Reviewerly/exHarmony huggingface dataset:
A Systematic Framework for State Channel Protocols Identification for Blockchain-Based IoT Networks and Applications

Adoption of Blockchain for the Internet of Things (IoT) has seen a steady increase of interest for overcoming some key challenges like security, privacy and decentralization. However, most blockchains are computationally expensive, have scalability bottlenecks and, if not adopted efficiently may lead to higher transactional costs which are not suitable for most IoT network deployments. State Channel protocols for blockchain networks have been one of the solutions for improving the scalability and minimizing the computation overhead in the system. In this paper, we analyse various state channel protocols and propose a systematic framework to decide upon choosing a specific protocol and apply them for IoT networks as per the needs and requirements of the enterprise.
Implementing Interactive 3-D Models in an Entry-level Engineering Course to Enhance Students’ Visualization

The ability to evaluate engineering elements, identify expected deformations, and predict possible failure mechanisms are critical skills for engineers.However, it has been observed that many undergraduate engineering students in applied mechanics courses struggle with applying these skills in engineering problems.Previous studies have shown that three-dimensional (3D) visualization can help students to improve spatial understanding, learn material more permanently and improve their creativity.In an attempt to build on this phenomenon, interactive 3D models using Augmented Reality (AR) were incorporated in a Mechanics of Materials course.This course is an entry level course and a major requirement for different engineering disciplines such as Civil, Mechanical, Biomedical, Materials Science, and Manufacturing Engineering.Two levels of learning were targeted in this study; applying and creating.For applying, 3D models were made available that could be viewed on a smartphone using an AR application or on a computer.The models were generated for problems that students in previous years found challenging or expressed difficulty in visualizing.Students were then encouraged to use this model to inform their problem solving.For creating, students were given the opportunity to generate 3D models based on textbook examples, indicate types of stresses and display deformed shapes.For both activities, student perceptions, best practices, and lessons learned are noted.Students were also asked to provide feedback about their experience and the effectiveness of AR models in their learning in class evaluation surveys.To evaluate the effect of using 3D models on students' performance, an independent study was conducted with students in the Mechanics of Materials course.In this two-problem study, one group only had access to a traditional two-dimensional (2D) schematic, while the other group had access to a 3D model.The experimental and control groups were then swapped for the second problem.The results of this initial study revealed that 3D models can significantly improve students' performance.It is anticipated that discussing the benefits and challenges associated with incorporating such activities, along with providing suggestions for incorporation, will help other institutions add similar activities to their engineering courses in an effort to improve student learning.
Introduction Virtual Reality for Learning Media in Schools in Indonesia

Abstract This paper aims to introduce Virtual Reality (VR) as a learning tool for students. As the first study (Case I), we present a VR simulation as a learning therapy tool using the VAKT method (visual, auditory, kinesthetic, tactile) for children with Autism Spectrum Conditions (ASC). The second example (Case II), VR was used as a display environment to explore biology learning for senior high school students. Finally, the result from our experiences and lessons learned, VR can be used as a tool for school learning both for regular students and ASC.
Legion: A K-12 HPC Outreach and Education Cluster

Outreach and education of High-Performance Computing (HPC) concepts pose the challenge of explaining and clarifying complex ideas and bringing them to a more digestible format. The current outreach done by the Holland Computing Center (HCC) of the University of Nebraska includes workshops, training, and tours with activities for groups ranging from K-12 students to researchers. The Legion Raspberry Pi cluster is a tool created to aid training and outreach HCC conducts with the Nebraska University and research community. The Legion cluster aids in demonstrating concepts such as what a computing cluster is and what its components are, the characteristics of different types of jobs ran on high-performance systems, and to expand outreach possibilities for HCC further. The cluster accomplishes the goal through scripted animations of lights on Raspberry Pis to demonstrate load, and through a series of LED bulbs ”connecting” the Pis in a representational network of a cluster environment. Using the cluster as an addition to existing outreach activities and events can then further aid the education of High-Performance Computing concepts.
Awareness of Crime-related Information and Concealed Information Detection method

Concealed information test based on P300 and machine learning has become increasingly popular in the fields of cognitive psychology. Numerous studies have set up mock crime scenario to identify changes in EEG cognitive components. However, only two kinds of subjects are taken into account in most previous studies. Therefore, if an innocent person had been aware of case-related information, i.e., probe items, which is likely to happen in practice, recognition capability will be significantly compromised. In order to simulate practical cases, three kinds of subjects needed to be discriminated, including guilty, innocent and informed. 36 subjects went through a mock crime scenario, and EEG signals obtained on 8 electrodes were analyzed. After preprocessing, the discrete wavelet packet decomposition was used to extract EEG features. Subsequently, a multi-scale wavelet kernel extreme learning machine classifier is proposed to recognize the group to which a specific subject belongs. To further reduce computation, Cholesky decomposition is introduced during the calculation of the output weights. Our results demonstrate that the proposed algorithm can achieve good recognition performance and has low computational burden.
Mental Models of Sustainability: The Degrowth Doughnut Model

Due to the concern that achieving human wellbeing through material development is “costing the Earth,” a mental model was developed to show in a single image the aspiration of social foundations of development to be achieved and biospheric boundaries not to be crossed. Anthromes analysis, combined with the cultural imperative of maintenance of global sustainability through coordinated transformation of social metabolism and its environmental impact, makes nation states the immediately available units of sustainability modeling. In this century humans must meet their needs equitably within the biophysical means of the planet. A downscaling of planetary boundaries and social wellbeing foundations (thresholds) to national level through calculations of the impacts and attainments of nation states’ socioeconomic activities makes the doughnut model a conceptual tool bringing sustainability closer to political a and organizational impact. To visualize the scale and the possible pathways for the transformation of national and global sociometabolic practices in the 21st century within the “degrowth doughnut” includes the boundaries and thresholds in three domains: cultural, socioeconomic, and biophysical. This way it aims to avoid the conceptually paralyzing trade-off between exclusively biophysical boundaries and exclusively social thresholds of the other doughnut models. Understanding that excesses and shortfalls of current and foreseeable socio-metabolic practices exist in cultural, socioeconomic, and biophysical aspects of nations’ social metabolism allows us to build on nations’ sustainability potentials. The aim of the model and its visual tool is to inform their populations about the direction and scale of the change strategies that they could adopt to contribute to the global effort of maintaining the planetary population within the safe and just operating space of the doughnut under known advantages and constraints of the 21st century.
A machine learning‐based memory forensics methodology for TOR browser artifacts

Summary At present, 96 % of the resources available into the World‐Wide‐Web belongs to the Deep Web , which is composed of contents that are not indexed by search engines. The Dark Web is a subset of the Deep Web , which is currently the favorite place for hiding illegal markets and contents. The most important tool that can be used to access the Dark Web is the Tor Browser . In this article, we propose a bottom‐up formal investigation methodology for the Tor Browser's memory forensics. Based on a bottom‐up logical approach, our methodology enables us to obtain information according to a level of abstraction that is gradually higher, to characterize semantically relevant actions carried out by the Tor browser. Again, we show how the proposed three‐layer methodology can be realized through open‐source tools. Also, we show how the extracted information can be used as input to a novel Artificial Intelligence‐based architecture for mining effective signatures capable of representing malicious activities in the Tor network. Finally, to assess the effectiveness of the proposed methodology, we defined three test cases that simulate widespread real‐life scenarios and discuss the obtained results. To the best of our knowledge, this is the first work that deals with the forensic analysis of the Tor Browser in a live system, in a formal and structured way.
Detecting Text in the Scene Text Image Using Fast Fourier Transform

Abstract Text in images express a lot of information for image understanding. Detecting and recognizing text facilitates many important applications. The characteristics of a text with different shapes and contour patterns require special computer vision to detect and recognize when compared to other objects in the image. Text detection is the process of detecting text in an image followed by surrounding text with a rectangular bounding box. It can be performed by using image-based or frequency-based techniques. Many applications require text detection in images ranging from automatic detection of traffic signs that help in the transportation system to assist blind people and others in need. The data of this study were Focused Scene Text with the JPG format. The method used to detect the text in images was Fast Fourier Transform (FFT). The final results of the study showed that 95% of the text could be detected in an image by various camera perspectives and was expected to help develop text recognition applications in an image.
ExclusiveOR-Discrete Cosine Transform- A Chaotic Algorithm for Image Encryption and Decryption

Image encryption is an operative method to shield images or videos from disrupting, while transporting them in a shared link for security reasons. The security levels are increased by applying various algorithms. In this literature, image encryption is done with chaotic methods. Chaotic representation like XOR representation is used to scramble the image data in a pixel by pixel limit. The input image is scrambled using P Fibonacci-XOR transform with masking coefficients and Discrete Cosine Transform (DCT). Later, the scrambled images got unscrambled by applying the inverse of the applied function. As a result, Chaotic concepts avoids iteration of the image data by the unintended persons and the performance of the ExclusiveOR-Discrete Cosine Transform XORDCT algorithm is analyzed by PSNR, NPCR, UACI and Correlation coefficient factors.
A Conceptual Model of Indonesian Question Answering System based on Semantic Web

Abstract This study aims to develop a conceptual model of question answering system in Bahasa using Semantic web technology. A trigger was built to periodically collect data scattered on the internet in a data center to shorten the query process on data without reducing the data completeness. The test case domain was automotive. This study used four types of ontologies: main ontology, ontologies from manufacturers, dealers, and rentals. This research has some novelties, such as solving the weakness of distributed and centralized data and combining the advantages of both by storing distributed data into a pool (centralized data) periodically, then querying the pool. Furthermore, it makes contribution by providing a conceptual model of Indonesian question answering system, particularly in automotive domain. This has some implications, such as extend the body of knowledge of computer science, especially semantic web and ontology fields.
Beyond the picture frame: The function of fixations in interactive tasks

When looking at an image, participants typically concentrate their fixations on a subset of locations that can be considered salient or important. A productive avenue of research has involved trying to explain how our eyes are guided to these locations. Here, I review research that investigates fixations in more interactive tasks, where participants move around or interact with the scene rather than viewing a picture. When walking or performing actions with real objects, fixations are closely linked to the current hand and body movements. In social interactions, gaze also provides a signal that can be interpreted by other people. In sequential tasks, fixations may serve multiple functions and when and where they are guided will change with different stages of the task. Interactive tasks can be studied in real-world settings or in the laboratory, and they provide important insights into the guidance and function of gaze.
Design of expert system for tool selection in digital forensics investigation

Abstract Along with the diverse types of electronic goods and digital evidence, techniques have also developed in conducting digital forensic investigations. Another effect is the increase in types and types of tools for conducting digital forensic investigations. The problem is the increasing number of digital forensic tools is not always developed in conjunction with the manual book. So this is what makes the investigator to check the tool to make it suitable for investigations. Choosing the right tool is very important in the investigation process, because each stage of the forensic investigation process has a different treatment for each of its staging. Seeing these conditions, then in this paper we make a system design to facilitate investigators in choosing the right digital forensic tool and in accordance with using an expert system. The design system shows that it’s still needed to confirm to an expert to get a match output if there is a new rule. Later this system will be implemented with website technology.
Human-Centered Symbiotic System Science

In previous paper we showed that Symbiotic System Science (SSS) is a growing scientific area which is taking a leadership role in fostering consensus on how best to bring about symbiotic relationships between autonomous systems. Capitalizing on SSS insights and development, the recognition that SAS (Symbiotic Autonomous Systems) are poised to have a revolutionary impact on society over the coming years is quite straightforward. The promise of SSS is to reveal a convenient roadmap to arrive to Human-centered Symbiotic System Science (HCSSS) to develop more reliable Human-centered Symbiotic System (HCSS), to fully utilize the capabilities of cognitive computing and brain-inspired system as support for more effective application of our higher human faculties. In present paper we discuss HCSSS to bring about symbiotic relationships between HCSS, as evidenced by the living human brain modalities, supported by the CICT OUM framework.
Towards an Automated Process to Categorise Tor’s Hidden Services

It has been argued that the anonymity the dark web offers has allowed criminals to use it to run a range of criminal enterprises, acting with impunity and beyond the reach of law enforcement. By designing a process that can identify sites based on their criminality, law enforcement officers can devote their resources to finding the people behind the sites, rather than having to spend time identifying the sites themselves. The scope of the study in this chapter is focused solely on Tor’s hidden services. The research problem was to identify what percentage of hidden services are accessible and how many of these are connected to criminal/illicit activities. Additionally, our research also aims to determine if it is possible to automate a system to identify sites of interest for law enforcement by categorising them based on the prevalent crime type of the hidden service. In this chapter, we look at how hidden services are set up. To facilitate this, an experiment was conducted where a hidden service was set up and hosted on the Tor network. It is connected to the Tor network and obtained an un-attributable IP address, identified over 12,800 .onion addresses from which it scraped the HTML from the home page, before checking this against a pre-determined list of keywords to identify illicit sites and categorise each of these dependant on their type of criminality. Our approach successfully identified criminal sites without the need for human interaction making it a very useful triage solution. Whilst further work is required before its categorisation process is sufficiently robust enough to provide an accurate, unquestionable strategic overview of hidden services, the tool in essence, works very well in achieving its primary function; to identify criminal sites across the dark web.
Computational quantitative syntax

Accounting for the constraints on the possible word orders of a sentence in a language and across the world languages is a core challenge for syntactic theory. In the spirit of computational quantitative syntax, in this paper we present quantitative evidence about Universal 18. We show that corpus data confirms a dispreference for the word order combination where adjectives precede but numerals follow the nouns (Adj-N and N-Num). We then investigate if this dispreference is better explained as a constraint expressed at the level of the dominant orders or at the level of individual structures. Corpus counts support the latter interpretation. Finally, we propose a formal model of how this bias against Adj-N-Num orders can be integrated in the grammar.
Similarity between referents constrains the processing of contrastive focus during reading

Contrastive focus implies a contrast between two elements. However, it is unclear whether and how any interplay between such a contrast and similarity between potentially contrasting elements might affect focus processing. Accordingly, we report an eye movement experiment investigating this issue. The experiment used a background story to introduce eight characters whose social identities were manipulated to be similar or dissimilar. Participants first read this background story, then a series of two-sentence discourses while their eye movements were recorded. Each discourse referred to two characters from the passage who had either similar or dissimilar identities, with one (the target character) either focused using the Chinese particle zhiyou (meaning only) or unfocused. The results showed a typical focus facilitation effect, such that target character names were processed more quickly when focused than unfocused. We also observed a main effect of the similarity/dissimilarly of characters and, crucially, an interaction between this variable and focus. This interaction was due to slower processing of a post-target region when the target character was focused and the two characters had similar rather than dissimilar identities, but no such effect when the target character was unfocused. The findings suggest that establishing a contrast between referents is effortful during reading when these have similar rather than dissimilar social identities and so are more difficult to differentiate. The distinctiveness of referents in a discourse context may therefore constrain the establishment of contrastive focus during reading. We discuss these findings in relation to current theories of focus interpretation.
Behavioral Feature Description Method Based on the Vector Module Ratio and Vector Angle of Human Body Structure

In the field of computer vision, the depth image sequence collected by depth camera is not sensitive to the interference of light, occlusion and background environment. Therefore, in recent years, it is often used to collect behavior data, from which the characteristics of bone joint points are extracted as behavior information. However, it is found that the direct use of joint coordinate information collected by depth camera for behavior recognition is easily affected by individual differences in behavior and changes in shooting distance. Considering the position information of human joints as well as the angle and length information of hidden limbs, based on skeleton data, a behavior feature description method based on the ratio of vector angle and vector mode of human structure is proposed. This method solves the above problems perfectly and obtains ideal results on the self built data set, which is suitable for simple daily behavior recognition.
Recursive Rules with Aggregation: A Simple Unified Semantics

Complex reasoning problems are most clearly and easily specified using logical rules, but require recursive rules with aggregation such as counts and sums for practical applications. Unfortunately, the meaning of such rules has been a significant challenge, leading to many disagreeing semantics. This paper describes a unified semantics for recursive rules with aggregation, extending the unified founded semantics and constraint semantics for recursive rules with negation. The key idea is to support simple expression of the different assumptions underlying different semantics, and orthogonally interpret aggregation operations using their simple usual meaning. We present formal definition of the semantics, prove important properties of the semantics, and compare with prior semantics. In particular, we present an efficient inference over aggregation that gives precise answers to all examples we have studied from the literature. We also applied our semantics to a wide range of challenging examples, and performed experiments on the most challenging ones, all confirming our analyzed results.
New Age MOOCS Teaching in Music Class of College

The widespread application of information technology in education has drove teaching reform. MOOCS is one kind of new teaching models that emerges in this context. How to effectively apply MOOCS teaching to music classroom teaching in high school and effectively improve the efficiency of music classroom teaching in college. The author explores the feasibility of MOOCS teaching mode in improving teacher-student relationship and teaching effect, innovating teaching strategies and promoting the teaching reform, and explore the promoting effect of MOOCS teaching mode on college music teaching from the aspects of teaching theory and practical application.