Miracle-dz/UniFAR-FTUs huggingface dataset:
Experience and Lessons in Building an ICS Security Testbed

Industrial control systems (ICS) control and monitor critical infrastructure, and if ICS security incidents occur, it will cause serious losses, and its security issues are gradually being taken seriously. Developing security testbed is an important part of ICS security research. However, the construction of ICS security testbed is a costly, labor intensive, and requires the cooperation of different professionals in the IT and automation fields. There is little literature on the experience and lessons learned in this field. We have developed a Multiple Scenarios Industrial Control System Testbed (MSICST). Based on the introduction of the architecture of MSICST, this paper focuses on several experience and lessons learned in building an ICS security testbed.
Fusion of Multiple Representations Extracted from a Single Sensor’s Data for Activity Recognition Using CNNs

With the emerging ubiquitous sensing field, it has become possible to build assistive technologies for persons during their daily life activities to provide personalized feedback and services. For instance, it is possible to detect an individual’s behavioral information (e.g. physical activity, location, and mood) by using sensors embedded in smartwatches and smartphones. To detect human’s daily life activities, accelerometers have been widely used in wearable devices. In the current research, usually a single data representation is used, i.e., either image or feature vector representations. In this paper, a novel method is proposed to address two key aspects for the future development of robust deep learning methods for Human Activity Recognition (HAR): (1) multiple representations of a single sensor’s data and (2) fusion of these multiple representations. The presented method utilizes Deep Convolutional Neural Networks (CNNs) and was evaluated using a publicly available HAR dataset. The proposed method showed promising performance, with the best result reaching an overall accuracy of 0.97, which outperforms current conventional approaches.
Bacteria shape classification by the use of region covariance and Convolutional Neural Network

In modern medical systems fast recognition of bacteria strain from microscopy image is very often done by the use of specialized computer systems. For these programs intelligent methodologies are very important tools. In this paper we present a model of bacteria recognition based on a composition of region covariance with Convolutional Neural Networks (CNN). In the first stage an input microscopy image is segmented by the use of region covariance model. Next these segments are forwarded to CNN for recognition of visible bacteria strains. Experiments were done for rod-shaped bacteria and spherical or nearly spherical shape bacteria. The results show high potential of the proposed methodology.
Lyapunov-Krasovskii functional approach to stability for delay neural network with small-world short-cuts

This paper considers the problem of stability for time-delay neural networks with small-world shortcuts. Most of the previous studies can't derived absolute stability criteria of systems with time-varying delay. But, in this paper, absolute stability criteria of systems with time-invariant delay and absolute stability criteria of systems with time-varying delay are derived by handled uncertain part of systems and constructing a suitable Lyapunov-Krasovskii functional(LKF). Several example show effectiveness of proposed method comparing with the previous.
Dilated 3D Convolutional Neural Networks for Brain MRI Data Classification

Benefiting from the research of machine learning (ML) and deep learning(DL), multivariate methods based on ML and DL have been the mainstream and successful analysis methods in Neural Engineering or Neuroimaging research, for example, assisting diagnosis based on brain Magnetic Resonance Imaging (MRI). However, many existing methods based on traditional ML methods cannot sufficiently extract discriminative features, especially feature patterns across long-distance brain areas, resulting in unsatisfactory classification performance. Designing an effective and robust classifier for different MRI images remains a challenge. In this paper, we introduced dilated 3D CNN method for classifying 3D MRI images combining CNN structure and dilated convolution with a small number of feature maps. We also presented a methodology framework based on dilated 3D CNN method, which can classify both single MRI images and image sequences. Our method and framework were evaluated on the structural MRI images of ADHD-200 dataset and fMRI images of a Schizophrenia dataset, demonstrating better performances than some other state-of-the-art methods.
Design and Implementation of Image Electronic Fence with 5G technology for Smart Farms

The 5G era brings the rapid development of the Internet of Things (IoT). New technologies, such as image recognition, have promoted the traditional agriculture to a new milestone. Through advanced research and development of image recognition, it can intelligently monitor the growth of crops and effectively reduce agricultural damages, while avoiding crops being stolen and reducing the manpower of farms. In this paper, we design and implement an image electronic fence based on the technology of image recognition and sensor fusion with 5G technology for smart farms. By applying cameras and beacon tags, our system can identify whether the incoming/leaving people are authorized or not at the entry and exit of farms. In addition, with the high speed and low latency of 5G technology, the video data of cameras can be transmitted in a flash and fused with the beacon detection. In this way, the incoming/leaving people can be identified efficiently so as to avoid the farms being damaged. Based on field trials, we validate that the identification accuracy of our system approaches 90% in average.
Efficient on-line learning with diagonal approximation of loss function Hessian

The subject of this paper is stochastic optimization as a tool for on-line learning. New ingredients are introduced to Nesterov’s Accelerated Gradient that increase efficiency of this algorithm and determine its parameters that are otherwise tuned manually: step-size and momentum decay factor. In this order a diagonal approximation of the Hessian of the loss function is estimated. In the experimental study the approach is applied to various types of neural networks, deep ones among others.
On the Influence of the Color Model for Image Boundary Detection Algorithms based on Convolutional Neural Networks

Image analysis and understanding are challenging tasks, usually having segmentation as a major step. Boundary detection is a type of segmentation which aims to highlight the boundaries of the objects in a scene. Models based on Convolutional Neural Networks (CNN) have presented promising results for boundary detection, where the input usually is the entire image or some patches, often described in the RGB color model. In this paper, we provide a qualitative analysis of boundary detection algorithms based on CNN but considering images in different color models. We have used the color models RGB, Lab, Luv, dRdGdB, YO1O2 and HSV for this analysis. The Holistically-Nested Edge Detection (HED) and Convolutional Encoder Decoder Network (CEDN) are the CNN's chosen due to their high performance. The benchmark BSDS is the boundary detection evaluator. Experiments show that the results of the edge detection process tend to be similar when training the CNN with weights randomly initialized, regardless of the color model used. For the HED architecture, the use of Lab and Luv color models has resulted in a significant improvement to the case of transfer learning and fine-tuning of weights.
Question Answering with Hierarchical Attention Networks

We investigate hierarchical attention networks for the task of question answering. For this purpose, we propose two different approaches: in the first, a document vector representation is built hierarchically from word-to-sentence level which is then used to infer the right answer. In the second, pointer sum attention is utilized to directly infer an answer from the attention values of the word and sentence representations. We evaluate our approach on the Children’s Book Test, a cloze-style question answering dataset, and analyze the generated attention distributions. Our results show that, although a hierarchical approach does not offer much improvement over a shallow baseline, it does indeed offer a large performance boost when combining word and sentence attention with pointer sum attention.
A Hierarchical Convolutional Neural Network for Malware Classification

Malware detection and classification is a challenging problem and an active area of research. Particular challenges include how to best treat and preprocess malicious executables in order to feed machine learning algorithms. Novel approaches in the literature treat an executable as a sequence of bytes or as a sequence of assembly language instructions. However, in those approaches the hierarchical structure of programs is not taken into consideration. An executable exhibits various levels of spatial correlation. Adjacent code instructions are correlated spatially but that is not necessarily the case. Function calls and jump commands transfer the control of the program to a different point in the instruction stream. Furthermore, these discontinuities are maintained when treating the binary as a sequence of byte values. In addition, functions might be arranged randomly if addresses are correctly reorganized. To address these issues we propose a Hierarchical Convolutional Network (HCN) for malware classification. It has two levels of convolutional blocks applied at the mnemonic-level and at the function-level, enabling us to extract n-gram like features from both levels when constructing the malware representation. We validate our HCN method on the dataset released for the Microsoft Malware Classification Challenge, outperforming almost every deep learning method in the literature.
Analysis of Object Detection Methods to Detect Traffic Flow

Traffic analysis has received more interest as smart cities become a reality. A key component of traffic analysis is detecting the amount of cars that pass certain points. However, limited research exists that explores methods for the car detection component in these systems. This paper will discuss different computer vision methods that can be used for the detection and analysis of vehicles on the road for active traffic flow analysis and implements them in an experiment to find the best method for the task of car detection. MobileNet and Haar-Cascade based methods are implemented and a compared according to performance and accuracy levels in real-world scenarios. Lastly, the results achieved from the experimental model will be discussed giving detail to why Haar cascade gives better performance and accuracy in most scenarios with an average frame rate of over 40 fps on HD video.
µL2Q: An Ultra-Low Loss Quantization Method for DNN Compression

Data quantization has been proved to be an effective method to compress deep neural networks (DNNs) by using less bits to represent the parameters and intermediate data. The bit width of the data directly affects the memory footprint, computing capability, and energy consumption during the computation of the DNN models. Although there have been numerous existing studies on data quantization, there is still no quantitative analysis of the existing quantization methods, which results in empirical quantization with unpredictable DNN accuracy loss. To address this problem, we propose an effective method, called ultra-low loss quantization (µL2Q), to provide DNN quantization schemes based on comprehensive quantitative data analysis. µL2Q builds the transformation of the original data to a data space with standard normal distribution, and then find the optimal parameters to minimize the loss of the quantization of a targeted bit width. In addition, we integrate the proposed µL2Q into a popular machine learning framework Caffe for convenient end-to-end DNN design and training. By comparing to the state-of-the-art DNN compression designs, µL2Q shows the greatest ability to maintain DNN accuracy after quantization. In the experiments, our proposed method can deliver 4.42%, 16.70%, 1.95%, 8.26% and 5.63% accuracy improvements on Lenet-5, Cifarnet, VGG7-64 and Resnet-18 (Top1/5), respectively, compared to the state-of-the-art solutions with the same compression ratio.
Community Detection with Indirect Neighbors based on Granular Computing in Social Networks

Social relations exist in real life widely, which can be abstracted into graphs to show various social networks. Community detection in social networks has become an important and effective methodology to understand the structure and function of real world networks. In this paper, the algorithm we proposed fully considers the influence of direct nodes and indirect nodes and expresses the relationship between users comprehensively. The algorithm runs on the social network graph based on a new granular computing method. Experimental results on bench mark data show the superiority of the proposed algorithm compared to other well-known methods.
GADGET: Using Gated GRU for Biomedical Event Trigger Detection

Biomedical event extraction plays an important role in the field of biomedical text mining, and the event trigger detection is the first step in the pipeline process of event extraction. Event trigger can clearly indicates the occurrence of related events. There have been many machine learning based methods applied to this area already. However, most previous work have omitted two crucial points: (1) Class Difference: They simply regard non-trigger as same level class label. (2) Information Isolation: Most methods only utilize token level information. In this paper, we propose a novel model based on gate mechanism, which identifies trigger and non-trigger words in the first stage. At the same time, we also introduce additional fusion layer in order to incorporate sentence level information for event trigger detection. Experimental results on the Multi Level Event Extraction (MLEE) corpus achieve superior performance than other state-of-the-art models. We have also performed ablation study to show the effectiveness of proposed model components.
Modified Adaptive 2-Step ICIC for Distributed MIMO System with User Clustering

Distributed multi-input multi-output (MIMO) system with user equipment (UE) clustering can boost up area spectrum efficiency by performing UE scheduling independently in all clusters. However, UEs locating at cell-edge area suffer from strong inter-cell interference (ICI). We have proposed a de-centralized adaptive 2-step ICI coordination (ICIC) which employs UE classification and selection of additional fractional frequency band based on received interference level, but UE clustering has not been considered. In this paper, we modify the adaptive 2-step ICIC so as to fit to distributed MIMO system with UE clustering. Multiple cell-edge UEs are scheduled simultaneously and adjust the cell-center UE/cell-edge UE timeslot ratio to keep an average UE connection probability similar to system without ICIC. Simulation results show that the modified adaptive 2-step ICIC improves the cell-edge UE link capacity without significantly degrading entire-cell UE link capacity.
Removing Movable Objects from Grid Maps of Self-Driving Cars Using Deep Neural Networks

We propose a technique for removing traces of movable objects from occupancy grid maps based on deep neural networks, dubbed enhanced occupancy grid map generation (E-OGM-G). In E-OGM-G, we capture camera images synchronized and aligned with LiDAR rays, semantically segment these images, and compute which laser rays of the LiDAR hit pixels segmented as belonging to movable objects. By clustering laser rays that are close together in a 2D projection, we are able to identify clusters that belong to movable objects and avoid using them in the process of generating the OGMs – this allows generating OGMs clean of movable objects. Clean OGMs are important for several aspects of self-driving cars’ operation (i.e., localization). We tested E-OGM-G using data obtained in a real-world scenario – a 2.6 km stretch of a busy multi-lane urban road. Our results showed that E-OGM-G can achieve a precision of 81.19% considering the whole OGMs generated, of 89.76% considering a track in these OGMs of width of 12 m, and of 100.00% considering a track of width of 3.4 m. We then tested a self-driving car using the automatically cleaned OGMs. The self-driving car was able to properly localize itself and to autonomously drive itself in the world using the cleaned OGMs. These successful results showed that the proposed technique is effective in removing movable objects from static OGMs.
Cross-project Defect Prediction via ASTToken2Vec and BLSTM-based Neural Network

Cross-project defect prediction (CPDP) as a means to focus quality assurance of software projects was under heavy investigation in recent years. In this paper, we propose a novel CPDP approach via deep learning. In particular, we model each program module via simplified abstract syntax tree (S-AST). For each node in S-AST, only the project-independent node type is remained and other project-specific information (such as name of variable and method) is ignored, so that the modeling method is project-independent and suitable for CPDP issue. Then we extract token sequences from program modules modeled as S-AST. In addition, to construct meaningful vector representations for token sequences, we propose a novel unsupervised embedding method ASTToken2Vec, which learns semantic information from S-AST’s natural structure. Finally, we use BLSTM (bi-directional long short-term memory) based neural network to automatically learn semantic features from vectorized token sequences and construct CPDP models. In our empirical studies, 10 real large-scale open source Java projects are chosen as our empirical subjects. Final results show that our proposed CPDP approach can perform significantly better than 5 state-of-the-art CPDP baselines in terms of AUC.
Dimensionality Reduction in Multilabel Classification with Neural Networks

A new neural network method for Dimensionality Reduction (DR) of the input feature space in Multilabel Classification (MC) problems is proposed and experimentally evaluated in this paper. The method (abbreviated as TCART-MR) can be used in two possible scenarios: either as a stand-alone DR pre-processing phase, preceding subsequent application of any particular MC algorithm, or as a compact MC approach in which TCART-MR is applied twice - first to DR task and then to MC problem with reduced input space.Extensive experimental results proved statistically relevant advantage of TCART-MR over three state-of-the-art approaches in DR domain (in the context of MC), as well as its superiority over 10 state-of-the-art MC algorithms listed in a recent MC survey paper. The MC tests were performed on a set of 9 benchmark problems and 16 evaluation measures (leading to 144 experimental cases in total).
Multitouch Interface is not Good for Spatial Navigation in Virtual Reality

In this study, a spherical-panoramic virtual reality (VR) system equipped with a game controller (e.g., a gamepad) and a multitouch interface (e.g., an iPad) was developed. The multitouch interface was expected to yield better user experience compared to the game controller. However, the experiments of spatial navigation using finger gestures in the VR system showed the opposite results. The multitouch interface was proven qualitatively and quantitatively inferior to the game controller in the experiments. Qualitatively, based on the results of evaluation conforming to ISO 9241–9 standard, the game controller was preferred in the spatial navigation. Quantitatively, the mean completion time performed by the participants on the game controller was better than that on the multitouch interface (p<.01) in both angular and linear navigation tasks. Furthermore, the participants also secured less performance error in average while using the game controller in linear navigation tasks (p<.01). All in all, as an ultimate implication, any future developments of VR-based application may need to consider an empirical fact that multitouch interface is not good for spatial navigation.
A New Permutation-Substitution Scheme Based on Henon Chaotic Map for Image Encryption

In this paper, a new permutation-substitution scheme based on Henon chaotic map is introduced to generate a set of secret keys equivalent to query image size $(\mathbf{m\ x\ n})$. The random floating values produced by Henon map have been invested to achieve the permutation and substitution processes of the query image. The performance evaluation of the running time for encryption processes significantly guarantees that the proposed cryptographic system reduces the running time compared to the state of art chaotic based image encryption schemes. Based on the experiments, the robustness of the proposed substitution and permutation approaches based encryption scheme has been evidenced against brute force and statistical attacks. The proposed encryption scheme exhibits efficient results which have a promotion in statistical analysis parameters such as; histogram distribution, key space analysis, initial key sensitivity, entropy benchmark, correlation coefficients and peak signal to noise ratio.
Predicting Household Water Consumption Events: Towards a Personalised Recommender System to Encourage Water-conscious Behaviour

Recommender systems assist customers to make decisions; however, the modest adoption of digital technology in the water industry means no such system exists for household water users. Such a system for the water industry would suggest to consumers the most effective ways to conserve water based on their historical data from smart water meters. The advantage for water utilities in metropolitan areas is in managing demand, such as low pressure during peak hours or water shortages during drought. For customers, effective recommendations could save them money. This paper presents a novel vision of a recommender system prototype and discusses the benefits both for the consumers and the water utility companies. The success of this type of system would depend on the ability to anticipate the time of the next major water use so as to make useful, timely recommendations. Hence, the prototype is based on a long short-term memory (LSTM) neural network that predicts significant water consumption events (i.e., showers, baths, irrigation, etc.) for 83 households. The preliminary results show that LSTM is a useful method of prediction with an average root mean square error (RMSE) of 0.403. The analysis also provides indications of the scope of further research required for developing a commercially successful recommender system.
Comparison of Probabilistic Models and Neural Networks on Prediction of Home Sensor Events

We present results and comparative analysis on the prediction of sensor events in a smart home environment with a limited number of binary sensors. We apply two probabilistic methods, namely Sequence Prediction via Enhanced Episode Discovery – SPEED, and Active LeZi – ALZ, as well as Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM) in order to predict the next sensor event in a sequence. Our dataset has been collected from a real home with one resident over a period of 30 weeks. The binary sensor events are converted to two different text sequences as dictated by SPEED and ALZ, which are also used as inputs for the LSTM networks. We compare the performance of the algorithms regarding the number of preceding sensor events required to predict the next one, the required amount of data for the model to reach peak accuracy and stability, and the execution time. In addition, we analyze these for two different sets of sensors. Our best implementation achieved a peak accuracy of 83% for a set with fifteen sensors including motion, magnetic and power sensors, and 87% for seven motion sensors.