement of labeling cost and accuracy. In this work, we propose a novel deep semi-supervised framework which can seamlessly handle both labeled as well as unlabeled data. The network has two important components: (a) the label prediction component predicts the labels for the unlabeled portion of the data and then (b) a common modality-invariant representation is learned for cross-modal retrieval. The two parts of the network are trained sequentially one after the other. Extensive experiments on three standard benchmark datasets, Wiki, Pascal VOC and NUS-WIDE demonstrate that the proposed framework outperforms the state-of-the-art for both supervised and semi-supervised settings.
Loss of miR-198 and -206 during primary tumor progression enables metastatic dissemination in human osteosarcoma

The metastatic dissemination is a complex multistep process by which tumor cells from a primary site enter into the systemic circulation to finally spread at distant sites. Even if this mechanism is rare at the tumor level, it remains the major cause of Osteosarcoma-patients’ relapse and mortality. MicroRNAs (miRNAs) have recently been described as novel epigenetics’ genes’ expression regulators actively implicated in cancer progression and dissemination. The understanding of their implication in the metastatic spreading could help clinicians to improve the outcome of osteosarcoma. We established the miRNA’s expression-profile between primary bone-tumors (PTs), circulating tumor cells (CTCs) and lung metastatic (META) samples from in vivo mice xenograft models. Our results show that the expression level of the miR-198 and -206 was decreased in META samples, in which the expression of the metastasis-related receptor C-Met was up-regulated. Those expression variations were validated in osteosarcoma patient biopsies from matching primary tumors and lung metastasis. We validated in vitro the endogenous miRNAs inhibitory effects on both migration and invasion, as well as we confirmed by luciferase assays that the C-Met receptor is one of their bona-fide targets. The anti-metastatic effect of these miRNAs was also validated in vivo, as their direct injections into the tumors reduce the number of lung-metastases and prolongs the overall survival of the treated animals. All together, our results suggest the absence of the miR-198 and -206 as powerful predictive biomarkers of the tumor cell dissemination and the rationale of their potential therapeutic use in the treatment of Osteosarcoma.
FKBP Ligands—Where We Are and Where to Go?

In recent years, many members of the FK506-binding protein (FKBP) family were increasingly linked to various diseases. The binding domain of FKBPs differs only in a few amino acid residues, but their biological roles are versatile. High-affinity ligands with selectivity between close homologs are scarce. This review will give an overview of the most prominent ligands developed for FKBPs and highlight a perspective for future developments. More precisely, human FKBPs and correlated diseases will be discussed as well as microbial FKBPs in the context of anti-bacterial and anti-fungal therapeutics. The last section gives insights into high-affinity ligands as chemical tools and dimerizers.
Red pepper seed water extract inhibits preadipocyte differentiation and induces mature adipocyte apoptosis in 3T3-L1 cells

BACKGROUND/OBJECTIVES Reducing the number of adipocytes by inducing apoptosis of mature adipocytes as well as suppressing differentiation of preadipocytes plays an important role in preventing obesity. This study examines the anti-adipogenic and pro-apoptotic effect of red pepper seed water extract (RPS) prepared at 4℃ (RPS4) in 3T3-L1 cells. MATERIALS/METHODS Effect of RPS4 or its fractions on lipid accumulation was determined in 3T3-L1 cells using oil red O (ORO) staining. The expressions of AMP-activated protein kinase (AMPK) and adipogenic associated proteins [peroxisome proliferator-activated receptor-γ (PPAR-γ), CCAAT/enhancer-binding proteins α (C/EBP α), sterol regulatory element binding protein-1c (SREBP-1c), fatty acid synthase (FAS), and acetyl-CoA carboxylase (ACC)] were measured in 3T3-L1 cells treated with RPS4. Apoptosis and the expression of Akt and Bcl-2 family proteins [B-cell lymphoma 2 (Bcl-2), Bcl-2-associated death promoter (Bad), Bcl-2 like protein 4 (Bax), Bal-2 homologous antagonist/killer (Bak)] were measured in mature 3T3-L1 cells treated with RPS4. RESULTS Treatment of RPS4 (0–75 µg/mL) or its fractions (0–50 µg/mL) for 24 h did not have an apparent cytotoxicity on pre and mature 3T3-L1 cells. RPS4 significantly suppressed differentiation and cellular lipid accumulation by increasing the phosphorylation of AMPK and reducing the expression of PPAR-γ, C/EBP α, SREBP-1c, FAS, and ACC. In addition, all fractions except ethyl acetate fraction significantly suppressed cellular lipid accumulation. RPS4 induced the apoptosis of mature adipocytes by hypophosphorylating Akt, increasing the expression of the pro-apoptotic proteins, Bak, Bax, and Bad, and reducing the expression of the anti-apoptotic proteins, Bcl-2 and p-Bad. CONCLUSIONS These finding suggest that RPS4 can reduce the numbers as well as the size of adipocytes and might useful for preventing and treating obesity.
Shape and Layout Imagery Creation Using BMI Based on Mechanical Kansei

The Mechanical Kansei means a decision capability to evaluate an impression unconsciously, intuitively with informational synthesis in order to mechanics. In a product conceptual design, to create structural shape and layout as solution principle from functional requirement is greatly dependent on the accumulated engineering knowledge, experience and know-how. These engineering senses are called as the Mechanical Kansei. If the Kansei cannot be well used to design product structural shape and layout which satisfied various functional requirements, its design process has a difficulty to draw creative structural shape and layout. In this study, we propose the new methodology of the interaction of a functional design and a structural design in a product conceptual design process for decision making of product design concept based on the Human Computing Interaction. This proposed system creates shape and layout imagery to interact between the Mechanical Kansei and topology optimization method based on the Fully Stressed Design. The response of the Mechanical Kansei is obtained by using Brain Machine Interface (BMI) from the Frontal Cortex handles a positive expectation and the Supplementary Motor Area is assigned to the physics simulation within a brain. The shape and layout imagery creation system is discussed via the verification result and the creation results. We confirmed that the system has created the shape and layout considering the Mechanical Kansei for the product conceptual design, and also it's an attractive product design structure.