Reviewerly/exHarmony huggingface dataset:
An efficient hardware-oriented dropout algorithm

This paper proposes a hardware-oriented dropout algorithm, which is efficient for field programmable gate array (FPGA) implementation. In deep neural networks (DNNs), overfitting occurs when networks are overtrained and adapt too well to training data. Consequently, they fail in predicting unseen data used as test data. Dropout is a common technique that is often applied in DNNs to overcome this problem. In general, implementing such training algorithms of DNNs in embedded systems is difficult due to power and memory constraints. Training DNNs is power-, time-, and memory- intensive; however, embedded systems require low power consumption and real-time processing. An FPGA is suitable for embedded systems for its parallel processing characteristic and low operating power; however, due to its limited memory and different architecture, it is difficult to apply general neural network algorithms. Therefore, we propose a hardware-oriented dropout algorithm that can effectively utilize the characteristics of an FPGA with less memory required. Software program verification demonstrates that the performance of the proposed method is identical to that of conventional dropout, and hardware synthesis demonstrates that it results in significant resource reduction.
Extraction and Recognition of Device Graphics in Process Flow Diagram

Design analysis on process flow diagram can find the logical error and improve efficiency. Existing software used to drawing the process flow diagrams (such as AutoCAD or Visio) does not have recognition and analysis function. For the images output by software, this paper describes the method of extracting and recognition the device graphics representing the valves, gauges, etc. Since the sizes of same device graphics may be different, the device graphics have to be extracted and normalized. In extraction, the points which are obtained by convolving the image with gradient operators, are used to positioning the device graphics. Considering the difficulty of extraction and recognition, the devices graphics are classified by shape. The device graphics with a circle are extracted by circle detection and normalized according to radius. The other device graphics are extracted, and normalized by the bounding rectangle after contours shrink. In recognition, the improved template matching and the recognition method based on structure are used separately to recognize different device graphics. After experimentation, the method described in this paper can significantly improve the recognition rate of various types of device graphics.
Development of Korean Tourist Information Platform Based on Big Data

The Korean Wave contents industry, which is growing steadily around the world, is now attracting foreign tourists interested in K-POP, movies, dramas, and cosmetics mainly in Asian countries. However, when foreign tourists have actually entered South Korea, they cannot fully enjoy the tourist courses planned by them before they go back to their countries in most cases due to time and spatial constraints. In addition, the return rate of foreign tourists who visited South Korea once was only 38% as of last year, which is much lower compared to 61% of the neighboring country Japan. Therefore, in this study, public data for individual regions opened by the government were collected by fully utilizing the rich IT infrastructures, which are an advantage of South Korea, to construct an integrated big data based tourism information visualization platform based on the web interlocked with tourism information service systems including GIS based maps thereby implementing a system to conduct opinion mining analysis with tourism destination evaluation comments collected through the web and accumulate or deduct the points of relevant tourism destinations according to positive or negative reputations.
Messaging Forensics In Perspective

This chapter presents the central theme and a big picture of the methods and technologies covered in this book (see Fig. 2.2). For the readers to comprehend presented security and forensics issues, and associated solutions, the content is organized as components of a forensics analysis framework. The framework is employed to analyze online messages by integrating machine learning algorithms, natural language processing techniques, and social networking analysis techniques in order to help cybercrime investigation.
Morality, Consciousness, and the Phenomenal World: A Future Art Curriculum

Once we come to see that many of our best theories are idealizations, we will also see why our best chance of understanding the world must be to have a plurality of ways of thinking about it. —Appi...
Re-Labeling for Real-time Semantic Segmentation in Specific Environments

In this paper, we perform re-labeling to evaluate the performance of semantic segmentation in a specific environment. By verifying the performance given new type of labels, the potential for future applications is confirmed. To check the performance, we use the BiSeNet V2 network which shows good performance in real-time semantic segmentation. In BiSeNet V2, low-level details and high-level semantics are individually trained to achieve high accuracy and real-time semantic segmentation performance. Using the reconstructed labels from the Cityscapes dataset composed of 2048x1024 images, the real-time semantic segmentation performance was evaluated, achieving an accuracy of 80.9% and a speed of 111 FPS.
Will You Trust This TLS Certificate?

Flawed TLS certificates are not uncommon on the Internet. While they signal a potential issue, in most cases they have benign causes (e.g., misconfiguration or even deliberate deployment). This adds fuzziness to the decision on whether to trust a connection or not. Little is known about perceptions of flawed certificates by IT professionals, even though their decisions impact high numbers of end users. Moreover, it is unclear how much the content of error messages and documentation influences these perceptions. To shed light on these issues, we observed 75 attendees of an industrial IT conference investigating different certificate validation errors. We also analyzed the influence of reworded error messages and redesigned documentation. We find that people working in IT have very nuanced opinions, with trust decisions being far from binary. The self-signed and the name-constrained certificates seem to be over-trusted (the latter also being poorly understood). We show that even small changes in existing error messages can positively influence resource use, comprehension, and trust assessment. At the end of the article, we summarize lessons learned from conducting usable security studies with IT professionals.
Detection of H2S Gas Concentration in Oil Refinery Stations by Using Drone

Detection and controlling of leaks of poisonous gases such as H2S gas in industrial workplaces have drawn the attention of many researchers. In this study, we have used quad-copter Drone (UAV) of 1200 g to detect the leak of H2S gas in one of the refineries in Iraq. The drone is equipped with many components including, but not limited to, global positioner system (GPS), and a hydrogen sulphide H2S sensor to detect the concentration of this dangerous gas in oil refinery facilities. It has four DC motors, and a battery. It flies fifty minutes before replacing the battery with a charged one. The drone has been customized utilizing mission planer or/and Arduino programming. The administrator controls the system by using radio frequency of a remote control. The estimating of the concentration of H2S gas is performed by utilizing MQ sensors connected with the drone. Ten positions were selected in order to test the concentrations of H2S gas, and all values were found in the range of 1.058–5.034 which are considered to be within the allowable limits. The methodology of this study is composed of hardware and software programming through the Arduino Uno. The result indicates that this technology is capable to protect the life of personnel in oil and gas facilities and consequently, this proposed method is promising and could be the basis of further research in this area.
Unsupervised Domain Adaptation for Person Re-Identification with Few and Unlabeled Target Data

Existing, fully supervised methods for person re-identification (ReID) require annotated data acquired in the target domain in which the method is expected to operate. This includes the IDs as well as images of persons in that domain. This is an obstacle in the deployment of ReID methods in novel settings. For solving this problem, semi-supervised or even unsupervised ReID methods have been proposed. Still, due to their assumptions and operational requirements, such methods are not easily deployable and/or prove less performant to novel domains/settings, especially those related to small person galleries. In this paper, we propose a novel approach for person ReID that alleviates these problems. This is achieved by proposing a completely unsupervised method for fine tuning the ReID performance of models learned in prior, auxiliary domains, to new, completely different ones. The proposed model adaptation is achieved based on only few and unlabeled target persons’ data. Extensive experiments investigate several aspects of the proposed method in an ablative study. Moreover, we show that the proposed method is able to improve considerably the performance of state-of-the-art ReID methods in state-of-the-art datasets.
Human Identification Metamorphoses in Virtual Space

Technological virtual reality is a synthesis of natural and artificial intelligence. This is the space of culture, which is articulated through the technologies of artificial intelligence systems. Emerging virtual reality not only complicates personal identification and self-identification but impacts on the definition of a human being in general. A virtual complex is being formed, which includes a system of material objects, social relations, as well as the values and attitudes of consciousness of a given society. In new, virtual conditions, a person may identify him/herself either with the individual bodily or spiritual characteristics, social status parameters, or an avatar, which is involved in various goals of communications. Existential risks of losing identity and self-identity become a real possibility for a human being these days. Humanities as an academic field has an important task today of defining strategies for protection of the holistic concept of a creative human personality.
Gameplay Analysis of Multiplayer Games with Verified Action-Costs

Abstract Measuring player skill cannot be done by considering their historical success alone as the relative skill of their opponents must be considered along with confounding factors such as luck and circumstance. With a specifically designed game, every possible player action can be attributed a cost, the value by which a player reduces their maximum probability of winning. By considering the costs of the actions made by a player we can obtain a more accurate representation of how skilful they are. We developed such a game, the mobile game RPGLite, and compared the actions players made with the cost values we had calculated. Through this analysis we made several observations about RPGLite which we share here to demonstrate the utility of action-costs for gameplay analysis. We show how they can be used to identify game states which players have difficulty making the best moves from, to measure how players learn over time and to compare the strengths and complexity of the characters of RPGLite. Commercial titles could benefit from similar tools—we discuss the feasibility of applying our approach to more complex games.
Securing core information sharing and exchange by blockchain for cooperative system

The privacy protection and information security are two crucial issues for future advanced artificial intelligence devices, especially for cooperative system with rich core data exchange which may offer opportunities for attackers to fake interaction messages. To combat such threat, great efforts have been made by introducing trust mechanism in initiative or passive way. Furthermore, blockchain and distributed ledger technology provide a decentralized and peer-to-peer network, which has great potential application for multi-agent system, such as IoTs and robots. It eliminates third-party interference and data in the blockchain are stored in an encrypted way permanently and anti-destroys. In this paper, a methodology of blockchain is proposed and designed for advanced cooperative system with artificial intelligence to protect privacy and sensitive data exchange between multi-agents. The validation procedure is performed in laboratory by a three-level computing networks of Raspberry Pi 3B+, NVIDIA Jetson Tx2 and local computing server for a robot system with four manipulators and four binocular cameras in peer computing nodes by Go language.
Abnormal/disguised writing

The best document for conducting a comprehensive examination and comparison of the writing is always the original. It is not always possible to reach a conclusion that the questioned writing is abnormal or disguised when examining a copy. Part of the examination process is to determine whether the writing contains any of the indicators of abnormal or disguised writing. This chapter describes some of those indicators and how they are to be evaluated.
Coincidence detection based quantum key distribution protocol

We exploit inherent randomness in photon number distribution of weak coherent pulses coupled with coincidence detection to perform quantum key distribution. This results in higher key rate over longer distance compared to decoy state protocol.
An Approach for Journal Summarization Using Clustering Based Micro-Summary Generation

Researchers are continuously faced with the task of reviewing numerous journal articles from different domains to understand the current trends in their study. This justifies the need for a summarization system that could aid a quick review of articles. The major challenge in summarization lies in distinguishing the more informative parts of a document from the less informative ones. Previous works focused majorly on impact-based summarization approach that depends on external data such as citation sentences, conference talks transcripts and references. This work presents a new full content-based approach for summarization of journal articles based on micro-summary generation to produce sufficiently informative summaries and to aid article review process. The approach focused on the segmentation of the journal articles into sections based on its peculiar structure and the application of an information retrieval sentence similarity-based clustering approach with k-means algorithm to produce micro summaries for each section. The generated micro-summaries are combined systematically to generate the final summary. Results from the evaluation of the proposed approach on seven selected journals proved promising with a good precision value close to 0.8, recall of 1.00 and F-measure of 0.9. The proposed approach also outperformed a similar clustering-based technique without microsummary in accuracy and speed. The proposed approach is expected to aid the article review process of researchers.
Inherent Disagreements in Human Textual Inferences

We analyze human’s disagreements about the validity of natural language inferences. We show that, very often, disagreements are not dismissible as annotation “noise”, but rather persist as we collect more ratings and as we vary the amount of context provided to raters. We further show that the type of uncertainty captured by current state-of-the-art models for natural language inference is not reflective of the type of uncertainty present in human disagreements. We discuss implications of our results in relation to the recognizing textual entailment (RTE)/natural language inference (NLI) task. We argue for a refined evaluation objective which requires models to explicitly capture the full distribution of plausible human judgments.
A Reference Method for Performance Evaluation in Big Data Architectures

This paper presents a reference method for performance evaluation in Big Data architectures, called by Improvement Method for Big Data Architectures (IMBDA) aiming to increase the performance, and consequently raising the quality of service provided. The method will contribute to small businesses and startups that have limited financial re-sources (impossible to invest in market solutions). The proposed approach considers the relationship of the processes in a data processing flow to find possible bottlenecks and optimization points. To this end, IMBDA collects system logs to compose functional metrics (e.g., processing time) and non-functional metrics (e.g., CPU and memory utilization, and other cloud computing infrastructure resources). The system stores these metrics in an external data analysis tool that investigates the correlation of performance between processes. The reference method applies to the architecture of a Big Data application, which provides solutions in fleet logistics. With the use of IMBDA, it was possible to identify performance bottlenecks, allowing the reconfiguration of the architecture to increase service quality at the lowest possible cost.
Local Structure and High-Order Feature Preserved Network Embedding Based on Non-Negative Matrix Factorization

Network embedding, as an effective method of learning the low-dimensional representations of nodes, has been widely applied to various complex network analysis tasks, such as node classification, community detection, link prediction and evolution analysis. The existing embedding methods usually focus on the local structure of the network by capturing community structure, first-order or second-order proximity, etc. Some methods have been proposed to model the high-order proximity of networks to capture more effective information. However, they are incapable of preserving the similarity among nodes that are not very close to each other in network but have similar structures. For instance, the nodes with similar local topology structure should be similar in embedding space even if they are not in the same community. Herein, we regard these structure characteristics as the high-order features, which reveals that the structure similarity between nodes is spatially unrelated. In light of above the limitations of existing methods, we construct the high-order feature matrix for mutually reinforcing the embedding which preserves the local structure. To integrate these features effectively, we propose LHO-NMF, which fuses the high-order features into non-negative matrix factorization framework while capturing the local structure. The proposed LHO-NMF could effectively learn the node representations via preserving the local structure and high-order feature information. In specific, the high-order features are learned based on random walk algorithm. The experimental results show that the proposed LHO-NMF method is very effective and outperforms other state-of-the-art methods among multiple downstream tasks.
Temporal data mining in nuclear site monitoring and in situ decommissioning

Temporal data mining (TDM) is an active and rapidly evolving area in big data science. Is it possible to borrow the concepts and algorithms of TDM and apply them to nuclear site monitoring and in situ decommissioning (ISD) research? Since all the data collected from the nuclear-decommissioning sites are time-specific, age-specific, and development stage–specific, they are ideal for TDM analysis to validate system performance and reveal unknown patterns of material failure, liquid leaking, and radiation field changing. Savannah River National Laboratory has established an ISD Sensor Network Test Bed—a unique, small-scale, and configurable environment—for the assessment of prospective sensors on actual ISD system at minimal cost. This chapter analyzed the baseline data collected by ISD test bed in recent years with the assistant of TDM algorithms/codes to find out frequency episodes in the event stream. The results have confirmed that TDM techniques and corresponding codes are effective tools to validate ISD performance, and the frequent episodes found in the data stream not only showed the daily cycle of the sensor responses but also established the response sequences of different types of sensors, which was verified by the actual experimental setup. Some abnormal patterns may have the potential for prediction of system failures.
The Role of the Information Channel in Visual Computing

Abstract Around seventy years ago, Claude Shannon, who was working at Bell Laboratories, introduced information theory with the main purpose of dealing with the communication channel between source and receiver. The communication channel, or information channel as it later became known, establishes the shared information between the source or input and the receiver or output, both of which are represented by random variables, that is, by probability distributions over their possible states. The generality and flexibility of the information channel concept can be robustly applied to numerous, different areas of science and technology, even the social sciences. In this chapter, we will present examples of its application to select the best viewpoints of an object, to segment an image, and to compute the global illumination of a three-dimensional virtual scene. We hope that our examples will illustrate how the practitioners of different disciplines can use it for the purpose of organizing and understanding the interplay of information between the corresponding source and receiver.
Object detection Cascaded canny edge with SURF method in quadcopter drone simulation

Natural disasters always make a huge problem for human beings. One of the problems is, it needs some time to identify where the victims are located. Inline, the impacted area still unstable and dangerous that could harm the SAR team. To handle this situation, we use Quadcopter to access it. A camera attached to it is used to detect victims, but the detection process needed a longer time. To reduce the time process, we implemented Cascaded Canny Edge and SURF. This method also improve detection accuracy up to 7%. The Canny Edge simplifies the image and creates a unique feature with a specific line that feeds the SURF method that reduces the computational process and time. This research reported a Cascaded canny edge with the SURF method which is compared with the only SURF method in detecting the object. The location of detected victims will be a target parameter to simulate a quadcopter which runs on gazebo. Total time response in searching victims is better using cascaded canny edge with SURF more 6% than the SURF only method.
Towards a Generalizable Comparison of the Maintainability of Object-Oriented and Service-Oriented Applications

While there are several theoretical comparisons of Object Orientation (OO) and Service Orientation (SO), little empirical research on the maintainability of the two paradigms exists. To provide support for a generalizable comparison, we conducted a study with four related parts. Two functionally equivalent systems (one OO and one SO version) were analyzed with coupling and cohesion metrics as well as via a controlled experiment, where participants had to extend the systems. We also conducted a survey with 32 software professionals and interviewed 8 industry experts on the topic. Results indicate that the SO version of our system possesses a higher degree of cohesion, a lower degree of coupling, and could be extended faster. Survey and interview results suggest that industry sees systems built with SO as more loosely coupled, modifiable, and reusable. OO systems, however, were described as less complex and easier to test.