Reviewerly/exHarmony huggingface dataset:
Legal Treatise

Abstract The legal treatise is a subject with a pedigree. Previous essays by A. W. B. Simpson, Morton Horwitz, Angela Fernandez and Markus Dubber, and Christopher Tomlins identify different ways that the treatise genre captures the essence of existing law and aspirations. Disembarking from these important contributions, this chapter draws upon such diverse fields as the history of the book, literary genre studies, information management, and the sociology of knowledge. It underscores the multiplicity of the treatise’s genre ecosystem. The legal treatise simultaneously serves as a site for the production of knowledge, a stepping stone or even substitute for codes, fabricated order, a mechanism for establishing authorial authority, a professional talisman, a technic of systematizing and organizing information, a legitimization of existing norms, and a means of constructing a national jurisprudence akin to the way a dictionary might define a national language. Focusing largely on common law treatises of the long nineteenth century, it interrogates how these competing goals might exist within a single literary artifact. What pressures threaten to unravel the legal treatise’s claims to monumental authority? And how can its power erode? The chapter’s salient contribution will be its highlighting the importance of time. The legal treatise is a genre which simultaneously asserts its place as a summa of legal knowledge and—as the commonplace historical introduction often underscores—while continuing to be subject to countless revisions. It is a text readied for obsolescence. Beneath the treatise’s façade of professional authority lies a profound anxiety about its own timelessness.
The Research on Internet Financial Flow Risk Measurement and Statistical Based on Analytic Hierarchy Process

Abstract As a decision-making method of qualitative and quantitative analysis, AHP has the advantages of flexibility, system, simplicity and convenience. It has important instrumental significance in the assessment of Internet financial liquidity risk. This paper adopts the concept and formation of Internet financial liquidity risk. The reason is discussed. On this basis, the analytic hierarchy process tool is introduced. The model is used as the carrier to measure and count the Internet financial risk, so as to provide a useful reference for the prevention and control of Internet financial flow risk.
Texture Image Segmentation Based on Stationary Directionlet Domain Probabilistic Graphical Model

In this paper, a stationary directionlet (SD) domain probabilistic graphical model (PGM) for texture image segmentation is proposed. Hidden markov chain (HMC) is a good tool to capture the persistence and clustering properties of the coefficients of SD transform. The homogeneous property of texture image is described by markov random field (MRF). Combining HMC and MRF in SD domain result in SDPGM. Image segmentation based on SDPGM, which is denoted as SDPGMseg, involves inferring the maximum a posterior (MAP) solution to class labels on the coefficients of SD transform. The segmentation result can be obtained by minimizing an energy function. Experiment results show that SDPGMseg can obtain better performance especially in homogeneous regions and boundaries of different regions.
Simulations and difficult problems

Abstract An authorship attribution investigation ideally begins with a well-defined set of possible authors and an adequate number of firmly attributed roughly contemporaneous long texts in the same genre by those authors. Many significant or intriguing problems, however, suffer from deficiencies or limitations that reduce the effectiveness or validity of some kinds of analysis and make others impossible. These problematic situations can be approached by creating simulations that are designed to overcome or mitigate the difficulties of the problems. The results of the simulations can be used to suggest at least tentative solutions. Here, simulations are used to investigate four difficult problems. One involves fewer and shorter texts than would be ideal–texts that are also chronologically earlier than the known texts by the target author. The second involves too small a number of well attributed texts by the authors in question, and initial uncertainty about the genres of the texts, the number of authors involved, and their genders. The third is a tricky case of co-authorship with only relatively vague and uncertain evidence about the nature and extent of each author’s contribution; here simulations with sections of well-attributed texts by the two authors are used to test Rolling Classify. The fourth addresses the sparsity of well-attributed and confidently-dated Early Modern plays, using simulations to evaluate Brian Vickers’ rare n-gram approach to the attribution of such plays.
The Study of Teleoperation Technology Based on Hand Gesture Recognition

Background: To solve the traditional manual teleoperation problems, this project explores a more natural method of manipulator teleoperation, sets up a fusion virtual teleoperation scene, uses hand gesture recognition technology to convert the operator’s hand action to the manipulator’s execution instruction, avoids the complexity which is caused by the operation translation and the posture handle control respectively. The three-dimensional scene reconstruction enables the operator to have a clear understanding of the three-dimensional situation of the whole teleoperation scene, and can perceive the spatial relationship between the operator, the manipulator and the target in real time, thus improving the execution efficiency of the teleoperation. Methods: This project used mature commercial depth camera devices (such as Kinect, Leapmotion, Intel Creative, ZED camera, etc.) as input device to identify operator’s hand movements and used a manipulator with multiple joints for teleoperation. The camera was installed on the wrist of manipulator or other related parts to obtain the images of operational objectives without changing the robot arm control system. The location and attitude of operational objectives was achieved by measuring the depth of data image, manipulator space location identification. After the completion of the operational objectives identification, the virtual hand, manipulator and the target were generated in the same virtual scene by using virtual reality technology and augmented reality technology to provide a stereoscopic display to the operator through the 3D display device. Based on stereo video image display technology, the position relation of manipulator and operational objectives in space was restored in virtual reality system, and the visual space location was realized by SLAM method. The operator will get the visual perception of the teleoperation scene with large field of view. The mapping relationship between the hand model and the manipulator was established to get the precise control, which can enhance the perception ability of the operator. Finally, an experimental system was formed to evaluate the applicability and effectiveness of interaction, which was modified according to the feedback results. An effective human-machine interaction method was proposed based on visual gesture recognition. Conclusion: Experiments show that the accuracy of the gesture operation is slightly lower than that of the handle operation, but it is easier to operate, and the operation is natural and smooth, which accords with the advantages of human-computer interaction habits. For scenes with low precision requirements, the advantages of gesture operation are obvious. It can be used as an operation mode of teleoperation. It has wide application prospects in the field of teleoperation system or task, such as space station, extraterrestrial exploration, robots, UAV and so on.
Multiplicative Linear Secret Sharing Without Interaction

As an essential building block in cryptosystem, linear secret sharing is widely used to safeguard the confidentiality and reliability of outsourced data. Though addition and constant multiplication are extremely easy thanks to the linear operation over shared secrets, how to efficiently multiply multiple shares remains an open problem. In this paper, we devised a non-interactive multiplication scheme based on Shamir’s secret sharing without parameter constrain. It is proved that our scheme is unconditionally secure if no more than k participants are compromised, meaning that both the security and access structure of Shamir’s scheme are immensely retained.
Augmenting the Operations on Cloud Virtual Forensic Data by Employing Probabilistic Data Structures

Background: Gathering and scrutinizing the different types of logs are the vital steps in the forensic domain. Logs are commonly gathered by the cloud service providers or by some third party layers which are governed by the cloud service providers. Security of the logs is a crucial issue as the logs can be tampered accidentally or intentionally by an employee in the cloud service provider’s organization or by the forensic investigator. Objective: The algorithm designed assists in verifying the tampering of the virtual instance logs which can be accidental or intentional. Verification process confirms that the confidentiality and integrity of the logs remains intact. Verification of the potential evidence for past logs is normally carried out by the forensic investigator and the auditor. Methods: The uniqueness of the research conducted in this paper is a technique which employs the cuckoo filter, which is supportive in proving the integrity of the potential evidences for past logs at a faster pace. The probabilistic data structures cuckoo filter and the bloom filter also support the concealment of logs. Results: The implemented system and the results observed in it, are very promising in the cloud forensic domain. The performance of the algorithm is better than the earlier system implemented with the log chain and the database. Conclusion: The secure framework designed in this research paper aids in proving the integrity of the virtual instance logs. The evidence verification process supports concealment of data also.
Research on Constructing Technology of Implicit Hierarchical Topic Network Based on FP-Growth

Topic extraction for books is of great significance in the development of intelligent reading systems, question answering systems and other applications. Compared with the theme of microblog and science and technology literature, the topic of book has the characteristics of multi-themes, hierarchization, networking, and information sharing. Therefore, the topic extraction of books must be more complicated and difficult. This article is based on solving the problems such as quick positioning of the relevant contents of the answer, cross-topic retrieval, and other issues in the intelligent reading system. Based on the topic trees extracted from the novel text chapters using the TF-IDF algorithm, the FP-GROWTH algorithm is used to mine the topic words. The association relationship, in turn, analyzes the hidden relationship between topics, and proposes and constructs an implicit hierarchical subject network (IHTN) of the novel text. The experimental results show that this method can completely extract the thematic network of novel texts, effectively extract the chapter relationships, significantly reduce the answer retrieval time in the question answering system, and improve the accuracy of the answers.
Preliminary Comparison of a Curved Public Display vs a Flat Public Display

In this paper, we describe how passers-by are affected by a curved public display compared to a flat public display. Through observing social behaviors of people around a curved public display, we found that firstly more people tend to stop in front of a curved display than a flat display. Secondly, a certain number of audience members viewed the public display at the nearest distance, and audience members at closer dwelling positions to the display tended to dwell longer than those who stay farther from the display. Furthermore, our observations reveal that, for both the curved display and the flat display, more passers-by were attracted and stopped to view the display when there was already an audience standing in front of the display, which is known as the honeypot effect. However, these audience members have shorter dwelling times than those of other audience members.
Human-Object Maps for Daily Activity Recognition

In the field of action recognition, when and where an interaction between a human and an object happens has the potential to be valid information in enhancing action recognition accuracy. Especially, in daily life where each activities are performed in longer time frame, conventional short term action recognition may fail to generalize do to the variety of shorter actions that could take place during the activity. In this paper, we propose a novel representation of human object interaction called Human-Object Maps (HOMs) for recognition of long term daily activities. HOMs are 2D probability maps that represents spatio-temporal information of human object interaction in a given scene. We analyzed the effectiveness of HOMs as well as features relating to the time of the day in daily activity recognition. Since there are no publicly available daily activity dataset that depicts daily routines needed for our task, we have created a new dataset that contains long term activities. Using this dataset, we confirm that our method enhances the prediction accuracy of the conventional 3D ResNeXt action recognition method from 86.31% to 97.89%.
Assessment of signature complexity

Traditionally, forensic document examiners (FDEs) assess the complexity of signature by the perceived level of difficulty that a simulator would have in mimicking the signature such that the simulation can pass examination by an FDE. As part of a signature examination, the FDE should sketch the signature in question and some of the reference signatures. This will provide the FDE with an estimation of the degree of difficulty a writer would have in simulating the questioned signature.
Pragmatic Relations in the Communication of Public Groups in the Russian Social Network <i>Vkontakte</i>

This research project deals with the analysis and classification of pragmatic relations with act-types in messages on the public message wall of three selected public groups in the Russian social network Vkontakte. The theories of Sinclair and Coulthard (1975), Korpimies (1978), Diekmannshenke (1999) and Tuor (2009) on exchange structures and act types served as a starting point for the development of a new classification of act types typical for communication on Vkontakte. The practical analysis revealed and classified 29 different act types which support the pragmatic relations between individual messages on the public message wall of Vkontakte. The analysis showed that a large group of acts in Vkontakte messages consists of multimedia-files such as audio-files, video-files, hyperlinks, which can support the pragmatic connection between messages. These files can form the single content of messages or interact with the verbal text in the messages.
Polysizemic Encryption: Towards a Variable-Length Output Symmetric-Key Cryptosystem

This short paper presents preliminary work towards developing a variable-length symmetric-key cryptosystem. Introduced are the definition of a new class of encryption mechanism termed Polysizemic Encryption and a reference implementation named the "Slinky Cipher". Three sub-classes of polysizemic ciphers are defined and examples of each are given. What makes a cipher polysizemic is discussed along with challenges associated with their evaluation. The Slinky Cipher is evaluated for its avalanche effect, and using the NIST statistical test suite. Preliminary baseline results show promising trends in terms of achieving provable security bounds, acceptable randomness ranges, and avalanche effect.
Blockchain-Based Authentication for Network Infrastructure Security

Network infrastructure devices form the backbone of a network. Any computer connecting to the network can communicate with these devices by running an appropriate software. Because of this, hackers could easily attack the network infrastructure. The reason why such attacks is possible is that there is no authentication mechanism in most of today’s networks. To solve this, this paper proposes a solution to the problem by introducing an authentication scheme. Blockchain technology is used to store authentication information because malicious tempering is difficult. Authenticated Devices Configuration Protocol (ADCP) is proposed to configure and form a secure overlay network infrastructure automatically. A working implementation has been built to validate the authentication design. Experimental results have also been presented that show the validation.
Artificial Intelligence and Copyright Protection

The proliferation of cultural creations using AI, and their increasing commodification, raises interesting questions from a copyright perspective. This paper addresses the copyright issues raised by works that are generated by AI. In particular, it assesses whether AI generated content may constitute an original, copyright protected work, and who is the author and owner of copyright in such a work, if at all. This is done from the perspective of international, EU and domestic copyright law (UK, France and US). The paper suggests that there is sufficient copyright protection for AI generated content such that reforms to the system are probably not needed.
Distributed autonomous learning framework

A distributed autonomous learning framework was developed by DAX LLC (Distributed Autonomous Everything) with the goal of revolutionizing what is known as the “eLearning Industry”. Although some online platforms share DAX’s vision for disrupting, democratizing, and improving accessibility to the online educational ecosystem, they do not offer the same level of security, adaptability, and appeal to the emerging computing technologies community as DAX. DAX’s learning framework is built upon its own private blockchain with user-guided adaptation through a suite of distributed applications. This sophisticated architecture serves to protect the student’s identity, provides verifiability of trust of historical records, and provides transparency of achievement for a given student encryption key. The inherent security of the private blockchain ensures computer hackers (or nefarious students) aren’t poisoning or “gaming” the system. The world is rapidly evolving and internet access creates a changing landscape for traditional education systems. DAX recognizes the need for educational systems to evolve beyond being solely centers of knowledge to becoming the enablers of information sharing and immersive learning. The distributed autonomous classroom concept presented herein is a novel eLearning concept designed to continue to evolve to meet the ever-changing demands of the globally connected, technologically savvy, student of tomorrow.
Persona revisited : filling in the gaps via the original script

In this article, I compare Bergman's original script of Persona with its final film version and discuss several differences between the two.I focus on three larger alternations, namely on omitting several of Elizabeth's replicas or transferring them to Alma as well as on a complete change of the ending scene.I speculate on how the dialogue and the scene would have changed the film if the director had not decided to modify or omit them, suggest possible reasons for such steps having been taken, and present arguments for how Bergman shifted to cinematic modernism and turned away from psychologizing during the process of making Persona.Furthermore, I breakdown and analyse the script version of the middle sequence of Persona, using positioning theory to uncover the motives behind Alma's and Elizabeth's verbal actions, and respectively the refusal of such action.
Evolving Training Scenarios with Measurable Variance in Learning Effects

One major cost driver in simulation-based training (SBT) and Intelligent Tutoring System (ITS) development is authoring scenario content. Effort is compounded when creating a catalog of scenarios to enable optimal scenario selection for the individual learner. Automated Scenario Generation (ASG) methods have been successful at creating variants of scenarios using procedural generation, evolution, or event templates with variable parameters. These methods typically describe boundaries within which content may vary, but do not describe how the variation will affect learning. The authors developed an evolutionary algorithm approach to generate variations in training scenario content that can integrate with the Generalized Intelligent Framework for Tutoring (GIFT) being developed by the Army Research Lab (ARL). One of the primary goals of the approach is to greatly increase not just surface variations that do not affect learning, but variations on scenario content and instructional feedback that make the scenarios measurably different as described by multidimensional learning effect metrics. The approach leverages a structured cognitive task analysis (CTA) which outputs a set of scenarios in a schema that directly drives the ITS. A small change to the CTA identifies dimensions and constraints of variation and the effect on learning from the variations. Both manual and machine learning approaches are being explored. The key contributions of our research are (a) developing domain-general computational measures of effect on learning for creative variation, (b) making evolution effective for many teaching and tutoring domains by evolving textual content in addition to event sequences, and (c) leveraging human expert input and usage data to influence and feed back into evolution. This paper summarizes the need for ASG, describes the novel approach, and shares the key contributions of our research.
Events and Objects Interaction Recognition using Aerial Videos

Event recognition becomes increasingly important and attracts growing interest from both academia and industry. It can provide awareness on possible threats and incidents such as acts of terrorism, illegal immigration, violence in crowded areas, and so on. It is essential to prepare and produce real-time and effective responses. However, it still faces great challenges when it comes to complex events that include more than one object/person. In this work, we target the problem of complex event recognition in visual surveillance by utilizing Unmanned Aerial Systems (UAS). Our approach is to detect and recognize relationship between events and object by determine set of predicates. We apply Markov Logic Network (MLN) to available online datasets to perform probabilistic inference for achieving this goal through grouping people together, who are doing same activities, and to recognize what these activities are. The performance of our approach is demonstrated on a number of existing videos obtained from UCLA that monitored wide spatial areas taking by flying UAS that contain complex relation between objects and activities.