Reviewerly/exHarmony huggingface dataset:
R Statistical Software

This chapter addresses an introduction to R software including the essential concepts, definitions, and expressions employed in R. The reader will be guided step-by-step on how to install R and RStudio to help beginners to comprehend the procedure. Considerable examples are provided to explain programming in R, and a simple interpretation for each function including the name and the intended job of each code is provided. Researchers will be fitted to employ R commands to perform various statistical analyses, become familiar with R scripts to define various variables, a vector, or matrix, and to set a new working directory for the work. Moreover, shapely and colorful plots can be generated by employing the built-in functions in R.
Arabic Text Stemming Using Query Expansion Method

Information retrieval is an activity of finding needed information that satisfies the information need of the users. Stemming is one of the technique uses to improve information retrieval search performance for different languages. The Arabic language is one of these languages that utilize stemming to reduce various words to their stems for both the query and corpus documents. However, most of the approaches that were based on stemming do not provide better results due to poor stemming algorithms. Specifically, root-based stemming produced low precision results as compared to higher recall. This paper presents a new query expansion approach to improve relevant search results. The new approach utilizes word synonyms and stemming. For word synonyms; a set of Arabic word synonyms were used to expands the query. For stemming, both the expanded query and the original corpus were then stemmed using Arabic stemming algorithm. The experiment on Quran datasets for classical and modern standard Arabic shows that the proposed approach achieved almost 15% MAP improvement results on modern standard Arabic dataset.
Dynamic analysis and adaptive obstacle avoidance algorithm of wave glider based on fuzzy control

This paper takes the wave glider as the research object. The wave glider is studied comprehensively from the aspects of motion mechanism, motion law and hydrodynamic performance. Firstly, based on the turbulence model of RANS equation, dynamic mesh method, the hydrodynamic performance of two-dimen sional tandem hydrofoils is analyzed, which has been developed twice. The paper puts forward the a obstacle avoidance algorithm of wave glider based on fuzzy control. Combining the principle of line-of-sight navigation with the idea of fuzzy control, the path planning of wave glider is carried out. The simulation and experimental results show that the algorithm can provide real-time and stable motion path of the wave glider, effectively avoid obstacles and safely reach the target point.
POSTER: Domain-Specialized Cache Management for Graph Analytics

In the domain of graph analytics, power-law graphs are prevalent. In such graphs, a small fraction of vertices are responsible for a large share of all graph connections. These richly-connected (hot) vertices inherently exhibit high reuse. However, this work finds that the state-of-the-art hardware cache management schemes struggle in capitalizing on their reuse due to highly irregular access patterns of graph analytics. In response, we argue in favor of leveraging software knowledge of graph data structures to accurately pinpoint hot vertices in hardware. To that end, we propose GRASP, a domain-specialized LLC management scheme that enables high cache efficiency for graph analytics with minimal modifications to existing cache structures.
Predicting the Torso Direction from HMD Movements for Walk-in-Place Navigation through Deep Learning

In this paper, we propose to use the deep learning technique to estimate and predict the torso direction from the head movements alone. The prediction allows to implement the walk-in-place navigation interface without additional sensing of the torso direction, and thereby improves the convenience and usability. We created a small dataset and tested our idea by training an LSTM model and obtained a 3-class prediction rate of about 90%, a figure higher than using other conventional machine learning techniques. While preliminary, the results show the possible inter-dependence between the viewing and torso directions, and with richer dataset and more parameters, a more accurate level of prediction seems possible.
Color image segmentation using saturated RGB colors and decoupling the intensity from the hue

Although the RGB space is accepted to represent colors, it is not adequate for color processing. In related works the colors are usually mapped to other color spaces more suitable for color processing, but it may imply an important computational load because of the non-linear operations involved to map the colors between spaces; nevertheless, it is common to find in the state-of-the-art works using the RGB space. In this paper we introduce an approach for color image segmentation, using the RGB space to represent and process colors; where the chromaticity and the intensity are processed separately, mimicking the human perception of color, reducing the underlying sensitiveness to intensity of the RGB space. We show the hue of colors can be processed by training a self-organizing map with chromaticity samples of the most saturated colors, where the training set is small but very representative; once the neural network is trained it can be employed to process any given image without training it again. We create an intensity channel by extracting the magnitudes of the color vectors; by using the Otsu method, we compute the threshold values to divide the intensity range in three classes. We perform experiments with the Berkeley segmentation database; in order to show the benefits of our proposal, we perform experiments with a neural network trained with different colors by subsampling the RGB space, where the chromaticity and the intensity are processed jointly. We evaluate and compare quantitatively the segmented images obtained with both approaches. We claim to obtain competitive results with respect to related works.
An Empirical Study of Students and Teaching Staff's Digital Competence in Western China

The society in the digital age is constantly evolving and advancing. At the same time, the emergence of new media, combined with a sea of access to information has also led to changes not only in our daily life but also in education and educational system. Information and communications technology (ICT) tools have already played an important role in our lives. In the aspect of education, we cannot ignore their influences and new challenges they have brought. Amid constantly-changing digital environment that contains the new conditions of interaction technologies, technological tools, digitization, informationalization and multimodality, the traditional literacy becomes outdated and thus the digital competence has become an important factor when people integrate into the contemporary society, helping them adapt to the modern trends and improve their competitiveness. The main objectives of this doctoral thesis are to investigate the actual status about digital competence of university students and teachers in China, especially western China, create their own digital competence profile.
The Insurability of Cyber Risk

In June 2017, the food and beverage conglomerate Mondelez International became a victim of the NotPetya ransomware attack. Around 1,700 of its servers and 24,000 of the company’s laptops were suddenly and permanently unusable. Commercial supply and distribution disruptions, theft of credentials from many users, and unfulfilled customer orders soon followed, leading to losses that totaled more than $100 million. Unfortunately, Zurich, which had sold the company a property insurance policy that included a variety of coverages, informed Mondelez in 2018 that cyber coverage would be denied under the policy based on the “war exclusion clause.” This case, now pending, will be a watershed moment for the cyber insurance industry, highlighting the great ambiguity around the insurability of certain types of cyber risk and the scope of coverage that insurers will provide in the case of a cyber incident. The literature on the insurability of cyber risk has focused all of its attention on questions of economic efficiency and viability. Scholarship has, for example, examined the actuarial challenges in cyber risk modeling and the likelihood for adverse selection resulting from information asymmetries and lack of historical claims data. Scholars have so far avoided a different set of considerations rooted not in economics but rather in public policy analysis of societal values. This paper lays the framework for such an analysis. Relying on traditional insurance and torts jurisprudence the paper makes the public policy case for limited legal interventions in the indemnification of three controversial categories of cyber harm: (1) acts of cyber terrorism or state-sponsored cyber operations; (2) extortion payments for ransomware attacks; and (3) administrative fines for violations of statutory data protection regulations. In so doing, the paper highlights systemic challenges to cyber insurance underwriting while explaining insurers’ role in increasing societal cyber posture by reducing the likelihood of moral hazard and suboptimal cyber-norms enforcement.
Dynamic interaction networks for image-text multimodal learning

Recently, there is a surge of interest in image-text multimodal representation learning, and many neural network based models have been proposed aiming to capture the interaction between two modalities with different forms of functions. Despite their success, a potential limitation of these methods is insufficient to model all kinds of interactions with a set of static parameters. To alleviate this problem, we present a dynamic interaction network, in which the parameters of the interaction function are dynamically generated by a meta network. Additionally, to provide necessary multimodal features that the meta network needs, we propose a new neural module called Multimodal Transformer. Experimentally, we not only make a comprehensively quantitative evaluation on four image-text tasks, but also show some interpretable analyses of our models, revealing the internal working mechanism of the dynamic parameter learning.
SpIitServe

Amazon Web Services (AWS) Lambdas and other "cloud functions" (CFs) offer much lower startup latencies than virtual machines (VMs) (tens/hundreds of milliseconds vs. a few/several minutes) with lower minimum cost. This makes it appealing to use them for handling unexpected spikes in simple, stateless workloads [2, 3, 5]. If the spike persists, additional VMs may be launched and CFs can be decommissioned when the VMs are ready (VMs are cheaper per unit resource procured than CFs). However, it is not immediately clear if using CFs for complex workloads - those involving significant state exchange among components - is similarly effective. Current CFs have several restrictions that may limit their efficacy: (i) relatively limited resource capacity, especially main memory (e.g., an AWS Lambda may only have up to 3GB memory), (ii) limited lifetime (e.g., Lambdas are terminated after 15 minutes), and (iii) limited support for sharing of intermediate state (e.g., Lambdas must employ an external storage system such as AWS S3). Contrary to conventional wisdom, we show that it is possible to exploit the faster startup times of CFs to improve cost and performance of autoscaling even for complex workloads.
Augmented Reality Based IOT Controller

Augmented reality enables computer generated graphics to be superimposed on real world objects. From 3D objects to animations, with the help of AR, one can create an interactive and assistance software applications. This project represents a framework for implementing an AR powered controller with interactive controlling of different devices that are connected to each other and communicate via the internet. The project aims to establish a more fundamental approach in integrating mobile AR with internet of things. The users are able to immerse themselves with a computer generated reality and use it to control vast amount of diverse objects and devices. The mobile application can be used with an AR headset for a better experience. This project produces two important contributions: 1) A mobile AR based application to control IOT enabled devices. 2) Implementation of the application using Unity 3D game engine. 3) Future scope of blending AR with IOT.
SMOTE Algorithm Variations in Balancing Data Streams

From one year to another, more and more vast amounts of data is being created in different fields of application. Great deal of those sources require real-time processing and analyzing, which leads to increased interest in streaming data classification field of machine learning. It is not rare, that many of those applications deal with somehow skewed or imbalanced data. In this paper, we analyze usage of smote oversampling algorithm variations in learning patterns from imbalanced data streams using different incremental learning ensemble algorithms.
Cyber KPI for Return on Security Investment

Cyber security return on investment (RoI) or return on security investment (RoSI) is extremely challenging to measure. This is partly because it is difficult to measure the actual cost of a cyber security incident or cyber security proceeds. This is further complicated by the fact that there are no consensus metrics that every organisation agrees to, and even among cyber subject matter experts, there are no set of agreed parameters or metric upon which cyber security benefits or rewards can be assessed against. One approach to demonstrating return on security investment is by producing cyber security reports of certain key performance indicators (KPI) and metrics, such as number of cyber incidents detected, number of cyber-attacks or terrorist attacks that were foiled, or ongoing monitoring capabilities. These are some of the demonstratable and empirical metrics that could be used to measure RoSI. In this abstract paper, we investigate some of the cyber KPIs and metrics to be considered for cyber dashboard and reporting for RoSI.
Solving Systems of Nonlinear Equations on Multi-core Processors

The paper proposes an approach to solving multidimensional systems of nonlinear equations based on the use of the OpenMP parallelization mechanism and the multicore architecture of modern computers. The software product, which performs the main function - the parallelization of the numerical solution of multidimensional SNE by the Newton method, is developed. The analysis of the speed and efficiency of calculations with different number of processor cores is carried out. As a result, appropriate estimates of the acceleration and efficiency coefficients were obtained. The proposed method is easily scaled to a different number of processor cores. A number of numerical experiments were conducted. The obtained results also indicate the possibility of further optimization of the computational process by developing the multi-core architecture of modern computers.
You get by with a little help: The effects of variable guidance degrees on performance and mental state

Since it can be challenging for users to effectively utilize interactive visualizations, guidance is usually provided to assist users in solving tasks. Guidance is mentioned as an effective mean to overcome stall situations occurring during the analysis. However, the effectiveness of a peculiar guidance solution usually varies for different analysis scenarios. The same guidance may have different effects on users with (1) different levels of expertise. The choice of the appropriate (2) degree of guidance and the type of (3) task under consideration also affect the positive or negative outcome of providing guidance. Considering these three factors, we conducted a user study to investigate the effectiveness of variable degrees of guidance with respect to the user's previous knowledge in different analysis scenarios. Our results shed light on the appropriateness of certain degrees of guidance in relation to different tasks, and the overall influence of guidance on the analysis outcome in terms of user's mental state and analysis performance.
Construction of Classroom Teaching Model Based on the 5G Communication Technology

The top-level design of China points out a direction for reform in education and teaching and it requires full use of emerging technology in promoting innovation of education teaching model. As a new generation of communication technology, 5G enters into our life gradually and reform of teaching mode is facing with a new development opportunity. In this study, problems in classroom teaching under the support of existing communication technology are analyzed and new scenes of classroom teaching supported by 5G technology are explored. A three-order 11-step classroom teaching model based on 5G is constructed by integrating AR immersion technology, remote front-projected holographic display, panorama immersion video technology, somatosensory teaching supporting technology and 8K video camera shooting. Specific operation process of the whole model is introduced. Finally, some conclusions are drawn and further research direction of the research team is pointed out.
360Drops: Mixed Reality Remote Collaboration using 360 Panoramas within the 3D Scene*

Mixed Reality (MR) remote guidance has become a practical solution for collaboration that includes nonverbal communication. This research focuses on integrating different types of MR remote collaboration systems together allowing a new variety for remote collaboration to extend its features and user experience. In this demonstration, we present 360Drops, a MR remote collaboration system that uses 360 panorama images within 3D reconstructed scenes. We introduce a new technique to interact with multiple 360 Panorama Spheres in an immersive 3D reconstructed scene. This allows a remote user to switch between multiple 360 scenes "live/static, past/present," placed in a 3D reconstructed scene to promote a better understanding of space and interactivity through verbal and nonverbal communication. We present the system features and user experience to the attendees of SIGGRAPH Asia 2019 through a live demonstration.
Bridging Theory and Practice with Query Log Analysis

Since large structured query logs have recently become available, we have a new opportunity to gain insights in the types of queries that users ask. Even though such logs can be quite volatile, there are various new observations that can be made about the structure of queries inside them, on which we report here. Furthermore, building on an extensive analysis that has been done on such logs, we were able to provide a theoretical explanation why regular path queries in graph database applications behave better than worst-case complexity results suggest at first sight.
A novel T-S fuzzy particle filtering algorithm based on fuzzy C-regression clustering

In this paper, a novel Takagi-Sugeno (T-S) fuzzy model particle filtering algorithm (TSF-PF) based on fuzzy C-regression clustering is proposed for uncertainty modeling of the target dynamic model with non-Gaussian noise. In the proposed algorithm, a generic semantic framework of the T-S fuzzy model is constructed to incorporate spatial feature information of a target into the particle filter, in which the spatial feature information is characterized by several semantic fuzzy sets. Meanwhile, a fuzzy C-regression clustering method based on correntropy is proposed to adaptively identify the premise parameters of the T-S fuzzy model, which is used to adjust the weight of models, and a Kalman filter is used to identify the consequent parameters. And then an efficient importance density function is constructed by using the proposed T-S fuzzy model, which can efficiently improve the robust and diversity of the sampling particles. Furthermore, in order to improve the real-time performance of the proposed algorithm, two improved T-S fuzzy model particle filtering algorithms are presented. The simulation results show that the tracking performance of the proposed algorithms are better than that of the traditional interacting multiple model (IMM), interacting multiple model unscented Kalman filter (IMMUKF), interacting multiple model particle filter (IMMPF) and interacting multiple model Rao-Blackwellized particle filter (IMMRBPF). Particularly, the proposed algorithms can accurately track the maneuvering target when the moving direction abruptly changes or the prior information of the target dynamic model is inaccuracy.
A clustering fuzzification algorithm based on ALM

In this paper, we propose a fuzzification method for clusters produced from a clustering process, based on Active Learning Method (ALM). ALM is a soft computing methodology which is based on a hypothesis claiming that human brain interprets information in pattern-like images. The proposed fuzzification method is applicable to all non-fuzzy clustering algorithms as a post process. The most outstanding advantage of this method is the ability to determine the membership degrees of each data to all clusters based on the density and shape of the clusters. It is worth mentioning that for existing fuzzy clustering algorithms such as FCM the membership degree is usually determined as a function of distance to the center of the clusters. In our proposed method, all data points of a cluster will play a role in order to determine the membership degrees. Consequently, the obtained membership degrees will depend on all of the data points of clusters, the amount of data points, and the density distribution of the clusters. Simulations prove the advantages of the proposed method.
An Interactive Mechanism to Improve Question Answering Systems via Feedback

Semantic parsing-based RDF question answering (QA) systems are to interpret users' natural language questions as query graphs and return answers over RDF repository. However, due to the complexity of linking natural phrases with specific RDF items (e.g., entities and predicates), it remains difficult to understand users' question sentences precisely, hence QA systems may not meet users' expectation, offering wrong answers and dismissing some correct answers. In this paper, we design an I nteractive M echanism aiming for PRO motion V ia users' fe edback to Q A systems (IMPROVE-QA), a whole framework to not only make existing QA systems return more precise answers based on a few feedbacks over the original answers given by RDF QA systems, but also enhance paraphrasing dictionaries to ensure a continuous-learning capability in improving RDF QA systems. To provide better interactivity and online performance, we design a holistic graph mining algorithm (HWspan) to automatically refine the query graph. Extensive experiments on both Freebase and DBpedia confirm the effectiveness and superiority of our approach.
A text analytics framework for automated communication pattern analysis

Human-coding reliant conversation analysis methods are ineffective when analyzing large volumes of data. In this paper, we propose a text analytics framework for automated conversation pattern analysis. This framework first extracts speech acts (i.e., activities) from conversation logs, and then analyzes their flow through frequent pattern mining algorithms to reveal insightful communication patterns. Using a real-world data set collected from a customer service center, we demonstrate the usefulness of the framework for identifying patterns that are associated with service quality outcomes. Our work has implications for the design of communication policies and systems for customer service management.