Miracle-dz/UniFAR-FTUs huggingface dataset:
Unequal Secrecy Protection for Untrusted Two-Way Relaying Systems: Constellation Overlapping and Noise Aggregation

This paper studies the secure information exchange of two-way relaying systems, where two terminal users communicate bi-directionally with the assistance of an untrusted relay. Each user is assumed to have two messages with distinct priorities: high priority and low priority. The high-priority messages (HPM) are first transmitted, for which we propose a constellation overlapping scheme such that the received signals at the relay overlap with each other and a high error floor is created to prevent the untrusted relay from deciphering the information. Upon the completion of HPM exchange, each user sends its low-priority message (LPM) by superposing it onto the previously decoded HPM from the other user. By exploiting the difference between the error patterns for HPM at the terminal users and the relay, channel noises in various time slots can be aggregated at the relay to secure the LPM transmission. The symbol error probability (SEP) at the terminal users as well as the untrusted relay is analyzed, from which it is shown that HPM is guaranteed to have greater reliability and higher secrecy level than that of LPM. However, the transmission of LPM enjoys lower implementation complexity and reduced system overhead, which fully demonstrates that the message-prioritization-based unequal secrecy protection framework can realize a good performance-complexity tradeoff. Simulation results exhibit the superiority of the proposed design compared to the existing competing solutions.
Ease or Privacy? A Comprehensive Analysis of Android Embedded Adware

With hundreds of millions of Android phone users in around 190 countries, the global smartphone market has witnessed extraordinary and explosive growth, which is escorted with the vast number of its applications. It has an open source code platform that encourages app developers to develop Android applications and introduce them free-of-cost in the market. Applications are considered as the heart of Android phone that drive innovation, leisure, ease of availability and compatibility with the mobile devices. Embedded adware or mobile advertising is rapidly finding its ways into android applications in the form of banner ads, rich media or interstitial ads. This paper aims to highlight risk of privacy leakage of end users by introducing an attack model and exploring attacks caused by in-application advertisements. It also presents theoretical framework by quantitative analytic approach to figure out the impact of embedded adware on Android user’s privacy.
Lighting Simulation and Validation for High Power LED Matrix Luminaire

In this work a lighting study of a high-power light-emitting diode (HP-LED) luminaire is presented. Some experimental simulations are performed in two case studies. The temperature behavior of the luminaire is also evaluated (with and without forced cooling) as increases the number of active LEDs in the luminaire. The illuminance behavior of each LED is still related as the luminaire temperature increases. Through a model of the luminaire designed in a stochastic ray tracing simulation software, a simulation is performed with 1 million rays obeying the same technical specifications of practical experimentation. The simulation results are compared with the results obtained in the practical experiment and the model is validated with an error of $\pm 3.8 \ {\%}$ along x-axis of target plane and $\pm 5.4 \ {\%}$ along y-axis of target plane.
In-Vivo Skin Capacitive Image Classification Using AlexNet Convolution Neural Network

Skin capacitive imaging is a novel technique which has been developed for skin hydration and skin solvent penetration measurements. This research is to assess the performance of deep learning in in-vivo skin capacitive image analysis using AlexNet model. The image classifier has been trained by using pretrained model to implement the specific feature extraction, prediction and classification specifically for the skin characteristics such as hydration level, skin damage level etc. There are over 1000 skin capacitive images used in this study. The objectives of the research are: feature extraction implementation using the pretrained model AlexNet; accuracy assessment of the model; and further improve the system for multiple features classification. The image classification programme shows a good result which has accuracy over 0.98, and the test images were classified correctly comparing with the experimental results of skin hydration, skin damaged level and the gender of the volunteers.
Impact of support characteristics and preparation method on photocatalytic activity of TiO2/ZSM-5/silica gel composite photocatalyst

Titanium dioxide (Degussa P25) was supported onto two different aluminosilicate zeolites (ZSM-5) and anchored on three silica gels using two separate preparation methods to study the effect of the catalyst components and the preparation method on the photoactivity of composite catalysts. The photoactivity was investigated by tracking phenol disappearance in a batch UVA light-emitting diode reactor. An easily separable photocatalyst with higher photoactivity than commercial Degussa P25 was developed using Degussa P25, ZSM-5 (SiO2/Al2O3 = 280) and silica gel (particle size 0.2–0.5 mm and pore size 40 Å). The optimum composition was found to be P25:ZSM-5:silica gel = 0.3 : 0.5 : 0.5 g l−1. SEM photographs show that the distribution of the composite catalyst components prepared without a binder was better than that prepared with a binder. The efficiency of photocatalytic ozonation of sulfamethoxazole (SMX) using the new photocatalyst was assessed and compared to that of commercially available Degussa P25. It was found that photocatalytic ozonation promoted the SMX disappearance and mineralization. PZS was superior to Degussa P25 with respect to photocatalysis and photocatalytic ozonation. The enhancement was attributed to the synergetic effect between adsorption, ozonation and/or photocatalytic oxidation.
Combining Common Words and Semantic Features for Sentence Similarity

Assessing the similarity of short texts or sentences is an important phase in many natural processing activities. The paper describes the importance and role of sentence similarity in various domains and also the paper provides a mechanism to calculate the similarity between short texts. The main idea in this paper is to extract the syntactic and semantic features between the sentences, to calculate the similarity between them. The syntactic features are evaluated by finding the common words between the sentences, whereas the semantic features are evaluated using the information content between the concepts of the sentences. For obtaining the information content between the concepts knowledge based measures are used. Three information content based measures are compared in this paper over bench mark sentence similarity dataset. The results show that the integration of syntactic and semantic features increases the performance of the system.
Flexible Graphene Based Films for Stepped Impedance Lowpass Microstrip Filter

A stepped impedance lowpass microstrip filter composed of graphene based film (GBF) microstrip is proposed in this work. The basic structure includes high-impedance and low-impedance line made up of high-conductivity graphene based films. The presented GBF filter is operating at 2.5 GHz. The experiment results show that microwave signals can transmit along the GBF microstrip lines effectively, and the insertion loss of GBF filter is less than 0.67 dB at passband. The GBF filter has a good performance, which is comparable to the traditional copper filter. Microstrip filter designed with high-conductivity GBF will play an irreplaceable role in wireless communication.
Passive and active rehabilitation control of human upper-limb exoskeleton robot with dynamic uncertainties

SUMMARY This paper investigates the passive and active control strategies to provide a physical assistance and rehabilitation by a 7-DOF exoskeleton robot with nonlinear uncertain dynamics and unknown bounded external disturbances due to the robot user's physiological characteristics. An Integral backstepping controller incorporated with Time Delay Estimation (BITDE) is used, which permits the exoskeleton robot to achieve the desired performance of working under the mentioned uncertainties constraints. Time Delay Estimation (TDE) is employed to estimate the nonlinear uncertain dynamics of the robot and the unknown disturbances. To overcome the limitation of the time delay error inherent of the TDE approach, a recursive algorithm is used to further reduce its effect. The integral action is employed to decrease the impact of the unmodeled dynamics. Besides, the Damped Least Square method is introduced to estimate the desired movement intention of the subject with the objective to provide active rehabilitation. The controller scheme is to ensure that the robot system performs passive and active rehabilitation exercises with a high level of tracking accuracy and robustness, despite the unknown dynamics of the exoskeleton robot and the presence of unknown bounded disturbances. The design, stability, and convergence analysis are formulated and proven based on the Lyapunov–Krasovskii functional theory. Experimental results with healthy subjects, using a virtual environment, show the feasibility, and ease of implementation of the control scheme. Its robustness and flexibility to deal with parameter variations due to the unknown external disturbances are also shown.
A Model for Eye and Head Motion for Virtual Agents

In this paper we propose a model for generating head and eye movements during gaze shifts of virtual characters, including eyelid and eyebrow motion. A user study with 30 participants was conducted to evaluate the communicative accuracy and perceived naturalness of the model. Results showed that the model communicates gaze targets with an accuracy that closely matches that of a human confederate, and participants subjectively rated the head and eye movements as natural (as opposed to artificial). The implementation can be used as-is in applications where virtual characters act as idle bystanders or observers, or it can be paired with a lip synchronization solution.
RSS-Based Cooperative Localization in Wireless Sensor Networks via Second-Order Cone Relaxation

In this paper, we consider the received signal strength-based cooperative localization problem in both known and unknown transmit power of target nodes. For the case of known transmit power, we treat the transmit power as a constant and derive a novel non-convex weighted least squares estimator which can be transformed into a second-order cone programming (SOCP) problem for reaching an efficient solution. For the case of unknown transmit power, we treat the transmit power as an additional unknown parameter and propose a hybrid maximum likelihood-SOCP algorithm to alternatively estimate the target node locations and transmit power. Simulation results confirm the effectiveness of the proposed methods in all considered settings.
An Incremental Approach to Structured Argumentation over Dynamic Knowledge Bases

Considering the structure of arguments allows users to analyze reasons for and against a conclusion; the warrant status of such a conclusion in the context of a knowledge base represents the main output of a dialectical process. A naive approach to computing such statuses is costly, and any update to the knowledge base potentially has a huge impact if done in this manner. We study the case of updates consisting of both additions and removals of pieces of knowledge in the Defeasible Logic Programming (DeLP) framework, first analyzing the complexity of the problem and then identifying conditions under which we can avoid unnecessary computations— central to this is the development of data structures to keep track of which results can potentially be affected by a given update. We also present experiments showing that our incremental algorithm yields significantly lower running times in practice, as well as overall fewer recomputations.
An Effective Crowdsourcing Data Reporting Scheme to Compose Cloud-Based Services in Mobile Robotic Systems

The smart device combined with artificial intelligence can act as robot system to perform data collection task. To minimize the data collection cost and to guarantee the quality of service (QoS) of tasks are two vital issue in such mobile robot system. Data collection platform and data reporter often needs to negotiate with each other before start of data collection which will generate a certain cost. Once the platform and the data reporter agree to the cooperation, data reporter will collect and report data for a period. However, in previous researches, it was often considered that data reporters can report data at any time without considering the cost of interaction and negotiation, which is not suitable for the practice. In this paper, we propose an efficiency cost data collection scheme (ECDCS) in which the data reporter is selected according to the contribution that all the data it collects have on the whole system rather than a single data samples. Because there exists correlation in data, matrix completion technology can be adopted to recover the missing data samples with partial data while guarantee the QoS of the task. So, a data reporter selection scheme ECDCS based on the matrix completion technology is proposed in which the selection is in terms of the cooperation effect of the reporters rather than a single data sample. The main goal is to select the reporter set with low cost and high QoS which has the best cooperative effect. By doing so, in the proposed data collection scheme, the missing of partial data can be tolerated which can reduce data collection cost while guarantee the QoS. The extensive experiments results indicate that the proposed scheme can effectively reduce the data cost while maintain the QoS of application.
A Comparative Analysis of Clinical Outcomes in Noninsertional Versus Insertional Tendinopathy Using PROMIS

Background. Achilles tendinopathy is a common clinical disorder. Utilizing the Patient-Reported Outcomes Measurement Information System, we aim to determine clinical response to nonoperative achilles tendinopathy rehabilitative care of insertional achilles tendinopathy compared to non-insertional achilles tendinopathy. Methods. Prospective Patient-Reported Outcomes Measurement Information System Physical Function, Pain Interference, and Depression scores were collected for patients with achilles tendinopathy at presentation and following a standard course of nonoperative care. A distribution-based method was used to determine the minimal clinically important difference. Descriptive statistics were reported and bivariate analysis was used to compare insertional achilles tendinopathy and non-insertional achilles tendinopathy. Receiver operating characteristic curve analysis was used to predict clinical improvement. Results. A total of 102 patients with an average follow-up of 68 days were included. For the non-insertional achilles tendinopathy group: Fifteen (46%), 12 (36%) and 9 (27%) patients reached clinical improvement for Physical Function, Pain Interference and Depression, respectively. For the insertional achilles tendinopathy group: Seventeen (25%), 20 (29%) and 22 (32%) patients reached clinical improvement for Physical Function, Pain Interference and Depression, respectively. Physical Function scores improved more in non-insertional achilles tendinopathy patients (4.0 vs. -0.046; p = 0.035) and more patients clinically improved (45.5% vs. 24.6%; p = 0.034). Patients with non-insertional and insertional achilles tendinopathy clinically improved functionally when initial Physical Function scores were equal to or lower than 40.25 and 38.08, respectively. Conclusions. Nonoperative care in achilles tendinopathy is often successful. The Patient-Reported Outcomes Measurement Information System can be used to evaluate and help determine clinical success. Levels of Evidence: Level II: Prospective comparative study
Multi-Lingual Information Retrieval Using Deep Learning

The task of finding data files related to an information need from a group of information resources is known as Information Retrieval. In this work, the author propose a multi-lingual information retrieval system using deep learning. Input to the system is a question in sentencing form that can be processed by NLP tools. In the preprocessing phase, part-of-speech tagging of the input sentence is performed. A three layer neural network is used for creating word to vector representation. The word2vec model continuous-bag-of-words (CBOW) is used for this purpose. Then related words are obtained via word-2-vec using deep learning RNN. RNN is the recurrent neural network. Finally, results are obtained by calculating the cosine similarity score. For multi-lingual results, bilingual mapping is performed using CFILTs bilingual corpus. The tourism dataset is used for experimentation purposes.
SLP-MCAF: Multiple Clusters of Connected Vehicles Authentication Framework in a Smart Life Platform

The development of the wireless Internet environment enables smart devices to be interconnected in various environments to provide convenient services to users. Specifically, in connected vehicle and smart home, the infotainment system and home electronics are connected to the Internet to individually provide services to users. The connected vehicle uses the display panel to show users music, video, navigation, and so on desired, or to develop a service that reduces the burden of driving and allows other activities by autonomous vehicles. However, these services are designed and developed independently on the platform and are not suitable for the concept of residential space of the future society. In this article, we propose a smart life platform by combining the concepts of connected vehicle and smart home into one platform. It also provides the SLP-MCAF to provide secure services within the platform. The SLP-MCAF strongly reinforces the authentication of objects and introduces clusters to improve availability so that services are not separated from the automobile or home, and services are continuous. Through simulation, we demonstrate the stability and efficiency of the proposed platform.
Lightweight PUF-Based Authentication Protocol for IoT Devices

Conventional cryptographic solutions to the security are expensive in terms of computing resources (memory and processing capacity) and power consumption. They are not suitable for the Internet of Things devices that have constrained resources. In this regard, physically unclonable functions (PUFs) have become an increasingly popular technology for building secure authentication in these systems. In this paper, we propose lightweight PUF-based authentication protocol. This was implemented on a wireless sensor network constructed using the resource-limited IoT devices: Zolertia Zoul re-mote. The functionality of the proposed scheme was verified using a server-client configuration. Then power consumption and memory utilisation of the proposed protocol were estimated and compared with the existing solutions, namely: DTLS (datagram transport layer security) handshake protocol and UDP (user datagram protocol). Our results indicate that the proposed PUF based authentication saves up to 45% power and uses 12% less memory compared to DTLS handshake authentication.
Robust Transmissions in Wireless-Powered Multi-Relay Networks With Chance Interference Constraints

In this paper, we consider a wireless powered multi-relay network in which a multi-antenna hybrid access point underlaying a cellular system transmits information to distant receivers. Multiple relays capable of energy harvesting are deployed in the network to assist the information transmission. The hybrid access point can wirelessly supply energy to the relays, achieving multi-user gains from signal and energy cooperation. We propose a joint optimization for signal beamforming of the hybrid access point as well as wireless energy harvesting and collaborative beamforming strategies of the relays. The objective is to maximize the network throughput subject to probabilistic interference constraints at the cellular user equipment. We formulate the throughput maximization with both the time-switching and power-splitting schemes, which impose very different couplings between the operating parameters for wireless power and information transfer. Although the optimization problems are inherently non-convex, they share similar structural properties that can be leveraged for an efficient algorithm design. In particular, by exploiting monotonicity in the throughput, we maximize it iteratively via customized polyblock approximation with reduced complexity. The numerical results show that the proposed algorithms can achieve close to optimal performance in terms of the energy efficiency and throughput
A Comparison of New Methods Based on ANNs for Detecting and Locating Faults in MTDC Systems

This work presents a protection scheme based on Artificial Neural Networks (ANNs) for fault detection and location in Multi Terminal Direct Current (MTDC) systems. To perform these tasks only local signals are used and communication links are not required. Three different schemes based on ANNs are evaluated (DC current samples, DC current spectrum and DC current wavelet coefficients), allowing a comparison between the advantages and disadvantages of each one. The proposed method has been tested for different fault locations and fault resistances in a four terminal MTDC system with three DC lines. The adopted MTDC system was modeled in ATP-Draw, while the proposed algorithm was developed in Matlab. The results show that the proposed scheme is able to properly detect and locate a fault within a few milliseconds.
Forecast-Driven Power Planning Approach for Microgrids Incorporating Smart Loads Using Stochastic Optimization

The main focus of this work is on improving the resilience of the standalone microgrids using the available forecast of both energy sources and loads, provided that some smart loads are incorporated in the system. The proposed approach assumes that the smartness of the loads facilitates an accurate prediction of their consumption as well as the possible time span for their operation. The kernel of the proposed approach is to provide a greedy-based scheduling scheme of a group of non-preemptive loads in order to reduce the net deficit between the aggregate load and the low-price power profile, and therefore, the levelized cost of energy (LCoE) can be minimized. Based on the driven forecast, the power routings between the components of the microgrid are investigated considering the scheduling candidates of the incorporated smart loads. Thus, the optimal schedule is selected so as to ensure the maximum utilization of the low-price power. A stochastic optimization method based on the genetic algorithms (GAs) is used to cut-down the massive searching space and provide the optimal schedule within a reasonable time. An illustrative example is used to carry out this work using a group of synthetically created loads representing different facilities inside a hospital in Gaza city. Simulation results show that the proposed algorithm can significantly reduce LCoE and meanwhile maximizing the utilization factor of the installed renewable energy sources.