Miracle-dz/UniFAR-FTUs huggingface dataset:
Adapting Word Embeddings to Traceability Recovery

Maintaining the traceability links of a software is tedious, error-prone task, but an essential requirement. Information retrieval has been approached to help to generate traceability links. Traceability links are usually determined by the similarity between two artifacts. However, methods are put forward mainly based on vector space model, topic model etc. which ignored the word semantic. According to that, this paper adapts the popular word embedding technique to traceability recovery tasks, and handle the out-of-vocabulary words at test time. In the end, a machine learning method is used (learning to rank) to improve our final result. Several contrast experiments are conducted on five public datasets, and the baseline methods are outperformed under the same condition.
Energy-Aware Cloud Architecture for Intense Social Mobile (Device to Device) 5G Communications in Smart City

Energy consumption in 5G cellular networks is a hot research topic by 2025 as D2D users are estimated to reach 35 billion. Till the date there is no accurate energy consumption mechanism provided for device to device communication. In this article we propose a new cellular communication architecture that integrates energy aware cloud computing with social aware device to device communication. The devices when active automatically get signed up over a universal cloud application with their Sensor Encounter Record (SER) information. This SER information gets updated continuously as the devices move between multiple social communities. The cloud computes the social network of mobile devices at regular intervals based on interests and location enabling them to share and download data directly using radio access networks (RAN). Socially aware D2D energy harvesting required the knowledge of the social ties between devices communicating within a limited distance. This computation mechanism using beacons is proved to be consuming lot of energy which is not calculated. The proposed architecture promises to be energy efficient in terms of energy consumption as it shifts the computation of social ties over to cloud with an unlimited capacity of resources and memory.
Development of River Current Hydrokinetic Siphoning Device

Water is essential to daily life and access to a steady supply of water can be a problem especially those who are living in far-flung areas. Others have to rely on the rain to collect water, but during the summer season, this poses an immense challenge. People have to exert a lot of manpower to collect water specifically those who are far and elevated from the water source. They have to buy water pumps, powered either by gasoline or electricity. Places that have running water like a river, is an example of a body of water that can be a source using its current. The purpose of the study is to develop a river current hydrokinetic siphoning device that can collect water without using electricity or fuel. The current of the river drives the turbine to rotate and supplies water to the Polyethylene (PE) Coil that collects water, as the other intake rotates, pressure is created to push the liquid upward. The prototype can be used in different types of water as long as the water current is enough to drive the turbine. It must be submerged $1/2$ ft. below water level and it is capable of siphoning water with a vertical height of 20 feet and can push water ranging from 492.15 to 656.17 feet in horizontal distance. The river current hydrokinetic siphoning device can collect 1 gallon of water for 1 minute and 30 seconds. The prototype has two floaters to adjust to different water levels. It is mounted on a concrete structure connected on both sides of the river to ensure its continuous operation.
Multimodal Foraging by Honey Bees Toward Optimizing Profits at Multiple Colonies

Honey bees single out from available foraging sources by evaluating the amount of energy needed to transport an article for livelihood. In this study, we propose a novel method based on the dynamic and distributed computing behavior of honey bees at distinct colonies in gathering multiple resources for meeting their demands at respective destinations by maximizing the profit. A computational model depicting the multidimensional swarm behavior in bee colonies is developed as a multiobjective optimization problem for optimizing the cost and time. The performance of the algorithm is evaluated by mapping it to a dynamic model of the multicommodity transportation problem with multiple optimizing parameters. The algorithm is found to terminate successfully in linear time at each destination accounting for uncertainties, which are the natural property of the real world, thus optimizing the objective function at all nodes.
Renewable Energy Sources Integration and Control in Railway Microgrid

The traffic rail increase implies an increase in the electric energy consumption. Hybridizing the railway substations with hybrid energy sources based on renewable energy sources and storage units connected to a dc bus may be a solution to contribute to the partial independence of energy producers in the sector of traffic rail. A smart control is highly recommended in order to avoid disturbing the traffic or the energy quality of railway lines. This paper proposes a reversible, self-adaptive, autonomous, and intelligent distributed generator connected to the catenary thanks to the dc-bus distributed control by the multiagent system. The results analysis has shown that the proposed control architecture can be a solution to face the issues related to the traffic railway issues.
Delay Impact on MPEG OMAF’s Tile-Based Viewport-Dependent 360° Video Streaming

With the continuously increasing attention that 360° video streaming is drawing, several solutions have been developed lately. They can be grouped into two main categories, with the first one being the viewport-independent category that consists of encoding the whole 360° video content using a particular projection, e.g., Equirectangular Projection or Cubemap Projection, without taking any viewport orientation into account. Such approaches waste resources since content not being watched by the user is encoded with the same visual fidelity as the content actually watched. The second category, the viewport-dependent category, relies on techniques that allow for viewport adaptivity. One approach is to apply viewport-dependent projections, wherein the spherical 360° video is mapped onto a rectangular frame in such a way that a specific viewport is mapped to a comparatively larger part of the rectangular frame than the rest of the content. Another approach, as specified in MPEG OMAF, is to use tile-based streaming, with the 360° video being offered in a tiled manner at various resolutions. Thus, each user can retrieve the tiles at different resolutions so that the high-resolution tiles match its viewport. Although viewport-dependent approaches allow providing better visual quality at the viewport, the end-to-end delay is critical, since it has an impact on the time needed by clients to adapt to user movements so that movements are reflected on the retrieved content. In this paper, we analyze the impact of the delay and introduce various means to reduce the impact on the observed fidelity.
Arbitrary-Oriented Dense Object Detection in Remote Sensing Imagery

Automatic object detection in remote sensing images is of significant importance with widespread practical applications. However, complex backgrounds, small size and dense arrangement of objects, as well as the various orientations of the target pose great challenges for current object detection algorithms. In this paper, an arbitrary-oriented dense object detection network is proposed to predict the object area using oriented bounding boxes. Firstly, we present a method to predict the object angle according to the features in the proposal, which does not increase computation costs by utilizing weight sharing. Then, a bound conversion algorithm is built to generate the oriented bounding box of an object according to the result of axis-aligned horizontal box and predicted angle information. In addition, we employ a two-stage NMS algorithm to reduce the omission ratio for dense objects by introducing oriented boxes to compute overlapping ratio. Detailed evaluations on the DOTA dataset demonstrate the effectiveness of the proposed method.
An On-Board Model-Based Condition Monitoring for Lithium-Ion Batteries

A model-based condition monitoring for lithium-ion (Li-ion) batteries involves estimating critical model parameters (e.g., capacity and impedance) and operational states [e.g., state of charge (SOC) and state of health]. This is important to design high-performance and safety-critical battery systems. Moreover, the tuning parameters of the condition monitoring algorithms significantly influence the performance of the algorithms. This paper proposes a real-time model-based condition monitoring for Li-ion batteries based on a real-time second-order resistor–capacitor electrical circuit battery model. The proposed method consists of an extended Kalman filter based online parameter identification and a smooth variable structure filter based state estimation. The two filters are systematically integrated to cooperate with each other and named hybrid filter (HF). Furthermore, a proposed particle swarm optimization based tuning parameter optimization is employed to find the optimal tuning parameters of the HF. The proposed method is compared with the dual extended Kalman filter (DEKF) via simulations and validated by experiments using a low-priced microcontroller. The results show that the proposed method can effectively choose the tuning parameters and has less computational complexity and higher accuracy of the SOC and capacity estimation than those of the conventional DEKF. Therefore, the proposed HF can be suitable for use in real-time embedded battery management system applications.
Contact Engineering of Trilayer Black Phosphorus With Scandium and Gold

High contact resistance keeps black phosphorus (BP) from fully wielding its excellent material property. Using first-principles calculations, we analyze the interfacial binding behavior and the impact of binding on the other layers of a trilayer BP. We found that the interfacial charge density and charge transfer of Scandium (Sc)-contacted trilayer BP are 2.67 and 3.29 times greater than Au-contacted trilayer BP, respectively. Moreover, the interfacial tunneling barrier height and width of Sc-contacted trilayer BP are 0 eV and 1.851 Å, which are significantly smaller than that of 5.1 eV and 2.447 Å observed in Au-contacted trilayer BP. All these facts suggest a strong bonding and efficient carrier transmission between Sc contact and trilayer BP substrate. Therefore, we conclude that the Sc electrode can lead to a superior performance that is consistent with the experiment.
Cascaded Static and Dynamic Local Feature Extractions for Face Sketch to Photo Matching

The automatic identification of a corresponding photo from a face sketch can assist in criminal investigations. The face sketch is rendered based on the descriptions elicited by the eyewitness. This may cause the face sketch to have some degrees of shape exaggeration that make some parts of the face geometrically misaligned. In this paper, we attempt to address the effect of these influences by a cascaded static and dynamic local feature extraction method so that the constructed feature vectors are built based on the correct patches. In the proposed method, the feature vectors from the local static extraction on a sketch and photo are matched using the nearest neighbors. Then, some <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> most similar photos are shortlisted based on the nearest neighbors. These photos are eventually re-matched using feature vectors from the local dynamic extraction method. The feature vectors are matched using the <inline-formula> <tex-math notation="LaTeX">$L_{1}$ </tex-math></inline-formula>-distance measure. The experimental results for The Chinese University of Hong Kong (CUHK) Face Sketch Database (CUFS) and CUHK Face Sketch FERET Database (CUFSF) datasets indicate that the proposed method outperforms the state-of-the-art methods.
On Enabling Technologies for the Internet of Important Things

The Internet of Things leverages Internet technology in cyber-physical systems (CPSs), but the protocols and principles of the Internet were designed for interacting with information systems, not cyber-physical systems. For one, timeliness is not a factor in any widespread Internet technology, with quality-of-service features having been routinely omitted for decades. In addition, for things, safety, freedom from physical harm, is even more important than information security, the focus on the Internet. Nevertheless, properties of the Internet are valuable in CPSs, including a global namespace, reliable (eventual) delivery of messages, end-to-end security through asymmetric encryption, certificate-based authentication, and the ability to aggregate data from a multiplicity of sources in the cloud. This paper discusses and surveys architectural approaches, communication protocols, and programming models that promise to bridge the gap, enabling the use of the Internet technologies even in safety-critical, cyber-physical applications such as factory automation and transportation. Specifically, we argue that smart gateways hosted on edge computers complement cloud-based services; they can provide tighter control over timing and security that is robust against network outages, play an active role in managing interactions between things, and isolate safety-critical services from best-effort services. We explain how time sensitive network technology can be leveraged to reliably orchestrate a multiplicity of things, and how augmenting our programming models with a well-defined notion of time can make systems more deterministic and more testable.
A Framework for Image Denoising Using First and Second Order Fractional Overlapping Group Sparsity (HF-OLGS) Regularizer

Denoising images subjected to Gaussian and Poisson noise has attracted attention in many areas of image processing. This paper introduces an image denoising framework using higher order fractional overlapping group sparsity prior to sparser image representation constraint. The proposed prior has a capability of avoiding staircase effects in both edges and oscillatory patterns (textures). We adopt the alternating direction method of multipliers for optimizing the proposed objective function by converting it into a constrained optimization problem using variable splitting approach. Finally, we conduct experiments on various degraded images and compare our results with those of several state-of-the-art methods. The numerical results show that the proposed fractional order image denoising framework improves the peak signal to noise ratio of an image by preserving the textures and eliminating the staircases effects. This leads to visually pleasant restored images which exhibit a higher value of Structural SIMilarity score when compared to that of other methods.
Roles of Review Numerical and Textual Characteristics on Review Helpfulness Across Three Different Types of Reviews

Understanding what factors make a helpful online review is critical to increase sales and drive revenue for online retailers. This paper examined the impacts of both reviews’ numerical and textual characteristics on review helpfulness across three different review types including comparative, suggestive, and regular reviews. With an analysis of 30 338 product reviews collected from http://Amazon.com, the results indicated that the effects of numerical characteristics of reviews on review helpfulness are stronger for regular reviews than those for suggestive and comparative reviews. The impacts of text sentiment on review helpfulness are more significant for suggestive and comparative reviews when compared with regular reviews. Moreover, the text complexity of reviews has a significant invert U-shaped relationship with review helpfulness, and the relationships are stronger for regular reviews when compared with suggestive and comparative reviews. Furthermore, text sentiment has a negative effect on review helpfulness, and the effect is stronger for suggestive reviews than that for comparative and regular reviews. Finally, we employ a random forest method to predict review helpfulness based on its numerical and textual characteristics. This paper found that review length is the most helpful factor in predicting the helpfulness of online reviews. Our findings also indicated that the importance of numerical characteristics is greater than that of textual characteristics across three different review types. The theoretical and practical implications of the findings are presented.
Fingerprint Liveness Detection Using an Improved CNN With Image Scale Equalization

Due to the lack of pre-judgment of fingerprints, fingerprint authentication systems are frequently vulnerable to artificial replicas. Anonymous people can impersonate authorized users to complete various authentication operations, thereby disrupting the order of life and causing tremendous economic losses to society. Therefore, to ensure that authorized users’ fingerprint information is not used illegally, one possible anti-spoofing technique, called fingerprint liveness detection (FLD), has been exploited. Compared with the hand-crafted feature methods, the deep convolutional neural network (DCNN) can automatically learn the high-level semantic detail via supervised learning algorithm without any professional background knowledge. However, one disadvantage of most CNNs models is that fixed scale images (e.g., $227\times 227$ ) are essential in the input layer. Although the scale problem can be handled by cropping or scaling operations via transforming an image of any scale into a fixed scale, they can easily cause some key texture information loss and image resolution degradation, which will weaken the generalization performance of the classifier model. In this paper, a novel FLD method called an improved DCNN with image scale equalization, has been proposed to preserve texture information and maintain image resolution. Besides, an adaptive learning rate method has been used in this paper. In the performance evaluation, the confusion matrix is applied into FLD for the first time as a performance indicator. The amounts of the experimental results based on the LivDet 2011 and LivDet 2013 data sets also verify that the detection performance of our method is superior to other methods.
Design and implementation of an SDN-enabled DNS security framework

The Domain Name System (DNS) is suffering from the vulnerabilities exploited to launch the cache poisoning attack. Inspired by biodiversity, we design and implement a non-intrusive and tolerant secure architecture Multi-DNS (MDNS) to deal with it. MDNS consists of Scheduling Proxy and DNS server pool with heterogeneous DNSs in it. And the Scheduling Proxy dynamically schedules m DNSs to provide service in parallel and adopts the vote results from majority of DNSs to decide valid replies. And benefit from the centralized control of software defined networking (SDN), we implement a proof of concept for it. Evaluation results prove the validity and availability of MDNS and its intrusion/fault tolerance, while the average delay can be controlled in 0.3s.
EEG Pathology Detection Based on Deep Learning

With the advancement of machine learning technologies, particularly deep learning, the automated systems to assist human life are flourishing. In this paper, we propose an automatic electroencephalogram (EEG) pathology detection system based on deep learning. Various types of pathologies can affect brain signals. Thus, the brain signals captured in the form of EEG signals can indicate whether a person suffers from pathology or not. In the proposed system, the raw EEG signals are processed in the form of a spatio-temporal representation. The spatio-temporal form of the EEG signals is the input to a convolutional neural network (CNN). Two different CNN models, namely, a shallow model and a deep model, are investigated using transfer learning. A fusion strategy based on a multilayer perceptron is also investigated. The experimental results on the Temple University Hospital EEG Abnormal Corpus v2.0. 0 show that the proposed system with the deep CNN model and fusion achieves 87.96% accuracy, which is better than some reported accuracy rates on the same corpus.
Topic Derivation in Weibo Through Both Interactions and Content

Social media represented by Weibo has become one of the most important platforms, covering all kinds of topics for information dissemination and day-to-day communications. As a result, topic derivation in Weibo can support various applications scenarios, including sentiment analysis, opinion controlling, market forecasting, etc. As traditional topic derivation in Weibo is mainly based on the short text of a weibo post, these methods usually encounter the data sparsity problem. To solve this problem, we find that both content and interactions can help improve the quality of topic derivation in Weibo. Thus, this paper proposed a method that additionally takes three typical interactions into features: mentioning, forwarding and the topic tags. The proposed method clusters the weibo posts and identifies the representative terms for each topic by matrix factorization technique. Our experimental results show that the proposed method performs better than advanced baseline methods in both topic clustering accuracy and keywords extraction.
2D Object Localization Based Point Pair Feature for Pose Estimation

To automate the bin picking task with robots, pose estimation is the key challenge which identifies and locates objects, thus the robot can pick and manipulate the object in an accurate and reliable way. This paper proposes a novel solution which combines a machine learning based 2D object localization and a non-machine learning based 3D pose estimation method to estimate the pose of randomly piled up industrial parts. Given an image of a scene, the target part is localized in 2D first and its result is then used to crop the point cloud of the target part. Using the cropped point cloud and Boundary-to-Boundary-using-Directional-Tangent-Line (B2B-DTL) point pair feature, a novel descriptor, the proposed method could estimate the pose of industrial parts whose point clouds lack key details, for example, the point cloud of ridges of a part. Our algorithm is evaluated against real scenes and its experimental results show that the proposed method is sufficiently accurate and its online computation time is short, which makes it could be used in the real factory environment.