Reviewerly/exHarmony huggingface dataset:
Different Chatbots for Different Purposes: Towards a Typology of Chatbots to Understand Interaction Design

Chatbots are emerging as interactive systems. However, we lack knowledge on how to classify chatbots and how such classification can be brought to bear in analysis of chatbot interaction design. In this workshop paper, we propose a typology of chatbots to support such classification and analysis. The typology dimensions address key characteristics that differentiate current chatbots: the duration of the user’s relation with the chatbot (short-term and long-term), and the locus of control for user’s interaction with the chatbot (user-driven and chatbot-driven). To explore the usefulness of the typology, we present four example chatbot purposes for which the typology may support analysis of high-level chatbot interaction design. Furthermore, we analyse a sample of 57 chatbots according to the typology dimensions. The relevance and application of the typology for developers and service providers are discussed.
Attacks on Digital Watermarks for Deep Neural Networks

Training deep neural networks is a computationally expensive task. Furthermore, models are often derived from proprietary datasets that have been carefully prepared and labelled. Hence, creators of deep learning models want to protect their models against intellectual property theft. However, this is not always possible, since the model may, e.g., be embedded in a mobile app for fast response times. As a countermeasure watermarks for deep neural networks have been developed that embed secret information into the model. This information can later be retrieved by the creator to prove ownership. Uchida et al. proposed the first such watermarking method. The advantage of their scheme is that it does not compromise the accuracy of the model prediction. However, in this paper we show that their technique modifies the statistical distribution of the model. Using this modification we can not only detect the presence of a watermark, but even derive its embedding length and use this information to remove the watermark by overwriting it. We show analytically that our detection algorithm follows consequentially from their embedding algorithm and propose a possible countermeasure. Our findings shall help to refine the definition of undetectability of watermarks for deep neural networks.
A Robust Localization Method for Unmanned Surface Vehicle (USV) Navigation Using Fuzzy Adaptive Kalman Filtering

Recently, multi-sensor navigation has emerged as a viable approach in autonomous vehicles' development. Kalman filtering has been widely applied in multi-sensor data fusion, and researchers are trialing variants of the Kalman Filter (KF) to improve the operational robustness of vehicles in a range of environments under varying dynamic constraints. This paper proposes a novel sensor data fusion algorithm employing an Unscented Kalman Filter (UKF) for the autonomous navigation of an Unmanned Surface Vehicle (USV). Since the navigational sensors on-board the USV are subject to operational uncertainties caused by equipment limitations and environmental disturbances, an improved UKF algorithm with the capability of adaptive estimation, namely fuzzy adaptive UKF data fusion algorithm, has been proposed to obtain reliable navigational information. The conventional UKF is capable of fusing a number of raw sensor measurements and generating relatively accurate estimations with proper a priori knowledge of system noise. To deal with systems that lack such information, a fuzzy adaptive estimation method is introduced to enhance the performance of the conventional UKF, making the algorithm capable of verifying and correcting the associated sensor noise in real time. The proposed fuzzy adaptive UKF data fusion algorithm has been tested and evaluated in different simulations modeled using practical maritime environments and the results are compared with the conventional UKF. The sensor measurements taken from a practical USV trial have also been applied to the proposed algorithm for further validation.
Gentle measurement of quantum states and differential privacy

In differential privacy (DP), we want to query a database about n users, in a way that “leaks at most ε about any individual user,” even conditioned on any outcome of the query. Meanwhile, in gentle measurement, we want to measure n quantum states, in a way that “damages the states by at most α,” even conditioned on any outcome of the measurement. In both cases, we can achieve the goal by techniques like deliberately adding noise to the outcome before returning it. This paper proves a new and general connection between the two subjects. Specifically, we show that on products of n quantum states, any measurement that is α-gentle for small α is also O( α) -DP, and any product measurement that is ε-DP is also O( ε√n) -gentle.
Empowerment on the Margins

Research in Human-Computer Interaction for Development (HCI4D) routinely relies on and engages with the increasing penetration of smartphones and the internet. We examine the mobile, internet, and social media practices of women community health workers, for whom internet access has newly become possible. These workers are uniquely positioned at the intersections of various communities of practice---their familial units, workplaces, networks of health workers, larger communities, and the online world. However, they remain at the margins of each, on account of difference in gender, class, literacies, professional expertise, and more. Our findings unpack the legitimate peripheral participation of these workers; examining how they appropriate smartphones and the internet to move away from the peripheries to fully participate in these communities. We discuss how their activities are motivated by moves towards empowerment, digitization, and improved healthcare provision. We consider how future work might support, leverage, and extend their efforts.
Implementing Multi-Touch Gestures with Touch Groups and Cross Events

Multi-touch gestures can be very difficult to program correctly because they require that developers build high-level abstractions from low-level touch events. In this paper, we introduce programming primitives that enable programmers to implement multi-touch gestures in a more understandable way by helping them build these abstractions. Our design of these primitives was guided by a formative study, in which we observed developers' natural implementations of custom gestures. Touch groups provide summaries of multiple fingers rather than requiring that programmers track them manually. Cross events allow programmers to summarize the movement of one or a group of fingers. We implemented these two primitives in two environments: a declarative programming system and in a standard imperative programming language. We found that these primitives are capable of defining nuanced multi-touch gestures, which we illustrate through a series of examples. Further, in two user evaluations of these programming primitives, we found that multi-touch behaviors implemented in these programming primitives are more understandable than those implemented with standard touch events.
THE DETERMINANTS OF ECONOMIC FREEDOM: A SURVEY

A large literature has found positive associations between economic freedom and income, growth, and a variety of other desirable outcomes. This paper surveys the literature that seeks to explain the causes of economic freedom. Some of the most consistent findings in this literature are that current levels of economic freedom are strongly correlated with past levels; freer countries have more difficulty continuing to improve their economic freedom; democracy and political freedom are positively associated with economic freedom; and inequality is negatively related. ( JEL E02, E14, O17, O43, P1)
Context-Reinforced Semantic Segmentation

Recent efforts have shown the importance of context on deep convolutional neural network based semantic segmentation. Among others, the predicted segmentation map (p-map) itself which encodes rich high-level semantic cues (e.g. objects and layout) can be regarded as a promising source of context. In this paper, we propose a dedicated module, Context Net, to better explore the context information in p-maps. Without introducing any new supervisions, we formulate the context learning problem as a Markov Decision Process and optimize it using reinforcement learning during which the p-map and Context Net are treated as environment and agent, respectively. Through adequate explorations, the Context Net selects the information which has long-term benefit for segmentation inference. By incorporating the Context Net with a baseline segmentation scheme, we then propose a Context-reinforced Semantic Segmentation network (CiSS-Net), which is fully end-to-end trainable. Experimental results show that the learned context brings 3.9% absolute improvement on mIoU over the baseline segmentation method, and the CiSS-Net achieves the state-of-the-art segmentation performance on ADE20K, PASCAL-Context and Cityscapes.
Long-Distance Path Planning for Unmanned Surface Vehicles in Complex Marine Environment

Many missions require unmanned surface vehicles to operate in complex environments over large distances. Marine environments are dynamic in nature and change over time as a result of tides, weather, and environmental restrictions. As a result, the available free traversal space for a marine vehicle in the given map can change over time. This requires the marine vehicle to dynamically generate and update the traversal map to represent the free space. This paper presents techniques for speeding up A* search on the nodes of visibility graphs. We use a quadtree representation of the marine environment to enable the algorithm to efficiently compute the nodes of the visibility graphs. We have developed an admissible heuristic that considers the large islands while estimating cost-to-go, and provide better estimates than a Euclidean distance-based heuristic. Due to the large size of the marine environment, the branching factor of the search tree can become large and reduce the efficiency of the path planner. We have developed an approach that focuses the search by only considering the child nodes lying in the local region around each node. Our experiments demonstrate that by focusing the search, we can reduce the computational time without significantly sacrificing the optimality of the computed path. We also present an extension to the algorithm for handling dynamic environments with time-varying free spaces. We incorporated the depth charts developed by the National Oceanic and Atmospheric Administration (NOAA), Silver Spring, MD, USA, along with the tide predictions to estimate the time-varying free space that can be used by the planner.
DL-Tags: DLT and Smart Tags for Decentralized, Privacy-Preserving, and Verifiable Supply Chain Management

Supply chain management enhanced by the Internet of Things (IoT) solutions integrate special tags (e.g., RFID, NFC, and QR-codes) with products to create Smart Tags, in addition to storing supplemental information about a product, which is also used to track products during their lifecycle. However, a product consumer has to implicitly trust the Smart Tag creator and other stakeholders within the supply chain that they are providing authentic data within a product's tag. The DL-Tags solution steps into this environment to offer a decentralized, privacy-preserving, and verifiable management of Smart Tags during a product's lifecycle. The solution is based on distributed ledger technology (DLT) and uses the Ethereum blockchain to mediate interactions between the stakeholders during a product's exchange process. By reaching a consensus on the product's description and state logged on the blockchain, all involved stakeholders and product consumers can verify the product's authenticity without revealing their identity. The paper describes the DL-Tags solution and includes a cost analysis of all implemented transactions on the Ethereum blockchain. The proposed solution provides evidence of the product's origin and its journey across the supply chain while preventing tag duplication and manipulation. It is among the first documented practical solutions using DLT and IoT for supply chain management, which is designed to be distributed ledger agnostic.
Hyperfusion-Net: Hyper-densely reflective feature fusion for salient object detection

Salient Object Detection (SOD), which aims to find the most important region of interest and segment the relevant objects/items in that region, is an important yet challenging task in computer vision and image processing. This vision problem is inspired by the fact that human perceives the main scene elements with high priorities. Thus, accurate detection of salient objects in complex scenes is critical for human-computer interaction. In this paper, we present a novel reflective feature learning framework, which results in high detection accuracy while maintaining a compact model design. The proposed framework utilizes a hyper-densely reflective feature fusion network (named HyperFusion-Net) to automatically predict the most important area and segment the associated objects in an end-to-end manner. Specifically, inspired by the human perception system and image reflection separation, we first decompose the input images into reflective image pairs by content-preserving transforms. Then, the complementary information of reflective image pairs is jointly extracted by an Interweaved Convolutional Neural Network (ICNN) and hierarchically combined with a hyper-dense fusion mechanism. Based on the fused multi-scale features, our method finally achieves a promising way of predicting salient objects, in which we cast the SOD as a pixel-wise classification problem. Extensive experiments on seven public datasets demonstrate that the proposed method consistently outperforms other state-of-the-art methods with a large margin.
A comprehensive study of chaos embedded bridging mechanisms and crossover operators for grasshopper optimisation algorithm

In recent years, the trend of embedding chaos in the optimization algorithms has grown multifold. Usually, the chaotic algorithms employ chaotic sequences instead of random numbers, in the exploration phase or they employ chaotic numbers for decision making in the exploitation phase. In literature, the positive impact of chaos over the performance of algorithms have been studied and reported. However, very little work is reported on the implications of chaos on the bridging mechanism between the exploration and exploitation phases. This paper presents implications of different chaotic sequences on the performance of Grasshopper Optimisation Algorithm (GOA). A bridging mechanism based on sinusoidal truncated function is proposed first, then 10 different normalize chaotic sequences are employed with this function. Along with this bridging mechanism, a chaotic operator derived crossover scheme is also proposed. These experiments evolve 11 different variants of GOA. The proposed modifications maintain an effective balance between exploration and exploitation phases. Simultaneously, it reduces the attraction, repulsion and comfort zone chaotically. New variants are benchmarked on latest 29 Congress on Evolutionary Computation-2017 (CEC-2017) functions. Efficacy of these variants is validated with several statistical tests and plots. Further, some real life challenging problem of engineering domain are considered for evaluating the efficacy of the proposed variants. These problems are Model Order Reduction (MOR) of control engineering, Protein Structure Prediction (PSP) of bio informatics and Frequency Modulated Sound Wave Parameter Synthesis Problem of parameter estimation. Results reveal that proposed variants exhibit better exploration and exploitation properties as compared to the parent algorithm.
High-resolution assessment of solar energy resources over the Arabian Peninsula

This study presents a high-resolution spatial and temporal assessment of the solar energy resources over the Arabian Peninsula (AP) from 38 years (1980–2017) reanalysis data generated using an assimilative Weather Research and Forecasting Solar model. The simulations are performed based on two, two-way nested domains with 15 km and 5 km resolutions using the European Centre for Medium-Range Weather Forecasts as initial and boundary conditions and assimilating most of available observations in the region. Simulated solar energy resources, such as the Global Horizontal Irradiance (GHI), Direct Normal Irradiance (DNI), and the Diffusive Horizontal Irradiance (DHI), are first validated with daily observations collected at 46 in-situ radiometer stations over Saudi Arabia for a period of four years (2013–2016). Observed and modelled data are in good agreement with high correlation coefficients, index of agreements, and low normalized biases. The total mean annual GHI (DNI) over the AP ranges from 6000 to 8500 Wh m−2 (3000 to 6500 Wh m−2) with significant seasonal variations. The diffuse fraction (the ratio of the DHI to the GHI) is high (low) over the northern (southern) AP in winter whereas it is high (low) over the central to southern (northern) AP during summer, indicating a significant modulation of the sky clearness over the region. Clouds over the northern AP in winter and the aerosol loading due to desert dust over the central and southern AP in summer are the major factors driving the variability of the DHI. The effects of dust and clouds are more pronounced in the diurnal variability of the solar radiation parameters. Our analysis of various solar radiation parameters and the aerosol properties suggest a significant potential for solar energy harvesting in the AP. In particular, the southeastern to northwestern Saudi Arabia are identified as the most suitable areas to exploit solar energy with a minimum cloud coverage over the region.
Human Behavior Deep Recognition Architecture for Smart City Applications in the 5G Environment

Human behavior recognition (HBR), as a critical link for further intelligent and real-time smart city application design, has attracted much more attention in recent years. Although the related technologies have been developed rapidly and many solid achievements have been already obtained, there is still a lot of space to deeply enhance the related research including the recognition structures, algorithms, and so on, to meet the increasing requirements of Smart City construction. In this article, we first review the conventional HBR structure, and analyze the problems and challenges for future smart city applications. Then a parallel and multi-layer deep recognition architecture (PMDRA) is discussed, which could have more powerful and ubiquitous feature extraction ability because of the hierarchical utilization of the deep learning network. Meanwhile, the quantity adjustment mechanism for DRUs and DLNUs could help for designing the actual architecture according to the requirements of real scenarios.
Enhancing IoT Security and Privacy Using Distributed Ledgers with IOTA and the Tangle

The Internet of Things (IoT) devices currently rely on centralized, brokered communication models which call for a number of security flaws and threats. Privacy & security remain a challenge owing to the existing systems being centralized - creating a single point of failure. In this paper, we lay out an architecture for incorporating a concept similar to Blockchain - the technology that underpins the cryptocurrency Bitcoin and many other cryptocurrencies to provide decentralized security and privacy for IoT devices. Currently, the issue with most IoT devices is that they lack the processing power, storage and computational overhead to run consensus algorithms to be an active part of the Blockchain network. The current issue with the blockchain on the other hand, is its low scalability and low number of transactions per second. We present an innovative approach using a technology called IOTA. This addresses some of the most important problems with existing blockchain infrastructure such as being able to run on lightweight devices with constrained memory. We eliminate transaction fees and the concept of mining which requires a large amount of processing power to compete with other miners. IOTA's main component is the Tangle, a guided acyclic graph (DAG) for transaction storage. We discuss creation of two IoT applications on the tangle - a smart utility meter system and a smart car transaction system which are connected to internet through Low Power Wide Area IoT networks.
Multiple Perspective of Cloud Computing Adoption Determinants in Higher Education a Systematic Review

Cloud computing has become a major talking point in recent times. An innovation such as cloud computing for higher education institutions (HEI) can be a cost effective means to operate their IT systems effectively without having to spend vast amounts of money on developing their IT infrastructure. HEIs also face the burden of several challenges e.g. limited infrastructure resources and IT budget, as well as limited teaching staff, technical experts, and IT skilled personnel. With support from a systematic literature review approach, this article identifies the key determinants of cloud adoption from a technological, organisational, environmental and personal perspectives. A total of 17 cloud adoption studies in the HEI context and their respected models from the period of 2012 to 2017 are reviewed and discussed. The findings suggest a lack of cloud adoption studies in the HEI domain from multiple perspectives, particularly in relation to the wider socio-technical concerns related to cloud adoption and future studies related to this research gap are deliberated.
Spatial-temporal pyramid based Convolutional Neural Network for action recognition

Convolutional Neural Networks (CNNs) usually use top-level appearance features of video frames for action recognition. However, these methods discard the implicit complementary advantages across different-scale appearance representations which are effective for object detection, instance segmentation and person re-identification. In this paper, a new spatial pyramid module is proposed to take full use of inherent multi-scale information of CNNs with nearly cost-free by which a bottom-up architecture with lateral connections is constructed for combining high-, mid-, low-level representations of CNNs into a hierarchical frame-level feature elaborately. Additionally, temporal relations at appropriate timescale are contributed to the identification of an action. To this end, we also propose a new temporal pyramid module in which frame-level features belonged to one video are reused by various timescale pooling approaches to get different time-grained features of snippets efficiently. Followed by snippet-relation reasoning, different timescale temporal relations are derived and accumulated for the comprehensive prediction. Unifying the proposed spatial and temporal pyramid modules, a novel network, Spatial-Temporal Pyramid Network (S-TPNet), is proposed to extract spatial-temporal pyramid features for action recognition in videos. Unlike previous models which boost performance at the cost of computation, S-TPNet can be trained in an end-to-end fashion with great efficiency. Extensive experiments on Kinetics, UCF101, and HMDB51 demonstrate that S-TPNet displays significant performance improvements compared with existing frameworks and obtains competitive performance with the state-of-the-arts.1