Miracle-dz/UniFAR-FTUs huggingface dataset:
Blockchain Meets Edge Computing: Stackelberg Game and Double Auction Based Task Offloading for Mobile Blockchain

Blockchain technology is developing rapidly and has been applied in various aspects, among which there are broad prospects in Internet of Things (IoT). However, IoT mobile devices are restricted in communication and computation due to mobility and portability, so that they can’t afford the high computing cost for blockchain mining process. To solve it, the free resources displayed on non-mining-devices and edge cloud are selected to construct collaborative mining network(CMN) to execute mining tasks for mobile blockchain. Miners can offload their mining tasks to non-mining-devices within a CMN or the edge cloud when there are insufficient resources. Considering competition for resource of non-mining-devices, resource allocation problem in a CMN is formulated as a double auction game, among which Bayes-Nash Equilibrium (BNE) is analyzed to figure out the optimal auction price. When offloading to edge cloud, Stackelberg game is adopted to model interactions between edge cloud operator and different CMNs to obtain the optimal resource price and devices’ resource demands. The mechanism realizes improving the mining utility in mining networks while ensuring the maximum profit of edge cloud operator. Finally, profits of mining networks are compared with an existing mode which only considers offloading to edge cloud. Under the proposed mechanism, mining networks obtain 6.86% more profits on average.
The Effect of Layout and Colour Temperature on the Perception of Tourism Websites for Mobile Devices

In e-commerce, the user interface design of a website is critical to its success. However, there is limited research on how colour and layout design elements influence the perception of e-commerce websites for mobile devices. To bridge this gap, we conducted an empirical study to investigate, how the layout of information and colour temperature of an e-commerce tourism website for mobile device influence essential Technology Acceptance Model (TAM) user experience (UX) design attributes and intention to use the website. The results of our Partial Least Square Path Modelling (PLSPM) showed that both interface design elements significantly influence perceived aesthetics, perceived enjoyment, perceived usefulness and intention to use. Specifically, layout (list = 0 and grid = 1) positively influences perceived aesthetics and perceived enjoyment, while colour temperature negatively influences perceived usefulness and intention to use. The first finding suggests that in tourism website design for mobile devices, a grid layout of products and services provides a better hedonic user experience than a list layout. Moreover, the second finding suggests that cooler-temperature (blue and green) tourism websites are viewed by users as more useful than warmer-temperature (orange and red) tourism websites. We discuss the implications of these findings in the context of website UX design for mobile devices in the tourism domain.
Mathematical Foundations of Adaptive Quantum Processing

Quantum information has the potential to revolutionize the way we store, process, transfer and acquire information [1,14,15,21,37]. In particular, quantum information offers exciting new approaches to secure communication, computation and sensing. However, in order to realize such technologies, we must first understand the effect that environmental noise has on a quantum system. This dissertation builds upon recent studies that have explored the underlying structure of quantum information and the effects of qubit channels in quantum communication protocols. This work is divided into five main chapters, with Chapter 1 being a brief introduction to quantum information. We then begin Chapter 2 by defining the error function for our qubit communication protocols. From there we explore the properties of our error functions and the topological space that they form. In Chapter 3 we consider the newly patented process Adaptive Quantum Information Processing, patent number US9838141 B2; originally outlined by Martin in [23]. We restate the adaptive scheme and exemplify its application through the Prepare and Send Protocol and Quantum Key Distribution. Applying our results from Chapter 2, we obtain an expression for the adaptability of unital channels in these two protocols and classify the channels that admit the most improvement. We dedicate Chapter 4 to the derivation of gravitational noise, and show that in certain circumstances gravity results in a channel that can be maximally improved in Adaptive QKD [3,14,16]. Lastly, we study the set of error functions through the lens of domain theory. Domain theory is a subset of mathematics that was developed in order to rigorously formalize computations. The first four chapters are all consequences of past discoveries in the mathematical structure of quantum channels. In Chapter 5 we characterize the set of error functions through domain theory, extending the mathematical foundations of quantum information. [12,18,20, 22, 23,25]. iii Dedicated to my Mom and Dad: this is the culmination of all the patience, time, and effort you put into me.
Loss of Function in GIGANTEA Gene is Involved in Brassinosteroid Signaling

Brassinosteroids (BRs) are plant steroid hormones that play essential roles in growth and development. Mutations in BR-signaling pathways cause defective in growth and development like dwarfism, male sterility, abnormal vascular development and photomorphogenesis. Transition from vegetative to reproductive growth is a critical phase change in the development of a flowering plant. In a screen of activation-tagged Arabidopsis, we identified a mutant named abz126 that displayed longer hypocotyls when grown in the dark on MS media containing brassinazole (Brz), an inhibitor of BRs biosynthesis. We have cloned the mutant locus using adapter ligation PCR walking and identified that a single T-DNA had been integrated into the ninth exon of the GIGANTEA (GI) gene, involved in controling flowering time. This insertion resulted in loss-of-function of the GI gene and caused the following phenotypes: long petioles, tall plant height, many rosette leaves and late flowering. RT-PCR assays on abz126 mutant showed that the T-DNA insertion in GIGANTEA led to the loss of mRNA expression of the GI gene. In the hormone dose response assay, abz126 mutant showed: 1) an insensitivity to paclobutrazole (PAC), 2) an altered response with 6-benzylaminopurine (BAP) and 3) insensitive to Brassinolide (BL). Based on these results, we propose that the late flowering and tall phenotypes displayed by the abz126 mutant are caused by a loss-of-function of the GI gene associated with brassinosteroid hormone signaling.
SentiNet: Detecting Localized Universal Attacks Against Deep Learning Systems

SentiNet is a novel detection framework for localized universal attacks on neural networks. These attacks restrict adversarial noise to contiguous portions of an image and are reusable with different images-constraints that prove useful for generating physically-realizable attacks. Unlike most other works on adversarial detection, SentiNet does not require training a model or preknowledge of an attack prior to detection. Our approach is appealing due to the large number of possible mechanisms and attack-vectors that an attack-specific defense would have to consider. By leveraging the neural network's susceptibility to attacks and by using techniques from model interpretability and object detection as detection mechanisms, SentiNet turns a weakness of a model into a strength. We demonstrate the effectiveness of SentiNet on three different attacks-i.e., data poisoning attacks, trojaned networks, and adversarial patches (including physically realizable attacks)-and show that our defense is able to achieve very competitive performance metrics for all three threats. Finally, we show that SentiNet is robust against strong adaptive adversaries, who build adversarial patches that specifically target the components of SentiNet's architecture.
Multilevel deep representation fusion for person re-identification

Abstract. Person re-identification (re-ID) aims at matching two pedestrian images across different cameras. Usually, the main scheme of re-ID based on deep learning includes two phases: feature extraction and metric calculation. We focus on how to extract more discriminative image features for re-ID. To address this problem, we propose a multilevel deep representation fusion (MDRF) model based on the convolutional neural network. Specifically, the MDRF model is designed to extract image features at different network levels through one forward pass. In order to produce the final image representation, these multilevel features are fused by a fusion layer. Then the final image representation is fed into a combined loss of the softmax and the triplet to optimize the model. The proposed method not only utilizes the abstract information of high-level features but also integrates the appearance information of low-level features. Extensive experiments on public datasets including Market-1501, DukeMTMC-reID, and CUHK03 demonstrate the effectiveness of the proposed method for person re-ID.
B*-Sort: Enabling Write-Once Sorting for Nonvolatile Memory

Nonvolatile random access memory (NVRAM) has been regarding a promising technology to replace DRAM as the main memory in embedded systems owing to its nonvolatility and low idle power consumption. However, due to the asymmetric read/write costs and limited lifetime of NVRAM, most of the existing fundamental algorithms are not NVRAM-friendly with their write pattern and write intensiveness. Thus, existing fundamental algorithms for NVRAM embedded devices has been revealed. For instance, as the sorting algorithm is one of the most fundamental algorithms, most of the existing sorting algorithms are not NVRAM-friendly because they impose heavy write traffic [i.e., <inline-formula> <tex-math notation="LaTeX">$\mathcal {O}(n\lg {n})$ </tex-math></inline-formula>] on main memory, where <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> is the number of unsorted elements. To resolve this issue, this article proposes a write-once sorting algorithm, namely B*-sort, to reduce the amount of write traffic on NVRAM-based main memory. B*-sort adopts a brand-new concept, i.e., <italic>tree-based sort</italic>, inspired by the binary-search-tree structure to achieve the <italic>write-once property</italic> which can guarantee the optimal endurance during the sorting process. According to the experimental results, B*-sort can achieve significant performance improvement for sorting on NVRAM-based systems.
Screening for Anxiety Disorders Among Schoolchildren with Asthma

Abstract The aim of this study was to perform screening for anxiety disorders among children with asthma and to reveal factors associated with general anxiety disorder and its specific forms. This was a cross-sectional study conducted among out-patients with asthma during routine visits to pediatricians. They were screened for anxiety disorders using SCARED self-reported questionnaire. Additional data were collected using specially designed questionnaire as well as the patient files. Statistical analysis was performed by the SPPS software using descriptive statistics and logistic regression. Study population consisted mostly of schoolchildren (n=58), 8-12 years old, and adolescents (13-17 years) (n=13). Approximately 33.8% respondents were positive for general anxiety disorder. The most common were separation anxiety and social anxiety, recorded among 49.3% and 32.4% of patients, respectively. Generalized anxiety and panic/somatic disorder were recorded in the same percentage of patients (21.3%), while avoiding school was the least frequent (14.08%). Influence of numerous factors was tested, but only the following showed significant effects: peak expiratory flow test was associated with general anxiety disorder, patient’s age and gender with PD, and living place, asthma control according to GINA and age on GAD. Parent’s smoking was associated with SAD, age and patient’s weight status with SPH, and GINA asthma control with SA. These findings suggest that anxiety disorders are common among children and adolescents with asthma. Various factors can be associated with general anxiety disorder and its specific forms, but some of them being preventable as avoiding smoking in the family.
Analysis and classification of Android malware

With the integration of mobile devices into our daily lives, smartphones are privy to increasing amounts of sensitive information. As of 2016, Android is the leading smartphone in popularity with sophisticated mobile malware targeting its data and services. Thus this thesis attempts to determine how accurate and scalable Android malware analysis and classification methods can be developed to robustly withstand frequent, and substantial, changes within the Android device and in the Android malware ecosystem. First, the author presents a comprehensive survey on leading Android malware analysis and detection techniques, and their effectiveness against evolving malware. Through the systematized survey, the author identifies underdeveloped areas of research which lead to the development of the novel Android malware analysis and classification solutions within in this thesis. This thesis considers the usefulness and feasibility of reconstructing highlevel behaviours via system calls intercepted while running Android apps. Previously, this method had only been rudimentarily implemented. However, the author was able to remedy this and developed a robust, novel, framework, to automatically and completely reconstructs all Android malware behaviours by thoroughly analysing dynamically captured system calls. Next, the author investigates the efficacy of using our reconstructed behavioural profiles, at different levels of abstractions, to classify Android malware into families. Experiments in this thesis show our reconstructed behaviours to be more effective, and efficient, than raw system call traces. To classify malware, we utilized support vector machines to achieve high accuracy, precision and recall. Deviating from previous methods, we further apply statistical classification to achieve near-perfect accuracies. Finally, the author explores an alternative Android malware analysis method using memory forensics. By extrapolating from these experiments, the author theorizes how to use this method to assist in capturing behaviours our previous methods could not, and how they could assist classification.
Speech Emotion Recognition: A Survey

Speech Recognition is a tremendous application from the history that is identification and conversion of spoken words into text. The performance and quality of work had increased a lot. This performance lead to the research work on Emotion Recognition based on the language spoken that is obtaining the kind of emotion from the spoken speech which is an application based on human-robot interactions. Emotions can be recognized in a better way using Speech processing, Artificial Intelligence techniques and linguistic semantics. Systems are given training in such a way to detect the emotions from the spoken utterances. This paper contains about the survey from the history to the present works that took place in the speech emotion recognition and also the experiment results. The survey contains about the works that took place from the by different scientists and their usage of different features, classifiers etc. The paper also holds three categories, one is different databases that are involved, second is what features are involved for representation of speech and third about the classification schemes. The survey also includes the conclusions of performances and limitations of current speech emotion recognition .
UDAT: Compound quantitative analysis of text using machine learning

Computing machines allow quantitative analysis of large databases of text, providing knowledge that is difficult to obtain without using automation. This article describes Universal Data Analysis of Text (UDAT) —a text analysis method that extracts a large set of numerical text content descriptors from text files and performs various pattern recognition tasks such as classification, similarity between classes, correlation between text and numerical values, and query by example. Unlike several previously proposed methods, UDAT is not based on frequency of words and links between certain key words and topics. The method is implemented as an open-source software tool that can provide detailed reports about the quantitative analysis of sets of text files, as well as exporting the numerical text content descriptors in the form of comma-separated values files to allow statistical or pattern recognition analysis with external tools. It also allows the identification of specific text descriptors that differentiate between classes or correlate with numerical values and can be applied to problems related to knowledge discovery in domains such as literature and social media. UDAT is implemented as a command-line tool that runs in Windows, and the open source is available and can be compiled in Linux systems. UDAT can be downloaded from http://people.cs. ksu.edu/∼lshamir/downloads/udat.
WEB-BASED SMART COMPLIANCE AUDITING USING ARTIFICIAL INTELLIGENCE: A PROACTIVE DECISIONS SUPPORT SYSTEM FOR FINANCIAL AUDITORS

This research focused on the application of computer in auditing financial records with particular reference to artificial intelligence as a decision support aid for achieving specific, measurable, attainable, relevant, and timely auditing result. We proposed machine learning to automatically coding account entries. Data necessary for auditing process are accessed by the machine and it learning by them then generates result to support the auditor decision. The auditing report was generated by verifying variable required for auditing through fast iterative processing and intelligent algorithm to access compliances with the finance rules of the organization.
Quantization noise reduction in PLLs using multiphase VCOs

This thesis presents the design and implementation of Sigma-Delta PLL with multi­ phase architecture. All the components of the system are designed in 0.18/zm TCMS digital CMOS process, excluding the loop-filter which is an external component. The frequency range of the PLL ranges from 10 MHz to 120MHz and an intended applica­ tion is the subhaxmonic injection signal in the injection locked VCOs and as a clock generator in the subharmonic ADCs. A multiphase architecture,which uses inherent multiphase outputs of the ring oscillator, is presented. The purpose of this architec­ ture is to decrease a quantization noise from the EA modulator. Both single-ended VCO and differential VCO are presented and compared in terms of the phase-noise performance, power consumption, and supply sensitivity. It has been found that single-ended design gives superior performance when the power supply is isolated.
Trust-Aware Decision Making for Human-Robot Collaboration

Trust in autonomy is essential for effective human-robot collaboration and user adoption of autonomous systems such as robot assistants. This article introduces a computational model that integrates trust into robot decision making. Specifically, we learn from data a partially observable Markov decision process (POMDP) with human trust as a latent variable. The trust-POMDP model provides a principled approach for the robot to (i) infer the trust of a human teammate through interaction, (ii) reason about the effect of its own actions on human trust, and (iii) choose actions that maximize team performance over the long term. We validated the model through human subject experiments on a table clearing task in simulation (201 participants) and with a real robot (20 participants). In our studies, the robot builds human trust by manipulating low-risk objects first. Interestingly, the robot sometimes fails intentionally to modulate human trust and achieve the best team performance. These results show that the trust-POMDP calibrates trust to improve human-robot team performance over the long term. Further, they highlight that maximizing trust alone does not always lead to the best performance.
Machine Learning Aided Air Traffic Flow Analysis Based on Aviation Big Data

Timely and efficient air traffic flow management (ATFM) is a key issue in future dense air traffic. The emerging demands for unmanned aerial vehicles and general aviation aircraft aggravate the burden of the ATFM. Thanks to the advanced automatic dependent surveillance-broadcast (ADS-B) technique, the aerial vehicles can be tracked and monitored in a real-time and accurate manner, providing possibility for establishing a more intelligent ATFM architecture. In this article, we first form an aviation Big Data platform by using the distributed ADS-B ground stations and the obtained ADS-B messages. By exploring the constructed dataset and mapping the extracted information to the routes, the air traffic flow between different cities can be counted and predicted, where the prediction task is implemented on the basis of two machine learning methods, respectively. The experimental results based on real-world data demonstrate that the proposed traffic flow prediction model adopting long short-term memory (LSTM) can achieve better performance, especially when abnormal factors in traffic control are considered.
Sentence Classification for Korean Dialog Engine

210 1. 서론 대화 엔진에서의 자연어 이해(Natural Language Understanding) 부분은 크게 (1) 입력 발화의 도메인 (Domain)을 결정하는 도메인 분류기, (2) 결정된 도메인 내에서 사용자가 실제 실행하고자 하는 의도(Intent)를 파악하는 의도 분류기, 그리고 (3) 요구 기능을 실행하 기 위한 파라미터를 얻어내는 엔티티(entity) 추출기로 이루어진다. 예를 들어, 사용자 입력 발화로 “아이유 노래 틀어줘” 가 주어질 경우, 도메인은 “음악”, 음 악 도메인 내에서의 사용자 의도는 “음악 재생”, 그리 고 실제 음악 재생을 위한 파라미터로서 가수명인 “아 이유” 엔티티가 추출되어야 한다. 이 중, 도메인 분류기와 의도 분류기는 모두 주어진 문장을 여러 개의 클래스 중 하나로 분류하는 문제로 정 의될 수 있다. 본 논문에서는, 카카오 미니 인공지능 스 피커에 적용된 한국어 문장 분류 시스템에 대하여 소개 한다. 빠른 처리 속도를 위하여 Convolutional Neural Network (CNN) [1] 을 이용하는 기계학습 기반 접근 방식 을 사용하였으며, 훈련 말뭉치에 등장하지 않는 표현들 을 처리하기 위해서 GloVe[2] 방식으로 한국어 뉴스 데 이터를 이용하여 훈련된 한국어 단어 의미 벡터들이 적 용되었다. 이 때, 영어와는 다른 한국어 특유의 어순으 로 인하여 발생하는 문제를 해결하기 위하여, 의존 문법 구문 분석기를 이용한 GloVe 계산 방식이 제안되었다. 또한, 대규모 말뭉치를 이용해 기훈련된 단어 의미 벡터 로는 얻을 수 없는, 대화 엔진 문제 및 도메인에 특화된 단어 의미 벡터를 얻어내면서 동시에 빠른 수행 속도를 유지하기 위해, 기존에 사용되던 캐릭터 기반 의미 벡터 [3] 대신 도메인 특화 단어 의미 벡터의 사용이 제안되 었다. 실험 결과, 본 논문에 제안된 한국어에 특화된 GloVe 계산 방법으로 훈련된 단어 의미 벡터는 자체 개발된 한 국어 단어 유사도 말뭉치를 이용한 실험에서 기존 GloVe 방식으로 훈련된 의미 벡터에 비해 약 0.7 %p 정도의 성 능 향상을 보였다. 또한, 이를 이용해 훈련된 문장 분류 시스템을 자체 구축된 테스트용 말뭉치에 적용하여 문장 당 평균 분류 시간 12.68 msec, 문장 단위 정확률 96.88 % 의 결과를 얻어낼 수 있었다. 본 논문의 기여는, (1) 한국어의 특성을 고려한 GloVe 의미 벡터 훈련 방법 제안; (2) 한국어 단어 의미 벡터 테스트를 위한 한국어 단어 유사도 말뭉치 구축; (3) 분 류에 걸리는 시간의 추가 소요를 방지하면서 풀고자 하 는 문제에 좀 더 특화된 단어 의미 벡터를 얻어내기 위 한 도메인 특화 의미 벡터의 제안; (4) 실제 사용자에게 제공되는 한국어 대화 엔진을 위한 문장 분류기의 전체 적인 네트워크 구조 제시로 정리될 수 있다.
WINkNN: Windowed Intervals’ Number kNN Classifier for Efficient Time-Series Applications

Our interest is in time series classification regarding cyber–physical systems (CPSs) with emphasis in human-robot interaction. We propose an extension of the k nearest neighbor (kNN) classifier to time-series classification using intervals’ numbers (INs). More specifically, we partition a time-series into windows of equal length and from each window data we induce a distribution which is represented by an IN. This preserves the time dimension in the representation. All-order data statistics, represented by an IN, are employed implicitly as features; moreover, parametric non-linearities are introduced in order to tune the geometrical relationship (i.e., the distance) between signals and consequently tune classification performance. In conclusion, we introduce the windowed IN kNN (WINkNN) classifier whose application is demonstrated comparatively in two benchmark datasets regarding, first, electroencephalography (EEG) signals and, second, audio signals. The results by WINkNN are superior in both problems; in addition, no ad-hoc data preprocessing is required. Potential future work is discussed.