Reviewerly/exHarmony huggingface dataset:
The role of narrative in collaborative reasoning and intelligence analysis: A case study

This paper explores the significance of narrative in collaborative reasoning using a qualitative case study of two teams of intelligence analysts who took part in an exercise using an online collaborative platform. Digital ethnographic methods were used to analyze the chat transcripts of analysts as they reasoned with evidence provided in a difficult, fictional intelligence-type problem and produced a final intelligence report. These chat transcripts provided a powerful "microscope" into the reasoning processes and interactions involved in complex, collaborative reasoning. We found that Individuals and teams used narrative to solve the kinds of complex problems organizations and intelligence agencies face daily. We observed that team members generated what we term "micro-narratives", which provided a means for testing, assessing and weighing alternative hypotheses through mental simulation in the context of collaborative reasoning. The creation of micro-narratives assisted in the teams' reasoning with evidence, an integral part of collaborative reasoning and intelligence analysis. Micro-narratives were combined into, and compared with, an ideal or 'virtual' narrative which informed the judgements the team came to in their final intelligence report. The case study developed in this paper provides evidence that narrative thought processes play an important role in complex collaborative problem-solving, reasoning with evidence and problem-solving. This is contrary to a widespread perception that narrative thinking is fundamentally distinct from formal, logical reasoning.
The Russian Language Test: Towards Assessing Text Comprehension

Reading comprehension relies on a variety of complex skills that are not effectively assessed by existing Russian language tests. At the same time, Russian textbooks are criticized both for their low text quality and high text complexity. This study addresses issues of Russian language proficiency and comprehension assessment with the development of the Russian Language Test (RLT). The RLT was constructed to measure proficiency relevant to textbook comprehension, such as grammar and vocabulary knowledge, establishing propositional meaning and inferencing. Results from this initial study including 81 fifth-grade and 94 ninth-grade students confirm that students struggle with grammatical inferences and identifying the main idea in a text. Additionally, three standardized Russian exams, VPR, OGE, EGE are analyzed, affording an overview of the testing system for the Russian language from the elementary through high school education levels. This study demonstrates promise for the use of the RLT as a language proficiency assessment and provides a broad context for understanding the current state of Russian language tests for native speakers.
Steganalysis of Intra Prediction Mode and Motion Vector-based Steganography by Noise Residual Convolutional Neural Network

Abstract In this paper, we present a universal steganalysis method for both intra prediction mode and motion vector-based steganography based on deep learning. Since the embedding process is eventually reflected in the modification of pixel values in decoded frames, we design a Noise Residual Convolutional Neural Network (NR-CNN) from the perspective of the spatial domain, which is the first CNN-based approach for this subject. In NR-CNN, feature extraction and classification modules are integrated into a unified and trainable network framework. It automatically learns features and implements classification in a data-driven manner, which effectively solves the existing problems. Experimental results show that NR-CNN has better performance of steganalysis than the related method.
Viability of Neural Networks for Core Technologies for Resource-Scarce Languages

In this paper, the viability of neural network implementations of core technologies (the focus of this paper is on text technologies) for 10 resource-scarce South African languages is evaluated. Neural networks are increasingly being used in place of other machine learning methods for many natural language processing tasks with good results. However, in the South African context, where most languages are resource-scarce, very little research has been done on neural network implementations of core language technologies. In this paper, we address this gap by evaluating neural network implementations of four core technologies for ten South African languages. The technologies we address are part of speech tagging, named entity recognition, compound analysis and lemmatization. Neural architectures that performed well on similar tasks in other settings were implemented for each task and the performance was assessed in comparison with currently used machine learning implementations of each technology. The neural network models evaluated perform better than the baselines for compound analysis, are viable and comparable to the baseline on most languages for POS tagging and NER, and are viable, but not on par with the baseline, for Afrikaans lemmatization.
Pengujian Black Box pada Aplikasi Penjualan Berbasis Web Menggunakan Teknik Boundary Value Analysis

In a software test, validation is one of the processes of checking whether the software meets the expected specifications and objectives or not. The data validation process has not been maximized can result in a mismatch of data that will be stored in the database. For example the amount of stock turned negative, or the number of digits entered can exceed the defined limits. This can be detrimental to the owner and user in using this application. Therefore, it is necessary to improve the quality of validation more accurately, so it can help the owner in terms of data accuracy. Applications are designed in such a way must go through a testing phase to ensure the quality of the software itself. Examples of tests can be said to be good if they have a chance of finding an error that cannot be revealed. Among the many ways of the Black Box testing methods, this study uses the Boundary Value Analysis testing method. The method tests the maximum number of limits and the minimum number of limits to produce a valid value, which is considered quite relevant for testing sales applications at PT Karunia Segar Kedua. The initial stage of the research needs to be done is to define the functionality to be tested, compile the test scheme, choose the material to be tested, determine the maximum and minimum number of limits in accordance with a predetermined database structure, conduct experiments, document research results, and draw conclusions. The conclusion is based on the testing has been done is software can be used properly, after an improvement is made on the errors found. However, this test has not been said to be perfect, because it is only done using a sample form. From the results of this test it is expected that the application can be used according to user requirements.
Obstacle Avoidance of Manipulators Based on Improved Artificial Potential Field Method

Obstacle avoidance is a key problem of robot motion planning. This paper introduces an improved method to realize obstacle avoidance of manipulators based on artificial potential field method which offers a relatively fast and effective way to solve the problem. We establish the attractive potential field function of the goal and the repulsive potential filed functions of obstacles in the workspace instead of the configuration space. Then a series of control points on the manipulator are chosen to be influenced by the total potential. A policy is proposed that only the end point is attracted to the goal while all selected points are repulsed by obstacles. Afterwards, we transfer the potential force of each point in workspace into the moment in configuration space to make the manipulator move by iteration of joint angles. The effectiveness of the method is confirmed by simulation experiments.
Synergizing Domain Expertise With Self-Awareness in Software Systems: A Patternized Architecture Guideline

To promote engineering self-aware and self-adaptive software systems in a reusable manner, architectural patterns and the related methodology provide an unified solution to handle the recurring problems in the engineering process. However, in existing patterns and methods, domain knowledge and engineers' expertise that is built over time are not explicitly linked to the self-aware processes. This linkage is important, as the knowledge is a valuable asset for the related problems and its absence would cause unnecessary overhead, possibly misleading results and unwise waste of the tremendous benefit that could have been brought by the domain expertise. This paper highlights the importance of synergizing domain expertise and the self-awareness to enable better self-adaptation in software systems, relying on well-defined expertise representation, algorithms and techniques. In particular, we present a holistic framework of notions, enriched patterns and methodology, dubbed DBASES, that offers a principled guideline for the engineers to perform difficulty and benefit analysis on possible synergies, in an attempt to keep "engineers-in-the-loop". Through three tutorial case studies, we demonstrate how DBASES can be applied in different domains, within which a carefully selected set of candidates with different synergies can be used for quantitative investigation, providing more informed decisions of the design choices.
Deceptive Previews: A Study of the Link Preview Trustworthiness in Social Platforms

Social media has become a primary mean of content and information sharing, thanks to its speed and simplicity.In this scenario, link previews play the important role of giving a meaningful first glance to users, summarizing the content of the shared webpage within their title, description and image.In our work, we analyzed the preview-rendering process, observing how it is possible to misuse it to obtain benign-looking previews for malicious links.Concrete use-case of this research field is phishing and spam spread, considering targeted attacks in addition to large-scale campaigns.We designed a set of experiments for 20 social media platforms including social networks and instant messenger applications and found out how most of the platforms follow their own preview design and format, sometimes providing partial information.Four of these platforms allow preview crafting so as to hide the malicious target even to a tech-savvy user, and we found that it is possible to create misleading previews for the remaining 16 platforms when an attacker can register their own domain.We also observe how 18 social media platforms do not employ active nor passive countermeasures against the spread of known malicious links or software, and that existing cross-checks on malicious URLs can be bypassed through client-and serverside redirections.To conclude, we suggest seven recommendations covering the spectrum of our findings, to improve the overall preview-rendering mechanism and increase users' overall trust in social media platforms.
Generation of quantum entangled states of multiple groups of qubits distributed in multiple cavities

Provided that cavities are initially in a Greenberger-Horne-Zeilinger (GHZ) entangled state, we show that GHZ states of $N$-group qubits distributed in $N$ cavities can be created via a three-step operation. The GHZ states of the $N$-group qubits are generated by using $N$-group qutrits placed in the $N$ cavities. Here, ``qutrit'' refers to a three-level quantum system with the two lowest levels representing a qubit while the third level acting as an intermediate state necessary for the GHZ state creation. This proposal does not depend on the architecture of the cavity-based quantum network and the way for coupling the cavities. The operation time is independent of the number of qubits. The GHZ states are prepared deterministically because no measurement on the states of qutrits or cavities is needed. In addition, the third energy level of the qutrits during the entire operation is virtually excited and thus decoherence from higher energy levels is greatly suppressed. This proposal is quite general and can in principle be applied to create GHZ states of many qubits using different types of physical qutrits (e.g., atoms, quantum dots, NV centers, various superconducting qutrits, etc.) distributed in multiple cavities. As a specific example, we further discuss the experimental feasibility of preparing a GHZ state of four-group transmon qubits (each group consisting of three qubits) distributed in four one-dimensional transmission line resonators arranged in an array.
Precise tomography of optical polarisation qubits under conditions of chromatic aberration of quantum transformations

In this work we present an algorithm of building an adequate model of polarizing quantum state measurement. This model takes into account chromatic aberration of the basis change transformation caused by the parasitic dispersion of the wave plates crystal and finite radiation spectral bandwidth. We show that the chromatic aberration reduces the amount of information in the measurements results. Using the information matrix approach we estimate the impact of this effect on the qubit state reconstruction fidelity for different values of sample size and spectral bandwidth. We also demonstrate that our model outperforms the standard model of projective measurements as it could suppress systematic errors of quantum tomography even when one performs the measurements using wave plates of high order.
Efficient Dual Attention Module for Real-Time Visual Tracking

Attention mechanisms are of great potential to enhance discriminative capacity and adaptability of Convolutional Neural Networks. However, due to speed requirements of trackers, it is inappropriate to introduce attention modules that need high computational-cost into tracking algorithms. To employ attention mechanisms in tracking field, we propose Efficient Dual Attention Module (EDAM) which is a lightweight and effective module to extract attention in channel and spatial dimensions. Firstly, we aggregate spatial and channel information with self-attention mechanism by a simplified Non-local Network, which is called Attention-wise Global Pooling. Next, we extract dual attention by modeling inter-channel and inter-position relationships and finally fuse them in a sequential manner. With the use of dual attention, the network adaptively attends to discriminative and robust features of targets. Extensive experiments on a standard tracking benchmark demonstrate that our tracker with an EDAM embedded achieves favorable performance against state-of-the-art trackers while runs at real-time speed.
DSAN: Double Supervised Network with Attention Mechanism for Scene Text Recognition

In this paper, we propose Double Supervised Network with Attention Mechanism (DSAN), a novel end-to-end trainable framework for scene text recognition. It incorporates one text attention module during feature extraction which enforces the model to focus on text regions and the whole framework is supervised by two branches. One supervision comes from context-level modelling branch and another comes from one extra supervision enhancement branch which aims at tackling inexplicit semantic information at character level. These two supervisions can benefit each other and yield better performance. The proposed approach can recognize text in arbitrary length and does not need any predefined lexicon. Our method achieves the current state-of-the-art results on three text recognition benchmarks: IIIT5K, ICDAR2013 and SVT reaching accuracy 88.6%, 92.3% and 84.1% respectively which suggests the effectiveness of the proposed method.
Application of 5G Technology in Education Informatization

The popularity of educational informatization and the emergence of smart learning spaces have significantly increased students' demand for personalized, convenient, and diversified learning content.The fifth generation mobile communication (5G) technology, with its characteristics of large bandwidth, ultra-high reliability, ultra-low time delay, large-scale connection of things, is becoming an enabling technology for perfecting the education network and even realizing education modernization.This paper focuses on the application of 5G technology in education informatization.After the analysis of major characteristics of education informatization, this paper analyzes the technical challenges faced by the education network of China, and demonstrates corresponding 5G application scenarios from the aspects of teaching, teaching research, and education management, thereby forming an overall view of education informatization based on 5G technology.By 2035, the 5G technology will be further promoted, which will constantly enrich the connotation and promote the reform of education scenes; and thus it will become the driving force for the innovation in China's education informatization.
Evolving Matrix-Factorization-Based Collaborative Filtering Using Genetic Programming

Recommender systems aim to estimate the judgment or opinion that a user might offer to an item. Matrix-factorization-based collaborative filtering typifies both users and items as vectors of factors inferred from item rating patterns. This method finds latent structure in the data, assuming that observations lie close to a low-dimensional latent space. However, matrix factorizations have been traditionally designed by hand. Here, we present Evolutionary Matrix Factorization (EMF), an evolutionary approach that automatically generates matrix factorizations aimed at improving the performance of recommender systems. Initial experiments using this approach show that EMF generally outperforms baseline methods when applied to MovieLens and FilmTrust datasets, having a similar performance to those baselines on the worst cases. These results serve as an incentive to continue improving and studying the application of an evolutionary approach to collaborative filtering based on Matrix Factorization.
What ifs

We develop a dynamic account of what if questions on which they re-pose questions inside local contexts introduced by their if-clauses subject to the felicity constraint that the resulting context is inquisitive. While this analysis is directly motivated by cases where a what if questioner challenges another speaker’s attempt to answer a current question under discussion (QUD) by seeming to re-ask this question over a more restricted contextual domain, it can also explain the flexibility of what if since other uses trigger accommodation with new QUDs to ensure that the post-suppositional inquisitivity condition is met. While QUD accommodation is a complex phenomenon that isn’t specific to just what if constructions, the pragmatic flexibility of what if furnishes a nice range of examples for investigating such repair. In the latter part of the paper, we focus on practical what if questions which trigger accommodation with QUDs that subserve the real-world domain goals of the speakers. We offer a systematic working theory of this accommodation within a formal model of discourse that involves goal stacks populated with both questions and decision problems tethered together by relevance. The larger contribution of this paper is to add to the understanding of how discourse felicity and update conditions at the level of speech acts can be encoded in natural languages. EARLY ACCESS
Crash, boom, bang

Audience participation is a prominent thread running through much of sound art practice, yet it remains largely absent from the sound art scholarship. In this article, I argue that the most widespread methodologies employed in sound art research – roughly split into the phenomenological branch and the object-oriented branch – are ill equipped to tackle the questions of sociality and participation. Instead, I offer a framework for the study of participation in sound art – and, more broadly, for sound aesthetics in general – rooted in the pragmatist tradition. My starting point is John Dewey’s conceptualization of an artwork as an aesthetic experience developing in cycles of doing and undergoing – a structure, he claims, present in both the creative process and the reception of artworks, putting them on equal footing. I then expand this notion by turning to the contemporary pragmatist trends in creativity studies, ANT and affordance theory, introducing the concepts of we-creativity, mediation and affordance. The second half of the article focuses specifically on affordance – a relationship between a sound artwork and its audience delimiting and facilitating the possibilities for participation. I discuss the low-level affordances (facilitating elementary action) for creative listening and soundmaking and high-level affordances (facilitating complex behaviors) for creativity, experimentation and connectivity. I conclude that the pragmatist framework allows to go beyond the subject- or object-centeredness of phenomenological or object-oriented methodologies, bringing to the foreground the relational and social character of sound art.
Unity3D-based app for 360VR subjective quality assessment with customizable questionnaires

This document describes an interactive virtual reality tool for subjective assessment designed under the scope of ITU-T Study Group 12 and the Video Quality Experts Group in the development of the future ITU-T Recommendation P.360-VR for the subjective assessment of 360 videos using Head-Mounted Displays (HMDs). The tool has been designed under the Unity3D engine to be used on multiple HMDs. Furthermore, it can be configured for a variety of custom or predefined questions while the video sequence is played and/or after its ending. The head tracking is also registered in degrees. The voting method is gaze-based using joysticks keys for confirmation.
Intuitionistic Non-normal Modal Logics: A General Framework

We define a family of intuitionistic non-normal modal logics; they can bee seen as intuitionistic counterparts of classical ones. We first consider monomodal logics, which contain only one between Necessity and Possibility. We then consider the more important case of bimodal logics, which contain both modal operators. In this case we define several interactions between Necessity and Possibility of increasing strength, although weaker than duality. For all logics we provide both a Hilbert axiomatisation and a cut-free sequent calculus, on its basis we also prove their decidability. We then give a semantic characterisation of our logics in terms of neighbourhood models. Our semantic framework captures modularly not only our systems but also already known intuitionistic non-normal modal logics such as Constructive K (CK) and the propositional fragment of Wijesekera's Constructive Concurrent Dynamic Logic.