Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptive convolution local and global learning for class-level joint representation of face recognition with single sample per person

Due to the absence of samples with intra-class variation, extracting discriminative facial features and building powerful classifiers are the bottlenecks of improving the performance of face recognition (FR) with single sample per person (SSPP). In this paper, we propose to learn regional adaptive convolution features which are locally and globally discriminative to face identity and robust to face variation. With collected generic facial variations, a novel class-level joint representation framework is presented to exploit the distinctiveness and class-level commonality of different facial features. In the proposed class-level joint representation with regional adaptive convolution feature (CJR-RACF), both discriminative facial features robust to various facial variations and powerful representation for classification with generic facial variations that can overcome the small-sample-size problem are fully exploited. CJR-RACF has been evaluated on several popular databases, including large-scale CMU Multi-PIE and LFW databases. Experimental results demonstrate the much higher robustness and effectiveness of CJR-RACF to complex facial variations compared to the state-of-the-art methods.
INSTRUCT: A Clustering Based Identification of Valid Communications in IoT Networks

Providing access control to the IoT devices is an essential task in today's ever-growing IoT network. IoT devices are deployed in smart homes, smart buildings, social infrastructures etc. Illegitimate users or malware should be denied access to these devices to protect the sensitive information collected by these devices and the login privileges of its operating system. This paper proposes INSTRUCT, a mechanism for providing access control by identifying valid communication in a network consisting of IoT devices using clustering techniques. INSTRUCT uses the fact that the IoT devices usually communicate with a fixed set of hosts/servers repetitively. By capturing the network traffic and learning the patterns out of the network traffic, this mechanism allows the automatic generation of access control list that can be deployed at the intermediate network switches. INSTRUCT proposes two different algorithms for TCP and UDP respectively. These algorithms are applied to two different IoT networks for evaluation. A signature-based manual analysis is used to compare with the automatically generated access control list from the algorithms. In our experiments, INSTRUCT achieved an accuracy of 100% as compared to the signature based analysis in identifying valid TCP communication. In the case of UDP, it is close to 95%.
Cache-Enabled Hierarchical Transmission Scheme for Fog Radio Access Networks

Fog radio access network (F-RAN) architecture is introduced, in which edge nodes are connected to a baseband unit via dedicated fronthaul links while also being endowed with caches, to reduce the burden on fronthaul links and the delivery latency. The full benefit of edge cache is leveraged on the design of content distribution and delivery strategies. In this paper, a novel cache-enable hierarchical transmission scheme is proposed. Specifically, the requested files cached at local cache are partially transmitted to the users. Then, the remain contents of requested files cached at local cache are transmitted to the users together with the uncached requested files. The goal is to maximize the delivery rate of F-RANs by jointly optimizing the beamformming vectors and the file partition factor under the constraints of the fronthaul capacity, the file size, and the transmit power per enhance remote radio head. We develop an effective method to solve the resulting difficult precoding optimization problem and provide numerical results to demonstrate the effectiveness of the proposed F-RAN design.
Bidirectional DC-DC Converter Topologies for Low-Voltage Battery Interface: Comparative Assessment

The growing interest towards efficient and reliable bidirectional DC-DC converter topologies for interfacing low-voltage (LV) batteries, eg. 48 V, with a high-voltage (HV) DC-link, eg. 400 V, has driven the research toward the investigation of many different topologies. In this paper, three isolated and bidirectional topologies have been selected and compared to each other, where these topologies are seen as the state-of-the-art topologies for battery-fed systems. The introduced comparison includes the number of used semiconductor devices and passive elements, the rated voltage and current of the different semiconductor devices, the forecasted switching and conduction losses using PLECS, and the estimated volume and power losses of the employed magnetic elements. Furthermore, the comparison includes simulation results using PLECS considering a 1.5 kW power level.
Gender Differences and Work-Family Conflicts among Emergency Physicians with Intention to Leave

Backgrounds The objective of this study was to investigate the relationships among intention to leave, emergency physician clinical activities, work-family conflicts, and gender differences in emergency physicians (EPs). Methods The survey instrument was a self-administered questionnaire containing basic demographic information and characteristics of clinical activities. The work-family conflicts were assessed by the Chinese version of the work-family conflict (WIF) scale. The questionnaires were mailed to board-certified EPs between January 2014 and August 2014. Student's t-test, Chi-square test, and one-way analysis of variance (ANOVA) were used to test the difference between subgroups. Logistic regression analysis was performed to determine the factors associated with intention to leave and gender differences. Results The study included 222 respondents for analysis after exclusions. Compared with physicians not planning to leave, those planning to leave ED practice showed higher dissatisfaction with their clinical work hours (50.0% versus 31.4%, p = 0.035) and night/day shift ratio (52.9% versus 31.0%, p = 0.013) and tended to work with night/day shift ratio exceeding 40% (67.6% versus 45.7%, p = 0.019). Female physicians were more likely to leave ED practice (females versus males, 26.5% versus 10.1%, p = 0.008). A significantly higher level of WIF scale was noted in the group with intention to leave ED practice (3.7 ± 0.6 versus 3.3 ± 0.7, p = 0.001). Conclusions Females and EPs with higher level of WIF scale were more likely to leave emergency clinical practice. Instead of the number of clinical practice hours, the satisfaction with the clinical work hours and night shift frequency were significantly associated with the intention to leave.
Heuristics For Efficient Sparse Blind Source Separation

Sparse Blind Source Separation (sparse BSS) is a key method to analyze multichannel data in fields ranging from medical imaging to astrophysics. However, since it relies on seeking the solution of a non-convex penalized matrix factorization problem, its performances largely depend on the optimization strategy. In this context, Proximal Alternating Linearized Minimization (PALM) has become a standard algorithm which, despite its theoretical grounding, generally provides poor practical separation results. In this work, we first investigate the origins of these limitations, which are shown to take their roots in the sensitivity to both the initialization and the regularization parameter choice. As an alternative, we propose a novel strategy that combines a heuristic approach with PALM. We show its relevance on realistic astrophysical data.
Emotion Label from ANEW Dataset for Searching Best Definition from WordNet

Emotions in a sentence can hold many roles in the field of sentiment analysis, knowing emotions can also be known through facial expressions, speech and text. In this paper we try to find the best definition for WordNet, by utilizing ANEW and ISEAR datasets. Sentences from WordNet that match the parameters of the two datasets will be searched for emotion, labeled according to the class and selected. This can be used in text mining. The result of this research showed that not all definitions in wordnet can accurately match all datasets and contexts
A Novel Online Generalized Possibilistic Clustering Algorithm for Big Data Processing

In this paper a novel efficient online possibilistic c-means clustering algorithm, called Online Generalized Adaptive Possibilistic C-Means (O-GAPCM), is presented. The algorithm extends the abilities of the Adaptive Possibilistic C-Means (APCM) algorithm, allowing the study of cases where the data form compact and hyper-ellipsoidally shaped clusters in the feature space. In addition, the algorithm performs online processing, that is the data vectors are processed one-by-one and their impact is memorized to suitably defined parameters. It also embodies new procedures for creating new clusters and merging existing ones. Thus, O-GAPCM is able to unravel on its own the number and the actual hyper-ellipsoidal shape of the physical clusters formed by the data. Experimental results verify the effectiveness of O-GAPCM both in terms of accuracy and time efficiency.
The Changing Role of Advisory Services in Information Technology Outsourcing

Information technology outsourcing continues to be an important industry and research topic. Besides the client-vendor relationship, advisory services have been identified as a key driver for successful ITO engagements. We investigate what activities and roles third-party advisory services take in ITO relationships, and how these roles do change over time. We conduct an exploratory, longitudinal case study of an ITO project with a triangular setup – client, vendor, and advisor. The analysis of the findings results in a preliminary process model with different roles and activities of the advisor. We identified problems and challenges as well as related mitigation measures from the client and especially from the advisor within the project. We evaluate if and how the third-party advisory has a positive respectively negative effect on the relationship between the client and vendor organization as well as on the outcomes of the ITO project.
Glove-Integrated Slotted Patch Antenna for Wearable UHF RFID Reader

We present a glove-integrated slotted patch antenna for a wearable Ultra High Frequency Radio Identification Technology (UHF RFID) reader operating at 866 MHz. We tested the prototype antenna made of copper foil adhered on low-permittivity Ethylene Propylene Diene Monomer (EPDM) foam material having the thickness of 4 mm. To characterize the antenna, we tested it wirelessly in communication with a common dipole type RFID tag to estimate its realized gain, radiation pattern and maximum tag read range it provides. We also analyzed the effects of variable separation between the antenna and the body to confirm stable operation required by the application. The results showed that the antenna feasible for the work glove applications providing the read range up to 360 cm with the reader’s output power of 28.4 dBm.
Image Captioning using Adversarial Networks and Reinforcement Learning

Image captioning is a significant task in artificial intelligence which connects computer vision and natural language processing. With the rapid development of deep learning, the sequence to sequence model with attention, has become one of the main approaches for the task of image captioning. Nevertheless, a significant issue exists in the current framework: the exposure bias problem of Maximum Likelihood Estimation (MLE) in the sequence model. To address this problem, we use generative adversarial networks (GANs) for image captioning, which compensates for the exposure bias problem of MLE and also can generate more realistic captions. GANs, however, cannot be directly applied to a discrete task, like language processing, due to the discontinuity of the data. Hence, we use a reinforcement learning (RL) technique to estimate the gradients for the network. Also, to obtain the intermediate rewards during the process of language generation, a Monte Carlo roll-out sampling method is utilized. Experimental results on the COCO dataset validate the improved effect from each ingredient of the proposed model. The overall effectiveness is also evaluated.
Retinal Blood Vessel Segmentation using combination of top-hat and h-maxima methods

the diagnosis of retinal image is a basic and important concept for Diabetic Retinopathy detection and analysis. The important role play in digital retinal image is Vessel segmentation in diagnosis of various diseases. In this paper we have been working on most important portion which is segmentation of blood vessel. Blood vessels are segmented via top-hat and h-maxima methods and for classification Convolutional Neural Networks (CNN) technique can be used to get better accuracy. The network is trained in such a manner that it automatically segments the blood vessels and classify weather it is normal or abnormal. High-end graphics processor unit i.e. GPU system is used for training on largely available images and display the outputs, and this also works for high-level classification task. We have implemented this on two publicly available databases (DRIVE and STARE). The performance parameter involves during classification are accuracy, sensitivity, and specificity [4].
Effect of cooking on glycemic index, antioxidant activities, α‐amylase, and α‐glucosidase inhibitory properties of two rice varieties

Abstract This study was designed to investigate the influence of cooking on the glycemic index (GI), amylose, and amylopectin contents of two rice varieties. Two rice varieties (foreign long rice and ofada) were sourced for, divided into two, one portion cooked and the other used raw. The sugar, starch, amylose, and amylopectin contents as well as glycemic indices, antioxidant properties, and the ability of the rice to inhibit carbohydrate‐hydrolyzing enzymes (α‐amylase and α‐glucosidase) were determined. In addition, polyphenol content was determined. The results revealed that cooking caused a significant increase in starch content of the rice varieties. In the same vein, cooking increases the amylopectin content but has no effect on the amylose content. In addition, cooking shows no effect on polyphenol content but significantly increases radical scavenging ability of rice varieties used in this study. Furthermore, cooking lower the α‐amylase and α‐glucosidase inhibitory properties of two rice varieties except for foreign rice. However, the inhibitory effect of both cooked and raw foreign and ofada rice on α‐amylase and α‐glucosidase coupled with their low GI may explain their important role in controlling blood glucose level.
Humans-in-the-loop: A Game-Theoretic Perspective on Adaptive Building Energy Systems

Efficient building energy management has attracted a great interest in diverse research areas due to significant potential energy savings. A remaining challenge is how to combine the efforts of engineers on improving the energy management system with approaches developed by social scientists to integrate the occupants actively in the energy management system. This paper proposes the formulation of a game between the building energy management system and occupants to agree on room temperature comfort bounds. Under mild assumptions on the cost functions of the occupants, we show that a generalized Nash equilibrium exists and it can be shown to equal the social optimum. The alternating direction method of multipliers is used to solve the resulting consensus optimization problem in a distributed way, with the building management system as coordinator. An advantage of the proposed method is that the building energy management system does not need to rely on an explicit model of the occupant behavior but due to the game theoretic approach, indirectly receives an adapted model at each iteration. An extensive numerical study demonstrates the efficacy of the proposed approach.
Distributed Cluster Formation and Power-Bandwidth Allocation for Imperfect NOMA in DL-HetNets

In this paper, we consider a non-ideal successive interference cancellation receiver-based imperfect non-orthogonal multiple access (NOMA) schemes whose performance is limited by three factors: 1) power disparity & sensitivity constraints; 2) intra-cluster interference (ICRI); and 3) intercell-interference (ICI). By quantifying the residual interference with a fractional error factor (FEF), we show that NOMA cannot always perform better than orthogonal multiple access (OMA) especially under certain receiver sensitivity and FEF levels. Assuming the existence of an offline/online ICI management scheme, the proposed solution accounts for the ICI which is shown to deteriorate the NOMA performance particularly when it becomes significant compared to the ICRI. Then, a distributed cluster formation (CF) and power-bandwidth allocation (PBA) approach are proposed for downlink heterogeneous networks (HetNets) operating on the imperfect NOMA. We develop a hierarchically distributed solution methodology, where BSs independently form clusters and distributively determine the power-bandwidth allowance of each cluster. A generic CF scheme is obtained by creating a multi-partite graph via partitioning user equipment with respect to their channel gains since NOMA performance is primarily determined by the channel gain disparity of cluster members. A sequential weighted bi-partite matching method is proposed for solving the resulted weighted multi-partite matching problem. Thereafter, we present a hierarchically distributed PBA approach which consists of the primary master, secondary masters, and slave problems. For a given cluster power and bandwidth pair, optimal power allocations and Lagrange multipliers of slave problems are derived in closed-form. While power allowance of clusters is updated by the secondary masters based on dual variables of slave problems, bandwidth proportions of clusters are iteratively allocated by the primary master as per the utility achieved by the secondary masters at the previous iteration. Finally, the proposed CF and PBA approaches under the operation of imperfect NOMA are investigated and compared to the OMA scheme by extensive simulations results in DL-HetNets.
A Compact Unidirectional Ultra-Wideband Circularly Polarized Antenna Based on Crossed Tapered Slot Radiation Elements

In this communication, a novel compact and unidirectional circularly polarized antenna based on the crossed printed tapered slot radiation elements is proposed with the ultra-wideband operation. The antenna consists of two crossed tapered slot radiation elements, a compact dual-feed network, and a dial-shaped parasitic metallic strip. The tapered slot element is applied and investigated for wideband coverage within a compact size. By using the proposed parasitic patches, an additional resonant point is formed to improve the operation bandwidth for the low band. To enhance the directivity in the low band, a dial-shaped parasitic metallic strip is loaded on the top substrate. With the proposed structure, a compact volume of <inline-formula> <tex-math notation="LaTeX">$0.33\lambda \times 0.33\lambda \times 0.3\lambda $ </tex-math></inline-formula> is obtained, where <inline-formula> <tex-math notation="LaTeX">$\lambda $ </tex-math></inline-formula> is the free-space wavelength at the lowest frequency of 1.85 GHz. A prototype is fabricated and measured for validation. The overlapping bandwidth of the 10 dB impedance bandwidth and the 3 dB axial ratio bandwidth is 105.8% (1.85–6 GHz).
Different Recombination in PID-affected Solar Modules from the Field

In this paper, some PID-affected solar modules dismounted from power plant were studied. And the obtained I-V curves of three substrings which were measured by solar simulator showed that the degradation of main electrical parameters ($\mathrm {V}_{\mathrm {o}\mathrm {c}}, \mathrm {I}_{\mathrm {s}\mathrm {c}}$ and $\mathrm {P}_{\max }$) were different. By analyzing the double-diode equivalent model of solar cell, it is further found that depletion region recombination current I02 and quality factor n2 increased, which can be attributed to the destruction of depletion region caused by PID. And then this result is proved by electroluminescence images and thermography images of three substrings.
A Multi-Layer Parallel LSTM Network for Human Activity Recognition with Smartphone Sensors

With the development of mobile communicationhuman activity recognition (HAR) with smartphones has attracted a lot of attentions in recent years. On the other hand, the appearance of deep learning technologies makes it possible to extract features automatically instead of hand-crafted extracting features in the traditional machine learning methods. Among deep model, CNN-based HAR methods dominate the studies compared to RNN-based methods. In this paper, we propose a RNN-based multi-layer parallel LSTM network to recognize human activities. The experimental results on the public UCI HAR dataset indicate that the proposed approach performs better than the traditional machine-learning methods, and achieves the similar performance as that of CNN, but it has lower computation complexity than CNN-based methods.
Dual-Resolution U-Net: Building Extraction from Aerial Images

Deep learning has been applied to segment buildings from high-resolution images with promising results. However, there still exist the problems stemming from training on split patches and class imbalances. To overcome these problems, we propose a dual-resolution U-Net that uses pairs of images as inputs to capture both high and low resolution features. We also employ a soft Jaccard loss to place more emphasis on the sparse and low accuracy samples. The images from different regions are further balanced according to their building densities. With our architecture, we achieved state-of-the-art results on the Inria aerial image labeling dataset without any post-processing.
Road-Segmentation-Based Curb Detection Method for Self-Driving via a 3D-LiDAR Sensor

The effective detection of curbs is fundamental and crucial for the navigation of a self-driving car. This paper presents a real-time curb detection method that automatically segments the road and detects its curbs using a 3D-LiDAR sensor. The point cloud data of the sensor are first processed to distinguish on-road and off-road areas. A sliding-beam method is then proposed to segment the road by using the off-road data. A curb-detection method is finally applied to obtain the position of curbs for each road segments. The proposed method is tested on the data sets acquired from the self-driving car of laboratory of VeCaN at Tongji University. Off-line experiments demonstrate the accuracy and robustness of the proposed method, i.e., the average recall, precision and their harmonic mean are all over 80%. Online experiments demonstrate the real-time capability for autonomous driving as the average processing time for each frame is only around 12 ms.
Influence of environmental factors like temperature and humidity on MEMS packaging materials.

Microelectromechanical systems (MEMS) and MEMS packaging solutions are gaining increased interests for electronic applications. These packages feature a variety of polymeric materials and composites e.g. fiber reinforced polymer laminates, insulating and conductive adhesives. Due to the sensitivity of MEMS devices to mechanical stress and environmental factors such as temperature and humidity, the influence of these factors on the device’s performance needs to be accounted for. In this contribution, a fabric woven glass fiber reinforced epoxy resin (BT epoxy resin) and two chosen adhesives commonly used in semiconductor and MEMS packaging have been considered and their dependency on environmental parameters have been studied with different testing methods. Based on the measured material properties a simulation process predicting the package under defined environmental loads is presented.