Miracle-dz/UniFAR-FTUs huggingface dataset:
Power Control Method Based on Users and Applications QoS Priorities(UAQP) in Femtocell Network

Femtocells, also known as Home enhanced Node B (HeNB), are low cost and low power wireless Access Point (AP). This device is habitually deployed in an indoor environment to provide in-building coverage enhancement. Although femtocells are low power base stations, a vast deployment of femtocells causes many technical problems such as: interferences, user Quality of Service (QoS) and power control and consumption of femtocells. To solve such problems, most exciting works focus on power control and QoS in femtocell networks. In this paper, we provide a new intelligent QoS and power control method, denoted Users and Applications QoS Priorities (UAQP) power control method. This method aims to optimize the energy consumption of Femtocell Base Station (FBS) through adjusting their transmitting power according to two types of priorities, users and applications priorities. The simulation demonstrates the effectiveness of our proposed power control method and their performance compared to fixed power control method.
SMP: A New Mechanism to Mitigate Control-Flow Hijacking Attacks

Software code written in low-level languages like C/C++ have many kinds of memory vulnerabilities (e.g., stack overflow, Use-after-free, etc.). Attackers could exploit these vulnerabilities to hijack control-flow and compromise the program. Existing defense mechanisms' (e.g., ASLR, DEP) security guarantee are incomplete, and widely studied defense mechanisms (e.g., CFI) have high overhead and stronger but also limited security guarantees [1]. In this paper, we introduced SMP (Shadow Memory Protection), a new defense mechanism that protects the program's control-flow from being maliciously tampered by protecting critical data in the program (e.g., indirect jump destination address). SMP has a better security-to-overhead ratio than most existing controlflow hijacking protection mechanisms. Experiments showed that SMP mechanism is effective (prevent all attacks in RIPE benchmark) and efficient (3.5% average overhead for C on SPEC CPU2006).
A Symmetrical Dual-Beam Bowtie Antenna With Gain Enhancement Using Metamaterial for 5G MIMO Applications

A symmetrical dual-beam end-fire bowtie antenna with gain enhancement is achieved by integrating three pairs of metamaterial (MTM) arrays for 5G MIMO applications. The first pair of MTM array with high refractive index (HRI) are deployed to form a wide beam antenna. The second pair of HRI MTM array are arranged along the end-fire direction (<italic>x</italic>-direction) in front of the radiators in order to split the single wide beam into dual beam. Besides, the third pair of anisotropic MTM array with HRI along <italic>x</italic>-direction and near zero refractive along <italic>y</italic>-direction are incorporated in front of the second pair of MTM array to improve the gain performance. The proposed technique is verified by both the full-wave electromagnetic simulation and experiment, and the simulated and measured results agree very well with each other. Moreover, the measured results reveal that the main beam directions of the proposed antenna point to <inline-formula><tex-math notation="LaTeX">$\pm 30^{\circ }$</tex-math></inline-formula> with respect to the end-fire direction (0<inline-formula><tex-math notation="LaTeX">$^{\circ }$</tex-math></inline-formula>) over 24.25–27.5 GHz, with a maximum gain of 7.4 dBi at 26 GHz and a 4.2 dB gain improvement compared to the wide beam antenna.
Kernel Support Vector Machines and Convolutional Neural Networks

Convolutional Neural Networks (CNN) have achieved great success in various computer vision tasks due to their strong ability in feature extraction. The trend of development of CNN architectures is to increase their depth so as to increase their feature extraction ability. Kernel Support Vector Machines (SVM), on the other hand, are known to give optimal separating surfaces by their ability to automatically select support vectors and perform classification in higher dimensional spaces. We investigate the idea of combining the two such that best of both worlds can be achieved and a more compact model can perform as well as deeper CNNs. In the past, attempts have been made to use CNNs to extract features from images and then classify with a kernel SVM, but this process was performed in two separate steps. In this paper, we propose one single model where a CNN and a kernel SVM are integrated together and can be trained end-to-end. In particular, we propose a fully-differentiable Radial Basis Function (RBF) layer, where it can be seamless adapted to a CNN environment and forms a better classifier compared to the normal linear classifier. Due to end-to-end training, our approach allows the initial layers of the CNN to extract features more adapted to the kernel SVM classifier. Our experiments demonstrate that the hybrid CNN-kSVM model gives superior results to a plain CNN model, and also performs better than the method where feature extraction and classification are performed in separate stages, by a CNN and a kernel SVM respectively.
Delaunay Triangulation Data Augmentation Guided by Visual Analytics for Deep Learning

It is well known that image classification problems can be effectively solved by Convolutional Neural Networks (CNNs). However, the number of supervised training examples from all categories must be high enough to avoid model overfitting. In this case, two key alternatives are usually presented (a) the generation of artificial examples, known as data augmentation, and (b) reusing a CNN previously trained over a large supervised training set from another image classification problem — a strategy known as transfer learning. Deep learning approaches have rarely exploited the superior ability of humans for cognitive tasks during the machine learning loop. We advocate that the expert intervention through visual analytics can improve machine learning. In this work, we demonstrate this claim by proposing a data augmentation framework based on Encoder-Decoder Neural Networks (EDNNs) and visual analytics for the design of more effective CNN-based image classifiers. An EDNN is initially trained such that its encoder extracts a feature vector from each training image. These samples are projected from the encoder feature space on to a 2D coordinate space. The expert includes points to the projection space and the feature vectors of the new samples are obtained on the original feature space by interpolation. The decoder generates artificial images from the feature vectors of the new samples and the augmented training set is used to improve the CNN-based classifier. We evaluate methods for the proposed framework and demonstrate its advantages using data from a real problem as case study — the diagnosis of helminth eggs in humans. We also show that transfer learning and data augmentation by affine transformations can further improve the results.
Systematic Analysis: Resistance to Traffic Analysis Attacks in Tor System for Critical Infrastructures

The threat of traffic analysis attacks against the Tor System is an acknowledged and open research issue, especially in critical infrastructures, motivating the need for continuous research into the potential attacks and countermeasures against this threat. This paper aims to provide an in-depth study into the driving technical mechanisms of the current state-of-art Tor System (Browser Bundle and Network) that aim to provide its benefits to anonymity and privacy online. This work presents the countermeasures that have been proposed and/or implemented against such attacks, in a collated evaluation to determine their effectiveness, suitability to Tor Project, and its design aims/goals.
Eye-Swipe Typing Using Integration of Dwell-Time and Dwell-Free Method

Computer becomes a part of human in learning and it can be used to type text entries using finger typing. However, handicapped people cannot type any text. Therefore, typing using eye movement is developed as an assistive technology for physically disabled people. Eye-swipe typing is a kind of on-screen keyboard that allows users to swipe through the first until the last letter of word using their eye gaze. Previous researchers implemented dwell-time method to select letter-by-letter. Then other researchers proposed dwell-free method to fasten typing. In this paper, combination of dwell-time and dwell-free method is proposed in order to achieve accuracy in typing. Edit distance algorithm is revised with neighbor weight to handle with neighbor-letter error from imperfect calibration of eye tracker. Then, number of word frequency and ratio of matching letters from edit distance of scan-path word with each word in database will be considered as score ranking. Words with top-5 score ranking will be recommended as candidate words.
Time Series Neural Networks for Real Time Sign Language Translation

Sign language is the primary mode of communication for the hearing and speech impaired and there is a need for systems to translate sign languages to spoken languages. Prior research has been focused on providing glove based solutions which are intrusive and expensive. We propose a sign language translation system based solely on visual cues and deep learning for accurate translation. Our system applies Computer Vision and Neural Machine Translation for American Sign Language (ASL) gloss recognition and translation respectively. In this paper, we show that an end to end neural network system is not only capable of recognition of individual ASL glosses but also translation of continuous sign language videos into complete English sentences, making it an effective and practical tool for sign language communication.
Lane Detection and Prediction under Hazy Situations for Autonomous Vehicle Navigation

Lane detection and tracking is essential concern in vision based autonomous vehicle navigation. This paper proposes a novel method based on probabilistic Hough transform and motion vector based analysis for detecting and tracking lane and lane departures. It addresses drawbacks of current systems under hazy situations with learning method based on previous tracking records of the system. In this method relevant lane mark features are extracted based on color variations. Therefore used HSL color model which gives higher color contrast of road surface and the markings. In order to reduce computation, processing algorithms are applied only to the region of interest (ROI). Edges of the selected regions are extracted with Canny Edge Detection. Since lane markings are made with set of straight lines, straight lines are extracted with Probabilistic Hough Transform. Extracted lines are analyzed to detect lane. To monitor lane departures vehicle’s motion is detected and tracked based on motion vector analysis with Lucas Kanade Optical Flow algorithm. Previous detection records are used to track the lane continuously in hazy situations and provide prediction on approximate lane for the roads without lane markings. Experiments were performed on set of videos taken from vehicles’ front camera mounted on dashboard. According to the experiments the proposed algorithm has 81.9% of sensitivity in lane detection and 63.5% in approximate lane predictions for the roads without lane markings. Lane detection algorithm has 83% precision, 68% recall and 0.75 F1 score with threshold brightness value 140. Algorithm can further improved to guide the autonomous vehicles to take accurate decisions on selecting safety lane to drive ahead in different situations.
Real-Time Monitoring System for University Buses using Available WiFi Networks and Travel Time Prediction

A real-time bus monitoring system is developed for students and faculty members of large universities that provide free bus services. It utilizes existing WiFi infrastructure to transmit and update GPS data, eliminating the need for cellular networks and reducing operation and maintenance costs. However, certain areas on campus may not have WiFi coverage. These areas are determined by an experiment that measures WiFi signals along bus routes to identify dead zones and create a WiFi connection map. Relevant factors for predicting bus travel time are analyzed, such as traffic condition, relationship and correlation between neighboring sections, and historical data, to provide a way of estimating the location of buses traveling along sections with no data connection. The result is that the system can alternate between using real-time GPS data when available or travel time prediction to give users a reliable and accurate way of making travel plans while on campus.
A Dataset for Electromyography-Based Dactylology Recognition

Historically, sign languages have been used as a way of communication by people with hearing loss. However, it is not trivial for other people to adopt sign language, due to its particular grammatical structure, rules, vocabulary, and so on. Recently, commercial wearable devices based on the electromyography technology became available to the wide audience at accessible cost. This work shows the process of creating a data set of electromyographic sign records for the Brazilian Sign Language (Língua Brasileira de Sinais - LIBRAS). The authors believe that, using such data set, it is possible to apply machine learning techniques to automatically recognize and translate Brazilian Sign Language to other languages, and thus improve the communication. To demonstrate the dataset potential, some classification methods were applied, achieving up to 89% of correctness.
Situational and Adaptive Context-Aware Routing for Opportunistic IoT Networks

The increased capabilities of embedded devices have made them smarter and able to adapt themselves to their users’ needs and preferences. Nevertheless, this adaptation has been limited to a single device or to a small number of them. Moreover, the Internet of Things (IoT) paradigm requires to coordinate a huge number of devices and to adapt them to their users’ context, which will lead to an increase in the traffic exchanged between these devices and the cloud. In this paper, the definition of Opportunistic Context-Virtual Networks (OCVNs), and a novel routing algorithm, namely Situational and Adaptive Context-Aware Routing (SACAR), are proposed to dynamically adapt the users context to the opportunistic IoT network environment they belong to at a given time. By taking advantage of the computing capacities of the nodes, and under simulations over a realistic reference scenario, SACAR outperforms other well-known opportunistic routing algorithms in terms of packet delivery ratio, reducing both average latency and network overhead.
Table Detection in Document Images using Foreground and Background Features

Table detection is an important step in many document analysis systems. It is a difficult problem due to the variety of table layouts, encoding techniques and the similarity of tabular regions with non-tabular document elements. Earlier approaches of table detection are based on heuristic rules or require additional PDF metadata. Recently proposed methods based on machine learning have shown good results. This paper demonstrates performance improvement to these table detection techniques. The proposed solution is based on the observation that tables tend to contain more numeric data and hence it applies color coding/coloration as a signal for telling apart numeric and textual data. Deep learning based Faster R-CNN is used for detection of tabular regions from document images. To gauge the performance of our proposed solution, publicly available UNLV dataset is used. Performance measures indicate improvement when compared with best in-class strategies.
Performance Oriented Block-Based Neural Network Model by Parallelized Neighbor's Communication

The structure and weight in Block-Based Neural Network (BBNN) are optimized by utilizing the genetic algorithm. The architecture of BBNN consists of a two-dimensional (2-D) array of basic block with four input/output nodes and connection weights for block's output. To propose easier hardware implementation like Field Programmable Gate Array (FPGA), integer weights are used in the basic block. Each block can be one of the four different basic types and the architecture of BBNN is configured with the combination of basic blocks internally configured. However, BBNN's structural change needs hardware reconfiguration and the cost is very high. To reduce the reconfiguration cost, Smart Block-based Neuron (SBbN) which has sufficient number of weights for all four types of basic block has been proposed. SBbN preserves all weights, even the unnecessary for some types, and thus it consumes redundant hardware resource. A new model of BBNNs, in which all weights in SBbN are used efficiently by modifying calculation procedures of outputs in basic blocks, has been proposed and it eliminates the resource redundancy of SBbN. However, the new approach, which both right and left's side nodes concurrently serve as input and output, cannot provide parallel computation in right and left signal flow. This paper presents a pipelined parallel computation with independent side nodes for each signal flow.
Real-Time Gender Detection in the Wild Using Deep Neural Networks

Gender recognition can be used in many applications, such as video surveillance, human-computer interaction and customized advertisement. Current state-of-the-art gender recognition methods are detector-dependent or region-dependent, focusing mostly on facial features (a face detector is typically required). These limitations do not allow an end-to-end training pipeline, and many features used in the detection phase must be re-learned in the classification step. Furthermore, the use of facial features limits the application of such methods in the wild, where the face might not be present. This paper presents a real-time end-to-end gender detector based on deep neural networks. The proposed method detects and recognizes the gender of persons in the wild, meaning in images with a high variability in pose, illumination an occlusions. To train and evaluate the results a new annotation set of Pascal VOC 2007 and CelebA were created. Our experimental results indicate that combining both datasets during training can increase the mAp of our gender detector. We also visually analyze which parts leads our network to make mistakes and the bias introduced by the training data.
Modelling the interaction between functioning and organogenesis in a stochastic plant growth model : Methodology for parameter estimation and illustration

The complex interaction loops between organogenesis, assimilate production and partitioning are a crucial core component of most functional-structural plant growth model; yet these three processes are seldom fully coupled and there is no consensus on how it should be done. In this context, the use of the internal trophic pressure as a regulating variable of the development processes is an option that has attracted increasing interest in recent years. Generalizing this approach is however hampered by the fact that it is a non-measurable quantity that can be only assessed through model parametric estimation, for which the methodology is not straightforward, especially when the model is stochastic.In this paper, our objectives are (i) to present a stochastic GreenLab model of plant growth (named ‘GL4’) with feedback effect of plant functioning, represented by the ratio of biomass supply to demand, on organogenesis, (ii) to illustrate some of the model properties on a virtual plant featuring the Roux architectural model, and (iii) to present the methodology for its parameter estimation and (iv) its application to two virtual test-cases. Such virtual fitting exercise allows a better understanding of the procedure as well as it thorough evaluation: it is therefore a prerequisite to any future application to real plants
Low Bits: Binary Neural Network for Vad and Wakeup

Nowadays, with the development of mobile Internet, more and more applications require small memory footprint, low power and high precision. In this paper, we first apply the state-of-the-art Binarized Neural Network (BNN) for voice activity detection (VAD) and Binarized Weight Network (BWN) architecture for wakeup. Unlike traditional networks, the weights and activations of BNN and weights of BWN are binary during training and real time processing, which drastically reduce memory, real time and energy. Our VAD system can achieve a good recall 91.1% with only 1k (bit) memory and nearly 44k (pJ) energy. Our wakeup system can achieve 90% accuracy and 4/h (four times per hour) false alarms with 230K (bit) memory and 4.99M (pJ) energy. Both of our system can get 32× memory saving and 5× faster on real time processing on GPUs. While during the real time testing, the Batch Normalization layer and posterior handing are too complex to get good performance, so we optimize them to make it possible to get much faster, smaller and low power VAD and wakeup system.
Cap: Exploiting Data Correlations to Improve the Performance and Endurance of SSD RAID

Parity-based RAID provides system-level fault tolerance. However, parity updates caused by small writes introduce lots of extra I/Os, degrading I/O performance and wearing SSDs out. It has been proposed to use Non-Volatile Memory (NVM) as a parity cache on an SSD RAID to postpone parity updates until the whole stripe has been updated. However, this often fails because of skewed distribution of hot data chunks within a stripe. In real workloads, it is often difficult to achieve a full-stripe update even after a long delay. In this paper, we propose a Correlation aware parity caching scheme, called Cap, for SSD-based RAIDs. The key idea behind Cap is to periodically reconstruct correlated hot data chunks into a new stripe. Since these data chunks have a strong correlation, they tend to be updated together within a short time span. This co-update within a stripe more efficiently utilizes the parity cache to convert partial-stripe updates into a full-stripe update. We have implemented Cap on a RAID-5 SSD array in Linux Kernel 4.3. Experimental results show that Cap improves the I/O bandwidth by 54%~145% compared with the Linux software RAID. Compared with the state-of-the-art parity caching scheme PPC, Cap improves the I/O bandwidth by 14%~31%.
Observer design for networked control systems implemented over WirelessHART

We study the design of state observers for nonlinear networked control systems (NCSs) that are implemented over WirelessHART (WH). WH is a wireless communication protocol for process automation applications. It is characterised by its multi-hop structure, slotted communication cycles, and simultaneous transmission over different frequencies. We present a solution based on the emulation approach. That is, given an observer designed with a specific stability property in the absence of communication constraints, we implement it over a WH network and we provide sufficient conditions on the latter, to preserve the stability property of the observer. In particular, we provide explicit bounds on the maximum allowable transmission interval. We assume that the plant dynamics and measurements are affected by noise and we guarantee an input-to-state stability property for the corresponding estimation error system.