Miracle-dz/UniFAR-FTUs huggingface dataset:
Computation Offloading with Virtual Resources Management in Mobile Edge Networks

The main requirement of the computation offloading service is the low service delay, which would correspond to a high Quality of Service (QoS). Recently, many works show that placing virtual resources (e.g., computing resource) in the Mobile Edge Network (MEN) to form a Mobile Edge Computing (MEC) system contributes to a lower service delay. However, due to the limited virtual resource in the MEN and the constraints of the wireless channel condition, only part of users can be served with low enough service delay. Moreover, considering the independent virtual machine (VM) environment on the MEC server in MEN for each user, efficient virtual resources management is needed. In this paper, we propose two computation offloading strategies combined with the virtual resources management to minimize the average service delay, where the virtual resources consists of the computing resources and the storage resources. One of the proposed strategies guarantees the fairness of users while the other one not. Then the optimal deployments of VMs with different strategies are obtained through the simulations. We also find and calculate the optimal proportion of these two kinds of virtual resource when there is only a limited budget for them.
Evaluation of Coverage and Mobility for URLLC via Outdoor Experimental Trials

The fifth generation mobile communications (5G) system will need to support ultra-reliable and low-latency communications (URLLC) to enable future mission-critical applications, e.g., self-driving cars and remote control. In this paper, we clarify coverage and mobility performance of URLLC by field experimental trials. To achieve URLLC requirements in 5G, we use a new frame structure, which adopts wider subcarrier spacing and acknowledgement/negative acknowledgement-less (ACK/NACK-less) retransmission to reduce the user-plane latency and improve the packet success probability, in this trial. We evaluate the URLLC coverage for several packet sizes using our prototype test-bed. The results show that URLLC requirement in 3GPP can be achieved in UL even when SNR is approximately -3.1 dB. Furthermore, we clarify that the requirement can be satisfied with 25 km/h mobility.
Emotion detection through fusion of complementary facial features

Facial Feature extraction is used in a number of applications including emotion detection. In the following approach various popular feature descriptors, including Gabor features, HOG, DWT were computed. We have fused features using Multiview Distance Metric Learning (MDML) which utilizes complementary features of the images to extract every known detail while eliminating the redundant features. Moreover MDML maps the features extracted from the dataset to higher discriminative space. The features belonging to the same class are brought closer and those that are from different classes are forced away by the MDML thereby increasing the accuracy of the classifier employed. CK+ Dataset has been used to conduct the experiments. Experimental results represent the efficacy of the method is 93.5% displaying the potential of the recommended manner.
Joint Effect of Maternal Tobacco Smoking and Pregestational Diabetes on Preterm Births and Congenital Anomalies: A Population-Based Study in Northern Italy

Smoking and pregestational diabetes (PGD) are recognized risk factors for adverse pregnancy outcomes, but to date, no population-based study has investigated their joint effects. Using hospital discharges, we identified all women with PGD delivering in Emilia-Romagna region during 2007–2010 matched 1 : 5 with parturients without diabetes. Our study endpoints were preterm births and congenital anomalies. We measured interaction between PGD and maternal smoking, by calculating excess prevalence and prevalence ratio due to interaction, relative excess risk due to interaction (RERI), attributable proportion (AP), and the synergy index (S). Analyses were performed in the overall study population and in the subgroup whose PGD was validated through diabetes registers. The study included 992 women with PGD (10.5% smokers) and 4788 comparison women (11.9% smokers). The effects of PGD and maternal tobacco smoking were greater than additive for both preterm birth (excess prevalence due to interaction = 11.7%, excess ratio due to interaction = 1.5, RERI = 2.39, AP = 0.51, S = 2.82) and congenital anomalies (excess prevalence due to interaction = 2.2%, excess ratio due to interaction = 1.3, RERI = 1.33, AP = 0.49, S = 5.03). Joint effect on both endpoints was confirmed in the subgroup whose PGD status was validated. In conclusion, we found that maternal tobacco smoking and PGD intensify each other's effect on preterm birth and congenital anomalies.
Physical Layer Security through Secure Channel Estimation

The random nature of wireless channel can be utilized to achieve secure communications. Recently, there have been some contributions towards finding practical approaches to provide physical layer security, but robust and secure channel estimates are critical in achieving secrecy. In this paper, we propose that in-band full-duplex communication can be utilized by the legitimate transceiver pair to mask the channel estimates from the eavesdropper. The proposed secure channel estimation technique comprises of two stages. In the first stage, the self- interference channel is estimated by respective nodes. In the second stage, in-band full-duplex transmission from both legitimate nodes is utilized to achieve channel estimates at legitimate nodes while providing equivocation at the eavesdropper. The performance analysis shows that the proposed scheme provides robust channel estimation at the legitimate nodes while maintaining equivocation at the eavesdropper.
FP-STALKER: Tracking Browser Fingerprint Evolutions

Browser fingerprinting has emerged as a technique to track users without their consent. Unlike cookies, fingerprinting is a stateless technique that does not store any information on devices, but instead exploits unique combinations of attributes handed over freely by browsers. The uniqueness of fingerprints allows them to be used for identification. However, browser fingerprints change over time and the effectiveness of tracking users over longer durations has not been properly addressed. In this paper, we show that browser fingerprints tend to change frequently—from every few hours to days—due to, for example, software updates or configuration changes. Yet, despite these frequent changes, we show that browser fingerprints can still be linked, thus enabling long-term tracking. FP-STALKER is an approach to link browser fingerprint evolutions. It compares fingerprints to determine if they originate from the same browser. We created two variants of FP-STALKER, a rule-based variant that is faster, and a hybrid variant that exploits machine learning to boost accuracy. To evaluate FP-STALKER, we conduct an empirical study using 98,598 fingerprints we collected from 1, 905 distinct browser instances. We compare our algorithm with the state of the art and show that, on average, we can track browsers for 54.48 days, and 26 % of browsers can be tracked for more than 100 days.
Optical Spectroscopic Ultrasound Displacement Imaging: A Feasibility Study

Photoacoustic imaging has been intensively studied in recent years and many of the achievements have already been applied in clinical diagnosis and therapy, e.g., using spectroscopic photoacoustic imaging to extract functional and molecular information. However, spectroscopic photoacoustic imaging requires expensive and bulky tunable laser source, which severely hinders its further development toward a portable device. In this paper, we propose a novel imaging method, named optical spectroscopic ultrasound displacement (OSUD) imaging, which enables optical spectroscopic imaging in deep scattering tissue using multiple low-cost continuous-wave laser sources and ultrasound imaging equipment. The principle of the OSUD imaging method will be introduced, followed by preliminary experimental results. The OSUD imaging may exploit the deep penetration merit of ultrasound and provide another pathway to provide spectroscopic optical absorption contrast in deep scattering tissue beyond commonly used photoacoustic imaging, which will be further validated in the future work.
Semantics Meet Saliency: Exploring Domain Affinity and Models for Dual-Task Prediction

Much research has examined models for prediction of semantic labels or instances including dense pixel-wise prediction. The problem of predicting salient objects or regions of an image has also been examined in a similar light. With that said, there is an apparent relationship between these two problem domains in that the composition of a scene and associated semantic categories is certain to play into what is deemed salient. In this paper, we explore the relationship between these two problem domains. This is carried out in constructing deep neural networks that perform both predictions together albeit with different configurations for flow of conceptual information related to each distinct problem. This is accompanied by a detailed analysis of object co-occurrences that shed light on dataset bias and semantic precedence specific to individual categories.
A Relay Selection Algorithm for Dual-User Amplify-and-Forward Systems in a Dense Relay Environment

We investigate a dual-user scenario where sources transmit their messages to the destinations by means of relays in an amplify-and-forward two-hop mechanism. In this scenario, the presence of co- channel interference requires the design of a suitable relay selection algorithm to minimize outage events. To this aim, we present a relay selection algorithm specifically tailored for amplify-and-forward relaying. The performance of the relay selection mechanism is evaluated analytically, in terms of outage probability, under the assumption that relay nodes are distributed according to a homogeneous Poisson point process. Numerical results show that the proposed algorithm outperforms a conventional one, based only on the measurement of the power received from the desired transmitters.
Joint Transceiver Design in Full-Duplex MISO Wireless Powered Communication Networks with User Cooperation

This paper investigates beamforming design of joint transceiver in full-duplex wireless powered communication networks for throughput maximization, where a multi-antenna hybrid access point (H-AP) broadcasts wireless energy to two distributed dual- antenna users and the users cooperatively transmit information signals via their individually harvested energy. Furthermore, the users can recycle self- energy from their own transmitted signals. H-AP acts as a transceiver to transmit energy and receive information simultaneously and amplified-and-forward relaying channel is applied from users to the H-AP. In this system, we aim to maximize the throughput by jointly optimizing the energy and information beamforming vector of H-AP. As the problem is non- convex, we first apply zero forcing method to cancel the self-interference at H-AP and then employ a series of efficient variable substitutions and a low-complexity algorithm to obtain the global optimum. Simulation results demonstrate that the proposed scheme can effectively improve the achievable throughput in the WPCN.
Text Classification of Flu-Related Tweets Using FastText with Sentiment and Keyword Features

In this study, we present a framework for fluprediction/detection based on the available data of Social Networking Sites (SNS). The framework uses a state-of-the-art text classifier, which is FastText, to classify Twitter posts into flu-related or flu-unrelated posts. The FastText based framework is trained and tested using a pre-labeled dataset and utilizing the features of sentiment analysis and predefined keyword occurrences in addition to textual features. Results show that the framework improves the accuracy, in addition to the efficiency of flu disease surveillance systems that use unstructured data such as posts of Social Networking Sites.
Rotational stability and visual performance 3 months after bilateral implantation of a new toric extended range of vision intraocular lens

Purpose To evaluate the clinical outcomes after implantation of Symfony® toric extended range of vision (ERV) intraocular lenses (IOLs) in subjects with preoperative corneal astigmatism. Setting Ifocus Øyeklinikk, Haugesund, Norway. Design Prospective non-comparative study. Patients and methods The study population consisted of 30 patients, with preoperative corneal astigmatism between 0.88 and 3.29 D, undergoing bilateral cataract removal or refractive lens exchange with implantation of Tecnis Symfony toric ERV IOLs. Main outcome measures were visual acuity, refraction, IOL axis rotational stability, patient and surgeon satisfaction, photic phenomena, and quality of life at 3 months. Results Postoperatively, mean binocular uncorrected distance, intermediate, and near visual acuities (logMAR) were −0.05±0.11, −0.03±0.08, and 0.09±0.10, respectively. A significant reduction in manifest cylinder (P<0.001) and spherical equivalent (P=0.001) was found after 3 months. Monocular and binocular corrected distance visual acuity also improved significantly with surgery (P<0.001). In terms of binocular uncorrected intermediate and near visual acuity, a total of 96% and 82% of patients achieved 0.1 logMAR (Snellen: 20/25) or better, respectively. Toric IOL axis showed a median rotation of 2.0 degrees (range 0.0–16.0 degrees) from the end of surgery to 3 months. The median patient satisfaction scores for distance, intermediate, and near vision were 9.0, 9.0, and 8.0, respectively. Fifty percent of patients reported complete spectacle independence and most patients (89%) were not bothered by any photic phenomena. Conclusion Bilateral implantation of the Symfony toric ERV IOL appeared to be safe and achieved good visual performance and high satisfaction.
Effects of Carnitine on Nutritional Parameters in Patients with Chronic Kidney Disease: An Updated Systematic Review and Meta-Analysis

Protein energy malnutrition is a common problem in patients with chronic kidney disease (CKD). Scattered reports indicate that supplementation of Carnitine may improve patients' clinical symptoms, with significant improvement in nutritional parameters. This systematic review was done to document the evidences of Carnitine effects in nutritional status of CKD patients. Peer-reviewed RCTs on Carnitine administration at any dose in CKD patients with at least four weeks of follow-up were including in the meta-analysis. Online databases (PubMed/Medline, ISI Web of Science, Embase, and Scopus) were searched to October 2017 using selected MeSH terms related to the study topic. Data was extracted independently by two reviewers using a standard form and then cross-checked. Statistical analyses were carried out with Comprehensive Meta-analysis software. Data are presented as standard mean difference (SMD) and 95% confidence interval (CI). According to the predefined criteria, a total of 14 randomized controlled clinical trials were included and screened for data extraction by two reviewers, separately. The preliminary results extracted from meta-analysis have shown that Carnitine can significantly increase the levels of albumin (SMD: -0.861; 95% CI: -1.321, -0.402), total protein (SMD: -0.418; 95% CI: -0.695, -0.141), total cholesterol (SMD: -0.350; 95% CI: -0.564, -0.135), LDL cholesterol (SMD: -0.362; 95% CI: -0.551, -0.173), transferrin (SMD: -1.465; 95% CI: -1.822, -1.108), and hemoglobin (SMD: -0.525; 95% CI: -0.732, -0.318); however there were no conclusive effects of Carnitine on body weight (SMD: -0.057; 95% CI: -0.404, 0.291) and BMI (SMD: -0.567; 95% CI: -1.548, 0.415), in pooled analyses. The results of this meta-analysis showed that there are considerable useful pieces of evidence so far about the effect of Carnitine on nutritional factors; however, there is still doubt about some evidences with this regard. It seems necessary to carry out clinical trials with stronger designs to evaluate the impact of these primary outcomes on the patients' clinical conditions. Having this evidences, the potential role of Carnitine in improving malnutrition consequences in CKD patients would be clearly defined.
Promises and Challenges in the Use of Consumer-Grade Devices for Sleep Monitoring

The market for smartphones, smartwatches, and wearable devices is booming. In recent years, individuals and researchers have used these devices as additional tools to monitor and track sleep, physical activity, and behavior. Their use in sleep research and clinical applications could address the difficulties in scaling up studies that rely on polysomnography, the gold-standard. However, the use of commercial devices for large-scale sleep studies is not without challenges. With this in mind, this paper presents an extensive review of sleep monitoring systems and the techniques used in their development. We also discuss their performance in terms of reliability and validity, and consider the needs and expectations of users, whether they are experts, patients, or the general public. Through this review, we highlight a number of challenges with current studies: a lack of standard evaluation methods for consumer-grade devices (e.g., reliability and validity assessment); limitations in the populations studied; consumer expectations of monitoring devices; constraints on the resources of consumer-grade devices (e.g., power consumption).
High Photo Sensing Performance With Electro-Optically Efficient Silicon Based ZnO/ZnMgO Heterojunction Structure

The bulk heterostructure having nanopillar growth on top surface of the proposed photodetector structure is a promising candidate of high detection for high resolution quantum optic and high speed optical communication. Here, we report Si-ZnO-ZnMgO having grown with aligned hexagonal nanopillars for high performance photo detector, due to having its high quantum efficiency. From the proposed structure (electro-optically modified) high responsivity of <inline-formula> <tex-math notation="LaTeX">$2.857\times 10^{2}$ </tex-math></inline-formula> AW<inline-formula> <tex-math notation="LaTeX">$^{-1}$ </tex-math></inline-formula> and detectivity of <inline-formula> <tex-math notation="LaTeX">$1.33\times 10^{15}$ </tex-math></inline-formula> cm Hz<inline-formula> <tex-math notation="LaTeX">$^{1/2}\,\,\text{W}^{-1}$ </tex-math></inline-formula> (3mm <inline-formula> <tex-math notation="LaTeX">$\times$ </tex-math></inline-formula> 3mm) was achieved and also the small performance degradation with increase of temperature shows thermal stability.
Design and implementation of an efficient tool to verify integrity of files uploaded to cloud storage

Cloud storage system (CSS) provides storage service through which users can upload their files for getting several benefits like ease of access, less cost and robustness. The uploaded files must be encrypted to achieve confidentiality. These files are replicated across multiple nodes distributed over various geographical boundaries by the CSS provider to ensure robust-ness. But these files may get corrupted unintentionally during transmission or intentionally by malicious users. To ensure the integrity of the uploaded file, an efficient integrity verifier tool is essential. In this paper, the design and implementation of an integrity verifier tool for files uploaded to cloud has been presented. Merkle Hash Tree (MHT) and digital signatures have been used to verify the integrity. The tool has been integrated with Dropbox Cloud Storage Provider and its performance results have been presented.
A Design of Non-Binary Turbo Codes over Finite Fields Based on Gaussian Approximation and Union Bounds

In this paper, we develop capacity-approaching non- binary turbo codes defined over high-order finite fields. Since the performance of turbo codes is characterized by the convergence behavior of iterative decoding and error floor in the high SNR region, we attempt to design the non-binary turbo codes based on the following two-stage optimization processes: In the first stage, we employ a Gaussian approximation method for non-binary turbo codes and analyze the convergence property of iterative decoding. After reducing the search space by the first stage, the best component convolutional codes are identified by examining the truncated union bound under the assumption of the uniform interleaver. Simulation results demonstrate that our non-binary turbo codes as designed above can outperform the conventional non-binary LDPC codes.
Impact of Quantized Side Information on Subchannel Scheduling for Cellular V2X

In Release 14, 3GPP completed a first version of cellular vehicle-to-everything (C-V2X) communications wherein two modalities were introduced. One of these schemes, known as \textit{mode-3}, requires support from eNodeBs in order to realize subchannel scheduling. This paper discusses a graph theoretical approach for semi- persistent scheduling (SPS) in \textit{mode-3} harnessing a sensing mechanism whereby vehicles can monitor signal-to-interference-plus-noise ratio (SINR) levels across sidelink subchannels. eNodeBs request such measurements from vehicles and utilize them to accomplish suitable subchannel assignments. However, since SINR values--herein also referred to as side information--span a wide range, quantization is required. We conclude that 3 bits per vehicle every 100 ms can provide sufficient granularity to maintain appropriate performance without severe degradation. Furthermore, the proposed algorithm is compared against pseudo-random and greedy SPS algorithms.
Coordinated Scheduling for Aircraft In-Cabin LTE Deployment under Practical Constraints

This paper proposes a coordinated multi-point transmission (CoMP) scheduling technique in combination with inter-cell interference coordination (ICIC) techniques, almost blank subframes (ABS) and reduced-power ABS (RP-ABS). It investigates the scheduler performance in the context of an aircraft in- cabin Long Term Evolution (LTE) system deployment. The scheduling problem is solved for an ideal case and a real-world case where LTE system constraints such as common modulation and coding scheme (MCS) assignment per user, channel quality indicator (CQI) reporting and LTE resource allocation (RA) types are considered in the scheduling problem in order to provide a meaningful comparison between real-world and ideal performance. The tradeoff between fairness, which is defined as the lowest variation in user data rates across a large user population, and system throughput is considered as the performance evaluation metric of the system. It is shown that when the LTE constraints are employed, the ideal user data rate and system throughput performance is drastically degraded regardless of the used ICIC technique. In the ideal case, where the LTE constraints are not considered, RP-ABS becomes the optimum tradeoff between fairness and system throughput which achieves a variation of 4.4 Mbps between the lowest and highest user rate, an average user rate of 3 Mbps and an average system throughput of 500 Mbps. However, when the LTE system constraints are taken into account, ABS becomes the optimum technique for the considered onboard system which achieves a variation of 1.7 Mbps, an average user rate of 1.5 Mbps and an average system throughput of 265 Mbps.