Miracle-dz/UniFAR-FTUs huggingface dataset:
Micro-Expression Recognition Based on the Spatio-Temporal Feature

Micro-expressions are brief and involuntary facial movements which reveal persons’ real emotions. Recognition of microexpression is a great challenge due to its properties of short duration and low intensity. To address this problem, we propose a ROI (Region of Interest)-based spatio-temporal feature named Dense Sampling Optical-flow’s Mean Magnitude and Angle (DS-OMMA) for micro-expression recognition. Namely, partitioning the facial region into some adaptive ROIs discovers the facial spatial structure, and optical flow explores the temporal information by capturing small muscular movements on the face. Moreover, dense sampling reduces the effect of noise caused by head movement or illumination. The proposed approach is evaluated on two spontaneous micro-expression datasets, i.e., CASME2 and CAS(ME)2. The experimental results show that our proposed DS-OMMA feature performs better than the baseline feature LBP-TOP and the state-of-the-art feature MDMO in recognition accuracy.
Urban Sprawl Classification Analysis Using Image Processing Technique in Geoinformation System

Urban Area has become peak attention around the world. Most of the countries are facing challenges in its growth, its impact and its resource management. Satellite images are used to monitor and control the growth of urban sprawl pattern. In this paper, satellite images of Salem city from tamilnadu, India has been considered as a study area for the analysis. Histogram equalization has been applied to the lansat satellite image of Salem city. Followed with the feature extraction in signature method. The extracted features like Built-up land, Mining, Agriculture crop land, Agriculture follow Land, water bodies, Waste land, forest are applied to maximum likelihood classification technique. It provide better result as compared to other existing technique.Urbangrowth has been predicted and its direction for extension of land area has been identified. The detected changes are analyzed from the year 1973to 2016.
Pro-Environmental Behavior and Green Information Systems Research - Review, Synthesis and Directions for Future Research

Understanding and promoting pro-environmental behaviors (PEB) is crucial to mitigate global warming, to reduce the use of natural resources, and to protect the biosphere. A major aim of green information systems (Green IS) is to support PEB, sustainable practices and decision-making on the individual, organizational, and societal level. In the past decade, many studies have investigated the role of Green IS on individual, organizational, and societal PEB. However, due to the interdisciplinary nature of the topic those studies are widely dispersed among different outlets. Therefore, we aim at providing a comprehensive review and synthesis of the literature. To structure prior research and to systematically identify gaps in the literature, we build upon the belief-action-outcome framework. Our analysis indicates that prior research mainly focused on action formation and outcome assessment indicating a void regarding belief formation. We also found a bias towards an isolated examination of either the micro-or macro-level. Thus, we argue that to understand the influence of Green IS on PEB on multiple levels more thoroughly, future research needs to investigate the complex interdependencies between the individual, organizational, and societal level.
A Study on Transaction Specification based Software Testing for Internet of Things

Automated testing techniques for software design specification include model-based approaches that generate test cases from a coding or state machine model of the interface, capture-replay methods that require the user to specify and demonstrate each test case individually, and modelling-language approaches that provide templates for abstract test cases. In this paper elucidates a method for automatic software analysis testing for IoT service. Nowadays real-time IoT software is used to satisfy the needs and functions of a user and it is assisted in this by the hardware. Software testing is generally used to detect bugs and errors and eliminate them to produce a high quality, reliable software. The automated testing depends on the kind of approach taken. This testing may be poorly implemented which results in a low quality software or it may be effectively implemented which results in a high quality software. Automated IoT software testing can decrease time and cost. When these techniques are identified, the cost and time can be reduced. The automated testing is based on three steps such as analyzing the test case, test execution and log analyzer. These steps provide user friendly software with high standard. The automated process provides test reusability and repeatability and saves the test coverage cost and time. In this paper compare the automated software testing for IoT transactions and system peripherals such as sensor, DMA controller, internal counters, physical layer, and virtual layer using IoT specification based software testing.
A Scalable and Multidirectional Rectenna System for RF Energy Harvesting

A rectenna system having multidirectional receiving capability and scalable antenna gain is presented for radio frequency (RF) energy harvesting. The technique of ray tracing was implemented to evaluate RF power collected by antennas with different half-power beam widths. The results indicate that different radiation features are suited to distinct scenarios. This further indicates that a scalable and multidirectional radiation characteristic is highly desired for energy-harvesting antennas because the level of ambient energy varies over time and space. Accordingly, a rectenna system is proposed that can scale in array size, antenna gain, direction of reception, and radiation pattern to enhance conversion efficiency and dc outputs. The proposed device is comprised of five rectenna cells. Each cell consists of a quasi-Yagi antenna with variable numbers of directors and one RF-to-dc energy-harvesting circuit. Thus, the proposed system can achieve either uniform coverage with enhanced antenna gain or highly directional characteristics at one/multiple incidence angle(s). A prototype is designed, fabricated, and tested in terms of each component and the entire system. The experimental results indicate that the proposed system can be useful in various scenarios, and it outputs enhanced power as scaling is performed in the direction of reception and antenna gain.
Design of Simplified Array Layouts for Preliminary Experimental Testing and Validation of Large AESAs

The problem of designing simplified and cost-effective versions of large phased arrays for a preliminary, but meaningful, experimental testing of a selected set of radiation requisites of the final/complete layouts is addressed. With reference to an array architecture composed by a wide set of equal (or different, even though limited to a finite number of sizes) functional blocks, each one containing the feeding network and the control line for a cluster of elements having independent transmit-receive modules, a novel and ad-hoc method is proposed for determining and selecting the minimum set of blocks of the fully populated array and the excitations of their elements to guarantee the fulfilment of a requested subset of performance of the final/complete arrangement. Representative results are shown and discussed to point out the peculiarities and the potential impact on the active electronically scanned array production cycle of the topic at hand as well as the effectiveness and the potentialities of the proposed design methodology.
Pool-based Recursive Construction Approach for Logistics Route Planning with Time Window

With the blossoming of online shopping, a lot of goods need be distributed to customers. For a logistics company, how to improve the delivery efficiency, reduce the logistics cost and satisfy the real logistical constraints including vehicle's capacity and customer's available time window is an important issue. This kind of problem is called Vehicle Routing Problem with Time Windows (VRPTW). Although a number of algorithms based on artificial intelligence have been proposed, most of them cannot efficiently solve VRPTW while the number of goods increases rapidly. In this paper, we propose a novel approach named Pool-based Recursive Constructor (PRC) to efficiently find a set of logistics routes by considering real logistics constraints. In PRC, an urgent value measurement, several customer selection strategies and a pool-based mechanism are proposed to evaluate the cost of each customer and select the most suitable customer for route constructing, recursively. Through the experimental evaluation based on two semi-real logistics datasets and comparison with two greedy strategies used by Kerry TJ Logistics, PRC shows an excellent performance in terms of route quality
Remote Software Update in Trusted Connection of Long Range IoT Networking Integrated With Mobile Edge Cloud

The Internet of Things (IoT) leads to intelligent services by collecting information from tiny sensor devices. In recent years, storage-less sensing devices have been used to implement IoT services. They depend on delivered software from a network server to operate service functions, and IoT services are based on collected user information. Therefore, it is important to maintain trusted connections during software delivery or data transmission. If a network connection is untrustworthy, stable data transmission cannot be achieved. Untrustworthy data connections cause many problems in IoT services. Therefore, this paper proposes a software update method in trusted connection of IoT networking. The proposed method employs a low-power wide area network (LPWAN) as a long-range IoT networking technology and uses a mobile edge cloud to improve computing efficiency in an access network that consists of IoT devices with insufficient resources. In the proposed method, the mobile edge cloud is integrated into a gateway and processes sensing data and remote software updates of LPWAN. IoT devices can receive software functions from the mobile edge cloud. The proposed method analyzes statistical information about connections in an access network and determines the LPWAN trusted connections. Then, software updates can be performed over the trusted connection. Using trusted connections leads to an increased packet delivery rate and reduced transmission energy consumption. The proposed method is compared with currently available systems through computer simulation and the proposed method’s efficiency is validated.
Multi-scale Fusion with Context-aware Network for Object Detection

Almost all of the state-of-the-art object detectors employ convolutional neural network (CNN) to extract feature. However, how to fully utilize spatial information is a challenge. In this paper, we propose an effective framework for object detection. Our motivation is that multi-scale representation and context are extremely important for object detection. For multi-scale representation, our mothed combines hierarchical feature maps to a fusion map, which has abundant spatial information and high-level semantics. For context, we exploit spatial information by stacking multi-region feature maps. The network is learned end-to-end, by minimize an objective function. Our network achieves competitive results, 75.9% mAP on PASCAL VOC 2007, 72.0% mAP on PASCAL VOC 2012 and 23.2% mAP on MS COCO. The speed of the network is 10 fps. Our studies demonstrate that multiscale representation and context can further improve performance of object detection.
Towards nano-patterned perovskite layers for enhanced absorption in solar cells

Organo-metal halide perovskites have revolutionized thin film photovoltaics by their outstanding performance improvements over the last years. Modifying the active layer by thermal nano-imprint lithography enables a great opportunity to modify and further enhance the optical properties. During the imprint, the perovskite layer changes tremendously. Crystal sizes and structure can be controlled by different molds, pressure and heat. Most importantly, the ability to transfer two-dimensional photonic structures inside the active layer enables enhanced photon absorption in the weakly absorbing wavelength region of thin perovskites.
Improving Video Captioning with Non-Local Neural Networks

Unlike static images, a video contains not only visual features but also more semantic meanings or relationships between the objects and scenes due to its temporal attribute. There have been many attempts to describe spatial and temporal relationships in videos, but the encoder-decoder based models are not enough to capture detailed relationships in videos. Specifically, a video clip often consists of several shots that seem to be unrelated, and simple recurrent model suffer from these change of shots. Recently, some studies have introduced the approach describing visual relations with relational reasoning on visual question answering and action recognition tasks. In this paper, we introduce an approach to capture temporal relationship with non-local block and boundary-awareness system. We evaluate our approach on Microsoft Video Description Corpus (MSVD, YouTube2Text) dataset. Experimental results show that non-local block applied along the temporal axis can improve video captioning performance on the MSVD dataset.
Evaluation of Optical Absorption in Cu2ZnSn(S,Se)4 Absorber Within Cell Structure by Fourier Transform Photocurrent Spectroscopy

We demonstrated to evaluate optical absorption spectra of Cu2ZnSn(S,Se)4 (CZTSSe) photo-absorbers within cell structure by Fourier transform photocurrent spectroscopy (FTPS). FTPS measurements were carried out for two series of CZTSSe thin film solar cells using window layer with different thickness. In the spectra, exponential tail was found below photon energy of 1.2 eV. We indicated that band gap energy and Urbach energy of CZTSSe photo-absorbers were determined from the spectra obtained by FTPS measurements for CZTSSe thin film solar cells.
Dual Band Wide-slot Printed Antenna for Wide Band Applications

In this paper, the design of the dual band wide-slot printed antenna for wideband applications is proposed. The antenna consists of a square radiating patch embedded in the wide-slot. Inverted U-shaped slot and pair of parallel rectangular slots in the ground plane are the main reasons for the notched structure and creation of dual bands. The First band ranges from 2.20 GHz - 3.04 GHz which covers Bluetooth (2.4 GHz), WLAN (2.4 GHz) and WiMAX (2.5 GHz). The second band ranges from 3.36 GHz – 5.64 GHz which can be used for WiMAX (3.5 GHz), WLAN (5.2 GHz) and WiMAX (5.5 GHz).
From Text to Video: Exploiting Mid-Level Semantics for Large-Scale Video Classification

Automatically classifying large scale of video data is an urgent yet challenging task. To bridge the semantic gap between low-level features and high-level video semantics, we propose a method to represent videos with their mid-level semantics. Inspired by the problem of text classification, we regard the visual objects in videos as the words in documents, and adapt the TF-IDF word weighting method to encode videos by visual objects. Some extensions upon the proposed method are also made according to the characteristics of videos. We integrate the proposed semantic encoding method with the popular two-stream CNN model for video classification. Experiments are conducted on two large-scale video datasets, CCV and ActivityNet. The experimanetal results validates the effectiveness of our method.
Deriving Word Embeddings Using Multilingual Transfer Learning for Opinion Mining

Deep learning (DL) is widely used in text mining applications. The input of DL consists of text words represented as dense vectors, named word embeddings. These word embeddings have been previously trained using datasets of large text corpus. Issues arise when the input text consists of words with unknown embeddings and when the embeddings do not capture the semantics of the domain of interest. In this study, we focus on the domain of opinion mining and propose a novel method for learning new embeddings based on limited training datasets. Our method is particularly tailored to address the challenge of multi-lingual opinion mining by exploiting transfer learning from the English language for which labeled data are available. We validate the proposed method by using a small dataset in the Greek language for creating new embeddings.
Infant Cry Detection in Adverse Acoustic Environments by Using Deep Neural Networks

The amount of time an infant cries in a day helps the medical staff in the evaluation of his/her health conditions. Extracting this information requires a cry detection algorithm able to operate in environments with challenging acoustic conditions, since multiple noise sources, such as interferent cries, medical equipments, and persons may be present. This paper proposes an algorithm for detecting infant cries in such environments. The proposed solution is a multiple stage detection algorithm: the first stage is composed of an eight-channel filter-and-sum beamformer, followed by an Optimally Modified Log-Spectral Amplitude estimator (OMLSA) post-filter for reducing the effect of interferences. The second stage is the Deep Neural Network (DNN) based cry detector, having audio Log-Mel features as inputs. A synthetic dataset mimicking a real neonatal hospital scenario has been created for training the network and evaluating the performance. Additionally, a dataset containing cries acquired in a real neonatology department has been used for assessing the performance in a real scenario. The algorithm has been compared to a popular approach for voice activity detection based on Long-Term Spectral Divergence, and the results show that the proposed solution achieves superior detection performance both on synthetic data and on real data.
Semiparametric Inference and Lower Bounds for Real Elliptically Symmetric Distributions

This paper has a twofold goal. The first aim is to provide a deeper understanding of the family of the real elliptically symmetric (RES) distributions by investigating their intrinsic semiparametric nature. The second aim is to derive a semiparametric lower bound for the estimation of the parametric component of the model. The RES distributions represent a semiparametric model, where the parametric part is given by the mean vector and by the scatter matrix, while the non-parametric, infinite-dimensional, part is represented by the density generator. Since, in practical applications, we are often interested only in the estimation of the parametric component, the density generator can be considered as nuisance. The first part of this paper is dedicated to conveniently place the RES distributions in the framework of the semiparametric group models. In the second part, building on the mathematical tools previously introduced, the constrained semiparametric Cramér–Rao bound (CSCRB) for the estimation of the mean vector and of the constrained scatter matrix of a RES distributed random vector is introduced. The CSCRB provides a lower bound on the mean squared error of any robust <inline-formula><tex-math notation="LaTeX">$M$ </tex-math></inline-formula>-estimator of mean vector and scatter matrix when no <italic>a priori</italic> information on the density generator is available. A closed-form expression for the CSCRB is derived. Finally, in simulations, we assess the statistical efficiency of the Tyler's and Huber's scatter matrix <inline-formula> <tex-math notation="LaTeX">$M$</tex-math></inline-formula>-estimators with respect to the CSCRB.
Grid-Current Control of a Differential Boost Inverter With Hidden LCL Filters

Single-phase differential boost inverter, built from two identical dc–dc boost converters, has been suggested as an alternative to the usual connection of a dc–dc boost converter to a standard full-bridge inverter. Some control schemes for controlling it have also been proposed, but mostly for voltage regulation, supplying a standalone load, rather than grid-current control. Moreover, it has never been identified that two LCL filters are hidden in the differential boost topology. Problems related to their associated resonances have therefore not been discussed, especially their variations with duty ratios. It is thus necessary here to unmask the hidden LCL filters, which upon clarification, permits vast knowledge from grid-current control of a standard LCL -filtered inverter to be extended for differential inverter control. However, the extension involves many challenges introduced by the varying resonance frequencies, nonlinearity, and many control options permitted by the differential boost inverter. These challenges are addressed now with simulation and experimental results provided for verifying direct grid-current regulation of a differential boost inverter.
Rotational Invariant Discriminant Subspace Learning For Image Classification

A novel discriminant analysis technique for feature extraction, referred to as Robust Discriminant Subspace (RDS) with L2,p+s-Norm Distance Maximization-Minimization (maxmin) is posed. In its objective, the within-class and between-class distances are measured by L2,p-norm and L2,s-norm, respectively, such that it is robust and rotational invariant. An efficient iterative algorithm is designed to solve the resulted objective, which is non-greedy. We also conduct some insightful analysis on the convergence of the proposed algorithm. Theoretical insights and effectiveness of our RDS are further supported by promising experimental results on several images databases.
A comparison of organic back contact materials for CdTe solar cells

Whilst significant research effort has been focused on inorganic back surface materials to establish a quasi-ohmic contact to CdTe solar cells, the use of organic layers is relatively unexplored, yet offers a highly controllable route to tailoring band offsets between CdTe and metal contacts. This work assesses the impact on CdTe device performance of three such organic materials; spiro-OMeTAD, poly(3-hexythiophene-2,5-diyl) (P3HT) and poly(9,9-di-n-octylfluorenyl-2,7-diyl) (PFO). Whilst PFO appears to increase the contact barrier with large rollover in JV curves, and severely limits performance, the inclusion of either P3HT or spiro-OMeTAD at the back contact was shown to effectively eliminate rollover in JV curves by reducing the contact barrier height, as confirmed by temperature dependent JV measurements. However, a reduced barrier height was not found to lead to a significant increase in cell efficiency, as inclusion of the organic layer results in an accompanying increase in series resistance.
Approximating Categorical Similarity in Sponsored Search Relevance

Sponsored Search is a major source of revenue for web search engines. Since sponsored search follows a pay-per-click model, showing relevant ads for receiving clicks is crucial. Matching categories of a query and its ad candidates have been explored in modeling relevance of query-ad pairs. The approach involves matching cached categories of queries seen in the past to categories of candidate ads. Since queries have a heavy tail distribution, the approach has limited coverage. In this work, we propose approximating categorical similarity of a query-ad pairs using neural networks, particularly CLSM. Embedding of a query (or document) is generated using its tri-letter representation which allows coverage of tail queries. Offline experiments of incorporating this feature as opposed to using the categories directly show a 5.23% improvement in AUC ROC. A/B testing results show an improvement of 8.2% in relevance.