Miracle-dz/UniFAR-FTUs huggingface dataset:
Achieving Privacy-Preserving Subset Aggregation in Fog-Enhanced IoT

Fog-enhanced IoT (Internet of Things) is a fast-growing technology in which many firms and industries are currently investing to develop their own real-time and low latency scenarios. Compared with the traditional IoT, fog-enhanced IoT can offer a higher level of efficiency and stronger security by providing local data pre-processing, filtering, and forwarding mechanisms. However, fog-enhanced IoT faces some security and privacy challenges, since fog nodes are deployed at the network edge and may not be fully trustable. In this paper, we present a new privacy-preserving subset aggregation scheme, called PPSA, in fog-enhanced IoT scenarios, that enables a query user to gain the sum of data from a subset of IoT devices. To identify the subset, inner product similarity of the normalized vectors in the query user side and each IoT device is securely computed. If the inner product is greater than the user’s specified threshold, IoT device’s data will be privately aggregated to form the final response. To successfully launch privacy-preserving subset aggregation in the proposed scheme, we employ the Paillier homomorphic encryption to encrypt user’s attribute vector, similarity threshold, IoT end-devices’ data, as well as the intermediate results. To the best of our knowledge, this work is the first one to address the privacy-preserving subset aggregation in fog-enhanced IoT. We analyze and extensively evaluate the efficiency and security of the proposed PPSA scheme, and the detailed analysis and results indicate that our proposed PPSA scheme can practically achieve privacy-preserving subset aggregation with significant communication and computational cost saving.
A 3-D Printed $E$ -Plane Waveguide Magic-T Using Air-Filled Coax-to-Waveguide Transitions

This article reports on a new class of broadband and fully 3-D printed <inline-formula> <tex-math notation="LaTeX">$E$ </tex-math></inline-formula>-plane coax-to-waveguide transition and a monolithically 3-D printed waveguide magic-T based on the transition. The transition is constructed by a section of air-filled rectangular coaxial transmission line (TL) that is placed between two broadband coax-to-waveguide probe transitions. It is used to interconnect the magic-T’s sum port and the waveguide T-junction. The incorporation of the transition reorients all the waveguide arms of the magic-T into the <inline-formula> <tex-math notation="LaTeX">$E$ </tex-math></inline-formula>-plane. Some <inline-formula> <tex-math notation="LaTeX">$X$ </tex-math></inline-formula>-band prototypes of the proposed transition and the magic-T are designed and implemented. Polymer-based additive manufacturing and copper electroplating techniques are employed to monolithically fabricate each prototype. The transition and the magic-T exhibit broadband and low-loss characteristics from 8.2 to 12.4 GHz with the measured performance well matched with the simulations. In addition, the power handling capability (PHC), including the peak PHC (PPHC) and the average PHC (APHC), of the magic-T is evaluated by simulations, showing that the proposed magic-T could handle 100 W of APHC.
Research and Design of a High-Order Differential N-path Filter

In recent years, N-path filters have been widely used in many fields such as RF communication and cognitive radio. In this paper, a high-order differential N-path filter is designed by introducing a series inductor in a traditional first-order N-path filter and cascading it into a high-order. The MOS switch used in the circuit is controlled by an external differential clock. The introduced series inductance resonates with the capacitor, reducing the loss and noise of the filter and extending the tunable range. With external differential clock control, even harmonic interference can be well eliminated during the filtering process. The transfer function of the high-order differential N-path filter is obtained by employing the adjoint network method, and which can greatly simplifies the mathematical derivation of the transfer function. The filter was designed using TSMC 0.18 CMOS process and the simulation results show that under the power supply voltage1.2V, the center frequency of the filter can be adjusted to a wide range of 0.2-3.0GHz, the out-of-band rejection is 23-58dB and the noise Figure is 4.6dB.
Witness: Detecting Vulnerabilities in Android Apps Extensively and Verifiably

Existing studies on detecting vulnerabilities in apps have two main disadvantages: one is that some studies are limited to detecting a certain vulnerability and lack comprehensive analysis; the other is the lack of valid evidence for vulnerability verification, which leads to high false alarms rate and requires massive manual efforts. We propose the concept of vulnerability pattern to abstract the characteristics of different attacks, e.g., their prerequisites and attack paths, so as to support detecting multiple kinds of vulnerabilities. Also, we present a zero false alarms framework which can find vulnerability instances precisely and generate test cases and triggers to validate the findings, by combing static analysis and dynamic binary instrumentation techniques. We implement our method in a tool named Witness, which currently can detect 8 different types of vulnerabilities and is extensible to support more. Evaluated on 3211 popular apps, Witness successfully detected 243 vulnerability instances, with better precision and more proofs than four existing tools.
Facial-Expression Recognition from Video using Enhanced Convolutional LSTM

We propose an enhanced convolutional long short-term memory (ConvLSTM) algorithm, i.e., Enhanced ConvLSTM, by adding skip connections in the spatial and temporal directions to conventional ConvLSTM to suppress gradient vanishing and use older information. We also propose a method that uses this algorithm to automatically recognize facial expressions from videos. The proposed facial-expression recognition method consists of two Enhanced ConvLSTM streams and two ResNet streams. The Enhanced ConvLSTM streams extract features for fine movements, and the ResNet streams extract features for rough movements. In the Enhanced ConvLSTM streams, spatio-temporal features are extracted by stacking the Enhanced ConvLSTM. We conducted experiments to compare a method using ConvLSTM with skip connections (proposed Enhanced ConvLSTM) and a method without them (conventional ConvLSTM). A method using Enhanced CovnLSTM had a 4.44% higher accuracy than the a method using conventional ConvL-STM. Also the proposed facial-expression recognition method achieved 45.29% accuracy, which is 2.31% higher than that of the conventional method.
Femtosecond Laser-assisted Fabrication of Fluorescent Boron Nitride Quantum Dots

As a kind of heavy metal-free quantum dot, boron nitride quantum dots (BNQDs) have attracted great interest due to their unique optical properties. Although there many synthesis methods of BNQDs, most of them require complex or time-consuming procedures, which limits the application of BNQDs. Here, we present a facile synthesizing method of few-layer BNQDs, which is based on liquid-phase ultra-short laser pulse ablation and ultrasonic-assisted exfoliation methods. The fabricated BNQDs are systematically characterized by atomic force microscope, transmission electron microscope, X-ray photoelectron spectroscopy, and Fourier-transform infrared spectroscopy. The products possess an average size of about 2.1 nm with few-layer thickness. Because of the abundant surface functional groups, the BNQDs show bright luminescence emission under UV light irradiation.
A 3–10-GHz CMOS Time-Domain Complex Dielectric Spectroscopy System Using a Contactless Sensor

This article presents an integrated time-domain (TD) microwave broadband dielectric spectroscopy (MBDS) system in the 3–10-GHz frequency span divided into nine subbands. The generated multitone input pulse with 1-GHz bandwidth is upconverted to RF frequencies in the transmitter using a broadband single-sideband (SSB) mixer to suppress the lower sideband of the excited signal and prevent the signal distortion at the configured dc-free direct conversion receiver with the center frequency of 500 MHz at the baseband. The upconverted pulse is passed through a contactless sensor in TD where the material under test (MUT) is placed in the middle of the two ultra-wideband (UWB) Vivaldi antennas coupled in their radiative near field. To identify both the real (<inline-formula> <tex-math notation="LaTeX">$\epsilon _{r}'$ </tex-math></inline-formula>) and imaginary (<inline-formula> <tex-math notation="LaTeX">$\epsilon _{r}''$ </tex-math></inline-formula>) parts of the MUT’s permittivity, the impact of the entire system on MUT characterization is alleviated by designing the system subblocks and the sensor with flat gain and constant group delay over the entire frequency range. The proposed MBDS system is fabricated in 65-nm CMOS and occupies an active area of 1.24 mm<sup>2</sup> while consuming 64~69 mW from a 1-V supply. As a proof of concept, the complex dielectric permittivity of different pure organic chemical liquids has been detected within the desired frequency range with a customized quartz-glass cuvette with the MUT volume of ~ 5 mL by calibrating the sensor using the variations in the phase and the magnitude after FFT with respect to the reference material (air). The system results in an rms error of less than 0.2% and 0.4% for <inline-formula> <tex-math notation="LaTeX">$\epsilon _{r}'$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$\epsilon _{r}''$ </tex-math></inline-formula>, respectively, compared with theory. To the best of our knowledge, this is the first CMOS TD MBDS system with a homodyne RF transceiver architecture including an on-chip UWB excitation pulse generation and the contactless sensor.
Clinical Observation on Treatment of Parkinson's Disease by Nourishing Liver and Kidney

Objective To observe the clinical efficacy of nourishing liver and kidney in the treatment of Parkinson's disease. Methods Total 72 subjects were randomly divided into two groups: treatment group in which 40 cases were treated by nourishing liver and kidney plus western medicine and control group in which 32 cases were treated by western medicine only. Results In Webster score,the effective rate was 97.5% in treatment group and 56.3% in control group; in UPDRS score, the effective rate was 75.0% in treatment group and 6.3% in control group,P 0.05,P 0.01. Conclusion This method is effective for Parkinson's disease.
Positive-Real Truncated Balanced Realization based Frequency-Weighted Model reduction

In this paper, a new frequency-weighted positive real balanced truncation technique is presented by using a combination of frequency-weighted model reduction with the positive real-truncated balanced realization (PR-TBR). The proposed technique yields passive reduced-order model for a given passive high order model with single-sided weighting only. The simulation results of the proposed technique are shown by using an example problem of RLC ladder network.
Table based K Nearest Neighbor for Word Categorization in News Articles

In this research, we propose the KNN version where words are encoded into tables, instead of numerical vectors, as the approach to the word categorization. In the previous works, the better text categorization performances from encoding texts into tables than into numerical vectors are shown and we try to make the mutual reinforcement between word and text categorization by connection them with each other. In this research, words are encoded into tables each of which consists of entries of text identifiers and their weights, the similarity metric between two tables which is based on the ratio of intersection to union, is defined, and the KNN algorithm is modified by adopting the proposed similarity metric. This research shows better results of the proposed KNN version which receives a table as input data than the traditional version, in categorizing words which are from news articles. In future, we connect mutually the word categorization with the text categorization for reinforcing them at same time.
Orthogonal Least Squares Based Incremental Echo State Networks for Nonlinear Time Series Data Analysis

This paper develops an incremental randomized learning method for an extended Echo State Network (<inline-formula> <tex-math notation="LaTeX">$\varphi $ </tex-math></inline-formula>-ESN), which has a reservoir with random static projection, to better cope with non-linear time series data modelling problems. Although the typical ESN can effectively improve the prediction performance of the network by extending a random static nonlinear hidden layer, since the input weights and biases of the hidden neurons in the extended static layer are randomly assigned, some neurons have little effect on reducing the model error, resulting in high model complexity, poor generalization and large performance fluctuation. A constructive incremental randomized learning method termed OLS-<inline-formula> <tex-math notation="LaTeX">$\varphi $ </tex-math></inline-formula>-ESN is proposed for generating the nodes of the extended static nonlinear hidden layer. Two-step training paradigm is adopted, namely, randomly assigning the input weights and biases of the hidden neurons in the extended static layer according to a supervisory mechanism and solving output weights by least squares algorithm. Based on Orthogonal Least Squares (OLS) search algorithm, the proposed supervisory mechanism is designed where an adaptive threshold is also set to better control the compactness of the generated learner model. Simulation results concerning both nonlinear time series prediction and system identification tasks indicate some advantages of our proposed OLS-<inline-formula> <tex-math notation="LaTeX">$\varphi $ </tex-math></inline-formula>-ESN in terms of more compact model and sound generalization.
HEVC/H.265 Intra Coding Based on the Human Visual System

High efficiency video coding (HEVC) is standard in the video compression field and performs well not only in video compression coding, but also in image compression. Regions of interest (ROIs) and just noticeable difference (JND), two human visual models, can accurately quantify human visual system (HVS) characteristics as pixel values. Using ROIs and JND to assist in evaluating image distortion can effectively reduce human visual redundancy and reflect authentic perceptual distortions. However, they are not readily applicable to the HEVC test model (HM) in the pixel domain. It is difficult to secure a suitable Lagrange multiplier <inline-formula> <tex-math notation="LaTeX">$\lambda $ </tex-math></inline-formula> and quantization parameter (QP) for the JND model in particular. This paper proposes different solutions for the use of rate control (RC) or not where an appropriate <inline-formula> <tex-math notation="LaTeX">$\lambda $ </tex-math></inline-formula> value is available for perceptual models. In RC, the proposed approach centers on a robust relationship between QP and achieved <inline-formula> <tex-math notation="LaTeX">$\lambda $ </tex-math></inline-formula>. And we also established a bit allocation technique using the related <inline-formula> <tex-math notation="LaTeX">$\lambda $ </tex-math></inline-formula> and expression for the RC model. Experimental results validate the rationality and effectiveness of the proposed method.
Real-Time Context-Aware Microservice Architecture for Predictive Analytics and Smart Decision-Making

The impressive evolution of the Internet of Things and the great amount of data flowing through the systems provide us with an inspiring scenario for Big Data analytics and advantageous real-time context-aware predictions and smart decision-making. However, this requires a scalable system for constant streaming processing, also provided with the ability of decision-making and action taking based on the performed predictions. This paper aims at proposing a scalable architecture to provide real-time context-aware actions based on predictive streaming processing of data as an evolution of a previously provided event-driven service-oriented architecture which already permitted the context-aware detection and notification of relevant data. For this purpose, we have defined and implemented a microservice-based architecture which provides real-time context-aware actions based on predictive streaming processing of data. As a result, our architecture has been enhanced twofold: on the one hand, the architecture has been supplied with reliable predictions through the use of predictive analytics and complex event processing techniques, which permit the notification of relevant context-aware information ahead of time. On the other, it has been refactored towards a microservice architecture pattern, highly improving its maintenance and evolution. The architecture performance has been evaluated with an air quality case study.
Image Classification on Visual Dictionary Optimization

There is the fact that the traditional k-means clustering training dictionary method has the problems of redundancy, high time complexity and easy to fall into local convergence, especially dictionary training has a direct influence on image classification effect. An improvement of the dictionary training method was proposed by this paper aiming to get the most optimal visual dictionary quickly. Firstly, an improved k-means clustering is presented by changing method of center selection. Then use it to train independent dictionaries for each category, and these dictionaries were all optimized with Word Activation Forces (WAFs). Finally, all categories of the dictionary are integrated to one dictionary. The optimized dictionary was obtained by using WAFs to optimize once again. The proposed method was verified on the classic image databases Caltech101 and Caltech256. The results showed that it improved not only the efficiency, but also the accuracy of image classification.
Tree Log Identity Matching using Convolutional Correlation Networks

Log identification is an important task in silviculture and forestry. It involves matching tree logs with each other and telling which of the known individuals a given specimen is. Forest harvesters can image the logs and assess their quality while cutting trees in the forest. Identification allows each log to be traced back to the location it was grown in and efficiently choosing logs of specific quality in the sawmill. In this paper, a deep two-stream convolutional neural network is used to measure the likelihood that a pair of images represents the same part of a log. The similarity between the images is assessed based on the cross-correlation of the convolutional feature maps at one or more levels of the network. The performance of the network is evaluated with two large datasets, containing either spruce or pine logs. The best architecture identifies correctly 99% of the test logs in the spruce dataset and 97% of the test logs in the pine dataset. The results show that the proposed model performs very well in relatively good conditions. The analysis forms a basis for future attempts to utilize deep networks for log identification in challenging real-world forestry applications.
Beyond Utility and Usability: Towards Affectability in Agile Software Requirements Engineering

As the relationship between users and interactive software systems becomes increasingly personal, the users are expecting more from these systems than just utility and usability. To that effect, in the context of agile development of interactive software systems, a framework for a user-centered and conceptual model-based process for engineering affective user stories is proposed, described, and illustrated. The implications of affect for market-oriented software development are emphasized, and challenges and limitations related to current state of requirements engineering education are discussed.
SAWANT: Smart Window Based Anomaly Detection Using Netflow Traffic

Network security becomes a big concern nowadays. Although many solutions have been developed to detect network anomalies, the number of successful attacks like DDoS, Phishing, and Spam are boosting dramatically. In this paper, a novel behavior-based method, called SAWANT, is proposed to detect malicious rate of network traffic. SAWANT uses deep learning architecture to analyze netflow data, in which several meaningful attributes are extracted using a sliding window technique. Extracted attributes are then taken to a deep learning structure to identify malicious rate of each window, that represents the rate of abnormal netflow records per the window size. Experimental results over the well-known labeled botnet traffic CTU 13 showed that SAWANT was highly accurate as more than 99% of predicted malicious rates were correct, while it required a very small number of records for training.
Ethical judgement in intelligent control systems for autonomous vehicles

The role of ethical judgement in autonomous control systems is gaining increasing prominence. In particular, there is increasing concern about ‘killer robots’, drones that can kill on their own, and intelligent autonomous vehicles such as driverless cars. Recent incidents involving autonomous vehicles in which humans have been killed or injured have raised questions about whether such vehicles can have an ethical dimension to their behavior so that they know when it is right or wrong to take over control from a human driver or hand control back, no matter how advanced their embedded artificial intelligence and sensor technology. This paper describes a fuzzy control approach to machine ethics that shows how it is possible for an ethics architecture to be part of a control system to calculate when taking over from a human driver is morally justified. One major advantage of the approach is that such an ethical reasoning architecture can generate its own data for learning moral rules and thereby reduce the possibility of picking up human biases and prejudices.
Mine-Like Object Sensing in Sonar Imagery with a Compact Deep Learning Architecture for Scarce Data

Detection of underwater mines is important for ensuring the safety of maritime routes. This paper presents a new approach for mine-like object sensing in sonar imagery. We propose a deep learning architecture that combines a convolution neural network and a hierarchical Gaussian process classifier. The proposed architecture is designed to improve the classification accuracy of the conventional convolutional neural network and to provide a well-calibrated measure of classification uncertainty. It can be trained in an end-to-end manner with labeled examples, or sonar snapshots, of underwater objects. To address the data scarcity in this application, we apply the generative adversarial network to produce extra sonar snapshots for training. Evaluated on a dataset of 349 sonar snapshots, the proposed method achieves an overall classification rate of 81.6%, which is significantly higher than the existing methods.