Reviewerly/exHarmony huggingface dataset:
Distributed, Collaborative Virtual Reality Application for Product Development with Simple Avatar Calibration Method

In this work we present a collaborative virtual reality application for distributed engineering tasks, including a simple avatar calibration method. Use cases for the application include CAD review, ergonomics analyses or virtual training. Full-body avatars with approximately natural motion characteristics are used to improve collaboration in terms of co-presence and embodiment. Through a calibration process, scaled avatars based on body measurements can be generated by users from within the VR application. For motion tracking, only the tracking capabilities of the VR devices used (head mounted display and hand controllers) are required, in order to achieve a low initialization effort for usage of the application. We demonstrate a simple, yet effective method for measuring the human body which requires these VR devices only. Tracking only the user's head and hands, we require inverse kinematics (IK) for avatar motion reconstruction. On the other hand, this requires a calibrated avatar, otherwise body postures and gestures will be misrepresented. In the first step of the demo, two users can create their calibrated avatar at the same time and then carry out a collaborative CAD review in the second step.
Comparative Analysis of Electricity Consumption at Home through a Silhouette-score prospective

Machine learning is a state-of-the-art sub-project of artificial intelligence, that is been evolved for finding large-scale intelligent analytics in the distributed computing environment. In this paper, we perform comparative analytics onto dataset collected for the electricity usage of home based on the K-mean clustering algorithm using comparison to silhouette score with a ratio of 1/8 dataset. The performance evaluation shows that, the comparison index is similar in numbers of silhouette score even if dataset is smaller than before.
Reducing correlation of random forest–based learning‐to‐rank algorithms using subsample size

Abstract Learning‐to‐rank (LtR) has become an integral part of modern ranking systems. In this field, the random forest–based rank‐learning algorithms are shown to be among of the top performers. Traditionally, each tree of a random forest is learnt using a bootstrapped copy of the training set, where approximately 63% of the examples are unique. The goal of using a bootstrapped copy instead of the original training set is to reduce the correlation between individual trees, thereby making the prediction of the ensemble more accurate. In this regard, the following question may be raised: how can we leverage the correlation between the trees in favor of performance and scalability of a random forest–based LtR algorithm? In this article, we investigate whether we can further decrease the correlation between the trees while maintaining or possibly improving accuracy. Among several potential options to achieve this goal, we investigate the size of the subsamples used for learning individual trees. We examine the performance of a random forest–based LtR algorithm as we control the correlation using this parameter. Experiments on LtR data sets reveal that for small‐ to moderate‐sized data sets, substantial reduction in training time can be achieved using only a small amount of training data per tree. Moreover, due to the positive correlation between the variability across the trees and performance of a random forest, we observe an increase in accuracy while maintaining the same level of model stability as the baseline. For big data sets, although our experiments did not observe an increase in accuracy (because, with larger data sets, the individual tree variance is already comparatively smaller), our technique is still applicable as it allows for greater scalability.
Visual Object Tracking in RGB-D Data via Genetic Feature Learning

Visual object tracking is a fundamental component in many computer vision applications. Extracting robust features of object is one of the most important steps in tracking. As trackers, only formulated on RGB data, are usually affected by occlusions, appearance, or illumination variations, we propose a novel RGB-D tracking method based on genetic feature learning in this paper. Our approach addresses feature learning as an optimization problem. As owning the advantage of parallel computing, genetic algorithm (GA) has fast speed of convergence and excellent global optimization performance. At the same time, unlike handcrafted feature and deep learning methods, GA can be employed to solve the problem of feature representation without prior knowledge, and it has no use for a large number of parameters to be learned. The candidate solution in RGB or depth modality is represented as an encoding of an image in GA, and genetic feature is learned through population initialization, fitness evaluation, selection, crossover, and mutation. The proposed RGB-D tracker is evaluated on popular benchmark dataset, and experimental results indicate that our method achieves higher accuracy and faster tracking speed.
Modeling Drone Pointing Movement with Fitts' Law

Although numerous studies have focused on interfaces for maneuvering drones, a method for evaluating these interfaces has not been established. A pointing experiment was carried out with a drone in this study. The results indicate that the target distance and target width affect the movement time and error rate while maneuvering. This is consistent with the results of previous pointing studies. Fitts' law was not a good fit (R^2 = 0.672), while the data fit well to a two-part model (R^2 = 0.993). Based on these results, we propose future experimental work that could contribute to improving drone interfaces.
Monitoring an untrusted light source with single-photon detectors in measurement-device-independent quantum key distribution

Measurement-device-independent quantum key distribution (MDI-QKD) is proposed to close the loopholes of the detection part in QKD systems, while it still requires the light source to be trusted. This assumption is not guaranteed in practice, leading to practical secure issues. In this paper, we propose a light source monitoring scheme with single-photon detectors for MDI-QKD, which can guarantee the security of the system under the untrusted source condition with both common decoy state method and the newly proposed five-intensity decoy state method. Moreover, our monitoring scheme performs almost the same as the ideal MDI protocol even considering the imperfect factors in practical systems. Compared to the previous scheme, our work improves the secure key rate and the transmission distance of practical MDI-QKD systems significantly.
Adaptive variable-structure basis function expansions: Candidates for machine learning

This paper proposes a novel top-down approach to rule-based fuzzy systems, one that begins with the product—an equation—and then addresses the unique features of the product, without requiring the reader to know anything about fuzzy sets and systems. The "products" are adaptive variable-structure basis function expansions, where "adaptive variable-structure" means that different subsets of its basis functions are active (non-zero) in different regions of the state space, something that occurs automatically by virtue of the structure of the basis functions, so that the products can be said to "adapt" to locations in the state space. These products are novel candidates for machine learning. Unique features of all products are: (1) Number of basis functions is no longer a variable and is established locally through type-1 or type-2 uncertainty partitioning of each variable; (2) Both coarse and fine sculpting of the state space are achieved, and are described in terms of first- and second-order partitions of the state space, respectively; and (3) Linguistic interpretability is obtained, which may be of value to an end-user. Learning about rule-based fuzzy systems can be greatly compressed by using the top-down approach that is described in this paper.
A Roman to Devanagari Back-Transliteration Algorithm based on Harvard-Kyoto Convention

Transliteration is the process to transcribe a script of one language into another, while, backward or back transliteration is converting back the transliterated text into its original script. The highly technical phonetic system of Sanskrit seems to have made the preparation of transliteration scheme quite arduous. This study is focused on development of a rule-based, grapheme model character alignment back-transliteration algorithm of Sanskrit script, transcribed ASCII(American Standard Code for Information Interchange)-encoded English to Devanagari, pursuant to the Harvard-Kyoto (HK) convention. Accordingly, the paper presents the context of the utility for such an algorithm. It also describes the various standard schemes available for transcribing Devanagari into Roman. A survey on the evolution of scripts in India suggests the Brahmi script as the foundation for the origin of variants like Devanagari. Since the nineteenth century, various transliteration schemes based on Roman script have evolved. The International Alphabet of Sanskrit Transliteration (IAST) schemes used diacritics to disambiguate phonetic similarities and seem to have induced much strenuous venture for the non-professionals. The ASCII-based, HK and its variant, Indian Language Transliteration (ITRANS) schemes do not use diacritics and hence accounted to be the simplest. Our rationale for the use of HK scheme, stem from its prime traits of Sanskrit Unicode encoding. We have also explained the Sanskrit alphabet and its classifications, which are incorporated into our proffered process. We appraise the complexity of our pseudo-coded algorithm and finally, we propose an extension of this work in the creation of similar tools, for other Indian languages that use the Devanagari script, such as Hindi and Marathi.
A Novel Approach of Image Encryption Using Chaos and Dynamic DNA Sequence

In this paper, an image encryption scheme based on dynamic DNA sequence and two dimension logistic map is proposed. Firstly two different pseudo random sequences are generated using two dimension Sine-Henon alteration map. These sequences are used for altering the positions of each pixel of plain image row wise and column wise respectively. Secondly each pixels of distorted image and values of random sequences are converted into a DNA sequence dynamically using one dimension logistic map. Reversible DNA operations are applied between DNA converted pixel and random values. At last after decoding the results of DNA operations cipher image is obtained. Different theoretical analyses and experimental results proved the effectiveness of this algorithm. Large key space proved that it is possible to protect different types of attacks using our proposed encryption scheme.
An Observational Investigation of Reverse Engineers' Process and Mental Models

Reverse engineering is a complex task essential to several software security jobs like vulnerability discovery and malware analysis. While traditional program comprehension tasks (e.g., program maintenance or debugging) have been thoroughly studied, reverse engineering diverges from these tasks as reverse engineers do not have access to developers, source code, comments, or internal documentation. Further, reverse engineers often have to overcome countermeasures employed by the developer to make the task harder (e.g., symbol stripping, packing, obfuscation). Significant research effort has gone into providing program analysis tools to support reverse engineers. However, little work has been done to understand the way they think and analyze programs, potentially leading to the lack of adoption of these tools among practitioners. This paper reports on a first step toward better understanding the reverse engineer's process and mental models and provides directions for improving program analysis tools to better fit their users. We present the initial results of a semi-structured, observational interview study of reverse engineers (n=15). Each interview investigated the questions they asked while probing the program, how they answered these questions, and the decisions made throughout. Our initial observations suggest that reverse engineers rely on a variety of reference points in both the program text and structure as well as its dynamic behavior to build hypotheses about the program's function and identify points of interest for future exploration. In most cases, our reverse engineers used a mix of static and dynamic analysis---mostly manually---to verify these hypotheses. Throughout, they rely on intuition built up over past experience. From these observations, we provide recommendations for user interface and program analysis improvements to support the reverse engineer.
Digital Silence and Liberating Stories

In August 2018, a student protest initiated in Bangladesh sought justice when two school students were run over by public bus. Student protesters were demonstrating on the street for days until they were physically attacked. Concurrent with the physical attacks, the country experienced a disconnect. Internet, restrictions on social media usage, and several high-profile arrests of people speaking about the incidents. These suppressive encounters created what we call a "digital silence." In response, we collected stories from people, which depict their effort to seek out information about the events unfolding and share their perspective of what happened. Through these in-the-moment stories, we see a glimpse of how the information suppression impacted people with varying proximity to the events, including protesters, bystanders, and family members. We also reflect on the benefit of the subtle defiance of storytelling for storytellers in the midst of this social justice effort.
A Low-Cost Hardware Accelerator for CCSDS 123 Predictor in FPGA

Hyperspectral images are a widely used remote sensing technique. These images are three-dimensional data structures, where the x and y axes contain spatial information and the z-axis contains spectral information or image bands. Usually, these bands can reach the order of hundreds, generating a considerable amount of information. Due to storage limitations and communication bandwidth, the use of compression techniques becomes essential. For spatial applications, a standard commonly used in the literature is CCSDS 123, developed by the Consultative Committee for Space Data Systems, which describes the algorithm for lossless compression of hyperspectral images. In this context, we implemented the prediction stage of the CCSDS 123 algorithm in the Xilinx Zynq-7000 FPGA. The developed processor provides a good trade-off to meet real-time requirements at low-cost.
NodeMOP

Node.js has become one of the most popular frameworks for general-purpose and server-side application development in JavaScript. However, due to its dynamic, asynchronous, event-driven programming model, Node.js applications are considered error-prone, and their correctness is hard to verify. Monitoring-Oriented Programming (MOP) is a Runtime Verification (RV) paradigm that aims at improving the safety and reliability of a software system. To the best of our knowledge, no practical RV framework targets JavaScript and Node.js applications.
Evaluating a Wearable Camera's Social Acceptability In-the-Wild

With increasing ubiquity, wearable technologies are becoming part of everyday life where they may cause controversy, discomfort and social tension. Particularly, body-worn "always-on" cameras raise social acceptability concerns as their form factors hinder bystanders to infer whether they are "in the frame". Screen-based status indicators have been suggested as remedy, but not evaluated in-the-wild. Simultaneously, best practices for evaluating social acceptability in field studies are rare. This work contributes to closing both gaps. First, we contribute results of an in-the-wild evaluation of a screen-based status indicator testing the suitability of the "displayed camera image" design strategy. Second, we discuss methodical implications for evaluating social acceptability in the field, and cover lessons learned from collecting hypersubjective self-reports. We provide a self-critical, in-depth discussion of our field experiment, including study-related behavior patterns, and prototype fidelity. Our work may serve as a reference for field studies evaluating social acceptability.
Semi-Supervised Classification With Graph Structure Similarity and Extended Label Propagation

Graph-based semi-supervised learning (GSSL) has attracted great attention over the past decade. However, there are still several open problems: (1) how to construct a graph that effectively represents the underlying structure of data and (2) how to incorporate label information of the labeled samples into a procedure of label propagation. Our solution mainly focuses on two aspects: (1) we propose a new graph construction technique by fusing local and global structural similarity (FLGSS). Based on an initial graph structure such as K-nearest neighbors (KNN), we utilize different types of link prediction algorithms to extract local and global graph structure information. These two types of structure information are fused into a graph structure that enhances the ability to represent the data correlation. (2) By incorporating the label correlation with feature similarity of samples, we propose an extended label propagation algorithm (ELP). Through experiments on three different types of datasets, it is shown that our method outperforms other widely used graph construction methods. The extended label inference algorithm achieves better classification results than some state-of-the-art methods. The proposed FLGSS method starts from KNN graph and two link prediction algorithms are performed to construct the graph. With the time complexity analysis, we theoretically deduce that the time complexity of FLGSS is not beyond that of KNN. Meanwhile, the time complexity of ELP remains the same as that of the traditional LP algorithm.
Lithopia

Lithopia is a prototype of a blockchain-managed fictional village that uses satellite and drone data to trigger smart contracts on the open source blockchain platform, Hyperledger. The project is testing the possibility of anticipatory governance of emerging blockchain and distributed ledger technologies (DLTs) by involving stakeholders in the design process over templates. The goal is to question the promises of blockchain governance happening over automation and smart contracts and to offer an alternative to the misuses of emerging technologies in the so-called predictive and anticipatory design. The prototype consists of a functional Node-RED dashboard used as an interface for the Hyperledger smart contracts and a design fiction movie about the lives of the Lithopians.
Self-Adaptive Artificial Intelligence

Machine learning tools, like deep neural networks, are perceived to be black boxes. That is, the only way of changing their internal data models is to retrain these models using different inputs. This is ineffective in dynamic systems that are prone to changes, like concept drift. A new promising solution is transparent artificial intelligence, based on the notions of interpretation and explanation, whose objective is to correlate the internal data models with predictions. The research question being addressed is whether we can have a self-adaptive machine learning system that is able to interpret and explain its data model in order for it to be controlled. In this position paper, we present our initial thoughts whether this can be achieved.
An Exploration of Responsive and Emotive Wearables through Research Prototyping

Responsive and Emotive wearables are concerned with the visualisation of environmental and physiological data. Four research prototypes were created for doctoral research to investigate the possibility that wearable technology could be used to create new forms of nonverbal communication using physiological data from the wearer's body. The research investigated who the audience might be for these research prototypes and their concerns and requirements. Through a lens of how these artifacts might they be used in social or formal contexts, the research gathered data from fifty potential users of emotive wearables and examined the usage and user preferences of such devices. Findings reflected the concerns of potential users from aesthetics and functionality to ethical and privacy issues.
Generating Narrations of Nested SQL Queries Using Context-free Grammars

In the software industry, Structured Query Language (SQL) remains a highly sought-after skill required by most employers. SQL is widely adopted as the preferred declarative language to manipulate data in relational databases. However, the process of comprehending written and new queries turns out to be difficult, especially for novices. To address this problem, a plethora of tools have been developed to assist a novice understand queries using different approaches via visualisation and narration. This paper describes a SQL Narrator designed using a Context-free Grammar that reads and generates narrations for queries using predefined templates. The SQL Narrator is able to narrate nested queries cascaded with balanced parentheses. The generated narrations can be used in assisting students to comprehend nested SQL queries in learning environments. A survey conducted on 161 participants showed that 98.1% agreed that they were able to understand nested queries using our SQL Narrator.
Improving Planning Performance in PDDL+ Domains via Automated Predicate Reformulation

In the last decade, planning with domains modelled in the hybrid PDDL+ formalism has been gaining significant research interest. A number of approaches have been proposed that can handle PDDL+, and their exploitation fostered the use of planning in complex scenarios. In this paper we introduce a PDDL+ reformulation method that reduces the size of the grounded problem, by reducing the arity of sparse predicates, i.e. predicates with a very large number of possible groundings, out of which very few are actually exploited in the planning problems. We include an empirical evaluation which demonstrates that these methods can substantially improve performance of domain-independent planners on PDDL+ domains.
Estimating a sharp convergence bound for randomized ensembles

When randomized ensembles such as bagging or random forests are used for binary classification, the prediction error of the ensemble tends to decrease and stabilize as the number of classifiers increases. However, the precise relationship between prediction error and ensemble size is unknown in practice. In the standard case when classifiers are aggregated by majority vote, the present work offers a way to quantify this convergence in terms of “algorithmic variance,” i.e. the variance of prediction error due only to the randomized training algorithm. Specifically, we study a theoretical upper bound on this variance, and show that it is sharp — in the sense that it is attained by a specific family of randomized classifiers. Next, we address the problem of estimating the unknown value of the bound, which leads to a unique twist on the classical problem of non-parametric density estimation. In particular, we develop an estimator for the bound and show that its MSE matches optimal non-parametric rates under certain conditions. (Concurrent with this work, some closely related results have also been considered in Cannings and Samworth (2017) and Lopes (2019).)