Reviewerly/exHarmony huggingface dataset:
Comparative study of existing approaches on the Task of Natural Language to Database Language

The use of databases is growing rapidly in many domains and the interaction to these systems requires an advanced knowledge of structural languages like SQL, XQuery, XPath, etc…, in order to correctly use the contents of database systems. In this paper, we introduce the pros & cons of the existing solutions on the task of Natural Language to Database Language including Xml and Relational ones. We focus on the ones that are making use of syntax parsing and the others that utilize deep learning techniques to get the right queries.
Becoming Invisible Hands of National Live-Fire Attack-Defense Cyber Exercise

This paper provides an extensive discussion of the design process for a new format of national live-fire exercise, called Cyber Conflict Exercise (CCE). CCE targets a real-time battlefield drill between red teams (RTs) who try to penetrate a multi-level organization network and blue teams (BTs) who try to defend against them. This is also a competition-based exercise between red teams and between blue teams. Exercise management teams (WTs, white team) want to be invisible hands to provide a dynamic and interesting battlefield experience to both RTs and BTs with balanced set-up. This paper discusses the challenges that need to be addressed in developing and operating CCE where WTs have little control over RTs or BTs. The devised technical and operational solutions to tackle these challenges are discussed.
Extracting Implicit Friends from Heterogeneous Information Network for Social Recommendation

With the popularity of online social networks, social relation data is becoming increasingly important to alleviate the data sparsity and cold-start problem of the traditional recommender systems. Social relations, such as trust or friend relationships, are used as complement source to user feedback data (e.g. item rating). However, using explicitly issued social relations directly may generate sub-optional recommendation results because of the inherent drawbacks of explicit social relations. To address the inherent drawbacks of explicit social relation, we incorporate top-k implicit friends, who can be identified from a heterogeneous information network established by user feedback and user social relation data, into a matrix factorization method to make social recommendations. Experimental results on real-world datasets FilmTrust and Douban show that our method can improve the performance of rating prediction, compared to the social recommender systems using explicit social relation and non-social recommender system.
Spatiotemporal Sequence Memory for Prediction using Deep Sparse Coding

Our brains are, "prediction machines", where we are continuously comparing our surroundings with predictions from internal models generated by our brains. This is demonstrated by observing our basic low level sensory systems and how they predict environmental changes as we move through space and time. Indeed, even at higher cognitive levels, we are able to do prediction. We can predict how the laws of physics affect people, places, and things and even predict the end of someone's sentence.
Smartphone-Based Intelligent Driver Assistant: Context Model and Dangerous State Recognition Scheme

The paper proposes the context model and the dangerous state recognition scheme for intelligent driver assistant system. The system is aimed at utilization of smartphone’s front-facing camera and other sensors for dangerous states reignition to prevent emergency and reduce the accidents probability. The proposed context model is divided into following types of contexts, driver context, vehicle context, road context, and environment context. The model shows how the smartphone front-facing camera and sensors as soon as accessible Internet services are used to support the proposed context types. Then, the context-based dangerous state recognition model is presented. The model calculates the computational power of the smartphone and based on this information implements the frame skipping algorithm to reduce the computation complexity for the smartphone processor. The implementation of the proposed frame skipping model shows that for the modern smartphones the complexity is decreased by three times in compare with usual scheme. The proposed context model and dangerous state recognition scheme has been implemented in Drive Safely system that is available in Google Play.
MIDCN: A Multiple Instance Deep Convolutional Network for Image Classification

For the image classification task, usually, the image collected in the wild contains multiple objects instead of a single dominant one. Besides, the image label is not explicitly associated with the object region, i.e., it is weakly annotated. In this paper, we propose a novel deep convolutional network for image classification under a weakly supervised condition. The proposed method, namely MIDCN, formulate the problem into Multiple Instance Learning (MIL), where each image is a bag which contains multiple instances (objects). Different with previous deep MIL methods which predict the label of each bag (i.e., image) by simply performing pooling/voting strategy over their instance (i.e., region) predictions, MIDCN directly predicts the label of a bag via bag features learned by measuring the similarities between instance features and a set of learned informative prototypes. Specifically, the prototypes are obtained by a newly proposed Global Contrast Pooling (GCP) layer which leverages instances not only coming from the current bag but also the other bags. Thus the learned bag features also contain global information of all the training bags, which is more robust and noise free. We did extensive experiments on two real-world image datasets, including both natural image dataset (PASCAL VOC 07) and pathological lung cancer image dataset, and show the results of the proposed MIDCN consistently outperforms the state-of-the-art methods.
Investigation of the Generational Differences of Two Types of Blog Writers

This study aimed to gain insights into the differences in perceptions of blog writing of two types of writers (i.e., digital natives [DNs] vs. digital immigrants [DIs]). The study focused on the generational literature and Web 2.0 as an online writing platform, investigating the generational differences in DN and DI writers' perceptions on a blog-based writing platform. The “WritingGen” blog was developed for this study to provide a web-based writing place to facilitate writers' writing and editing practices. An empirical study was conducted involving 34 Taiwanese blog writers with five hypotheses to be verified. Data were analyzed using independent samples t tests and logistic regression. The results revealed that the DN writers have significantly more positive attitudes toward blog writing, higher frequencies in blog-based writing behavior, perceived higher satisfaction, and higher knowledge acquisition than the DI writers. Based on these findings, pedagogical implications are provided.
Semantic-Based Feedback Recommendation for Automatic Essay Evaluation

With the development of sophisticated e-learning platforms, educational recommender systems and automatic essay evaluation are becoming an important feature in e-learning systems. Most of the works in educational recommendation techniques are focused on recommending learning materials or learning activities to the learners. In this paper, we proposed and implemented a semantic-based feedback recommendation approach for automatic essay evaluation, which will allow assessors to interact with automatic essay evaluation systems, give feedback on learner’s essay solution in the form of textual comments and provide recommendation to other similar essay solution based on the solution which the assessor has given textual feedback. To compute the semantic similarity and to provide feedback recommendation, we used neural word embedding and relaxed word mover’s similarity. The proposed approach achieves high-performance accuracy, compared to the state-of-the-art methods, according to our experimental results.
Finger Gesture Recognition Based on 3D-Accelerometer and 3D-Gyroscope

Gesture-based interaction, as a natural way for human-computer interaction, has a wide range of applications in the ubiquitous computing environment. The latest research findings reveal that user's arm and hand gestures are likely to be identified with ease using the motion sensors worn on the wrist, but it is not clear how much of user's finger gestures can be recognized. This paper presents a method, which is capable of recognizing the bending of fingers, based on input signals from the 3D-accelerometer and 3D-gyroscope worn on the wrist. Features from Time-domain and Frequency-domain are extracted. Gestures are recognized by five classifiers, and the recognition results were then compared with each other. In this paper, maximal information coefficient is adopted for examining the effect of features on the gesture classification. Besides, we work out a faster calculation method, which is based on the features of top 30 maximal information coefficient. Our present results can be widely applied for medical rehabilitation and consumer electronics control based on gesture interaction.
Blocks' Network: Redesign Architecture Based on Blockchain Technology

The internet is a global network that uses communication protocols. It is considered the most important system reached by humanity, which no one can abandon [1]. However, this technology has become a weapon that threatens the privacy of users, especially in the client-server model, where data is stored and managed privately. Additionally, users have no power over their data that store in a private server, which means users' data may interrupt by government or might be sold via service provider for-profit purposes. Furthermore, blockchain is a technology that we can rely on to solve issues related to client-server model if appropriately used. However, blockchain technology uses consensus protocol, which is used for creating an incontrovertible system of agreement between members across a distributed network [2]. Thus, the consensus protocol is used to slow all member down from generating too fast in order to control the network creation pattern, which leads to scalability and latency problems. This paper will present a platform that leverages modernize blockchain called Blocks' Network. The system is taking into consideration the issues related to privacy and confidentiality from the client-side model, and scalability and latency issues from the blockchain technology side. Blocks' network is a public and a permissioned network that use a multi-dimensional hash to generate multiple chains for the purpose of a systematic approach. The proposed platform is users' assembly point to create a decentralized network via P2P protocols. The system has a high data flow due to frequent use by participants (e.g., the use of the Internet). Besides, the system will store all traffic of the network overtly via Blocks' Network.
Dropout with Tabu Strategy for Regularizing Deep Neural Networks

Abstract Dropout has been proven to be an effective technique for regularizing and preventing the co-adaptation of neurons in deep neural networks (DNN). It randomly drops units with a probability of p during the training stage of DNN to avoid overfitting. The working mechanism of dropout can be interpreted as approximately and exponentially combining many different neural network architectures efficiently, leading to a powerful ensemble. In this work, we propose a novel diversification strategy for dropout, which aims at generating more different neural network architectures in less numbers of iterations. The dropped units in the last forward propagation will be marked. Then the selected units for dropping in the current forward propagation will be retained if they have been marked in the last forward propagation, i.e., we only mark the units from the last forward propagation. We call this new regularization scheme Tabu dropout, whose significance lies in that it does not have extra parameters compared with the standard dropout strategy and is computationally efficient as well. Experiments conducted on four public datasets show that Tabu dropout improves the performance of the standard dropout, yielding better generalization capability.
On XOR lemmas for the weight of polynomial threshold functions

A multilinear polynomial p is said to sign-represent a Boolean function f:{−1,1}n→{−1,1} if f(x)=sgn(p(x)) for all x∈{−1,1}n. In this paper, we consider the length and weight of polynomials sign-representing Boolean functions of the form ⊕kf, the XOR of k copies of f on disjoint sets of variables. Firstly, we show that for an infinite family of functions f, a naive construction does not yield a shortest polynomial sign-representing ⊕kf. More precisely, we give a construction of polynomials sign-representing ⊕kANDn whose length is strictly smaller than the k-th power of the minimum length of a polynomial sign-representing ANDn, for every k≥2 and n≥2 except for k=n=2. Previously, such polynomials were known only for n=2 (Sezener and Oztop, 2015). A similar result for the weight is also provided. Secondly, we introduce a parameter vf⁎ of a Boolean function f and show that the k-th root of the minimum weight of a polynomial sign-representing ⊕kf converges between vf⁎ and (vf⁎)2 as k goes to infinity.
Implementation of an E-Learning Platform in Hybrid Clouds

The provision of computing facilities and services has been revolutionized by cloud computing where virtual resources that are scalable are increasingly offered as services over the internet. Starting out as provision of Software as a Service (SaaS), cloud computing has evolved to Platform as a Service (PaaS) whereby scalable, large-scale computing resources such as data centers are offered as services to Infrastructure as a Service (IaaS) where a complete computing infrastructure and computing resources are integrated as a service to clients. The application model of hybrid clouds including public clouds and private clouds is current mainstream of cloud technology with various advantages such as elastic computing resource, load balance, geo-replication (like those from public cloud), safety, privacy and cost effectiveness (like those from private cloud). Based on previous work on cloud-based electronic design automation for integrated circuit design, implementation of an e-learning platform in hybrid clouds using Eucalyptus private cloud and Openshift public cloud is performed in this study. There are eight virtual machines in private cloud and the same number of virtual machines in public cloud. Each virtual machine in the hybrid clouds is running with Linux operating system, Nginx HTTP server, Wordpress CMS, and shared MySQL database. In hybrid clouds outside sixteen virtual machines, an additional Nginx reverse proxy is running for load balancing due to mass visiting request. In this study, e-learning platform is developed with using Wordpress CMS and PHP language to realize various functions such user identification, WYSIWYG course authoring, video & audio learning material with text, online quiz, and discussion. The Eucalyptus, Openshift, Nginx, Wordpress, and MySQL utilized in this study are all free open source codes that much improvements of the e-learning platform in hybrid clouds is very possible for future works.
Comparative Analysis of Intelligent Personal Agent Performance

Intelligent Personal Assistant (IPA) devices such as Google Home and Amazon Echo have become commodity hardware and are well-known in the public domain. Leveraging these devices as speech-based interfaces to bespoke conversation agent (CA) systems in vocabulary-specific domains exposes their underlying Automatic Speech Recognition (ASR) transcription error rates, which are usually hidden behind a probability matching of utterance to intent (slot filling). We present an evaluation of the two aforementioned IPA's isolated word and phrasal recognition rates together with an improvement scheme associated with a Contextual Multiple Classification Ripple Down Rules (C-MCRDR) CA knowledge-base system (KBS). When measuring isolated-word word error rates (WER) for a human speaker, Google Home achieved an average WER of 0.082 compared to 0.276 for Amazon Echo. Computer-generated utterances unsurprisingly had much poorer recognition rates, with WER for Google Home and Amazon Echo of 0.155 and 0.502 respectively. For phrasal tests, Google Home had an average WER of 0.066 in comparison to the Amazon Echo WER of 0.242 when processing human-sourced sentences. We applied a rule-based transcription error-correcting scheme for isolated words and achieved correct recognition rates of 100% for the Google Home in five of the isolated word data sets, and across all isolated words datasets we improved the initial average WER of 0.082 to 0.0153, a significant decrease of 81.34%.
Using Burstiness for Network Applications Classification

Network traffic classification is a vital task for service operators, network engineers, and security specialists to manage network traffic, design networks, and detect threats. Identifying the type/name of applications that generate traffic is a challenging task as encrypting traffic becomes the norm for Internet communication. Therefore, relying on conventional techniques such as deep packet inspection (DPI) or port numbers is not efficient anymore. This paper proposes a novel flow statistical-based set of features that may be used for classifying applications by leveraging machine learning algorithms to yield high accuracy in identifying the type of applications that generate the traffic. The proposed features compute different timings between packets and flows. This work utilises tcptrace to extract features based on traffic burstiness and periods of inactivity (idle time) for the analysed traffic, followed by the C5.0 algorithm for determining the applications that generated it. The evaluation tests performed on a set of real, uncontrolled traffic, indicated that the method has an accuracy of 79% in identifying the correct network application.
An efficient framework of utilizing the latent semantic analysis in text extraction

The use of the latent semantic analysis (LSA) in text mining demands large space and time requirements. This paper proposes a new text extraction method that sets a framework on how to employ the statistical semantic analysis in the text extraction in an efficient way. The method uses the centrality feature and omits the segments of the text that have a high verbatim, statistical, or semantic similarity with previously processed segments. The identification of similarity is based on a new multi-layer similarity method that computes the similarity in three statistical layers, it uses the Jaccard similarity and the vector space model in the first and second layers respectively, and uses the LSA in the third layer. The multi-layer similarity restricts the use of the third layer for the segments that the first and second layers failed to estimate their similarities. Rouge tool is used in the evaluation, but because Rouge does not consider the extract’s size, we supplemented it with a new evaluation strategy based on the compression rate and the ratio of the sentences intersections between the automatic and the reference extracts. Our comparisons with classical LSA and traditional statistical extractions showed that we reduced the use of the LSA procedure by 52%, and we obtained 65% reduction on the original matrix dimensions, also, we obtained remarkable accuracy results. It is concluded that the employment of the centrality feature with the proposed multi-layer framework yields a significant solution in terms of efficiency and accuracy in the field of text extraction.
TULIP: A Fully Incentive Compatible Blockchain Framework Amortizing Redundant Communication

Blockchain technology allows running ordered transactions within an immutable public ledger in a decentralized manner. It consists of two processes: advertisement where the participants become aware of the new transactions and chain extension where the validated transactions are added into a block by extending the chain. Existing blockchain protocols have mainly focused on the chain extension, though the advertisement process has not been dealt with in depth. More specifically, transaction advertisement is fulfilled in an altruistic manner and the same gossip-like propagation mechanism is used for both transactions and blocks. Consequently, the existing protocols are not only incompatible within a rational setting where participants try to maximize their benefit but also suffering from inefficient bandwidth usage. In this work, we propose Tulip, a two-layered blockchain framework, which enriches any blockchain protocol with respect to the aforementioned advertisement issues. Tulip treats the advertisement and chain extension processes separately. Transactions are collected and verified by a transaction collector, whereas extending the chain with a new block is done by a round leader. In order to achieve a generic framework, we model the basic functionalities of a ledger. These functionalities are realized by any existing blockchain protocol and responsible for the chain extension process. Thanks to the modular structure of Tulip, the chain extension process can be combined with an incentive compatible and bandwidth-efficient advertisement process. Tulip can be built upon any blockchain protocol while preserving the ledger properties persistence and liveness. Moreover, it enhances the liveness into strong liveness property which requires only one honest participant, rather than all, to advertise a transaction. In this manner, to the best of our knowledge, Tulip forms the first blockchain protocol having incentive compatible chain extension and transaction advertisement processes. Finally, we show that Tulip can be used to reduce redundant bandwidth usage by up to 99%.
Improved Differential Evolution Algorithm to Solve the Advertising Method Selection Problem

This article proposes a methodology to resolve the advertising method selection problem (AdSP). The use of different advertising methods for the same product can generate different responses in terms of the product&rsquo;s sales volume. Companies selling products have limited resources for advertising, with challenges such as budget and time constraints. It is necessary that the correct advertising method is selected in order to increase the maximum profit, given these limited resources. In the present study, a mathematical model was developed to represent the AdSP, and optimization software (OS) was utilized to optimally resolve it. However, a larger problem can prevent OS from optimally resolving the problem within a reasonable timeframe. To overcome this challenge, the authors developed a metaheuristic called the improved differential evolution algorithm (IDE), which combines three metaheuristics: (1) The differential evolution algorithm (DE); (2) the iterated local search (ILS); and (3) the adaptive large neighborhood search (ALNS). The performance of IDE reflects the best elements of these three methods. The computational results show that IDE can generate solutions that are similar to the optimal solutions obtained by OS while using 69.25% less computational time than OS. IDE improved the efficiency compared with the three original component methods. Moreover, IDE also found better solutions than those found by the original DE, ILS, and ALNS.
Dynamic Monitoring of Data Center Slices

Slicing is a move towards segmentation of resources and deployment of NFV for the purpose of enhanced services and applications on globally shared resources. The slicing approach in this paper considers Data Center slicing and the VIM on-demand model. We focus on the monitoring of Data Center slices, showing what is needed from the monitoring perspective and how the monitoring should be done. The proposed monitoring approach is validated on a platform that supports the on-demand creation of lightweight VIM instances.