Reviewerly/exHarmony huggingface dataset:
Challenges and Design Considerations for Multimodal Asynchronous Collaboration in VR

Studies on collaborative virtual environments (CVEs) have suggested capture and later replay of multimodal interactions (e.g., speech, body language, and scene manipulations), which we refer to as multimodal recordings, as an effective medium for time-distributed collaborators to discuss and review 3D content in an immersive, expressive, and asynchronous way. However, there exist gaps of empirical knowledge in understanding how this multimodal asynchronous VR collaboration (MAVRC) context impacts social behaviors in mediated-communication, workspace awareness in cooperative work, and user requirements for authoring and consuming multimedia recording. This study aims to address these gaps by conceptualizing MAVRC as a type of CSCW and by understanding the challenges and design considerations of MAVRC systems. To this end, we conducted an exploratory need-finding study where participants (N = 15) used an experimental MAVRC system to complete a representative spatial task in an asynchronously collaborative setting, involving both consumption and production of multimodal recordings. Qualitative analysis of interview and observation data from the study revealed unique, core design challenges of MAVRC in: (1) coordinating proxemic behaviors between asynchronous collaborators, (2) providing traceability and change awareness across different versions of 3D scenes, (3) accommodating viewpoint control to maintain workspace awareness, and (4) supporting navigation and editing of multimodal recordings. We discuss design implications, ideate on potential design solutions, and conclude the paper with a set of design recommendations for MAVRC systems.
Semantics of higher-order probabilistic programs with conditioning

We present a denotational semantics for higher-order probabilistic programs in terms of linear operators between Banach spaces. Our semantics is rooted in the classical theory of Banach spaces and their tensor products, but bears similarities with the well-known semantics of higher-order programs a la Scott through the use of ordered Banach spaces which allow definitions in terms of fixed points. Our semantics is a model of intuitionistic linear logic: it is based on a symmetric monoidal closed category of ordered Banach spaces which treats randomness as a linear resource, but by constructing an exponential comonad we can also accommodate non-linear reasoning. We apply our semantics to the verification of the classical Gibbs sampling algorithm.
Graduality and parametricity: together again for the first time

Parametric polymorphism and gradual typing have proven to be a difficult combination, with no language yet produced that satisfies the fundamental theorems of each: parametricity and graduality. Notably, Toro, Labrada, and Tanter (POPL 2019) conjecture that for any gradual extension of System F that uses dynamic type generation, graduality and parametricity are ``simply incompatible''. However, we argue that it is not graduality and parametricity that are incompatible per se, but instead that combining the syntax of System F with dynamic type generation as in previous work necessitates type-directed computation, which we show has been a common source of graduality and parametricity violations in previous work. We then show that by modifying the syntax of universal and existential types to make the type name generation explicit, we remove the need for type-directed computation, and get a language that satisfies both graduality and parametricity theorems. The language has a simple runtime semantics, which can be explained by translation to a statically typed language where the dynamic type is interpreted as a dynamically extensible sum type. Far from being in conflict, we show that the parametricity theorem follows as a direct corollary of a relational interpretation of the graduality property.
Blockchain—Opportunities and challenges for accounting professionals

Abstract Everybody is talking about blockchain, which is being positioned as the solution for all business ills. Blockchain is a revolutionary technology, but its power and efficacy are only as good as its application. Blockchain has the potential to transform or even eliminate some of the accounting functions that are currently taken for granted, but it also creates risks of its own that must be considered. The technology is still in its formative stage and is certain to undergo meaningful changes in the future. Entities using this technology will have to weigh its benefits, but also build safeguards to protect against its vulnerabilities.
Self-perceptions of English and Welsh constables and sergeants preparedness for online crime

Purpose Cybercrime is the greatest threat facing law enforcement agencies in England and Wales. Although these crimes are transnational by nature, the burden of response has been placed on line officers. Not all officers, however, believe they are capable of responding to calls involving cybercrime. The current study, using latent class analysis (LCA) on a large sample of English and Welsh officers, finds two types of officers: those prepared (39 percent) and those unprepared (61 percent). Using logistic regression to predict who falls into either classification, the authors find that training and age are the best predictors of latent membership. Implications for policy and future research are discussed. The paper aims to discuss these issues. Design/methodology/approach In this study, the authors used LCA to determine the number and character of unobserved categories of officers in how they deal with cybercrime. Findings The LCA indicated there are two distinct categories of police in the English and Welsh constabulary: those prepared (39 percent) and those unprepared (61 percent). Training and age were the two key determinants of this classification. Research limitations/implications LCA is an exploratory analysis technique that requires additional validation to confirm the findings of any one study. Practical implications The salience of training in helping officers feel prepared to deal with cybercrime cases as well as victims was demonstrated. A full 60 percent of the officers in this study fell in the “unprepared” category, which continues to highlight the limitations of local police to handle cybercrime cases; nevertheless, almost 40 percent of officer could be considered ready when responding to cybercrimes. Social implications As the harm cybercrime brings to our financial and social well-being, law enforcement agencies will be required to improve their response capabilities. Most current cybercrime responses address technical issues related to online fraud and abuse, but officers often perceive the problem as outside their legal and geographic jurisdiction. Knowing how officers perceive cybercrime as well as their own capabilities will allow us to begin changing enforcement policies, training capacity and individual response efficacy. Originality/value This study involved a sample of English and Welsh constables and sergeants to classify their cybercrime readiness. The analysis and particular data are unique to the study of cybercrime.
Digital Media as a New Means of Mediation (Part Two)

The article continues the cycle “Digital media as a new means of mediation”. The first article of the cycle is devoted to discussing theoretical and methodological perspectives of perceiving digital media as a specific means of mediation, combining components both of a sign and of a tool. The second article highlights how some traditional types of activity are transformed in digital contexts. Particularly, peculiarities of reading hypertexts (hypermedia structures) are discussed in comparison with printed texts. A brief analysis of play activity, mediated by digital media, is presented. Characteristic traits of cyber-communication are discussed. It is argued that transformation of the existing social practices influences the social situation of development at different stages of human development and determines the contemporary socio-cultural background of the development of higher mental functions and processes. The article indicates opportunities for further research on the impact that digital media have on the development of thinking, attention and memory in users of different age groups.
A Web-Based Skin Disease Diagnosis Using Convolutional Neural Networks

Skin diseases are reported to be the most common disease in humans among all age groups and a significant root of infection in sub-Saharan Africa.The diagnosis of skin diseases using conventional approaches involves several tests.Due to this, the diagnosis process is seen to be intensely laborious, time-consuming and requires an extensive understanding of the domain.The enhancement of computer vision through artificial intelligence has led to a more straightforward and quicker way of detecting patterns in images, which can be harnessed to equip diagnosis process.Despite the breakthrough in technology, the dermatological process in Ghana is yet to be automated, making the diagnosis process complicated and time-consuming.Hence, this study sought to propose a web-based skin disease detection system named medilab-plus using a convolutional neural network classifier built upon the Tensorflow framework for detecting (atopic dermatitis, acne vulgaris, and scabies) skin diseases.Experimental results of the proposed system exhibited classification accuracy of 88% for atopic dermatitis, 85% for acne vulgaris, and 84.7% for scabies.Again, the computational time (0.0001 seconds) of the proposed system implies that any dermatologist, who decides to implement this study, can attend to not less than 1,440 patients a day compared to the manual diagnosis process.It is estimated that the proposed system will enhance accuracy and offer fasting diagnosis results than the traditional method, which makes this system a trustworthy and resourceful for dermatological disease detection.Additionally, the system can serve as a realtime learning platform for students studying dermatology in medical schools in Ghana.
Efficient Method and Architecture for Real-Time Video Defogging

Real-time video defogging has a huge demand in intelligent transportation, advanced driver assistance systems (ADAS), long-range surveillance, autonomous aerial vehicles and endoscopic surgery. Most of these applications are constrained by stringent frame rate, power and memory budget. Till date, the methods devised for such requirements are very rare. Therefore, this paper proposes an efficient method and very-large-scale integration (VLSI) architecture for resource-constrained embedded system targeting real-time video defogging. The method and architecture are co-designed to achieve high throughput while consuming less resources and power. The architecture is divided into four parts namely, atmospheric light estimation unit, airlight adjustment unit, transmission estimation unit, and pixel restoration unit. The atmospheric light estimation unit employs a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3\times 3$ </tex-math></inline-formula> tile based approach that eliminates the requirement of large buffer memory for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$15\times 15$ </tex-math></inline-formula> support size. The transmission is estimated using dark channel prior and gradient threshold based Gaussian filtering approach. In order to overcome flickering artifacts, an adaptive airlight updating scheme is employed. From the quantitative and qualitative evaluations, it is observed that the proposed method outperforms the existing hardware approaches. Furthermore, the field-programmable gate array (FPGA) and application-specific integrated circuit (ASIC) implementations of the architecture achieve a high throughput of 200 MPixels/s and 600 MPixels/s, respectively. The ASIC implementation dissipates 9.03 mW power at 200MHz. Moreover, the proposed design does not require any external memory such as dynamic random-access memory (DRAM), thus making it suitable for on-chip processing that can be closely integrated with an image sensor.
A general framework for secondary constructions of bent and plateaued functions

In this work, we employ the concept of composite representation of Boolean functions, which represents an arbitrary Boolean function as a composition of one Boolean function and one vectorial function, for the purpose of specifying new secondary constructions of bent/plateaued functions. This representation gives a better understanding of the existing secondary constructions and it also allows us to provide a general construction framework of these objects. This framework essentially gives rise to an infinite number of possibilities to specify such secondary construction methods (with some induced sufficient conditions imposed on initial functions) and in particular we solve several open problems in this context. We provide several explicit methods for specifying new classes of bent/plateaued functions and demonstrate through examples that the imposed initial conditions can be easily satisfied. Our approach is especially efficient when defining new bent/plateaued functions on larger variable spaces than initial functions. For instance, it is shown that the indirect sum methods and Rothaus’ construction are just special cases of this general framework and some explicit extensions of these methods are given. In particular, similarly to the basic indirect sum method of Carlet, we show that it is possible to derive (many) secondary constructions of bent functions without any additional condition on initial functions apart from the requirement that these are bent functions. In another direction, a few construction methods that generalize the secondary constructions which do not extend the variable space of the employed initial functions are also proposed.
Coupled IGMM-GANs with Applications to Anomaly Detection in Human Mobility Data

Detecting anomalous activity in human mobility data has a number of applications, including road hazard sensing, telematics-based insurance, and fraud detection in taxi services and ride sharing. In this article, we address two challenges that arise in the study of anomalous human trajectories: (1) a lack of ground truth data on what defines an anomaly and (2) the dependence of existing methods on significant pre-processing and feature engineering. Although generative adversarial networks (GANs) seem like a natural fit for addressing these challenges, we find that existing GAN-based anomaly detection algorithms perform poorly due to their inability to handle multimodal patterns. For this purpose, we introduce an infinite Gaussian mixture model coupled with (bidirectional) GANs—IGMM-GAN—that is able to generate synthetic, yet realistic, human mobility data and simultaneously facilitates multimodal anomaly detection. Through the estimation of a generative probability density on the space of human trajectories, we are able to generate realistic synthetic datasets that can be used to benchmark existing anomaly detection methods. The estimated multimodal density also allows for a natural definition of outlier that we use for detecting anomalous trajectories. We illustrate our methodology and its improvement over existing GAN anomaly detection on several human mobility datasets, along with MNIST.
ConvoKit: A Toolkit for the Analysis of Conversations

Jonathan P. Chang, Caleb Chiam, Liye Fu, Andrew Wang, Justine Zhang, Cristian Danescu-Niculescu-Mizil. Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue. 2020.
Randomized Smoothing of All Shapes and Sizes

Randomized smoothing is a recently proposed defense against adversarial attacks that has achieved state-of-the-art provable robustness against L2 perturbations. Soon after, a number of works devised new randomized smoothing schemes for other metrics, such as L1 or L-infinity; however, for each geometry, substantial effort was needed to derive new robustness guarantees. This begs the question: can we find a general theory for randomized smoothing? In this work we propose a novel framework for devising and analyzing randomized smoothing schemes, and validate its effectiveness in practice. Our theoretical contributions are as follows: (1) We show that for an appropriate notion of “optimal”, the optimal smoothing distributions for any “nice” norm have level sets given by the Wulff Crystal of that norm. (2) We propose two novel and complementary methods for deriving provably robust radii for any smoothing distribution. Finally, (3) we show fundamental limits to current randomized smoothing techniques via the theory of Banach space cotypes. By combining (1) and (2), we significantly improve the state-of-the-art certified accuracy in L1 on standard datasets. On the other hand, using (3), we show that, without more information than label statistics under random input perturbations, randomized smoothing cannot achieve nontrivial certified accuracy against perturbations of L-infinity-norm Omega(1/sqrt(d)), when the input dimension d is large. We provide code in github.com/tonyduan/rs4a.
3-D Measurement Method for Nonuniform Reflective Objects

For 3-D measurement of nonuniform reflective objects, it is necessary to ensure measurement accuracy, reduce the number of additional images, and improve measurement efficiency. To satisfy these requirements, we first present a pixel matching method for projected and captured images. Second, we obtain the optimal projected image from two additional images. Finally, we propose a method to estimate reasonable exposure time. Experimental results show that the method can reconstruct 3-D shape of the metal, plastic, and colored objects with minimum of root mean square error (RMSE) 0.095, 0.068, and 0.079, respectively.
Improving bug report triage performance using artificial intelligence based document generation model

Abstract Artificial intelligence is one of the key technologies for progression to the fourth industrial revolution. This technology also has a significant impact on software professionals who are continuously striving to achieve high-quality software development by fixing various types of software bugs. During the software development and maintenance stages, software bugs are the major factor that can affect the cost and time of software delivery. To efficiently fix a software bug, open bug repositories are used for identifying bug reports and for classifying and prioritizing the reports for assignment to the most appropriate software developers based on their level of interest and expertise. Owing to a lack of resources such as time and manpower, this bug report triage process is extremely important in software development. To improve the bug report triage performance, numerous studies have focused on a latent Dirichlet allocation (LDA) using the k-nearest neighbors or a support vector machine. Although the existing approaches have improved the accuracy of a bug triage, they often cause conflicts between the combined techniques and generate incorrect triage results. In this study, we propose a method for improving the bug report triage performance using multiple LDA-based topic sets by improving the LDA. The proposed method improves the existing topic sets of the LDA by building two adjunct topic sets. In our experiment, we collected bug reports from a popular bug tracking system, Bugzilla, as well as Android bug reports, to evaluate the proposed method and demonstrate the achievement of the following two goals: increase the bug report triage accuracy, and satisfy the compatibility with other state-of-the-art approaches.
The Effect of Skimming Method to Improve Students’ Ability in Reading Comprehension on Narrative Text.

This research deals with the effect of using skimming method in teaching reading comprehension smk negeri 2 pematangsiantar on narrative text. The problem of this research are “what is the students’ ability in reading without using skimming method at Grade XI students’ of SMK N.2 Pematangsiantar. What is the students’ ability in reading by using skimming method at Grade XI students’ of SMK N.2”. The purpose of this research are to find out the students’ ability in reading without using skimming method at grade XI students in SMK N 2 Pematangsiantar on reading narrative text, to find out the the students ability in reading by using skimming method to the skill of grade XI students in SMK N 2 Pematangsiantar on reading narrative text. To answer the problem the researchers uses theories: Micklukey and Jefferies (2007), Grellet (2000), Milter (2001), Jhon and Dara (2005), David Nunan (1991), Douglas Newton (2000), Douglas Brown (2003), Rae Pica (2000), Roger Brown (1973), Ronald Carter (1998), Scoot Thornburry (2002), Wilga Rivers (1987), Diane Larsen (1990), Judi Willis (2008), Jack and Theodore (1986). The research design is a quantitative research. As an experimental research, it aims to know the effect of the technique that given and apply whether it influences to the object or no. In this research in collecting the data the researcher has to do three steps, they are pre-test, treatment, post-test. The researchers find out that the effect of using skimming method can increase the students’ skill in reading of narrative text. It can be proven from the test of students’ score in posttest of experimental class which the treatment the researchers did the treatment by using skimming method. By using skimming method, the students can increase their ability in reading of narrative text.
Benchmarking Continuous Dynamic Optimization: Survey and Generalized Test Suite

Dynamic changes are an important and inescapable aspect of many real-world optimization problems. Designing algorithms to find and track desirable solutions while facing challenges of dynamic optimization problems is an active research topic in the field of swarm and evolutionary computation. To evaluate and compare the performance of algorithms, it is imperative to use a suitable benchmark that generates problem instances with different controllable characteristics. In this article, we give a comprehensive review of existing benchmarks and investigate their shortcomings in capturing different problem features. We then propose a highly configurable benchmark suite, the generalized moving peaks benchmark, capable of generating problem instances whose components have a variety of properties, such as different levels of ill-conditioning, variable interactions, shape, and complexity. Moreover, components generated by the proposed benchmark can be highly dynamic with respect to the gradients, heights, optimum locations, condition numbers, shapes, complexities, and variable interactions. Finally, several well-known optimizers and dynamic optimization algorithms are chosen to solve generated problems by the proposed benchmark. The experimental results show the poor performance of the existing methods in facing new challenges posed by the addition of new properties.
Federated Knowledge Distillation

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