Reviewerly/exHarmony huggingface dataset:
Why is Twitter so popular in Japan?

Abstract Across the countries of the world, Japan can rightly claim to be a great “Twitter nation” ( Akimoto 2011 ). Japanese people like to tweet anytime and anywhere. Although the popularity of Twitter in Japan is often associated with the large information capacity of Japanese character sets ( Wagner 2013 ), Neubig and Duh (2013) prove that this is not necessarily the case. Our research compares two sets of data (300 tweets for each) posted by Japanese and Americans, and demonstrates that Japanese tweets contain more monologic features, or show a higher degree of monologicity, than Americans’ tweets. Also, more than 60% of the sentence-ending forms in the Japanese tweets do not encode explicit addressee orientation. The study reveals that it is not the Japanese unique character sets, but the grammatical devices for monologization that linguistically allow Japanese users to enjoy the fullest benefits of online anonymity and addressee underspecification provided by Twitter.
An Automatic Identification Algorithm for Encrypted Anti-counterfeiting Tag Based on DWT-DCT and Chen’s Chaos

The production, distribution, and consumption of counterfeit goods have been increasing at an alarming rate around the world. In order to resist the bad influence of fake and inferior products, an automatic encryption algorithm for anti-counterfeiting tags based on DWT-DCT and Chen's chaos is proposed in this paper. Chen's chaos is used to encrypt anti-counterfeiting tags on the basis of anti-counterfeiting technology in the algorithm, and the feature vectors are extracted from the encrypted tags by DWT-DCT. Then we set up the corresponding feature vector database. The normalized correlation coefficient (NC) is used to realize the automatic identification of encrypted anti-counterfeit tags. The experimental results show that the algorithm has a good robustness to common and geometrical attacks and has a larger key space to resist attacks such as brute-force attack and other deciphering methods. The results of our experiments indicate that the proposed algorithm is satisfactory in term of the higher security and extraordinary speed as compared to the existing algorithms.
The Coaxing Architecture of Reddit’s r/science: Adopting <i>Ethos</i> -Assessment Heuristics to Evaluate Science Experts on the Internet

Concerned with how individuals assess scientific experts on the Internet, our research investigates the virtual r/science subreddit and their popular Ask-Me-Anything (AMA) series, where scientists engage with an audience for a question-and-answer forum focused on their research. We attend primarily to the Reddit architecture and social norms, examining their role in mediating ethos – that is, the scientist’s performance of expertise. We argue that r/science AMAs promote the adoption of simple ethos-assessment heuristics that enable participants, with little cognitive investment, to presume the trustworthiness and credibility of participating scientists and therefore defer to their expertise.
Ontology-Based Modelling for Learning on Bloom’s Taxonomy Comprehension Level

The taxonomy of educational objectives allows specifying the level of assessments and making the learning process gradual progress over subsequent levels. Out of six levels for the cognitive domain, the most problematic level for e-learning task is the comprehension level because it includes such tasks as interpreting, summarizing and inferring which is typically manually graded. The lack of comprehension-level assessments in e-learning leads to students attempting to solve more difficult tasks without developing comprehension skills, which complicates the learning process.The article discusses using subject domain representation in the form of an ontology model which can be used to visualize the concepts and their relations for students, serve as a basis of a question-answering system for assessing student’s comprehension of the subject domain, generating a natural-language explanation of the student’s mistakes and creating adaptive quizzes. The requirements for the ontology to capture the comprehension level of the subject domain are formulated. The article discusses various application for such ontological models in the learning process.
Population Statistics Algorithm Based on MobileNet

Abstract In today’s society, intelligent video surveillance plays an important role in social security, traffic scheduling, national security and other fields. One of the research hotspots is people statistics based on image processing, which has strategic significance in practical applications. Aimed at the problem that the low accuracy in the actual application scenario, the limited hardware resources, and the low operation efficiency, this paper proposes a multi-feature target detection model based on the lightweight deep learning network MobileNet [1], which can be used in intelligent terminals. The basic feature-extraction network MobileNet as a lightweight network can provide a flexible alternative configuration in terms of efficiency and accuracy. The underlying detection network selects a single deep nerual network, named SSD [2]. The algorithm can achieve multi-scale target detection, and uses the target position and category to perform one-time regression. In this paper, the activation function of SSD is changed into SeLU (scaled exponential linear units) [3], which improves the robustness of the algorithm. At the same time, the work of sample diversity and data enhancement has been made, and the characteristics of the human body above the shoulders have been fully utilized. Experiments have shown that the improved network structure based on MobileNet has higher detection accuracy, lower delay, excellent robustness, while the number of model parameters is effectively reduced.
Saliency and Background Prior-Based Residential Area Detection for SAR Images

Due to the lack of color, the strong speckle noise, and the complex background clutter, target detection in synthetic aperture radar (SAR) images is a challengeable task. A novel saliency and background prior (SBP)-based residential area detection method for SAR images is proposed in this letter. It has three major advantages compared with other methods: 1) in saliency analysis, it deeply exploits the image feature and conceives a new texture representation using the amplitude of partitioning Fourier transform (pFT), which compensates for the lack of color and spectrum information in SAR; 2) it employs the superpixel-level background prior and monitors the average intensity level (AIL) of each superpixel for generating accurate outlines of residential areas; and 3) two regional feature-based indices are presented to select the background clutter, and the results serve as a modification to saliency analysis. Experiments using ALOS PALSAR images show that the proposed method has great priority in both quality and quantity over competing methods by extracting integrated residential areas with clear boundaries.
QoE-Aware Video Collaborative Distribution Mechanism in Cloud Radio Access Networks

In this paper, a video collaborative distribution mechanism is studied in Cloud Radio Access Networks (C-RANs) with object to guarantee the quality of experience (QoE) for different users. Specifically, a framework which enables the remote radio head (RRH)-to-device (R2D) technology to cooperate with the device-to-device (D2D) technology is constructed to transmit video traffic efficiently. Besides, a new QoE evaluation model is built in terms of the transition characteristics of video quality version and the interruption characteristics of video transmissions. Then, the optimal choice of video quality version is studied to achieve a good tradeoff among the video quality, interruption and smoothness for a target user. Moreover, a resource allocation policy is proposed to reduce the mean latency caused by video interruption of the whole network. Simulation results verify that the proposed mechanism performs better than other existing ones when the latency jointly caused by the quality version transition and the transmission interruption is sensitive to the users.
Exploration of Virtual Reality-Based Online Shopping Platform

The first online shopping transformed traditional shopping experience with the development of computer and network in 1984. In traditional shopping, people are able to physically interact with the products by multitude of senses: sight, hearing, taste, smell, and touch before making a purchase. In contrast, online shopping is totally different: people are restricted by the size of 2D screen and can only use the “browser to search keywords” or the “classification” to find the products of interest. Both shopping behaviors have their advantages to satisfy human beings. Therefore, the challenge of this research lies in how to create a more natural online shopping platform by incorporating online shopping and physical shopping? This research aims to use Virtual Reality (VR) device as an alternative bridge to break the boundaries between physical and virtual shopping stores. Our long term project is to create a “VR online shopping platform system”. While this research focuses on the preliminary stage to explore the simulation of VR online shopping platform, the future study will implement the system and apply it to Amazon and evaluate the possibility and feasibility.
Semantic Maps for Knowledge Management of Web and Social Information

We are deluged by a growing amount of data coming from heterogeneous sources, like web sites and social networks, and finding relevant information to extract potentially useful knowledge is becoming every day more challenging. The focus of this chapter is on describing techniques for knowledge representation and management of Web and social media data by semantic information modeling. Specifically, the following methods exploiting semantic information are described: (i) a method that provides a compact and structured representation of the concepts in a document (typically web pages) in form of graphs, ready for classification and agglomeration; (ii) a method to represent and synthesize the information content of Twitter conversations in the form of semantic maps, from which the main topics and orientation of tweeters may easily be read. From the obtained results, we observe that both methods provide promising performance.
Control-Flow Carrying Code

Control-Flow Integrity~(CFI) is an effective approach in mitigating control-flow hijacking attacks including code-reuse attacks. Most conventional CFI techniques use memory page protection mechanism, Data Execution Prevention~(DEP), as an underlying basis. For instance, CFI defenses use read-only address tables to avoid metadata corruption. However, this assumption has shown to be invalid with advanced attacking techniques, such as Data-Oriented Programming, data race, and Rowhammer attacks. In addition, there are scenarios in which DEP is unavailable, e.g., bare-metal systems and applications with dynamically generated code. We present the design and implementation of Control-Flow Carrying Code~(C^3), a new CFI enforcement without depending on DEP, which makes the CFI policies embedded safe from being overwritten by attackers. C3 embeds the Control-Flow Graph (CFG) and its enforcement into instructions of the program by encrypting each basic block with a key derived from the CFG. The "proof-carrying" code ensures that only valid control flow transfers can decrypt the corresponding instruction sequences, and that any unintended control flow transfers or overwritten code segment would cause program crash with high probability due to the wrong decryption key and the corresponding random code bytes obtained. We implement C3 on top of an instrumentation platform and apply it to many popular programs. Our security evaluation shows that C3 is capable of enforcing strong CFI policies and is able to defend against most control-flow hijacking attacks while suffering from moderate runtime overhead.
Placing big graph into cloud for parallel processing with a two-phase community-aware approach

Big graphs are so large that their analysis often rely on the cloud for parallel processing. Data placement, as a key pre-processing step, has a profound impact on the performance of parallel processing. Traditional placement methods fail to preserve graph topologies, leading to poor performance. As the community is the most common structure of big graphs, in this work, we present a two-phase community-aware placement algorithm to place big graphs into the cloud for parallel processing. It can obtain a placement scheme that preserves the community structure well by maximizing the modularity density of the scheme under memory capacity constraints of computational nodes of the cloud in two phases. In the first phase, we design a streaming partitioning heuristic to detect communities based on partial and incomplete graph information. They form an initial placement scheme with relatively high modularity density. To improve it further, in the second phase, we put forward a scale-constrained kernel k-means algorithm. It takes as input the initial placement scheme and iteratively redistributes graph vertices across computational nodes under scale constraints until the modularity density cannot be improved any further. Finally, experiments show that our algorithm can preserve graph topologies well and greatly support parallel processing of big graphs in the cloud.
Fatigue failure monitoring of 316L stainless steel coupons using optical fibre based distributed strain sensing

We demonstrate the application of optical frequency domain reflectometry to detect, locate and track the propagation of fatigue cracks in simple beam-shaped stainless-steel specimens.To do so we recorded the strain distribution along the entire length of hot rolled and additively manufactured 316L steel specimens with a spatial resolution of 1 mm using an embedded optical fibre, and we evaluated fatigue induced damage under four-point bending load cycles.Our findings are threefold.First, we show that the onset of fatigue damage can be detected using our methodology based on a damage index adapted to optical frequency domain reflectometry measurements, which allows alerting for potential failure.We also show that our optical fibre mounting and embedding technique enables the fibre to survive critical failure of the steel specimen.In addition, we obtain strain profile measurements with a spatial resolution that allows linking the strain distribution with imperfections in the four-point bending set-up.
Evaluating Response Delay of Multimodal Interface in Smart Device

Multimodal interface based on natural language processing (NLP) technology is becoming more and more popular. Many studies show that response delay is a key factor to evaluate multimodal interface performance that can influence naturalness and fluency of interaction experience. However, few studies have been conducted to define the optimum response delay time. Focused at multimodal interface with voice as dominant modality, in this paper, we built a research framework to evaluate response delay according to user perception during the voice interaction, in which the system output process was divided into three successive stages. We carried out two experiments to evaluate the influence of response delay time in different stages, a smart speaker with screen and a smart TV were involved in the experiments. The first experiment focused on automatic speech recognition (ASR) feedback delay time, and the second experiment was designed to investigate the influence of both query response delay time and loading response delay time. We defined the satisfying and acceptable delay time for each stage respectively, which could be used as the references to improve corresponding technical performance.
Consciousness and Subconsciousness as a Means of AGI’s and Narrow AI’s Integration

The present article concentrates on the cognitive architecture of the agent capable to form AGI. In the process, an attempt to bridge the existing gap between AGI and narrow AI methods is made. There are two main blocks to do this - Consciousness and Subconsciousness, each of which solves the following classes of tasks: predictive analytics, prescriptive analytics, executive analytics, reflexive analytics, goal analytics, abstraction analytics, and attention analytics. Consciousness uses conscious memory and inference mechanisms. Subconscious uses models and methods of narrow AI and unconscious memory. In order to transfer information from Subconsciousness to Consciousness, emotions, insights, intuitive decisions, algorithms or sequences of actions, expectations, feelings, desires or their hierarchies, abstractions, and attention zones are used. Both conscious and unconscious memory use the metagraph model of knowledge. There is a mechanism for knowledge transfer from unconscious memory to conscious. Acquiring knowledge from the information coming from external channels is performed in parallel to conscious and unconscious memory. Similarly, information output to external channels and performing actions are based on the knowledge of both conscious and unconscious memory. AGI learning occurs due to the expansion of the number of production rules in Consciousness, as well as increasing the number of models and improving the quality of meta-learning algorithms in Subconsciousness.
Micro-Level Analysis and Visualization of Tennis Shot Patterns with Fractal Tables

Sports data analysis and visualization can be a useful tool for gaining insights into the games. In this paper, we present a new technique to analyze and visualize shot patterns in tennis matches. Tennis is a complicated game that involves a rich set of tactics and strategies. The current tennis data analyses are usually conducted at a high level that often fail to show useful patterns and nuances embedded in low level data. Based on a very detailed database of professional tennis matches, we have developed a system to analyze the serve and shot patterns so that a user can explore questions such as "What are the favorite patterns of this player? What are the most effective patterns for this player?" This can help tennis experts, players, and fans gain deeper insight into the sport.
Recognition Algorithm for Biological and Criminalistics Objects

This paper describes the results of a work to develop an algorithm for analyzing images of embossed impressions in paper documents under oblique lighting. The described algorithm could also be used for recognition of similarly-structured objects, for example, some of biological structures. This type of analysis is necessary during forensic analysis of certain security features of paper documents. Part of this analysis is determining to which category new, uncategorized impression belongs to. This research explores the potential for automation of this task using neural networks. The core element of the algorithm is a neural network which determines the similarity between two embossed impressions. The paper describes the structure of the algorithm, a method for creating an image database for training and testing, as well as testing results for proposed algorithm.
Deep Contextualized Word Embeddings for Universal Dependency Parsing

Deep contextualized word embeddings (Embeddings from Language Model, short for ELMo), as an emerging and effective replacement for the static word embeddings, have achieved success on a bunch of syntactic and semantic NLP problems. However, little is known about what is responsible for the improvements. In this article, we focus on the effect of ELMo for a typical syntax problem—universal POS tagging and dependency parsing. We incorporate ELMo as additional word embeddings into the state-of-the-art POS tagger and dependency parser, and it leads to consistent performance improvements. Experimental results show the model using ELMo outperforms the state-of-the-art baseline by an average of 0.91 for POS tagging and 1.11 for dependency parsing. Further analysis reveals that the improvements mainly result from the ELMo’s better abstraction ability on the out-of-vocabulary (OOV) words, and the character-level word representation in ELMo contributes a lot to the abstraction. Based on ELMo’s advantage on OOV, experiments that simulate low-resource settings are conducted and the results show that deep contextualized word embeddings are effective for data-insufficient tasks where the OOV problem is severe.
User-Item-Based Hybrid Recommendation System by Employing Mahout Framework

Recommendation systems are gaining popularity nowadays. Generally, recommendations are used in multiple areas like music, movies online products, news articles, texts, study articles, search engine queries, and social networking tags. Recommender systems use machine learning techniques and data mining algorithms to predict what items should suggest to the users based on some previous information related to the users and their relations to the items. It basically offers the users a limited number of products and services which he/she would like to get among the vast amount of all available items. The growth of information on the Internet, as well as the number of visitors to Web sites, is producing many choices for a customer, but many of those are irrelevant to them. A recommendation system filters this data and refers to the filter’s data to the users. The recommendation system is totally based on its training data, the performing algorithms and the recommending approach. There are many popular approaches like user-based collaborative filtering, item-based collaborative filtering, content-based filtering, and hybrid models. We have implemented three different architectures, item-based, user-based, and factor-based hybrid models in order to build recommender system for an artist. In order to cope up with the huge amount of data, we have used Apache Mahout on top of Hadoop. Mahout is basically a framework and an open source project of Apache. Generally, it is being used for creating scalable machine learning algorithms. Using Mahout, we can fasten the process. Mahout is ready-to-use framework, helps programmers for doing data mining tasks on a large scale of data.
Learning Distributed Cooperative Policies for Security Games via Deep Reinforcement Learning

A rich amount of literature is available for solving the problem of finding equilibrium strategies in two-player security games that harness the power of integer linear programming (ILP). However, in practice, most security games are accurately modeled with multiple agents where ILP methods either fail to find the optimal solution or the state space is large enough making ILP methods an impractical solution. In this paper, we consider a multi-agent security game setting and propose MultiOptGrad: a novel deep reinforcement learning-based solution to learn distributed optimal policies for defenders. Additionally, using MultiOptGrad we built an reinforcement learning framework for robotic bodyguards that recommend deployment strategies for them in a coordinate system. To demonstrate the effectiveness of our proposed solution, we consider an urban security game where a team of robotic bodyguards are protecting a VIP from physical assault in the presence of neutral and/or adversarial bystanders. Our empirical analysis has shown that MultiOptGrad outperformed quadrant load-balancing (QLB): a hand-engineered technique for solving the VIP protection problem.