Reviewerly/exHarmony huggingface dataset:
Embodied Human-Computer Interactions through Situated Grounding

In this paper, we introduce a simulation platform for modeling and building Embodied Human-Computer Interactions (EHCI). This system, VoxWorld, is a multimodal dialogue system enabling communication through language, gesture, action, facial expressions, and gaze tracking, in the context of task-oriented interactions. A multimodal simulation is an embodied 3D virtual realization of both the situational environment and the co-situated agents, as well as the most salient content denoted by communicative acts in a discourse. It is built on the modeling language VoxML [7], which encodes objects with rich semantic typing and action affordances, and actions themselves as multimodal programs, enabling contextu-ally salient inferences and decisions in the environment. VoxWorld enables an embodied HCI by situating both human and computational agents within the same virtual simulation environment, where they share perceptual and epistemic common ground.
Fine-grained Language Identification with Multilingual CapsNet Model

Due to a drastic improvement in the quality of internet services worldwide, there is an explosion of content generation and consumption. This has led to an increasing diversity of audiences who want to consume media outside their linguistic familiarity/preference. Hence, there is an increasing need for real-time and fine-grained content analysis services, including language identification, content transcription, and analysis. Accurate and fine-grained spoken language detection is an essential first step for all the subsequent content. Current techniques in spoken language detection may lack in final language prediction accuracy, require large amounts of training data, or finally may deal with work with a small number of languages. In this work, we present a real-time language detection approach to detect spoken language from noisy and readily available data sources through a Capsule Network (CapsNet) architecture. Further, we show that the CapsNet can effectively detect whether data samples belong to none of the languages on which the model was trained. We compare our results with our baseline based on a combination of recurrent networks and attention mechanism.
Profiling Developers Through the Lens of Technical Debt

Context: Technical Debt needs to be managed to avoid disastrous consequences, and investigating developers' habits concerning technical debt management is invaluable information in software development. Objective: This study aims to characterize how developers manage technical debt based on the code smells they induce and the refactorings they apply. Method: We mined a publicly-available Technical Debt dataset for Git commit information, code smells, coding violations, and refactoring activities for each developer of a selected project. Results: By combining this information, we profile developers to recognize prolific coders, highlight activities that discriminate among developer roles (reviewer, lead, architect), and estimate coding maturity and technical debt tolerance.
Real-Time Target Detection in Visual Sensing Environments Using Deep Transfer Learning and Improved Anchor Box Generation

Visual perception is critical and essential to understand phenomenon and environments of the world. Pervasively configured devices like cameras are key in dynamic status monitoring, object detection and recognition. As such, visual sensor environments using one single or multiple cameras must deal with a huge amount of high-resolution images, videos or other multimedia. In this paper, to promote smart advancement and fast detection of visual environments, we propose a deep transfer learning strategy for real-time target detection for situations where acquiring large-scale data is complicated and challenging. By employing the concept of transfer learning and pre-training the network with established datasets, apart from the outstanding performance in target localization and recognition can be achieved, time consumption of training a deep model is also significantly reduced. Besides, the original clustering method, k-means, in the You Only Look Once (YOLOv3) detection model is sensitive to the initial cluster centers when estimating the initial width and height of the predicted bounding boxes, thereby processing large-scale data is extremely time-consuming. To handle such problems, an improved clustering method, mini batch k-means++ is incorporated into the detection model to improve the clustering accuracy. We examine the sustainable outperformance in three typical applications, digital pathology, smart agriculture and remote sensing, in vision-based sensing environments.
Security Compliance in Agile Software Development: A Systematic Mapping Study

Companies adopting agile development tend to face challenges in complying with security norms. Existing research either focuses on how to integrate security into agile methods or on discussing compliance issues of agile methods but independently of the regulation type, in particular of security standards. A comprehensive overview of this scattered field is still missing and we know little about how to achieve security compliance in agile software development. Existing secondary studies (mapping studies and literature reviews) analyze publications on secure agile development, but they do not analyze implications of security standard compliance, e.g., integration of specific standard requirements or compliance assessments. To close this gap, we report on a systematic mapping study. Starting with a set of 2,383 papers, our work distills 11 relevant publications addressing security compliance in agile software development. With this study, we contribute by describing the maturity of the field, as well as domains where security compliant agile software engineering was investigated. Moreover, we make explicit which phases of a secure development process are covered by the field and which agile principles are analyzed when aiming at compliance with international security standards, country-specific security regulations, industry-specific security standards, and other well-known security frameworks.
An Automatic Calibration Method for AVM Cameras

We introduce a method for the efficient calibration of around-view-monitoring (AVM) cameras. Particularly, we introduce two situations that require calibration because of the characteristics of AVM cameras: a situation wherein cameras are shipped from the manufacturing line and another situation wherein some cameras are distorted during operation and need recalibration. In this study, the calibration method for shipped cameras is defined as the factory mode and that for recalibration is defined as the service mode. In the factory mode, two circular patterns placed at a regular distance are used to ensure the maximum accuracy while requiring minimum calibration. In the service mode, as a recalibration method, we developed a robust method that considers various environments using parallel parking lines that can be easily installed in general service centers. In the factory mode, we confirmed that the AVM-camera-calibration error was within 5.66 cm when the two circular patterns were located at a certain distance within a certain range. However, in the service mode, we achieved camera movement angle error equal to or less than 0.1° using the parking-line-detection result.
Graph Unfolding Networks

The technique of recursive neighborhood aggregation has dominated the implementation of existing successful Graph Neural Networks (GNNs). However, the recursive information propagation across layers inevitably brings in extra calculations, potentially large variance, and difficulty of parallel computation. In this paper, we propose Graph Unfolding Networks (GUNets) as an alternative mechanism of recursive neighborhood aggregation for graph representation learning. Comparing to generic GNNs, our proposed GUNets are efficient, robust and practically effective. At their core, GUNets unfold the local structure of every node, i.e. the rooted tree, to a set of trajectories, and then adopt set function to capture the topology of the rooted subtree, which is more convenient for parallel computation than the recursive neighborhood aggregation process. More importantly, through a specific design of the set function, our architecture enables efficient and robust learning on large-scale graphs without resorting to any pruning of the rooted subtree that is usually necessary in generic GNNs. Extensive experiments on five large datasets (the number of nodes ranges from 104 to 106) show that our GUNets achieve comparable or even better results than current successful GNNs while gaining significantly more efficiency and lower accuracy variance. Codes can be found at github.com/GUNets/GUNets.
Secure Fine-Grained Keyword Search With Efficient User Revocation and Traitor Tracing in the Cloud

Fine-grained searching is an important feature in multi-user cloud environment and a combination of attribute-based encryption (ABE) and searchable encryption (SE) is used to facilitate it. This combination provides a powerful tool where multiple data owners can share their data with multiple data users in an independent and differential manner. In this article, the authors have used key-policy design framework of attribute-based encryption to construct the multi-keyword search scheme where access rights assigned to a data user are associated with his/her secret key. This leads to a situation where a data user can abuse his secret key to distribute it illegally to the unauthorized users to perform search over the shared data which is not intended for him/her. Therefore, to track such kind of key abusers the authors have embedded an extra functionality of tracing the traitors. For this purpose, each user is assigned a unique identity in the form of binary string where each bit represents an attribute related to his identity. In addition to the normal attributes, the access structure of a user also possesses identity-related attributes which are hidden from the user along with some normal attributes. Hence, the proposed scheme supports partial anonymity. Further, in the event of user revocation the proposed scheme efficiently handles the system update process by delegating the computationally intensive tasks to the cloud server. Finally, the proposed scheme is proved secure under Decisional Bilinear Diffie-Hellman (DBDH) assumption and decision linear assumption in the selective security model.
Privacy-Preserving Machine Learning Using Federated Learning and Secure Aggregation

Over the past few years, machine learning has been responsible for the rapid advancements in fields such as computer vision, natural language processing and speech recognition. No small part of this success is due to data becoming more and more available, often being collected in privacy-invasive ways. The aim of this work is to study the use of two privacy-preserving solutions for training machine learning models: Federated Learning (FL) and Secure Multiparty Computation (MPC). Federated learning is a subfield of machine learning that allows training models on a large, decentralized corpus of data residing on edge devices like smartphones. Instead of sharing data, users collaboratively train a model by only sending weight updates to a server. By leveraging secure multiparty computation, it can be ensured that the server cannot inspect any individual user's update. To assess the feasibility of these approaches in different settings, a client-server architecture was implemented in Python and multiple experiments were run on datasets made available by LEAF in order to investigate ways of improving the overall performance of the models trained in a federated manner.
Dual Autoencoder Network with Swap Reconstruction for Cold-Start Recommendation

Cold-start is a long-standing and challenging problem in recommendation systems. To tackle this issue, many cross-domain recommendation approaches are proposed. However, most of them follow a two-stage embedding-and-mapping paradigm, which is hard to be optimized. Besides, they ignore the structure information of the user-item interaction graph, resulting in that the embedding is insufficient to capture the latent collaborative filtering effect. In this paper, we propose a Dual Autoencoder Network (DAN), which implements cross-domain recommendations to cold-start users in an end-to-end manner. The graph convolutional network (GCN) based encoder in DAN explicitly captures high-order collaborative information in user-item interaction graphs. The two-branched decoder is proposed for fully exploiting the data across domains, and therefore the elaborate reconstruction constraints are obtained under a domain swapping strategy. Experiments on two pairs of real-world cross-domain datasets demonstrate that DAN outperforms existing state-of-the-art methods.
Coarse-to-Fine Segmentation on LiDAR Point Clouds in Spherical Coordinate and Beyond

Active sensors such as LiDARs (light detection and ranging) are popular in autonomous driving systems for perception and localization. Existing perception approaches process the rich 3D LiDAR point clouds for object detection, tracking and recognition. These methods generally require an initial segmentation procedure containing two steps: (1) filter points as ground and non-ground points, and (2) cluster the non-ground points into objects. Leveraging a range image created in Spherical coordinates, this paper proposes a field-tested coarse-to-fine 3D point cloud segmentation framework to achieve both speed and accuracy. Under this framework, a basic version and an advanced version of segmentation algorithms are presented. In the basic version, we demonstrate how a coarse-to-fine scheme is applied in a range image for ground filtering and object clustering. In the advanced version, we move forward to reduce the processing time and correct the motion distortion by directly dealing with the data packets instead of the range image. Tests in the well-known KITTI dataset and field experiments in public roads have shown that the method significantly improves the speed of 3D point cloud segmentation whilst maintains good accuracy.
Image-to-Image Translation Method for Game-Character Face Generation

Traditional image-to-image translation methods effectively change the style; however, these methods have several limitations in shape changing. Particularly, current image-to-image translation technology is not effective for changing a real-world face image to the face of a virtual character. To solve this problem, we propose a novel unsupervised image-to-image translation method that is specialized in facial changes accompanied by radical shape changes. We apply two feature loss functions specialized for faces in an image-to-image translation technique based on the generative adversarial network framework. The experimental results show that the proposed method is superior to other recent image-to-image algorithms in case of face deformations.
Analyzing Deep Learning for Time-Series Data Through Adversarial Lens in Mobile and IoT Applications

Predictive analytics using the time-series data collected from various types of sensors is a fundamental task that enables diverse mobile and Internet of Things applications including smart health. Deep-learning-based solutions are increasingly employed to solve such tasks because of their ability to directly process raw sensor data to achieve high accuracy as opposed to using human-engineered features. However, there are no principled studies on analyzing deep models for multivariate time-series data in adversarial settings. In this article, we propose a novel framework referred as a multivariate time-series adversarial lens (MTS-AdLens) to analyze deep models for wearable and mobile sensing systems through the adversarial lens in a realistic setting. We make three main contributions toward this goal. First, we introduce highly effective black-box attacks that expose significant vulnerabilities of deep models for multivariate time-series input space. Specifically, we show that deep models are vulnerable to attacks on limited channels. Second, inspired by our vulnerability analysis, we propose a novel technique to improve the robustness of the model. Third, we perform comprehensive experiments on data collected from real tasks to validate all our claims. Our results show the effectiveness of MTS-AdLens in identifying the vulnerabilities of deep models and in improving their robustness to realistic attacks.
A Reproducibility Study of Deep and Surface Machine Learning Methods for Human-related Trajectory Prediction

In this paper, we compare several deep and surface state-of-the-art machine learning methods for risk prediction in problems that can be modelled as a trajectory of events separated by irregular time intervals. Trajectories are the abstract representation of many real-life data, such as patient records, student e-tivities, online financial transactions, and many others. Given the continuously increasing number of machine learning methods to predict future high-risk events in these contexts, we aim to provide more insight into reproducibility and applicability of these methods when changing datasets, parameters, and evaluation measures. As an additional contribution, we release to the community the implementations of all compared methods.
Refactoring the FreeBSD Kernel with Checked C

Most modern operating system kernels are written in C, making them vulnerable to buffer overflow and buffer over-read attacks. Microsoft has developed an extension to the C language named Checked C which provides new source language constructs that allow the compiler to prevent NULL pointer dereferences and spatial memory safety errors through static analysis and run-time check insertion. We evaluate the use of Checked C on operating system kernel code by refactoring parts of the FreeBSD kernel to use Checked C extensions. We describe our experience refactoring the code that implements system calls and UDP and IP networking. We then evaluate the refactoring effort and the performance of the refactored kernel. It took two undergraduate students approximately three months to refactor the system calls, the network packet (mbuf) utility routines, and parts of the IP and UDP processing code. Our experiments show that using Checked C incurred no performance or code size overheads.
Creating a 4D Photoreal VR Environment to Teach Civil Engineering

Motivated by recent developments in AR/VR technology and accessibility, an interdisciplinary team at a Sino-foreign Higher Education Institution in China have been engaged in the creation of a virtual reality learning resource that will be offered to students and educators in the institution's Civil Engineering department. In Civil Engineering education, it is difficult to explain to students how classroom theories translate into real life solutions and field trips can help resolve this problem. However, these must overcome fundamental concerns associated with student site visits, which include safety, logistics and accessibility for disadvantaged students. The presented case study details the development of a virtual field trip that is accessible from simple phone-based virtual reality headsets. The photoreal virtual field trip was constructed through multiple site visits to capture data and the experience now provides students with an opportunity to view the entire construction process of a university library. This paper contributes to current research by exploring the challenges and conjugate solutions encountered whilst building the virtual reality environment, and by presenting the first known photoreal virtual field trip that allows students to autonomously move both spatially and chronologically around a construction site. This will benefit other educational practitioners who are contemplating similar initiatives.
JUSTIN: An Audience Participation Game With A Purpose for Collecting Descriptions for Artwork Images

This paper presents JUSTIN standing for Japanese Ukiyo-e Streaming That Improves Narrative, a game system that is designed for collecting descriptive data for artwork images (Japanese ukiyo-e in this study). The proposed game is the first "Audience Participation Game With a Purpose (APGWAP)," which combines two existing concepts: Audience Participation Game and Game With A Purpose. The APGWAP concept is aimed at addressing problems that computers can hardly solve by utilizing human participation on a live streaming platform. The descriptive data of ukiyo-e images can be beneficial in humanity research and in promoting cultural heritage. However, such data are currently insufficient, sparse or poor in quality. JUSTIN proves to be a powerful tool in exploiting crowd-sourcing for addressing such problems through solid evidence provided from the results of conducted experiments in this work in terms of the quality of collected descriptions as well as player experience.
Private Capital Flows, Real Sector Growth and Institutional Quality in Africa

Abstract The article examines the interconnections between capital flows, institutions and real sector growth in Africa. We show that capital flows (foreign direct investment, private debt and equity) have no direct favourable impact on the real sector. Initial assessments show that countries with robust institutional frameworks can benefit significantly from capital flows, as institutions moderate the positive impact of capital flows on real sector growth. Our marginal effect analyses show that the impact of private capital on real sector components is dependent on the type of capital, sector and the percentile level on institutions, in some cases, at the 90th percentile. © 2020 John Wiley &amp; Sons, Ltd.
2Lbp-RRNS: Two-Levels RRNS With Backpropagation for Increased Reliability and Privacy-Preserving of Secure Multi-Clouds Data Storage

Cloud storage as service is the mainstream technology used to retain digital data. However, there are significant risks for confidentiality, integrity, and availability violation associated with the loss of information, denial of access, technical failures, etc. In this article, we propose a two-level 2Lbp-RRNS scheme based on a Redundant Residue Number System with a backpropagation and hamming distance mechanisms for increasing reliability of a configurable and secure multi-cloud data storage. We provide a theoretical analysis of the 2Lbp-RRNS solution as an extension of the classical 2L-RRNS and a variant of fully homomorphic encryption for privacy-preserving, parallel processing, and scalability. We formulate, explain, and prove its main properties to extend existing knowledge within the limits of the critical bounding RRNS assumptions. We show that 2Lbp-RRNS can identify and recover more errors than traditional 2L-RRNS. We provide the upper bounds of the traditional threshold 2L-RRNS and our solution to estimate the number of detectable and correctable errors. We study various data access scenarios and show that it detects 1.58$\times $ and corrects 3.37$\times $ more errors than 2L-RRNS, on average. We also provide efficient implementations of encoding and decoding algorithms MRC8, and MRC16 based on the Mixed-Radix system, Finite Ring Neuronal Network, and signed binary window method. We evaluate encoding/decoding speeds using three algorithms: Mignotte, MRC8, and MRC16. The experimental system includes seven cloud storages: DropBox, GoogleDrive, OneDrive, Sharefile, Box, Egnyte, and Salesforce. To assess the efficiency of the system on real data, we vary scenarios of the first and second levels. The results show that our solution outperforms MRC8 by 2.53$\times $ (1.78$\times $ ), and Mignotte by 4.83$\times $ (11.43$\times $ ) for the encoding (decoding) speed, respectively.