Reviewerly/exHarmony huggingface dataset:
Self-Supervised Monocular Depth Estimation via Effective Local Consistency and Multi-scale Attention

Recent studies have shown that pixel-level depth estimators can be learned through the self-supervised method, resulting in progress in depth prediction. However, due to the complexity of realistic scenarios, the assumption of brightness constancy, which is the basis of the optimization function designed by existing methods, is invalid in practice. As a result, the optimization target may be chosen in an inefficient manner, and thus affecting the performance of depth estimation. This paper tackles this issue by integrating local consistency into the framework. Specially, we exploit the gradient from depth map to generate a valid mask, which is capable of locating the key region and optimizing the photometric loss by ensuring the local consistency of masked points between target image and synthesis image. In addition, we combine the depth maps from multiple scales to effectively exploit multi-scale features and excavate detailed information of depth map. Comprehensive evaluation results on the KITTI dataset demonstrate that the proposed algorithm greatly improves the ability of capturing details of depth changes and achieves more accurate results.
Knowledge Management in 2016

This article will very briefly indicate some current topics and developments in the field of cognitive engineering (CE), knowledge engineering (KE) and knowledge management (KM). From there, it will present an extensive look at the development and usage of new Delphi model, starting with its origin in the 1980's and later, its successful usages with modifications into the early 2000's. An effort will be made to show how that new Delphi, called the small-group Delphi paradigm (SGDP), can now be again modified to take advantage of expansions in the field of KE/KM and of the great strides in communication and computer technology. Next, an example of how such an up-dated, modified SGDP could be applied to a current, serious problem in aviation, as well as an indication of other issues for which this new Delphi also could provide useful results.
Literature Review

In this chapter, we have addressed the literature of the various methods used in related work to vision-based control.
Remote Attestation of Large-scale Virtual Machines in the Cloud Data Center

With the development of cloud computing, remote attestation of virtual machines has received extensive attention. However, the current schemes mainly concentrate on the single prover, and the attestation of a large-scale virtualization environment will cause TPM bottleneck and network congestion, resulting in low efficiency of attestation. This paper proposes CloudTA, an extensible remote attestation architecture. CloudTA groups all virtual machines on each cloud server and introduces an integrity measurement group (IMG) to measure virtual machines and generate trusted evidence by a group. Subsequently, the cloud server reports the physical platform and VM group's trusted evidence for group verification, reducing latency and improving efficiency. Besides, CloudTA designs a hybrid high concurrency communication framework for supporting remote attestation of large-scale virtual machines by combining active requests and periodic reports. The evaluation results suggest that CloudTA has good efficiency and scalability and can support remote attestation of ten thousand virtual machines.
Dark Web Analytics : A Comparative Study of Feature Selection and Prediction Algorithms

The value and size of information exchanged through dark-web pages are remarkable. Recently Many researches showed values and interests in using machine-learningmethods to extract security-related useful knowledge from those dark-web pages. In this scope, our goals in this research focus on evaluating best prediction models while analyzing traffic leveldata coming from the dark web.Results and analysis showed that feature selection played an important role when trying to identify the best models. Sometimes the right combination of features would increase the model’saccuracy. For some feature set and classifier combinations, the Src Port and Dst Port both proved to be important features.When available, they were always selected over most other features. When absent, it resulted in many other features being selected to compensate for the information they provided. The Protocol feature was never selected as a feature, regardless of whether Src Port and Dst Port were available
Symbolic Recurrence Quantification Analysis and its Application

In this paper, symbolic recurrence analysis (SRA) and joint symbolic recurrence analysis (JSRA) are proposed as novel measures to quantify the recurrence characteristics of systems. The construction of symbolic recurrence method avoids the nuisance of approaching parameter ε and symbolic recurrence plot (SRP) provides a richer perspective than traditional recurrence plot (RP) since it is represented by the symbols of color. Besides, symbolic recurrence quantification analysis (SRQA) is further proposed to interpret the structure shown in the SRP quantitatively. The method is proved to be effective in the experiments of synthetic data and real data. In the research of specific model, sliding window algorithm and multiscale algorithm are combined to obtain valuable dynamic information and multiscale information of nonlinear properties hidden in systems. As demonstrated in simulation models, the method can identify the chaos, certainty and chaotic transition states in single variable system, as well as detect the multiscale joint recurrence property of bivariate system. In addition, it shows that this method well distinguishes the different characteristics before, during and after the financial crisis of financial system and reveals distinctive-shaped structures in multiscale which indicate the inherent relation between three stock markets. The proposed method brings together recurrent plots and symbolic dynamics to empower researchers with effective means to visualize and quantify complex behavior in dynamic system.
Design of unmanned plant monitoring system based on cloud platform

Abstract Aiming at the phenomenon that people are keen to cultivate green plants in life, but because of busy work and business trips. They forget to water in time and cause plants to die due to lack of water. Based on this, this paper proposes a plant unmanned monitoring system design based on cloud platform. Using STM32 as the main control chip and ESP8266 wireless module as the communication module. Using the Internet of Things cloud service technology to send the data collected in the smart home to the cloud combine various sensors and hardware circuit design to realize remote control of the intelligent watering system. The tested system has stable operation, low design cost, easy expansion of functions, strong real-time effect, high precision and broad application prospects. The system debug results show that the design can meet the unmanned online real-time monitoring of plants and it is suitable for home use.
QoS-aware online mechanism for dynamic VM provisioning in cloud market using Q-learning

Cloud provider (CP) leases various resources such as CPUs, memory and storage in the form of virtual machine (VM) instances to clients over internet. This paper tackles the issue of quality of service (QoS) provisioning in cloud environment. We examine using Q-learning for provisioning VMs in the cloud market. The extracted decision function should decide when rejecting new request for VMs that violate QoS guarantee. This problem requires the reward for CP be maximised while simultaneously meeting a quality of service (QoS) constraints. These complex contradicting objectives are embedded in our Q-learning model that is developed and implemented as shown in this paper. Numerical analysis shows the ability of our solution to earn significantly higher revenue than alternatives.
A Survey on Anomalous Topic Discovery in High Dimensional Data

Generally, finding of an unusual information i.e. anomalies from discrete information leads towards the better comprehension of atypical conduct of patterns and to recognize the base of anomalies. Anomalies can be characterized as the patterns that don't have ordinary conduct. It is likewise called as anomaly detection. Anomaly detection procedures are for the most part utilized for misrepresentation detection in charge cards, bank extortion; organize interruption and so on. It can be eluded as, oddities, deviation, special cases or exception. Such sort of patterns can't be seen to the diagnostic meaning of an exception, as uncommon question till it has been incorporated legitimately. A bunch investigation strategy is utilized to recognize small scale clusters shaped by these anomalies. In this paper, we show different techniques existed for recognizing anomalies from datasets which just distinguishes the individual anomalies. Issue with singular anomaly detection strategy that identifies anomalies utilizing the whole highlights commonly neglect to identify such anomalies. A strategy to recognize bunch of anomalous information join show atypical area of a little subset of highlights. This technique utilizes an invalid model to for commonplace topic and after that different test to identify all clusters of strange patterns.
Activity

Ascertaining the degree of correspondence between audit area assertions or direct subject matter and audit criteria is a professional mandate. Under normal circumstances, audit area planning, study, and testing permit IT audit team members submitting working papers for review by the in-charge IT auditor. Preceding IT audit report preparation, the in-charge IT auditor must review documented inconsistencies and departures from applicable IT principles discovered during the engagement. Moreover, the in-charge IT auditor must apply an in-depth understanding of what constitutes appropriate audit evidence. Chapter 7 conveys evidential working papers expectations that assist in ensuring appropriate audit engagement fieldwork. Chapter 7 also discusses the assessment of audit findings and the evaluation of audit working papers.
Non-Compliant Mobile Device Usage and Information Systems Security: A Bystander Theory Perspective

This study tested the context of employees using their devices for both work and personal use, and non-compliant device usage of a person potentially resulting in Information Systems (IS) security threat to personal as well as work data and/or the devices. Integrating bystander and protection motivation theory (PMT) perspectives this paper studies bystanders' responses to IS security threats and the extent to which a perceived security threat motivates individual intention to act, in the context of non-compliant mobile device usage behaviors. It tests the role of an individual's threat perceptions to protect their own IS security, and as a bystander, protecting their peers or the IS security of their organization. Data collected from 431 individuals support the hypotheses that security awareness predicts perceived severity and protection motivation. Evaluation apprehension and diffusion of responsibility inhibit bystander's intentions to act against non-compliant mobile device usage behaviors, while awareness facilitates it. Theoretical contributions and practical implications of the research are discussed.
Performance Comparison of Phoneme Modeling using MFCC and IHCC Features with various Optimization Algorithms for Neural Network Architectures

Human speech signal is rich with information such as the identity of the speaker, the spoken message, emotional and physical state of the speaker, the spoken language, gender, age and other information. Automatic Speech Recognition (ASR) involves complex tasks aimed at recognition and translation of human speech to text by computers. Phoneme Recognition means recognizing the phonemes associated with speech utterance and is a part of ASR. Developing a phonetic engine and enhancing its performance can lead to significant improvement in ASR. In this paper we propose Artifical Neural Network (ANN) based Phoneme Modeling. We compare performance of speech features such as Inner Hair Cell Coefficients (IHCC) and Mel-Frequency Cepstral Coefficient (MFCC) using various neural network architectures, different optimization algorithms, change in input data vector dimensionality (corresponding to different contextual information) and also increasing the number of epochs. Experiments were carried out on TIMIT database. Our experimental results indicates MFCC performs much better than IHCC feature and the best optimization algorithm is found to be SGD for IHCC and Adagrad for MFCC features by trying out with different sizes of input vector, changing the number of training iterations, training algorithms for various neural network architectures.
Multi-target relative distance perception based on monocular image

These years, target detection has been frequently used in autonomous driving, UAV scene analysis and robot vision due to its technological breakthroughs. Aiming at the problem that the category and category probability information provided by target detection is insufficient in the field of autonomous driving, a multi-target relative distance perception algorithm based on monocular images is proposed. Using monocular depth estimation to obtain a depth image, apply the target bounding box information to the depth map to find the location of the target, and then combine the relative distance perception based on pixel values to get relative distance information. The results of our experiments show that the proposed viewpoint adds a new information compared with traditional target detection, can provide relative distance information, and can help to realize the assistant driving of short distance straight line.
An image encryption scheme based on digital fractional Fourier transform and chaos theory

This research presented an image encipherment technology adopting the fractional Fourier transform and chaos theory. To expand the key space of the encipherment protection technology, we adopt the sampling digital fractional Fourier transform instead of traditional digital Fourier transform. Then, the improved double random phase masks are proposed to achieve the goal of making the encryption result close to the noise image and solve the problems of large key storage space. Based on the above works, we designed an image encryption scheme and the specific implementation details are given. In order to prove the validity and security of our designed encryption algorithm, we analyzed the effectiveness of the designed encipherment algorithm in four aspects through several experiments. The results proved that the designed image encryption algorithm not only has outstanding key safety level and great anti-statistical attack ability, but also has great anti-data loss ability and anti-noise ability.
Kinect-Based Limb Rehabilitation Methods

Within the context of health informatics, this article discusses how real-time information of human skeleton movements can be conveniently captured via the use of Kinect-based deep sensor. It highlights an effective action recognition method, using Unity3D to form virtual models of characters and scenes. Kinect somatosensory cameras can now identify one's motion data and provide feedback on the virtual role model to complete the avatar drive. This is to achieve real-time simulation modeling as well as drive operation instructions based on the design and identification verification to complement limb rehabilitation. The hardware of such a health informatics-related system is simple, inexpensive, and highly meaningful in term of augmenting user experience. In conclusion, the suggested approach can contribute significantly towards aiding physical rehabilitation therapy
SocialBully: A Social Information-Driven Cyberbullying Detector with Similarity-Based Word Embedding

Cyberbullying depicts the form of bullying that is spread through information and communication technologies, most common of which is through the Internet. When compared to traditional bullying, cyberbullying can spread quicker and reach a wider audience and, because of this, can have severe effects on people’s mental health. In this paper, we propose a new cyberbullying detection method – SocialBully. SocialBully systematically integrates both users’ social information and their posted textual information to achieve a high detection accuracy. It uses a new word embedding method “SimWord” to represent words based on the similarity of their co-occurrence vectors and exploits a graph embedding method to obtain the social representation of users. Based on these representations, SocialBully uses a bidirectional Long Short-Term Memory to detect bullying texts. Extensive experiments are conducted on four real-world datasets to validate the effectiveness of the proposed method.
Probability

This is an edited and enhanced transcript (with notes and references added) from a lecture on "Probability", prepared as background material for the Minds and Machines course (SYMSYS 1/PSYCH 35/LINGUIST 35/PHIL 99) at Stanford University. From a video recorded July 30, 2019, as part of a series of lectures funded by a Vice Provost for Teaching and Learning Innovation and Implementation Grant to the Symbolic Systems Program at Stanford, with post-production work by Eva Wallack. Topics include Basic Probability Theory, Conditional Probability, Independence, Philosophical Foundations, Subjective Probability Elicitation, and Heuristics and Biases in Human Probability Judgment.
Introduction to Graphs

Graphs are key data structures that find numerous applications. A graph has a number of nodes some of which are connected. This simple structure can be used to represent many real-life applications such as a road network, communication network and a biological network. In fact, any network may be modeled by a graph and the methods of graph theory can be implemented conveniently to solve various problems in these networks. We define graphs, review types, operations on graphs and graph representations in this chapter to form the basic background for further chapters in this part.
이론적 접근을 통한 기업의 사이버 보안 관리 및 문화 형성

Businesses in any country are vulnerable to cyber threats. The rise of Internet of Things, cloud computing, and communication platforms contributed to a drastic increase in those threats. Therefore, it is of importance to address the threats and risks that businesses face. Considering the significance of this situation, this paper aims to provide a theoretical understanding of cyber security management for businesses via literature review. Research framework in this work consists of three steps, (i) Cyber security risk management frameworks, (ii) Cyber security management measures, and (iii) Cyber security culture. Cyber security risk management frameworks can be adopted on the strategic level, and cyber security risk management measures can be used on the practical level. If these are taken successfully, cyber security culture is to be cultivated for the perpetuation of cyber security management within a business. In this paper, first, various models and elements of the frameworks and measures are explained, and second, a balanced approach is discussed in order to make a success of cyber security management. It is expected to lay the foundation for conceptualisation of cyber security management.
Research on Translation Teaching Combining Online News and Online Artificial Intelligence Translation

With the significant improvement of online artificial intelligence translation quality, the paper is intending to figure out the attitude of the students towards the translation class combining the fresh online news and online artificial intelligence translation to engage the students and foster the students’ "readers" awareness by focusing on the exact meaning of online translated sentences; and then the paper puts forward the related suggestions.
Distributed Reduced Kernel Extreme Learning Machine

Extreme learning machines (ELM) are popular in the field of pattern recognition and machine learning. The kernel extension of ELM (KELM) presents a better performance than traditional ELM. Although the KELM is able to solve complex nonlinear problems, it is time-consuming and memorydemanding when dealing with a large-size kernel matrix. Introducing reduced kernel technique can dramatically cut down the computational load and memory usage. However, as the amount of training data grows exponentially, it is not effective for a single worker to store the kernel matrix, making it infeasible for data mining in a centralized manner. In this paper, a distributed reduced kernel method for training ELM over decentralized data (DRKELM) is proposed. In the DRKELM, we randomly assign data to different nodes. The communication between nodes is fixed and does not depend on the size of the training data on each node, but on the network topology. Different from the existing reduced kernel ELM, the DRKELM is a fully distributed training algorithm based on the method of alternating direction method of multiplier (ADMM). Experiment with the large scale data set finds that the distributed method can achieve almost the same results as the centralized algorithm and even takes less time to a large extent. It greatly reduces the computation time consumption.
Recursive LMMSE Sequential Fusion with Multi-Radar Measurements for Target Tracking

By simply stacking all converted measurements, recursive LMMSE (linear minimum mean square error) filtering for a single radar has been extended to the case of centralized fusion with multiple radars. To further improve the performance of the LMMSE centralized fusion, [1] ranks all scalar measurements from multiple radars dimension by dimension, and then recombines these measurements for LMMSE filtering. However, due to the inherent shortcomings of centralized fusion, they have potential limitations in practical application. In this paper, we first develop an information filtering form of the recursive LMMSE filter by equivalent transformation, to avoid the inverse operation of innovation covariance. Then, a recursive LMMSE sequential fusion with multi-radar measurements is presented depending on the information filter. The sequential fusion is theoretically optimal in the sense that it is equivalent to the LMMSE centralized fusion. Numerical examples show that the recursive LMMSE sequential fusion with recombined multi-radar measurements performs better in terms of estimation accuracy.
Mutual Correlation-Based Anonymization for Privacy Preserving Medical Data Publishing

Currently the industry is focused on managing, retrieving, and securing massive amounts of data. Hence, privacy preservation is a significant concern for those organizations that publish/share personal data for vernacular analysis. In this chapter, the authors presented an innovative approach that makes use of information gain of the quasi attributes with respect to sensitive attributes for anonymizing the data, which gives the fruitfulness of an attribute in classifying the data elements, which is a two-way correlation among attributes. The authors show that the proposed approach preserves better data utility and has lesser complexity than former methods.
Quantum Wavelet Packet Transforms

Quantum wavelet packet transform (QWPT) may play an important role in quantum information processing. In this chapter, the authors design quantum circuits of a generalized tensor product (GTP) and a perfect shuffle permutation (PSP). Next, they propose multi-level and multi-dimensional (1D, 2D and 3D) QWPTs, including Haar QWPT (HQWPT), D4 QWPT (DQWPT) based on the periodization extension and their inverse transforms for the first time, and prove the correctness based on the GTP and PSP. Furthermore, they analyze the quantum costs and the time complexities of the proposed QWPTs and obtain precise results. The time complexities of HQWPTs is at most six basic operations on 2n elements, which illustrates high efficiency of the proposed QWPTs.