Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards a Terminology Unification in Software Interoperability

Interoperability is commonly referred as a software quality sub-characteristic. Some proposals for formalizing interoperability include sets of concepts linked to this phenomenon. Such concepts differ among the proposals, resulting in a different terminology for referring to the same thing. Non-unified terminology of interoperability concepts exhibits three main problems: homonymy, synonymy and missing concepts. Due to such problems, crucial aspects of the interoperability process could be either misunderstood or just omitted, resulting in an ambiguous and incomplete description of interoperability. To solve these problems, we propose a terminology unification of interoperability concepts. This unification is based on published studies about the interoperability formalization. Results lead us to recognize six main concepts useful to unify the interoperability vocabulary.
Spatial- Temporal Attention for Image Captioning

Inspired by the work in human translation, when translating a sentence, we can not generate a word without looking back at the previous words of the sentence. In addition, generating a sentence for an image needs spatial information. In this paper, we address a novel spatial-temporal attention approach which combines previous, current and visual information. To get a more correct sentence for an image, our model decides whether the spatial or temporal information is more important during word generation. In the experiment, we verify our method on the most popular dataset: Microsoft COCO. The results show that our method performs well.
Salophen Copper(II) Complex‐Assisted Click Reactions for Fast Synthesis of 1,2,3‐Triazoles Based on Naphthalene‐1,4‐dione Scaffold, Antibacterial Evaluation, and Molecular Docking Studies

The salophen copper(II) complex was successfully used for the efficient synthesis of new 1,2,3‐triazoles based on the naphthalene‐1,4‐dione scaffold. The reaction of 2‐chloro‐3‐(prop‐2‐yn‐1‐yloxy)naphthalene‐1,4‐dione or 2,3‐bis(prop‐2‐yn‐1‐yloxy)naphthalene‐1,4‐dione with aromatic azides in the presence of a low copper catalyst (loading 1 mol‐%) afforded 2‐chloro‐3‐[(1‐phenyl‐1H‐1,2,3‐triazol‐4‐yl)methoxy]naphthalene‐1,4‐dione or 2,3‐bis[(1‐phenyl‐1H‐1,2,3‐triazol‐4‐yl)methoxy]naphthalene‐1,4‐dione, respectively. The advantages of these reactions are short reaction times, high‐to‐excellent reaction yields, operational simplicity, and mild experimental conditions. The new 1,2,3‐triazoles obtained were screened for their in vitro antibacterial activities and were subjected to molecular docking studies.
Double Resonance Two-Photon Interrogations of Hydrogen Molecular Ions for Determination of Fundamental Constants

Rotational and rovibrational transitions of cold trapped HD+ ions may be probed by double resonance two-photon spectroscopy and detected by photodissociation. The two-photon transition rates and the lightshifts are calculated using the two-photon tensor operator formalism. The experimental accuracy is estimated at the $\pmb{10^{-12}}$ level. An adjustment of selected Doppler-free lines of HD+ and H2 + may provide an insight into the proton radius puzzle, may lead to an independent determination of the Rydberg constant, and may improve the values of the proton-to-electron and deuteron-to-proton mass ratios beyond the 10−11 level.
Probabilistic Load Forecasting Using an Improved Wavelet Neural Network Trained by Generalized Extreme Learning Machine

Competitive transactions resulting from recent restructuring of the electricity market, have made achieving a precise and reliable load forecasting, especially probabilistic load forecasting, an important topic. Hence, this paper presents a novel hybrid method of probabilistic electricity load forecasting, including generalized extreme learning machine for training an improved wavelet neural network, wavelet preprocessing and bootstrapping. In the proposed method, the forecasting model and data noise uncertainties are taken into account while the output of the model is the load probabilistic interval. In order to validate the method, it is implemented on the Ontario and Australian electricity markets data. Also, in order to remove the influence of model parameters and data on performance validation, Friedman and post-hoc tests, which are non-parametric tests, are applied to the proposed method. The results demonstrate the high performance, accuracy, and reliability of the proposed method.
Systematic evaluation of stroke thrombectomy in clinical practice: The German Stroke Registry Endovascular Treatment

Background Endovascular treatment has become standard of care for the treatment of acute ischemic stroke with large vessel occlusion. However, patients treated in clinical practice differ from the selected populations randomized in clinical trials. Aims The German Stroke Registry Endovascular Treatment (GSR-ET) aims at a systematic evaluation of outcome, safety, and process parameters of endovascular stroke treatment in standard of care in Germany. Methods The GSR-ET is an academic, independent, prospective, multicenter, observational registry study. Participating stroke centers from all over of Germany consecutively enroll patients transferred to the angiography suite with an intention to be treated with endovascular stroke treatment. Patients receive regular care. Data are collected as part of clinical routine. Baseline clinical and procedural information and clinical follow-up information after 90 days are recorded. Here, we present an analysis of baseline data of the first 1662 patients included in the GSR-ET. Results The registry was established in June 2015. By 31 December 2017, 1662 patients were enrolled in 23 active sites. Mean age was 72 ± 13 years, 50% were female, and median National Institutes of Health Stroke Scale on admission was 15 (IQR 10–19), 88% had anterior circulation occlusion. Median ASPECT score was 8 (IQR 7–10) prior to intervention. Fifty-nine percent of patients received intravenous thrombolysis prior to thrombectomy. Mean “onset-to-groin” time was 224 ± 176 min. Conclusions Baseline characteristics of stroke patients undergoing thrombectomy in clinical practice differ from those in the randomized trials. The GSR-ET will provide valuable insights into practices of endovascular treatment in routine care of acute ischemic stroke. (GSR-ET ClinicalTrials.gov Identifier: NCT03356392.)
Random Draw Forest: A Salient Index for Similarity Search Over Multimedia Data

The approximate nearest neighbor(ANN) search over high dimensional multimedia data has become an unavoidable service for online applications. Returning fast and high-quality results of unknown queries are the largest challenge that most algorithms faced with. Locality Sensitive Hashing(LSH) is a well-known ANN search algorithm while suffers from inefficient index structure and poor accuracy in the distributed scheme. The traditional index structures have most significant bits(MSB) problem, which is their indexing strategies have an implicit assumption that the bits from one direction in the hash value have higher priority. In this paper, we propose new content-based index called Random Draw Forest(RDF), which not only applies a content-based partition strategy to reduce the search range for fast query response, but also uses the shuffling permutations on hash values to solve the most significant bits problem. We also study the trade-off between query's efficiency and accuracy after applying our partition strategy. In the experiment, we show the effect of parameters and the salient performance of RDF compared with other LSH-based methods to meet the online ANN search.
Flow Network Models for Online Scheduling Real-Time Tasks on Multiprocessors

We consider the flow network model to solve the multiprocessor real-time task scheduling problems. Using the flow network model or its generic form, linear programming (LP) formulation, for the problems is not new. However, the previous works have limitations, for example, that they are classified as offline scheduling techniques since they establish a flow network model or an LP problem considering a very long time interval. In this study, we propose how to construct the flow network model for online scheduling periodic real-time tasks on multiprocessors. Our key idea is to construct the flow network only for the active instances of tasks at the current scheduling time, while guaranteeing the existence of an optimal schedule for the future instances of the tasks. The optimal scheduling is here defined to ensure that all real-time tasks meet their deadlines when the total utilization demand of the given tasks does not exceed the total processing capacity. We then propose the flow network model-based polynomial-time scheduling algorithms. Advantageously, the flow network model allows the task workload to be collected unfairly within a certain time interval without losing the optimality. It thus leads us to designing three unfair-but-optimal scheduling algorithms on both continuous and discrete-time models. Especially, our unfair-but-optimal scheduling algorithm on a discrete-time model is, to the best of our knowledge, the first in the problem domain. We experimentally demonstrate that it significantly alleviates the scheduling overheads, i.e., the reduced number of preemptions with the comparable number of task migrations across processors, in comparison with an existing algorithm on the discrete-time model.
Tracking Objects with Severe Occlusion by Adaptive Part Filter Modeling - In Traffic Scenes and Beyond

Vision-based object tracking approach has been drawing increased attention from both the academia and the industry in recent years. One of its most successful applications is the monitoring system, which can be installed on infrastructures or on mobile platforms (e.g., vehicles) to track pedestrians or bicyclists in a pre-defined region and further to prevent probable accidents. Despite tremendous progress achieved, the task of visual tracking is still challenging, especially in dealing with severe occlusions, where the tracker may fail due to the abrupt change of object appearance. Such case is common not only in traffic scenarios but also in other tracking tasks. Aiming to tackle this problem, in this paper, we propose a new tracking approach by adopting part based trackers, which are built in the form of correlation filter. In this approach, the occluded object parts are identified by leveraging the knowledge derived from image features and filter responses. With the help of a masking process, visible object areas are acquired in a pixel-wise precision and utilized to build part filters. As both the number and size of part filters are adapted to the current object appearance, the influence of occlusions can be significantly suppressed. Experimental results on traffic sequences demonstrate that our tracker performs robust against occlusion, especially in cases, where long term and severe occlusions appear. A further experiment on the standard benchmark proves that our approach outperforms state-of-the-art methods in tracking various object classes under varied circumstances. Furthermore, the proposed tracker is sophisticatedly designed and is feasible for real time applications.
Examining the Effects of Altered Avatars on Perception-Action in Virtual Reality

In virtual reality (VR), avatars are graphical representations of people. Previous research highlights benefits of having a self-avatar when perceiving-acting while embedded in a virtual environment. We studied the effect that an altered avatar had on the perception of one’s action capabilities. In Experiment 1, some participants acted with a normal, or faithful, avatar whereas another group of participants used an avatar with an extended arm, all in virtual reality. Experiment 2 utilized the same methodology and procedure as Experiment 1, except that only a calibration phase occurred in VR, whereas other phases were completed in the real world. All participants performed reaches to various distances presented visually. Results showed that calibration to altered dimensions of avatars is possible after receiving feedback while acting with the altered avatar. Calibration occurred more quickly when feedback was used to transition from a normal avatar to an altered avatar than when transitioning from the altered avatar back to the normal avatar without feedback. The implications of these findings for training in virtual reality simulations and for transfer to the real world are discussed, along with the implications for the concept of an embodied action schema.
Spam Reviews Detection Framework Based on Heterogeneous Information Network (HIN)

Nowadays social media plays an important role in our day-to-day activities. Specifically, in the past few years, online social websites such as Facebook, Twitter and WhatsApp are evolving as one of the major sources of communication for internet users, in order to keep in touch with their friends. However, Spam reviews generated on a website results in huge financial gain only for competitors, whereas it is a major loss for both customers and organization. In the literature, the existing techniques for Spam reviews detection suffer due to issues such as limited datasets and lack of proper classification methods, which results in inefficiency of the systems. In order to solve these problems, this paper proposes a new framework, which models the given review dataset, using Heterogeneous Information Network (HIN) concept and solves the spam detection problem by means of clearly identifying the spam reviews present in a website. The performance of the proposed framework, which is evaluated using real-world labeled datasets of Amazon website, illustrate its better performance, in terms of weight calculations based on meta-path concepts.
Foreground Gated Network for Surveillance Object Detection

Object detection in surveillance videos is challenging due to the requirement of real-time detection of small objects in presence of motion blurs and over-exposures at night. False positives caused by above challenges on background areas are more critical in surveillance videos compared to general object detection. Existing state-of-the-art object detection methods often suffer from performance declines, or become time consuming by heavily relying on the temporal correlations between sequential frames. In this paper, we propose a novel real-time method for object detection in surveillance videos to address the above challenges. The proposed architecture employs an efficient BS-RNN block to distill foreground and fine-tunes the unsupervised parameters by minimizing the detection loss. A feature gating block follows to acquire feature-level masks to amplify activations on foreground objects while suppressing irrelevant background regions. We also introduce a new type of feedback connection from object detections to the BS-RNN, which plays an important role in handling static foreground objects such as cars and pedestrians waiting in front of traffic lights. Extensive experiments demonstrate that Foreground Gated Network (FGN) outperforms the compared methods in both accuracy and speed.
A Two-Layer Energy Management System for Microgrids With Hybrid Energy Storage Considering Degradation Costs

The integration of renewable energy source (RES) and energy storage systems (ESS) in microgrids has provided potential benefit to end users and system operators. However, intermittent issues of RES and high cost of ESS need to be placed under scrutiny for economic operation of microgrids. This paper presents a two-layer predictive energy management system (EMS) for microgrids with hybrid ESS consisting of batteries and supercapacitors. Incorporating degradation costs of the hybrid ESS with respect to the depth of charge and lifetime, long-term costs of batteries and supercapacitors are modeled and transformed to short-term costs related to real-time operation. In order to maintain high system robustness at minimum operational cost, a hierarchical dispatch model is proposed to determine the scheduling of utilities in microgrids within a finite time horizon, in which the upper layer EMS minimizes the total operational cost and the lower layer EMS eliminates fluctuations induced by forecast errors. Simulation studies demonstrate that different types of energy storages can be utilized at two control layers for multiple decision-making objectives. Scenarios incorporating different pricing schemes, prediction horizon lengths, and forecast accuracies also verify the effectiveness of the proposed EMS structure.
Diffusion Leaky Zero Attracting Least Mean Square Algorithm and Its Performance Analysis

Recently, the leaky diffusion least-mean-square (DLMS) algorithm has obtained much attention because of its good performance for high input eigenvalue spread and low signal-to-noise ratio. However, the leaky DLMS algorithm may suffer from performance deterioration in the sparse system. To overcome this drawback, the leaky zero attracting DLMS algorithm is developed in this paper, which adds an <inline-formula> <tex-math notation="LaTeX">$l_{1}$ </tex-math></inline-formula>-norm penalty to the cost function to exploit the property of sparse system. The leaky reweighted zero attracting DLMS algorithm is also put forward, which can improve the estimation performance in the presence of time-varying sparsity. Instead of using the <inline-formula> <tex-math notation="LaTeX">$l_{1}$ </tex-math></inline-formula>-norm penalty, in the reweighted version, a log-sum function is employed as the substitution. Based on the weight error variance relation and several common assumptions, we analyze the transient behavior of our findings and determine the stability bound of the step-size. Moreover, we implement the steady state theoretical analysis for the proposed algorithms. Simulations in the context of distributed network system identification illustrate that the proposed schemes outperform various existing algorithms and validate the accuracy of the theoretical results.
Association between interleukin 4 (IL-4) VNTR, gene polymorphism, and breast cancer susceptibility in Iranian population: experimental and web base analysis.

BACKGROUND Breast cancer (BC) is one of the most common types of cancer and the second leading cause of cancer death among women. Epidemiological studies showed that BC is linked to genetic and environmental factors, and inheritance plays a key role in the pathobiology of this disease. Interleukin 4 (IL-4) is a key differentiation cytokine and is produced by Th2 and activates Th2 development. Hence the current study aimed to assess the possible association between interleukin 4 (IL-4) VNTR polymorphism, and BC susceptibility in a sample of Iranian population. MATERIAL AND METHODS IL-4 VNTR polymorphism was evaluated in 150 women with BC and 150 age-matched healthy women by polymerase chain reaction method. RESULT Among 3 possible alleles for IL-4 gene, we only observed 2 alleles. Current findings indicate that RP2/RP2 genotypes can be regarded as potent protective factors against breast cancer (OR = 0.929 [95%CI, 0.929-0.995]). CONCLUSION Our result showed that the RP2/RP2 genotype of the IL-4 VNTR polymorphism could be a protective factor for BC susceptibility (Tab. 2, Fig. 1, Ref. 46).
Local cliques in ER-perturbed random geometric graphs

Random graphs are mathematical models that have applications in a wide range of domains. We study the following model where one adds Erd\H{o}s--R\'enyi (ER) type perturbation to a random geometric graph. More precisely, assume $G_\mathcal{X}^{*}$ is a random geometric graph sampled from a nice measure on a metric space $\mathcal{X} = (X,d)$. The input observed graph $\widehat{G}(p,q)$ is generated by removing each existing edge from $G_\mathcal{X}^*$ with probability $p$, while inserting each non-existent edge to $G_\mathcal{X}^{*}$ with probability $q$. We refer to such random $p$-deletion and $q$-insertion as ER-perturbation. Although these graphs are related to the objects in the continuum percolation theory, our understanding of them is still rather limited. In this paper we consider a localized version of the classical notion of clique number for the aforementioned ER-perturbed random geometric graphs: Specifically, we study the edge clique number for each edge in a graph, defined as the size of the largest clique(s) in the graph containing that edge. The clique number of the graph is simply the largest edge clique number. Interestingly, given a ER-perturbed random geometric graph, we show that the edge clique number presents two fundamentally different types of behaviors, depending on which "type" of randomness it is generated from. As an application of the above results, we show that by using a filtering process based on the edge clique number, we can recover the shortest-path metric of the random geometric graph $G_\mathcal{X}^*$ within a multiplicative factor of $3$, from an ER-perturbed observed graph $\widehat{G}(p,q)$, for a significantly wider range of insertion probability $q$ than in previous work.
Compensation of Industrial CMOS Camera and Image Enhancement for Sounding Rocket Astrophysical Experiments

Suborbital missions of sounding rockets along the parabolic trajectory are commonly used for the Earth’s near space environment research or for the astrophysical observations with the time duration only in the order of several minutes. Such short-term missions are usually performed with a low-cost equipment as the exact opposite of long-term satellite missions with high-end and expensive technologies. In this paper there is described the usage of a low-cost industrial CMOS camera for the confirmation of accurate pointing of the Roentgen telescope during the Vela supernova observation. It requires a robust method of the image post-processing and the compensation of real properties of the camera system faced to the extreme space environment. Also a specific mode of the image acquisition is required due to temperature changes during the suborbital mission. As the result of our work, a low-cost camera could be used for simple and short-term tasks in space instead of an expensive space qualified system.
Alternating Direction Method of Multipliers for Sparse and Low-Rank Decomposition Based on Nonconvex Nonsmooth Weighted Nuclear Norm

Sparse and low-rank decomposition (SLRD) poses a big challenge in many fields. The existing methods are used to solve SLRD problem via formulating approximations of sparse and low-rank matrices. These conventional methods consider the approximation of the low-rank matrix as its nuclear norm, which is a convex surrogate function of the rank. Since these approaches simultaneously minimize all the singular values, and thus the rank may not be well approximated in practice. In this paper, we extend the nonconvex nonsmooth weighted nuclear norm to approximate the low-rank matrix and formulate a general form nonconvex nonsmooth sparse and low-rank matrices decomposition problem. Hence, we can adopt the alternating direction method of multipliers to solve this nonconvex nonsmooth problem and analyze its convergence. Simulation results and discussions are given to validate the proposed method.
1-Port Vector Network Analyzer Calibration Technique Using Three Lines

This paper proposes a calibration technique for 1-port vector network analyzers (VNA) using three lines as the measurement standard. In the method, the reflection coefficient of a device under test and the circuit errors, such as the directivity, matching, and tracking, are simultaneously obtained. The advantage of the method is that results without the effects of the circuit errors are obtained, no assumption is required for the characteristics of the standard lines and theory, and the simple SI traceability can be established.