Miracle-dz/UniFAR-FTUs huggingface dataset:
Threat Modeling of Wireless Attacks on Advanced Metering Infrastructure

Cyber physical systems are playing an essential role to increase the efficiency of integrated computational and physical elements. Especially, Advanced Metering Infrastructure proves to be useful for customers and consumer companies for efficient usage of energy and its cost calculations. However, such systems are more prone to cyber-attacks because of frequent real time communication between the providers and customers. The wireless and wired networks are used as the source of communication between the various components of AMI. In this study, we have done threat modelling to identify possible wireless attacks to compromise the AMI assets. We have used STRIDE and DREAD models for threat modelling. One of the major contributions of this study is classification and risk ratings of wireless attacks using STRIDE and DREAD models.
A Multi-feature Fusion Based Method For Urban Sound Tagging

Noise pollution is one of the serious issues for citizens. Mapping urban noise is essential to improve the quality of life for residents and construction for smart cities. Yet, most cities lack effective classification or tagging methods to monitor urban noise. To tackle this challenge, we propose a multi-feature fusion based method for urban sound tagging (UST). This method combines various features and Convolutional Neural Networks (CNNs) to predict whether noise of pollution is present in a 10-second recording. Log-Mel, harmonic, short-time Fourier transform (STFT) and Mel Frequency Cepstral Coefficents (MFCC) spectrograms are fed into different CNN architectures. And a fusion method is applied to make the final outputs. The proposed method is evaluated on the DCASE2019 task5 dataset and achieves a macro-AUPRC score of 0.68, outperforming the baseline system of 0.54.
A Secure and Privacy-Preserved Road Condition Monitoring System

Connected vehicular cloud computing (CVCC), a combination of VANETs and cloud computing, is recently emerged as a promising paradigm for road condition monitoring task. However, for the efficient utilization of the advantages of CVCC, there is a necessity to look into the security and privacy requirements of this system. A vehicle needs to be authenticated for the genuinity of its road condition monitoring report. Also, there is a need to preserve privacy against the trusted but curious agents of the cloud server. Wang et al. recently proposed a cloud-based road condition monitoring scheme, and attempted to tackle these issues. Unfortunately, we discovered that this scheme has critical security and privacy issues that must be addressed. Since, the Roadside Units (RSUs) that have been used in their scheme are trusted but curious, they may collude with each other and successfully reveal some sensitive information like the trajectory of the vehicle user. Moreover, their scheme fails to provide anonymity and unlinkability for the vehicle user. Furthermore, an attacker can be successful to induce forged emergency case through replay attack. Apart from these security and privacy issues, the cloud server in their scheme cannot segregate a threshold number of current reports, from a threshold number of the combination of old and current reports. This paper proposes a secure and privacy-preserved road condition monitoring system. The security analysis of the proposed system is performed, which ensures that the proposed system is secure.
The Design and Manufacturing of a Small Monopole Antenna with Minimized Electromagnetic Radiation Health Effects

Both the antenna miniaturization and bandwidth account for recent trends in designing modern antennas. This research is about the design of an ultra-wideband (UWB) planar monopole antenna, examining the specific absorption rate (SAR) induced by that antenna on a simplified human head model. The motives of the research are to miniaturize the antenna, targeting the UWB performance with minimized electromagnetic interference with the human brain fluid. An UWB small monopole antenna was designed, manufactured and tested (as a prototype); both the SAR and the electric field were simulated on a basic human head model. During the design process, as the antenna is grounded on one side, the performance is enhanced through impedance matching with the feed; electromagnetic (EM) far-field is strengthened too. Results from the grounded and ungrounded models at 3 GHz helped on the final prototype selection. A grounded monopole was selected, manufactured and the measured results proved the correctness of simulation results. The existing works in this area showed that computer simulation is a reliable method in studying electromagnetic radiation effects: sizing parameters and design properties are flexibly changed in simulation software. For main results, apart from the manufactured and tested prototype, the nearly zero SAR was achieved by simulation using the highfrequency structure simulator (HFSS) as a software tool; the brain fluid was directly placed in the main radiation near field beam of the antenna. The EM interference was studied on the modeled head and brain, in this research work; however, the same interference seriously affects the body’s organs which contain a big quantity of fluid, such as the heart, the cheeks, etc. Therefore, both the users and manufacturers of EM energy radiating devices are recommended to refer to their SAR design specifications before utilization, especially when those devices are intended to be used in closeness with the human body.
Deep Group-Shuffling Dual Random Walks With Label Smoothing for Person Reidentification

Person reidentification (ReID) is a challenging task of finding a target pedestrian in a gallery set collected from multiple nonoverlapping camera views. Recently, state-of-the-art ReID performance has been achieved via an end-to-end trainable deep neural network framework, which integrates convolution feature extraction, similarity learning and reranking into a joint optimization framework. In such a framework, the similarity is learned via an embedding network, the reranking is conducted with a random walk, and the whole framework is optimized with a cross-entropy-based verification loss. Unfortunately, the embedding net is difficult to train well because their two-dimensional outputs mutually interfere each other when using the conventional random walk. In addition, the supervision information has not been fully exploited during the training phase due to the binary nature of the verification loss. In this paper, we propose a novel approach, called group-shuffling dual random walks with label smoothing (GSDRWLS), in which random walks are performed separately on two channels—one for positive verification and one for negative verification—and the binary verification labels are properly modified with an adaptive label smoothing technique before feeding into the verification loss in order to train the overall network effectively and to avoid the overfitting problem. Extensive experiments conducted on three large benchmark datasets, including CUHK03, Market-1501 and DukeMTMC, confirm the superior performance of our proposal.
FPGA implementation of an algorithm that enables color constancy

We designed a digital circuit that converts raw image sensor data into illumination-independent color information, and implemented it on a field-programmable gate array (FPGA). The coding algorithm used here is based on the center / surround retinex model, which is inspired by color perception of humans and by the visual signal processing in the visual nervous system. The designed digital circuit contains the following functions: integration of multiple exposure images, logarithmic compression, and difference-of-Gaussian spatial filtering. We built an image sensor system by interfacing a CMOS image sensor to the FPGA. We examined the performance of the system by presenting Mondrian to the image sensor under varying color of illumination. As a result, the system successfully reduced the influence of the color of the illumination.
Equipment Maintenance Decision Model Based on Degradation Data and Failure Data

The degradation of data and failure data is used to make the maintenance decision of equipment in this paper. Firstly, the Wiener process is used to build the degradation model which is according to the degradation data of the equipment. Then, the random distribution coefficient of failure threshold is estimated by the failure data of the equipment, and we also derive the analytical expression of the remaining useful lifetime (RUL) distribution of the equipment. Finally, the maintenance decision model is established according to the renewal-reward theory and RUL prediction data which can achieve optimal maintenance strategy of equipment. The simulation example shows that the method in this paper can prolong the run time cost of equipment and effectively reduce the life cycle which has broad application prospects.
HawkesEye: Detecting Fake Retweeters Using Hawkes Process and Topic Modeling

Retweets are essential to boost the popularity of a tweet, and a large number of fake retweeters can contribute heavily to this aspect. We define a fake retweeter as a Twitter account that retweets spammy tweets, retweets an abnormally large amount of tweets in a short period, or misuses a trending hashtag to promote events irrelevant to the topic of discussion. We introduce an up-to-date, temporally diverse, trend-oriented labeled dataset to address the problem of fake retweeter detection. We develop a novel classifier, called HawkesEye which makes predictions based on a temporal window, in contrast to existing approaches which require a graph-like relationship between tweet entities, or the presence of the entire retweeting timeline of a retweeter. HawkesEye utilizes both temporal and textual information using a class-specific topic model and Hawkes processes. Experiments on our curated dataset show significant improvement over four state-of-the-art methods, with precision and recall scores of 0.964 and 0.960 on a balanced dataset, respectively – HawkesEye beats the best baseline by 6.16% and 25.98% relative improvement in terms of precision and recall, respectively. We also diagnose our model to understand the advantages and pitfalls of the underlying mechanism. We believe that the extent of this study is not restricted to Twitter, but generalizable to other social media systems such as Facebook and Instagram with similar reposting capabilities.
Outage Performance of Cooperative Underlay Cognitive Radio with Non-Orthogonal Multiple Access

In this paper, we propose a downlink cognitive underlay relay networks with non-orthogonal multiple access (CR-NOMA), where a secondary source node communicates with two secondary destination nodes with the help of multi secondary relays. We derive exact expressions of the outage probability for two secondary destination nodes with fixed relaying and partial relay selection. The effect of imperfect channel state information on partial relay selection is also investigated. The provided numerical results confirm that the derived expressions for the outage probabilities are in excellent agreement with the Monte-Carlo simulations.
A Parallel Deep Neural Network Using Reviews and Item Metadata for Cross-Domain Recommendation

Cross-domain recommendation is an effective technique to alleviate the data sparsity problem in recommender systems by utilizing the information from relevant domains. In this paper, we propose Cross-domain Deep Neural Network (CD-DNN) for the cross-domain recommendation. CD-DNN solves the rating prediction problem by modeling users and items using reviews and item metadata, which jointly learns features of users and items from not only the target domain but also other source domains. Latent factors for users and items are learned by several parallel neural networks, and the relevance of user features and item features is learned by maximizing prediction accuracy. CD-DNN builds a single mapping for user features in the latent space, so that the network for user is optimized together with item features from other domains. Experimental results indicate that the proposed CD-DNN significantly outperforms other state-of-the-art recommendation approaches on four public datasets of Amazon and it alleviates the data sparsity problem by leveraging more data across domains.
High-quality waveform generator from fundamental frequency, spectral envelope, and band aperiodicity

This paper introduces a waveform generation algorithm from three speech parameters (fundamental frequency fo, spectral envelope, and band aperiodicity). The conventional speech analysis/synthesis system based on a vocoder mainly has a waveform generator based on pitch synchronous overlap and add (PSOLA). Since it uses the fast Fourier transform (FFT) to generate the vocal cord vibration, the processing speed is proportional to the fo. The algorithm also uses the spectral representation of the aperiodicity, whereas the band aperiodicity is mainly used in speech synthesis applications such as statistical parametric speech synthesis. We propose a waveform generation algorithm that reduces the computational cost and memory usage without degrading the synthesized speech. The algorithm utilizes excitation signal generation by directly using the band aperiodicity. The computational cost in a certain period is fixed because the excitation signal is filtered and processed by the overlap-add (OLA) algorithm. We used the re-synthesized speech to perform two evaluations for the processing speed and sound quality. The results showed that the sound quality of speech synthesized was almost the same by our proposed algorithm as by the conventional algorithm. The proposed algorithm can also reduce computational cost and memory usage.
A Coarse-to-Fine Generalized-ICP Algorithm With Trimmed Strategy

In this paper, we introduce a modified Generalized Iterative Closest Point (GICP) algorithm by presenting a coarse-to-fine strategy. Our contributions can be summarized as: Firstly, we use adaptively a plane-to-plane probabilistic matching model by gradually reducing the neighborhood range for given two point sets. It is an inner coarse-to-fine iteration process. Secondly, we use an outer coarse-to-fine strategy to bridge the point-to-point and plane-to-plane registration for refining the matching. Thirdly, we use the trimmed method to gradually eliminate the effects of incorrect correspondences, which improves the robustness of the methods especially for the low overlap cases. Moreover, we also extend our method to the scale registration case. Finally, we conduct extensive experiments to demonstrate that our method is more reliable and robust in various situations, including missing points, noise and different scale factors. Experimental results show that our approach outperforms several state-of-the-art registration methods.
Trust Computation in Online Social Networks Using Co-Citation and Transpose Trust Propagation

Evaluating trust and distrust between users in online social networks is an important research problem. To address this problem, we provide a method for estimating continuous trust /distrust value between unconnected users. Our method is based on co-citation and transpose trust propagation. We determine on average how differently two users trust or are trusted by other users, and how differently a user trusts another user from how it is trusted by that user. Using these differences, we estimate four partial trust estimates and compute the final trust value from trustor to trustee as the weighted average of these partial estimates. We propose a basic framework that maximizes accuracy, robustness and coverage and show how we can further improve the accuracy at a lower coverage. We perform experiments on real world trust related networks that show that our proposed method outperforms recent state of the art trust computation methods in terms of accuracy and robustness on commonly used datasets.
Beamforming and Scheduling Techniques for Multibeam DVB-S2X Over Rainy Fading Satellite Channel

The extension of the digital video broadcasting second generation standard (DVB-S2X) for transmission over satellite has been introduced recently with its novel superframe structure, which is a key enabler for applying interference management techniques, such as precoding, to multibeam high throughput geosynchronous (GEO) satellite systems operating in the Ka-band. This paper presents the first full design of DVB-S2X system with multi-user-multiple-input-single-output (MU-MISO DVB-S2X), with most of its modulation and coding schemes (MODCODs), over rainy fading channels. The atmospheric impairments on the Ka-band satellite channel are considered, especially the rainfall effect, which is the most effective atmospheric impairment that degrades the system performance. Two rainy fading channels are designed, one for tropical region and the other for temperate region, using real rain data from these areas. In addition, the user scheduling influence on the bit error rate (BER) performance of MU-MISO DVB-S2X system is tested and compared with the conventional MISO DVB-S2X system. Simulation results show that the proposed system can achieve a significant improvement in terms of BER performance with at least 20 dB for 128 amplitude and phase shift keying (128APSK) MODCOD over the tropical channel and 14 dB for 32APSK MODCOD over temperate channel when the number of users is six. The enhancement in error rates proves that MU-MISO DVB-S2X system with scheduling can be the key solution for DVB-S2X system performance degradation in fading channels, especially rainy fading channels.
Attribute Estimation Using Multi-CNNs from Hand Images

The human hand is one of the primary biometric traits in person authentication. A hand image also includes a lot of attribute information such as gender, age, skin color, accessory, and etc. Most conventional methods for hand-based biometric recognition rely on one distinctive attribute like palmprint and fingerprint. The other attributes as gender, age, skin color and accessory known as soft biometrics are expected to help identify individuals but are rarely used for identification. This paper proposes an attribute estimation method using multi-convolutional neural network (CNN) from hand images. We specially design new multi-CNN architectures dedicated to estimating multiple attributes from hand images. We train and test our models using 11K Hands, which consists of more than 10,000 images with 7 attributes and ID. The experimental results demonstrate that the proposed method exhibits the efficient performance on attribute estimation.
A Comprehensive Simulation Platform for Space-Air-Ground Integrated Network

Space-air-ground integrated network (SAGIN) is envisioned as a promising solution to provide cost-effective, large-scale, and flexible wireless coverage and communication services. Since realworld deployment for testing of SAGIN is difficult and prohibitive, an efficient SAGIN simulation platform is requisite. In this article, we present our developed SAGIN simulation platform which supports various mobility traces and protocols of space, aerial, and terrestrial networks. Centralized and decentrallized controllers are implemented to optimize the network functions such as access control and resource orchestration. In addition, various interfaces extend the functionality of the platform to facilitate user-defined mobility traces and control algorithms. We also present a case study where highly mobile vehicular users dynamically choose different radio access networks according to their quality of service (QoS) requirements.
A Resource Usage Intensity Aware Load Balancing Method for Virtual Machine Migration in Cloud Datacenters

To provide robust infrastructure as a service (IaaS), clouds currently perform load balancing by migrating virtual machines (VMs) from heavily loaded physical machines (PMs) to lightly loaded PMs. The unique features of clouds pose formidable challenges to achieving effective and efficient load balancing. First, VMs in clouds use different resources (e.g., CPU, bandwidth, memory) to serve a variety of services (e.g., high performance computing, web services, file services), resulting in different overutilized resources in different PMs. Also, the overutilized resources in a PM may vary over time due to the time-varying heterogeneous service requests. Second, there is intensive network communication between VMs. However, previous load balancing methods statically assign equal or predefined weights to different resources, which lead to degraded performance in terms of speed and cost to achieve load balance. Also, they do not strive to minimize the VM communications between PMs. We propose a Resource Intensity Aware Load balancing method (RIAL). For each PM, RIAL dynamically assigns different weights to different resources according to their usage intensity in the PM, which significantly reduces the time and cost to achieve load balance and avoids future load imbalance. It also tries to keep frequently communicating VMs in the same PM to reduce bandwidth cost, and migrates VMs to PMs with minimum VM performance degradation. We also propose an extended version of RIAL with three additional algorithms. First, it optimally determines the weights for considering communication cost and performance degradation due to VM migrations. Second, it has a more strict migration triggering algorithm to avoid unnecessary migrations while still satisfying Service Level Objects (SLOs). Third, it conducts destination PM selection in a decentralized manner to improve scalability. Our extensive trace-driven simulation results and real-world experimental results show the superior performance of RIAL compared to other load balancing methods.
Numerical and Experimental Testing of Underwater Gripper with Adjustable Stiffness Joints

This paper presents numerically and experimentally obtained results for an underwater multi-joint gripper for which joint stiffness can be adjusted using a differential gear mechanism. By adjusting the joint stiffness, the gripper can achieve an enveloping grasp of various objects. First, we numerically investigated the grasping performance based on equations of motion including the hydrodynamic effects. Additionally, we conducted preliminary experiments to assess the gripper performance in a circulating water tank. From the numerical and experimentally obtained results, we confirmed that the gripper can maintain its posture and hold an object stably, even in water flows.
An Unsupervised Method for Web User Interest Analysis

In this paper, we propose an unsupervised method to determine dominating user interests from browsing behavior data. This method identifies group of users with similar interests by partitioning a similarity graph, in which nodes are users and edges represent the similarities in browsing behavior between the users. Two users are considered similar if they have viewed the same content. We consider and compare several types of similarity measures, which are calculated at different granularity levels of content: at web page level, page category level, and topic level as identified by topic modeling techniques. A hierarchical graph clustering algorithm is used to perform partitioning process to capture the natural hierarchy of user clusters and producing intuitive features for understanding the content linked to each cluster. For evaluation, we perform an experiment on a browsing behavior dataset from a university’s Web portal. Our system can discover interesting information like web user interests, who is interested in which content.