Miracle-dz/UniFAR-FTUs huggingface dataset:
High-Speed Pattern Matching Architecture on Limited Connectivity FPGA

State-of-the-art Field Programmable Gate Arrays (FPGA) are characterized by high frequency of operation, big volume and high bandwidth of its internal memory, which makes them very promising for rapid pattern matching systems realization. Large-scale data structures with high-level of internal connectivity of its elements require parallel data processing architectures with correspondent level of internal connectivity of its blocks. In this paper, we discuss different fields of application of the suggested by the authors approach to map one-dimensional input pattern to the two-dimensional FPGA processing structure with limited connectivity of its elements. Experimental and theoretical research have shown that synthesized on FPGA multicluster architectures for patterns with finely granulated structure matching are characterized by more than 90% utilization of FPGA memory bandwidth, low time delay of 3–5 clock cycles. For input patterns with coarse structure, effectiveness of FPGA memory elements use can decrease to 80%, data processing time delay increases to 5–6 clock cycles.
QoE-Oriented Rate Control and Resource Allocation for Cognitive M2M Communication in Spectrum-Sharing OFDM Networks

With the development of wireless communication systems, it is particularly essential to maximize the quality of experience (QoE) of machine-to-machine (M2M) communication. In this paper, we propose a new QoE-oriented uplink rate control and resource allocation scheme for the Internet of Things (IoT) network, by introducing an evaluation model based on mean opinion score (MOS) for different machine-type communication (MTC) devices. The existing works are only dedicated to solving the short-term resource allocation problems by considering the current transmission time slots, which cannot handle long-standing problems. To this end, based on the recently developed Lyapunov optimization, we convert the original long-term optimization problem into the admission rate control subproblem and the resource allocation subproblem in each time slot. To solve the joint power optimization and sub-channel selection subproblems, Gale–Shapley algorithm is utilized to formulate it as a two-dimensional matching problem, and the preference lists are established by the transmission rate and signal to interference plus noise ratio (SINR). In the proposed algorithms, a priority mechanism is employed to ensure fairness. The simulation results demonstrate that without prior knowledge of the data arrivals and sub-channel statistics, the proposed algorithms can significantly improve the overall perceived quality from the users’ perspective.
Reference Architectures for Security-by-Design IoT: Comparative Study

IoT projects require significant attention at the design stage, as they are particularly vulnerable systems. Security by design must be considered both at the architectural level and in the system deployment. This article validates the application of multi-criteria decision model to compare architecture. We will analyse the capabilities of ten reference architectures for IoT using the Analytic Hierarchical Process (AHP). They are compared using the requirements described by the ITU as criteria. Our comparative study aims to determine the architecture that best meets the availability security requirements.
vReLU Activation Functions for Artificial Neural Networks

ReLU (Rectified Linear Units) layers are widely used in all deep learning architectures. To improve ReLU's performance, some ReLU variants have been proposed in the literature. In this paper, we analyze and compare various kinds of ReLU variants with vReLU: a V-shaped ReLU activation function for artificial neural networks. In particular, we compare and test Leaky ReLU (LReLU), Exponential Linear Unit (ELU) and Scaled Exponential Linear Units (SELU) with vReLU on two datasets: MNIST and not-MNIST. We also analyze these ReLU variants' impact on mean and deviation values for deep artificial neural networks. The comparison and analysis show that vReLU activation function has good performance for classification tasks using artificial neural networks.
Mobility-Aware Task Offloading and Migration Schemes in Fog Computing Networks

Fog computing is an extension of cloud computing, which emphasizes distributed computing and provides computing service closer to user equipments (UEs). However, due to the limited service coverage of fog computing nodes (FCNs), the moving users may be out of the coverage, which would cause the radio handover and execution results migration when the tasks are off-loaded to FCNs. Furthermore, extra cost, including energy consumption and latency, is generated and affects the revenue of UEs. Previous works rarely consider the mobility of UEs in fog computing networks. In this paper, a generic three-layer fog computing networks architecture is considered, and the mobility of UEs is characterized by the sojourn time in each coverage of FCNs, which follows the exponential distribution. To maximize the revenue of UEs, the off-loading decisions and computation resource allocation are jointly optimized to reduce the probability of migration. The problem is modeled as a mixed integer nonlinear programming (MINLP) problem, which is NP-hard. The problem is divided into two parts: tasks off-loading and resource allocation. A Gini coefficient-based FCNs selection algorithm (GCFSA) is proposed to get a sub-optimal off-loading strategy, and a distributed resource optimization algorithm based on genetic algorithm (ROAGA) is implemented to solve the computation resource allocation problem. The proposed algorithms can handle the scenario of UEs’ mobility in fog computing networks by significantly reducing the probability of migration. Simulations demonstrate that the proposed algorithms can achieve quasi-optimal revenue performance compared with other baseline algorithms.
EMERGING TRENDS AND FUTURE COMPUTING TECHNOLOGIES: A VISION FOR SMART ENVIRONMENT

With the rapid technology augmentation, it becomes necessary to find complementary emerging computing technologies. This paper highlights future computing technologies, emerging trends and industry buzz to identify most prominent technologies in India. In the emerging technologies, the market is perceiving the entry of local vendors covering such areas as the Internet of Things (IoT), Robotic Process Automation offerings and Machine Learning based technologies. Some technologies are of transformational nature and results in the foundation of new ecosystem these are, Internet of Things with its associated applications and Machine Learning. Technologies on innovation trigger take more time for wide market acceptance. Main objective of this paper is to present a future vision for smart environment which can provide knowledge accumulation and new directions to new researchers in the related field.
Path-Following Control of an AUV: A Multiobjective Model Predictive Control Approach

The path-following (PF) problem of an autonomous underwater vehicle (AUV) is studied, in which the path convergence is viewed as the main task while the speed profile is also taken into consideration as a secondary task. To accommodate the prioritized PF tasks, a novel multiobjective model predictive control (MPC) (MOMPC) framework is developed. Two methods, namely, weighted sum (WS) and lexicographic ordering, are investigated for solving the MOMPC PF problem. A logistic function is proposed for the WS method in an attempt to automatically select the appropriate weights. The Pontryagin minimum principle is subsequently applied for the WS-MOMPC implementation. The implicit relation between the two methods is shown, and the convergence of the solution with the MOMPC PF control algorithms is analyzed. Simulation studies on the Saab SeaEye Falcon AUV demonstrate the effectiveness of the proposed MOMPC PF control.
Automatic Classification of Chinese Herbal Based on Deep Learning Method

In today's society, people's living standards are getting better and better. At the same time, many problems have also appeared in the diet, which has led to an increase in the incidence of diseases. Chinese herbal medicine has been widely used in the treatment of many diseases. But it is a problem for the collection and classification of Chinese herbal medicines. There are a wide variety of Chinese herbal medicine plants, and there are also some Chinese herbal medicine plants that look very similar. Even a taxonomist can hardly distinguish every herbal medicine, let alone for beginners. So we designs a method to automatically identify and classify Chinese herbal medicines by processing images and deep learning method, which can greatly reduce the workload, and improve the efficiency of work. The technology of Chinese herbal medicine recognition and identification based on image processing and deep learning method can effectively overcome the shortcomings of manual recognition that require rich experience. At present, deep learning is more and more popular, especially for image classification, so we use GoogLeNet to classify 50 kinds of Chinese herbal medicine by their images under natural conditions with complex backgrounds. And the method achieved good performance. TOP-1 achieved an accuracy of 62.8%, and TOP-5 achieved an accuracy of 89.4%.
NON-RUMINANT PRODUCTION

https://doi.org/10.17523/bia.2021. v78.e1498 Bol. Ind. Anim. , Nova Odessa, v. 78, 2021 ACURÁCIA E CONFIABILIDADE DE DOIS ESTIMADORES DE PESO CORPORAL BASEADOS EM MENSURAÇÕES LINEARES EM EQUÍDEOS Resumo Estimativas para determinar o peso corporal de um equino são rotineiramente usadas quando uma balança não está disponível. Todavia, é fundamental realizar um diagnóstico da acurácia e confiabilidade das predições destes estimadores, pois o manejo nutricional, dosificação de fármacos, entre outros, exigem o conhecimento do peso. Assim, objetivou-se avaliar a acurácia e confiabilidade de predições de peso estimado de equídeos utilizando dois modelos de cálculos e discutir o reflexo das predições no manejo dos animais. Foram pesados 71 equídeos machos (40 Muares e 31 Equinos) com idade adulta, em balança de precisão (peso controle). Medidas de perímetro torácico e comprimento corporal foram realizadas e utilizadas para estimar o peso corporal usando os modelos. A acurácia foi avaliada via análise do índice de adequação, comparação entre o quadrado do erro de predição, critério de informação Delta Akaike's e decomposição do erro quadrático médio da predição. Para avaliação econômica, criaram-se três cenários de produção: simples (vermifugação + volumoso), tradicional (vermifugação + volumoso + concentrado) e tradicional com suplementação (vermifugação + volumoso + concentrado + suplementação). Valores econômicos foram verificados em pesquisa de preço na região Centro Oeste do Brasil e convertidos em dólar. Avaliou-se as diferenças de percentagem dos custos utilizando teste de Fisher. O modelo mais adequado para o perfil morfométrico dos animais foi o que usa combinação entre perímetro torácico e comprimento corporal. Foram significativas (P<0,05) as diferenças para os valores econômicos nos cenários de produção. Prejuízos usando o modelo inadequado são 10% superiores em relação ao controle, assim em um estabelecimento com 20 equídeos que usa este modelo, os prejuízos são ≈ 10 mil dólares/ano. Na ausência da balança, é fundamental avaliar qual modelo de cálculo será mais compatível com biotipo corporal do rebanho, pois todo tipo de manejo que depende do peso corporal dos animais, pode gerar prejuízos econômicos significativos.
Prevalence of extracranial carotid atherosclerosis in the patients with coronary artery disease in a tertiary hospital in Malaysia

Abstract There is limited information regarding the prevalence of extracranial carotid atherosclerosis in the patients with coronary artery disease (CAD) undergoing coronary artery bypass graft (CABG) surgery in South East Asia. The primary objective was to assess the prevalence of extracranial carotid stenosis, raised carotid intima media thickness (CIMT), and plaques in the patients with CAD undergoing elective CABG. The secondary objective was to evaluate the risk factors for extracranial carotid atherosclerosis. A total of 119 consecutive patients with CAD undergoing elective CABG in a tertiary hospital in Malaysia were recruited. Data on the demographic characteristics and risk factors were collected. The ultrasound carotid Doppler findings comprising of raised CIMT, plaques, and stenosis in the extracranial carotid vessels were recorded. The mean age of the patients was 64.26 ± 10.12 (range 42–89). Most of the patients were men (73.1%). The patients consisted of 44 (37%) Malays, 26 (21.8%) Chinese, and 49 (41.2%) Indians. A total of 67 (56.3%) patients had raised CIMT, 89 (74.8%) patients had plaques, and 10 (8.4%) patients had stenosis in the internal and common carotid arteries. The mean age of patients with plaques was higher compared to those without plaques (66.00 ± 9.63 vs 59.10 ± 9.92, P = .001). The body mass index (BMI) of patients with stenosis was higher compared to those without stenosis (28.35 ± 4.92 vs 25.75 ± 3.16, P = .02). The patients with plaques were more likely to be older, whereas the patients with carotid stenosis were more likely to have higher BMI.
High-Accuracy Entity State Prediction Method Based on Deep Belief Network Toward IoT Search

The state of physical entity in the Internet of Things (IoT) has an obvious time-varying characteristic. Preliminarily selecting candidate entities by predicting their current state when searching match entities from massive ones can effectively reduce the communication overhead of IoT search system. The existing methods are all based on shallow learning theories whose performances are very limited. Thus, a high-accuracy entity state prediction method (HESPM) based on deep learning theory is proposed. The model of HESPM is built by utilizing the deep belief network. Then the contrastive divergence algorithm is adopted to train the model. Therefore, the dynamic evolution trend of entity state can be accurately perceived and the future entity state can be precisely predicted. Simulation results demonstrate the effectiveness of HESPM in enhancing the prediction accuracy and communication overhead performances.
Factors Affecting Performance of Web Flows in Cellular Networks

Studies show that more than 95% of the traffic generated by smartphones typically consists of short-lived TCP flows towards websites. The content of such websites often is distributed across multiple servers which requires clients to resolve multiple DNS names and establish multiple TCP connections to fetch the webpage in its entirety. Studies have shown that network latency in a mobile network (attributed to DNS lookup and TCP connect times) contributes heavily to poor experience when browsing such websites. However, there is little understanding of the factors that contribute to high DNS lookup and TCP connect times. In this paper, we take this further by measuring the Domain Name System (DNS) lookup time and the TCP connect time to popular websites from ~25K subscribers of a cellular network operator in Finland. Using a month-long dataset (Oct 2016) of these measurements, we show that LTE offers considerably faster DNS lookup time compared to legacy cellular networks (such as HSPA+ and UMTS). We also show that the model of the device and the proximity of the DNS server to the subscribers impacts the DNS lookup time. Furthermore, the TCP connect time is also affected by the radio technology. We observe that LTE offers a significantly low latency profile such that the TCP connect time to popular websites is reduced by ~80% compared to legacy cellular networks. The presence of ISP caches also considerably improves TCP connect times. Using a ping test, we also observe that legacy radio technologies (such as HSPA+ and UMTS) suffer from higher packet loss than LTE.
Unsupervised Images Generation Based on Sloan Digital Sky Survey with Deep Convolutional Generative Neural Networks

Convolution neural networks (CNN) has gain huge successful in computer vision applications and image classification in recent years. However, unsupervised learning with CNN attracts less attention than supervised learning. In this work, we use deep convolutional generative adversarial neural networks (DCGANs) to generate images. The model is trained by various star and galaxy images from Sloan Digital Sky Survey. In this paper, we also took a gird search to choose the best value for hyper-parameters and several methods to help to stabilize the training process and promise a good quality of the output. Based on several experiments, we demonstrate that our approach keeps the training procedure stable and the generator and discriminator are both good for unsupervised leaning.
Network revenue management with dependent demand under overlapping segments

There are many simplified assumptions that do not conform to the actual conditions in the traditional revenue management models, including the independence of customer demand and the assumption on the passive customers. More recently, there has been a growing body of researches on choice-based revenue management, which considers customers' particular preference as well as strategic behavior. The most basic model on this topic is the dynamic programming model but it is difficult to effectively solve, so a lot of approximation methods emerge, one of which is the famous choice-based deterministic linear program (CDLP). The drawback of CDLP is that the number of variables is exponentially related to the number of products so it has to use column generation or other algorithms for solving. Another approximation model sales-based linear program (SBLP) is easier to solve compared to CDLP. But the two models are both based on the disjoint segments case, which has limitations in reality. This paper addresses the network choice-based revenue management problem with overlapping segments. We develop a modified sales-based linear program based on the generalized demand model GAM rather than MNL model adopted by most of the choice models. In the calculation process, we find that the new model can be solved very quickly, and the results are very similar to the CDLP value.
Optimal User Pairing for Downlink Non-Orthogonal Multiple Access (NOMA)

In this letter, we explore user pairing in a downlink non-orthogonal multiple access (NOMA) network. As power allocation inherently intertwines with user pairing, a joint user pairing and power allocation problem is considered to optimize the achievable sum rate (ASR) with minimum rate constraint for each user, which is a mixed integer programming problem. To solve this non-convex problem, we first obtain the optimal power allocation in an NOMA system with only 2 users; then analyze the user pairing problem in a simplified situation, i.e., an NOMA system with four users. Finally, we obtain the closed-form globally optimal solution in a general NOMA system. Extensive performance evaluations are conducted to compare the ASRs of the NOMA and OMA systems. Results show that the performance of the NOMA system with the proposed optimal user pairing is significantly better than that of the OMA system, as well as the performance of the NOMA system with random user pairing.
Application of modern technologies of mathematical simulation for the development of medical equipment

This paper describes the use of application Finite Difference Time Domain (FDTD) technique of computational modeling for development and simulation waveguide antennas for microwave radiometry of cervix cancer. The motivation for this work focuses on the development of this new intracavitary antenna. Slot waveguide antenna was selected as optimum using 500 MHz bandwidth and center frequency 3.8 GHz. The new sensor was tested and validated on different phatoms of biological tissues. Results suggest sufficient characteristics of new antenna for potential use in cervix cancer detection and cervix research.
Deep Learning-Based Implementation of Hate Speech Identification on Texts in Indonesian: Preliminary Study

This paper presents an implementation of hate speech identification task for text data written in Indonesian language. There are some studies purposed for similar problem, but all of them use classical machine learning approach, whose heavily depends on the feature engineering. Switching the domain of data set means that the feature engineering should be redone. To address this issue, this preliminary research proposes another method based on deep learning approach which needs no feature engineering and is also adaptive to the varying context. Using data sets sourced from Twitter posts, the proposed method gives better result of 94.5% F1-score at a minimum.
IP Spoofing Detection for Preventing DDoS Attack in Fog Computing

One of the biggest security threats to the availability of service in Fog Computing has been identified as Distributed Denial of Service (DDoS), whose attributes include IP address spoofing. In this paper, various methods for identifying spoofed IP packets in Fog Computing are discussed. In order to match the Operating System (OS) of incoming packet from its database, we proposed a Host-Based OS fingerprinting that uses both active and passive technique. Consequently, the proposed technique was implemented using the Xen Cloud Platform (XCP).
A Robust and Real-Time Approach for License Plate Detection

License plate detection (LPD) plays an important role in intelligent transportation system. Many LPD methods work effectively under particular scenarios or strict assumptions. However, when the processed vehicle license plate images lie in a more complex environment, such as different illuminations, diverse state of the weather, various camera equipment rotations and so on, these methods usually lead to poor performance. Therefore, in this paper, we present a novel method based on convolutional neural network (CNN) to perform high-accuracy and real-time vehicle license plate detection. By using an improved CNN, the license plate features from the bottom and high levels of the network are extracted and integrated to achieve precise and real-time detection. In terms of detection accuracy and detection efficiency, experimental results on the public dataset AOLP demonstrate that the approach proposed in this paper outperforms other existing state-of-the-art approaches. Also, the proposed approach is robust to varieties of complicated scenarios, exhibiting great practicability.
A Tag-Based Personalized News Recommendation Method

Although the news tags show the topic information that is closely related to the news content, the existing personalized news recommendation methods usually ignore the value of tags. In this paper, the probability relation graph among tags is first established by mining the potential correlation among different tags. On this basis, we leverage the term frequency-inverse document frequency (TF-IDF)to find the way of calculating the weights of tags and propose the method of calculating the correlation degree between tags by means of the conditional probability. Finally, a personalized news recommendation method based on the probability relation graph among tags is proposed by leveraging the weights of tags and the correlation degree between tags, and the validity of the proposed method is verified by the experiments on the real data set.
LTE Signal Fingerprinting Device-Free Passive Localization Robust to Environment Changes

This paper shows the feasibility of an LTE signal fingerprinting Device Free Passive localization system. The experimental setup considers one single LTE receiver in the monitored room. The paper shows that when ambient signals such as LTE are used, the use of RSSI as fingerprint provides very poor performance. CSI-based approaches demonstrate much better performance. Moreover, the system using CSI shape and statistical descriptors rather than direct CSI vectors is characterized by more stable performance also when the room configuration is changed (i.e., the furniture is moved).