nils-herrmann/clean_scirepeval huggingface dataset:
Machine Learning for Wireless Networks with Artificial Intelligence: A Tutorial on Neural Networks

Next-generation wireless networks must support ultra-reliable, low-latency communication and intelligently manage a massive number of Internet of Things (IoT) devices in real-time, within a highly dynamic environment. This need for stringent communication quality-of-service (QoS) requirements as well as mobile edge and core intelligence can only be realized by integrating fundamental notions of artificial intelligence (AI) and machine learning across the wireless infrastructure and end-user devices. In this context, this paper provides a comprehensive tutorial that introduces the main concepts of machine learning, in general, and artificial neural networks (ANNs), in particular, and their potential applications in wireless communications. For this purpose, we present a comprehensive overview on a number of key types of neural networks that include feed-forward, recurrent, spiking, and deep neural networks. For each type of neural network, we present the basic architecture and training procedure, as well as the associated challenges and opportunities. Then, we provide an in-depth overview on the variety of wireless communication problems that can be addressed using ANNs, ranging from communication using unmanned aerial vehicles to virtual reality and edge caching.For each individual application, we present the main motivation for using ANNs along with the associated challenges while also providing a detailed example for a use case scenario and outlining future works that can be addressed using ANNs. In a nutshell, this article constitutes one of the first holistic tutorials on the development of machine learning techniques tailored to the needs of future wireless networks. This research was supported by the U.S. National Science Foundation under Grants CNS-1460316 and IIS-1633363. ar X iv :1 71 0. 02 91 3v 1 [ cs .I T ] 9 O ct 2 01 7
RF POWER AMPLIFIERS RF and Microwave Power Amplifier and Transmitter Technologies — Part 2 By

6a. BASIC TECHNIQUES FOR RF POWER AMPLIFICATION RF power amplifiers are commonly designated as classes A, B, C, D, E, and F [19]. All but class A employ various nonlinear, switching, and wave-shaping techniques. Classes of operation differ not in only the method of operation and efficiency, but also in their power-output capability. The power-output capability (“transistor utilization factor”) is defined as output power per transistor normalized for peak drain voltage and current of 1 V and 1 A, respectively. The basic topologies (Figures 7, 8 and 9) are single-ended, transformer-coupled, and complementary. The drain voltage and current waveforms of selected ideal PAs are shown in Figure 10.
Software Defined Network-based Scalable Resource Discovery for Internet of Things

Geo-distributed and heterog eneous Internet of Things (IoT) devices can gener ate hug e amoun t of da ta. Inefficien t manag emen t of IoT-da ta promotes netw ork cong estion and increases computa tional overhead on the data-processing entities. Traditional netw orking architecture, tha t is lack of functional abstr action and monitoring capabilities, often fails to meet the dynamics of IoT. Softw are Define Netw ork (SDN) can be a viable al terna tiv e of the traditional netw orking architecture while dealing with IoT. In SDN, manag emen t, monitoring and context sensing of the connected componen ts are sim plifie and can be customized. In this paper , SDN-sensed contextual inf orma tion of differen t componen ts (computa tional entities, netw ork, IoT devices) are combined together to facilita te scalable resource discov ery in IoT. The proposed policy targ ets balanced processing and cong estion-less forw arding of IoT-da ta. Through sim ula tion studies, it has been demonstr ated tha t the SDN-based resource discov ery in IoT outperf orms the traditional netw orking based approaches in terms of resource discov ery time and Quality of Service (QoS) satisf action rate. Received on 09 April 2017; accepted on 13 July 2017; published on 25 September 2017
Security trends and advances in manufacturing systems in the era of industry 4.0

The next industrial revolution will incorporate various enabling technologies. These technologies will make the product lifecycle of the manufacturing system efficient, decentralized, and well-connected. However, these technologies have various security issues, and when integrated in the product lifecycle of manufacturing systems can pose various challenges for maintaining the security requirements such as confidentiality, integrity, and availability. In this paper, we will present the various trends and advances in the security of the product lifecycle of the manufacturing system while highlighting the roles played by the major enabling components of Industry 4.0.
Fast K-means for Large Scale Clustering

K-means algorithm has been widely used in machine learning and data mining due to its simplicity and good performance. However, the standard k-means algorithm would be quite slow for clustering millions of data into thousands of or even tens of thousands of clusters. In this paper, we propose a fast k-means algorithm named multi-stage k-means (MKM) which uses a multi-stage filtering approach. The multi-stage filtering approach greatly accelerates the k-means algorithm via a coarse-to-fine search strategy. To further speed up the algorithm, hashing is introduced to accelerate the assignment step which is the most time-consuming part in k-means. Extensive experiments on several massive datasets show that the proposed algorithm can obtain up to 600X speed-up over the k-means algorithm with comparable accuracy.
Indoor signal propagation of LoRa technology

With an increasing number of small devices, the concept of LPWANs becomes more popular. The most important requirements on LPWANs include long battery lifetime, low price of devices, ability to connect millions of devices and long range coverage; the long range coverage is the most critical issue of this technology. In this paper we discuss indoor signal propagation of LoRa technology. At the beginning of the paper, there is an introduction to LoRa technology. Later, the paper presents a measurement of an indoor propagation of LoRa signal in a reinforced concrete building in Prague, the Czech Republic. The measurement is realized with one mobile transmitter and one receiver with a fixed location for each experiment. The measurement is performed for two experiments, one with the receiver in the basement of the building, one with the receiver on the roof of the building. Results of the measurement are graphically visualized for both measurement experiments at the end of this paper and show that the best signal coverage is for the roof location, where it includes also flats of neighboring entrances.
Cognitive and emotional influences in anterior cingulate cortex

Anterior cingulate cortex (ACC) is a part of the brain's limbic system. Classically, this region has been related to affect, on the basis of lesion studies in humans and in animals. In the late 1980s, neuroimaging research indicated that ACC was active in many studies of cognition. The findings from EEG studies of a focal area of negativity in scalp electrodes following an error response led to the idea that ACC might be the brain's error detection and correction device. In this article, these various findings are reviewed in relation to the idea that ACC is a part of a circuit involved in a form of attention that serves to regulate both cognitive and emotional processing. Neuroimaging studies showing that separate areas of ACC are involved in cognition and emotion are discussed and related to results showing that the error negativity is influenced by affect and motivation. In addition, the development of the emotional and cognitive roles of ACC are discussed, and how the success of this regulation in controlling responses might be correlated with cingulate size. Finally, some theories are considered about how the different subdivisions of ACC might interact with other cortical structures as a part of the circuits involved in the regulation of mental and emotional activity.
Can neural machine translation do simultaneous translation?

We investigate the potential of attention-based neural machine translation in simultaneous translation. We introduce a novel decoding algorithm, called simultaneous greedy decoding, that allows an existing neural machine translation model to begin translating before a full source sentence is received. This approach is unique from previous works on simultaneous translation in that segmentation and translation are done jointly to maximize the translation quality and that translating each segment is strongly conditioned on all the previous segments. This paper presents a first step toward building a full simultaneous translation system based on neural machine translation.
A Survey of FPGA-Based Neural Network Inference Accelerator 11 : 3

Recent researches on neural network have shown signicant advantage in machine learning over traditional algorithms based on handcraed features and models. Neural network is now widely adopted in regions like image, speech and video recognition. But the high computation and storage complexity of neural network inference poses great diculty on its application. CPU platforms are hard to oer enough computation capacity. GPU platforms are the rst choice for neural network process because of its high computation capacity and easy to use development frameworks. On the other hand, FPGA-based neural network inference accelerator is becoming a research topic. With specically designed hardware, FPGA is the next possible solution to surpass GPU in speed and energy eciency. Various FPGA-based accelerator designs have been proposed with soware and hardware optimization techniques to achieve high speed and energy eciency. In this paper, we give an overview of previous work on neural network inference accelerators based on FPGA and summarize the main techniques used. An investigation from soware to hardware, from circuit level to system level is carried out to complete analysis of FPGA-based neural network inference accelerator design and serves as a guide to future work.
Smart Home automation system using IR, bluetooth, GSM and android

In this busy life while away from home we may tend to forget to switch off the AC appliances which cause wastage of energy. Misuse of power energy can be curtailed by automating the devices and appliances. In Home automation household appliances and residential house features like gates, curtains etc are controlled automatically or semi-automatically. Some of the currently available systems provide a view of the house from a web application; but this can cause trouble to the user. Because user must access the web each time he/she wishes to view the status of the home appliances. In this paper a home automation system that uses IR remote, Bluetooth and GSM to control AC appliances using android app is introduced that is easy to use over the traditional method of the switch. Therefore, the motivation behind the development of this system is to let people know about these technologies, and make the system as simple as possible for an ordinary person to understand. The result of this research is the implementation of home automation system which involves control and automation of home appliances through mobile application from remote locations.
A CONCEPTUAL PRIVACY FRAMEWORK FOR PRIVACY-AWARE IOT HEALTH APPLICATIONS

Internet of things (IoT) is intensely gaining reputation due to its necessity and efficiency in the computer realm. The support of wireless connectivity as well as the emergence of gadgets alleviates its usage essentially in governing systems in various fields. Though these systems are ubiquitous, pervasive and seamless, an issue concerning consumers’ privacy remains debatable. This is most evident in the health sector, as there is an immaculate rise in terms of awareness amongst patients where data privacy is concerned. In this paper, we propose a framework modelling the privacy requirements for IoT-based health applications. We have reviewed several privacy frameworks to derive at the essential principles required to develop privacy-aware IoT health applications. The proposed framework presents important privacy requirements to be addressed in the development of novel IoT health applications.
DESIGN AND ANALYSIS OF COMPOSITE LEAF SPRING IN LIGHT VEHICLE

This project describes design and experimental analysis of composite leaf spring made of glass fiber reinforced polymer. The objective is to compare the load carrying capacity, stiffness and weight savings of composite leaf spring with that of steel leaf spring. The design constraints are stresses and deflections. The dimensions of an existing conventional steel leaf spring of a light commercial vehicle are taken. Same dimensions of conventional leaf spring are used to fabricate a composite multi leaf spring using E-Glass/Epoxy unidirectional laminates. Static analysis of 2-D model of conventional leaf spring is also performed using ANSYS 10 and compared with experimental results. Finite element analysis with full load on 3-D model of composite multi leaf spring is done using ANSYS 10 and the analytical results are compared with experimental results. Compared to steel spring, the composite leaf spring is found to have 67.35% lesser stress, 64.95% higher stiffness and 126.98% higher natural frequency than that of existing steel leaf spring. A weight reduction of 76.4% is achieved by using optimized composite leaf spring.
A survey on classification techniques for opinion mining and sentiment analysis

Opinion mining is considered as a subfield of natural language processing, information retrieval and text mining. Opinion mining is the process of extracting human thoughts and perceptions from unstructured texts, which with regard to the emergence of online social media and mass volume of users’ comments, has become to a useful, attractive and also challenging issue. There are varieties of researches with different trends and approaches in this area, but the lack of a comprehensive study to investigate them from all aspects is tangible. In this paper we represent a complete, multilateral and systematic review of opinion mining and sentiment analysis to classify available methods and compare their advantages and drawbacks, in order to have better understanding of available challenges and solutions to clarify the future direction. For this purpose, we present a proper framework of opinion mining accompanying with its steps and levels and then we completely monitor, classify, summarize and compare proposed techniques for aspect extraction, opinion classification, summary production and evaluation, based on the major validated scientific works. In order to have a better comparison, we also propose some factors in each category, which help to have a better understanding of advantages and disadvantages of different methods.
Demo2Vec: Reasoning Object Affordances from Online Videos

Watching expert demonstrations is an important way for humans and robots to reason about affordances of unseen objects. In this paper, we consider the problem of reasoning object affordances through the feature embedding of demonstration videos. We design the Demo2Vec model which learns to extract embedded vectors of demonstration videos and predicts the interaction region and the action label on a target image of the same object. We introduce the Online Product Review dataset for Affordance (OPRA) by collecting and labeling diverse YouTube product review videos. Our Demo2Vec model outperforms various recurrent neural network baselines on the collected dataset.
CardiacNET: Segmentation of Left Atrium and Proximal Pulmonary Veins from MRI Using Multi-View CNN

Anatomical and biophysical modeling of left atrium (LA) and proximal pulmonary veins (PPVs) is important for clinical management of several cardiac diseases. Magnetic resonance imaging (MRI) allows qualitative assessment of LA and PPVs through visualization. However, there is a strong need for an advanced image segmentation method to be applied to cardiac MRI for quantitative analysis of LA and PPVs. In this study, we address this unmet clinical need by exploring a new deep learning-based segmentation strategy for quantification of LA and PPVs with high accuracy and heightened efficiency. Our approach is based on a multi-view convolutional neural network (CNN) with an adaptive fusion strategy and a new loss function that allows fast and more accurate convergence of the backpropagation based optimization. After training our network from scratch by using more than 60K 2D MRI images (slices), we have evaluated our segmentation strategy to the STACOM 2013 cardiac segmentation challenge benchmark. Qualitative and quantitative evaluations, obtained from the segmentation challenge, indicate that the proposed method achieved the state-of-the-art sensitivity (90%), specificity (99%), precision (94%), and efficiency levels (10 seconds in GPU, and 7.5 minutes in CPU).
Accelerating Binarized Neural Networks: Comparison of FPGA, CPU, GPU, and ASIC

Deep neural networks (DNNs) are widely used in data analytics, since they deliver state-of-the-art accuracies. Binarized neural networks (BNNs) are recently proposed optimized variant of DNNs. BNNs constraint network weight and/or neuron value to either +1 or −1, which is representable in 1 bit. This leads to dramatic algorithm efficiency improvement, due to reduction in the memory and computational demands. This paper evaluates the opportunity to further improve the execution efficiency of BNNs through hardware acceleration. We first proposed a BNN hardware accelerator design. Then, we implemented the proposed accelerator on Aria 10 FPGA as well as 14-nm ASIC, and compared them against optimized software on Xeon server CPU, Nvidia Titan X server GPU, and Nvidia TX1 mobile GPU. Our evaluation shows that FPGA provides superior efficiency over CPU and GPU. Even though CPU and GPU offer high peak theoretical performance, they are not as efficiently utilized since BNNs rely on binarized bit-level operations that are better suited for custom hardware. Finally, even though ASIC is still more efficient, FPGA can provide orders of magnitudes in efficiency improvements over software, without having to lock into a fixed ASIC solution.
WikiQA — A question answering system on Wikipedia using freebase, DBpedia and Infobox

Answering (QA) system is the task where arbitrary question IS posed in the form of natural language statements and a brief and concise text returned as an answer. Contrary to search engines where a long list of relevant documents returned as a result of a query, QA system aims at providing the direct answer or passage containing the answer. We propose a general purpose question answering system which can answer wh-interrogated questions. This system is using Wikipedia data as its knowledge source. We have implemented major components of a QA system which include challenging tasks of Named Entity Tagging, Question Classification, Information Retrieval and Answer Extraction. Implementation of state-of-the-art Entity Tagging mechanism has helped identify entities where systems like OpenEphyra or DBpedia spotlight have failed. The information retrieval task includes development of a framework to extract tabular information known as Infobox from Wikipedia pages which has ensured availability of latest updated information. Answer Extraction module has implemented an attributes mapping mechanism which is helpful to extract answer from data. The system is comparable in results with other available general purpose QA systems.
Phishing websites detection through supervised learning networks

Phishing is an unlawful activity of making gullible people to reveal their insightful information into fake websites. The Aim of these phishing websites is to acquire confidential information such as usernames, passwords, banking credentials and some other personal information. Phishing website looks similar to legitimate website therefore people cannot make difference among them. Today users are heavily relying on the internet for online purchasing, ticket booking, bill payments, etc. As technology advances, the phishing approaches being used are also getting progressed and hence it stimulates anti-phishing methods to be upgraded. In this paper, we have implemented two algorithms named Adaline and Backpropion along with the support vector machine to enhance the detection rate and classification.
Cyberbullying and traditional bullying in adolescence : Differential roles of moral disengagement , moral emotions , and moral values

The aim of this study was to investigate whether different aspects of morality predict traditional bullying and cyberbullying behaviour in a similar way. Students between 12 and 19 years participated in an online study. They reported on the frequency of different traditional and cyberbullying behaviours and completed self-report measures on moral emotions and moral values. A scenario approach with open questions was used to assess morally disengaged justifications. Tobit regressions indicated that a lack of moral values and a lack of remorse predicted both traditional and cyberbullying behaviour. Traditional bullying was strongly predictive for cyberbullying. A lack of moral emotions and moral values predicted cyberbullying behaviour even when controlling for traditional bUllying. Morally disengaged justifications were only predictive for traditional, but not for cyberbullying behaviour. The findings show that moral standards and moral affect are important to understand individual differences in engagement in both traditional and cyberforms of bUllying.
Towards Viable Intrusion Detection Methods For The Automotive Controller Area Network

The Controller Area Network (CAN) in cars is critical to their safety and performance and is now regarded as being vulnerable to cyberattack. Recent studies have looked at securing the CAN and at intrusion detection methods so that attacks can be quickly identified. The CAN has qualities that distinguish it from other computer networks, while the nature of car production and usage also provide challenges. Thus attack detection methods employed for other networks lack appropriateness for the CAN. This paper surveys the methods that have been investigated for CAN intrusion detection, and considers their implications in terms of practicability and requirements. Consequent developments that will be needed for implementation and research are suggested.
Multi-task Adversarial Network for Disentangled Feature Learning

We address the problem of image feature learning for the applications where multiple factors exist in the image generation process and only some factors are of our interest. We present a novel multi-task adversarial network based on an encoder-discriminator-generator architecture. The encoder extracts a disentangled feature representation for the factors of interest. The discriminators classify each of the factors as individual tasks. The encoder and the discriminators are trained cooperatively on factors of interest, but in an adversarial way on factors of distraction. The generator provides further regularization on the learned feature by reconstructing images with shared factors as the input image. We design a new optimization scheme to stabilize the adversarial optimization process when multiple distributions need to be aligned. The experiments on face recognition and font recognition tasks show that our method outperforms the state-of-the-art methods in terms of both recognizing the factors of interest and generalization to images with unseen variations.