Reviewerly/exHarmony huggingface dataset:
Multi-Class Weather Classification from Still Image Using Said Ensemble Method

In the field of computer vision, multi-class outdoor weather classification is a difficult task to perform due to diversity and lack of distinct weather characteristic or features. This research proposed a novel framework for identifying different weather scenes from still images using heterogeneous ensemble methods. Our approach is based on a method called Selection Based on Accuracy Intuition and diversity (SAID) of stacked ensemble algorithms. This involves the extraction of histogram of features from different weather scenes. The blending and boosting of different weather features using stacked ensemble algorithms increases recognition rate of different weather conditions compared to other classification and ensemble methods. The paper presents academic and practitioners a new insight into diversity of heterogeneous ensemble methods for solving the challenges of weather recognition from still images.
OmniMR: Omnidirectional Mixed Reality with Spatially-Varying Environment Reflections from Moving 360° Video Cameras

We propose a new approach for creating omnidirectional mixed reality (OmniMR) from moving-camera 360° video. To insert virtual computer-generated elements into a moving-camera 360° video, we reconstruct camera motion and sparse scene content via structure from motion on stitched equirectangular video (the default output format of current 360° cameras). Then, to plausibly reproduce realworld lighting conditions for these inserted elements, we employ inverse tone mapping to recover high dynamic range environment maps which vary spatially along the camera path. We implement our approach into the Unity rendering engine for real-time object rendering with dynamic lighting and user interaction. This expands the use and flexibility of 360° video for mixed reality.
Iterated extended Kalman filter for airborne electromagnetic data inversion

The iterated extended Kalman filter (IEKF) is a tool within the theory of optimal estimation used for nonlinear problems. The IEKF minimises variance in the estimation error in terms of a probabilistic approach. Despite the special terminology, the Kalman filter algorithm minimises the objective function, representing the normalised squared difference between the measured and calculated vectors for the parameters of a selected model. It works like the weighted least squares method – a conventional method for airborne electromagnetic data inversion. In this article, I describe the essence of the Kalman approach to solving inverse problems. I show how one-dimensional inversion with lateral constraints can be performed in terms of the Kalman filter. The described algorithm takes account of the measurement noise, which is specified as the dispersion of signals in the corresponding measurement channels at high altitude. A specific covariance matrix representation allows use of the corresponding Kalman filter calculation methods. They provide numerical stability of the algorithm. The Kalman approach makes it possible to combine modern techniques used in airborne survey data processing. Some examples of Kalman filter use in frequency-domain airborne data processing are given.
A Filter-Wrapper based Feature Selection for Optimized Website Quality Prediction

A Website quality model essentially consists of a set of criteria used to determine if a website reaches certain levels of fineness. Quality attributes are imperative in predicting quality of a website. However large set features may increase model complexity and computational time for prediction. In this study an initial set of 13 quality attributes is prepared and two-stage feature selection based on hybrid filter-wrapper approach is used to optimize website quality prediction performance. In the first stage, each feature is ranked using the Information Gain (IG) method. In the second stage, Firefly Search Algorithm (FA) is applied to features which are ranked higher in the first stage and a feature sub-selection is carried out. In order to evaluate the effectiveness of feature selection method experiments are conducted using four baseline classification algorithms on a dataset prepared by 700 websites. The experimental results show that the proposed model is able to achieve high classification performance. An average of 36.53% reduction in features has been observed which leads to 0.015 seconds of average reduction in time for building a classifier and an average improvement of 5.420% in classification accuracy.
Should I Interfere' AI-Assistants' Interaction with Knowledge Workers

Artificial Intelligence (AI) assistants have been a hot topic for a few years. Popular solutions - such as Google Assistant, Microsoft's Cortana, Apple's Siri, and Amazon Alexa - are becoming resourceful AI-assistants for general users. Apart from some mishaps, those assistants have a successful history in supporting people's everyday tasks. The same cannot be said in industry-specific scenarios, in which AI-assistants are still a bet. Companies combining AI with human expertise and experience can be stand out in their industry. This is particularly important for industries that rely their strategic decision-making processes on knowledge workers actions. More than another system, AI-assistants are new players in the human-computer interaction. But how and when should an AI-assistant interfere in a knowledge worker task? In this paper, we present findings from a case study using the Wizard of Oz approach in an oil and gas company. Our findings begin to answer that question for what kind of interference knowledge workers in that domain would accept from an AI-assistant.
Context-aware recommendations via sequential predictions

Recommender systems have been widely applied to produce recommendations tailored by user preferences. Context-aware recommender systems additionally take context information (such as time, location, weather, companion, etc) into consideration to generate better recommendations, due to the fact that user tastes may vary from contexts to contexts. In this paper, we propose a novel recommender for context-aware recommendations in which we estimate the user preferences by sequential predictions. Our experimental results based on multiple real-world data sets demonstrate that the proposed approach is able to outperform the state-of-the-art context-aware recommendation techniques.
Long-Short Term Memory Neural Network Stability and Stabilization using Linear Matrix Inequalities

A global asymptotic stability condition for Long Short-Term Memory neural networks is presented in this paper. A linear matrix inequality optimization problem is used to describe this global stability condition. The linear matrix inequality formulation can be viewed as a way for stabilization of Long Short-Term Memory neural networks since the networks' weight matrices and biases can be essentially treated as control variables. The condition and how to compute numerical values for the weight matrices and biases are illustrated by some examples.
How the Choice of Distance Measure Influences the Detection of Prior-Data Conflict

The present paper contrasts two related criteria for the evaluation of prior-data conflict: the Data Agreement Criterion (DAC; Bousquet, 2008) and the criterion of Nott et al. (2016). One aspect that these criteria have in common is that they depend on a distance measure, of which dozens are available, but so far, only the Kullback-Leibler has been used. We describe and compare both criteria to determine whether a different choice of distance measure might impact the results. By means of a simulation study, we investigate how the choice of a specific distance measure influences the detection of prior-data conflict. The DAC seems more susceptible to the choice of distance measure, while the criterion of Nott et al. seems to lead to reasonably comparable conclusions of prior-data conflict, regardless of the distance measure choice. We conclude with some practical suggestions for the user of the DAC and the criterion of Nott et al.
Hyperbolic SVD‐based Kalman filtering for Chandrasekhar recursion

The problem of numerical instability of the classical Kalman filter (KF) still remains one of the most important topics in engineering literature. For improving its robustness with respect to roundoff errors, the singular value decomposition (SVD) methodology has been proposed for implementing the underlying classical KF Riccati recursion. In this study, SVD-based filtering is derived for an alternative KF mechanisation that is based on the so-called Chandrasekhar recursion and yields a family of fast KF implementations. The new methodology involves hyperbolic SVD (HSVD) factorisation rather than usual SVD utilised in the Riccati-based filtering. The results of numerical study indicate that the HSVD-based filtering strategy outperforms the conventional Chandrasekhar-based KF while solving ill-conditioned state estimation problem. Together with the existed Cholesky-based algorithms, they are the preferred implementations when solving applications with high reliability requirements within the class of fast Chandrasekhar-based KF implementations.
Bit-Slicing FPGA Accelerator for Quantized Neural Networks

Deep Neural Networks (DNNs) become the state-of-the-art in several domains such as computer vision or speech recognition. However, using DNNs for embedded applications is still strongly limited because of their complexity and the energy required to process large data sets. In this paper, we present the architecture of an accelerator for quantized neural networks and its implementation on a Nallatech 385-A7 board with an Altera Stratix V GX A7 FPGA. The accelerator's design centers around the matrix-vector product as the key primitive, and exploits bit-slicing to extract maximum performance using low-precision arithmetic.
A Conceptual Study on the Emergence of Cryptocurrency Economy and Its Nexus with Terrorism Financing

One of the noteworthy developments in the world economy is the cryptocurrency in general and the bitcoin in particular. Although several types of cryptocurrency are in operation in the current digital economy, the most prevalent is the bitcoin, which was launched formally in 2009 by an individual or group known under the pseudonym Satoshi Nakamoto. The value of bitcoin has increased to such an extend that it reached 19.7 billion US dollars by January 2, 2018 (Statista, 2018). As the bitcoin price touches a new high day by day, various terrorist organizations are using this cryptocurrency to anonymously finance their grotesque terrorist activities around the world by bypassing the surveillance mechanism of the banking system of the respective countries. Against this backdrop, this chapter aims to understand the mechanism of cryptocurrencies in general and the bitcoin in particular. Finally, it also endeavors to identify the trend of the bitcoin economy and its impact on nefarious activities in general and terrorism financing in particular. It has been revealed from the study that cryptocurrency economy has become so popular across the world that it has created an alternative virtual economy devoid of regulations from a specific country or a group of countries. By using vector error correction model (VECM), it had been observed that there exists a statistically significant long-run association between terrorist incidences and bitcoin transaction/circulation in the panel of 12 countries for 2010–2016. However, there is a huge concern over its way of operation and its unholy nexus with terrorism financing.
Sign Language Recognition

In this paper, we propose an approach, for sign language recognition, that makes use of a virtual reality headset to create an immersive environment. We show how features from data acquired by the Leap Motion controller, using an egocentric view, can be used to automatically recognize a user signed gesture. The Leap features are used along with a random forest for real-time classification of the user's gesture. We further analyze which of these features are most important, in an egocentric view, for gesture recognition. To test the efficacy of our proposed approach, we test on the 26 letters of the alphabet in American Sign Language in a virtual environment with an application for learning sign language.
Designing an Escape Room in the City for Public Engagement with AI-enhanced Surveillance

Escape the Smart City is a critical pervasive game that uses an escape room format to help players develop an understanding of the implications of urban surveillance technologies. Set in downtown Amsterdam, players work together as a team of hackers to stop the mass deployment of an all-seeing AI-enhanced surveillance system. In order to defeat the system players need to understand its attributes and exploit its weaknesses. Novel gameplay elements include locating hidden surveillance cameras in the city, discovering and exploiting algorithmic biases in computer vision, and exploring new techniques to avoid facial recognition systems. This work makes two distinct contributions to the CHI community: first, it introduces critical pervasive games as an approach to engage the public in complex sociotechnical issues, and second, it experiments with the escape room format as a platform for critical play.
IPANDA

In this paper, we introduced the concept of a hybrid product which combines a digital game with physical experiences and discussed practical recommendations for hybrid products development in the domain of wildlife conservation for children. IPANDA including hardware and software applications with sensing technology was able to gather real-time environmental data and connected to one virtual wildlife experiencing environmental challenges regarding its living habits. Children who play the product can learn about the environment around them and foster wildlife protection awareness. To evaluate our conceptual system, we created a preliminary prototype and conducted user study within the semi-structured interview and Smileyometer. Our striking findings revealed IPANDA as a promising tool to serve as groundwork to encourage children to explore the physical environment and gain potential wildlife protection education.
Contemporary requirements challenges and issues

Requirements Engineering (RE) is regarded as key to software project success and has been researched and practiced for decades. With the growing maturity and complexity of software development, however, the contemporary RE environment has been changing so that the intertwining of requirements with implementation and organizational contexts has been observed and emphasized. What relevant RE challenges and issues exist in this contemporary RE environment merits investigation. This paper, therefore, reports an empirical study on the current requirements challenges and issues focusing on projects in enterprise software development environments that affect multiple (more than three) applications. Data were obtained by survey and interviews from a total of 26 practitioners in 11 organizations. Through analyzing the data, a collection of requirements challenges and issues were identified. The findings indicate that identifying the ramifications of changes and balancing the level of design and solutions in requirements are becoming important in contemporary RE for enterprise software development environment.
A Method for Anomaly Prediction in Power Consumption using Long Short-Term Memory and Negative Selection

To identify and predict anomalous power consumption, this paper proposes a method based on Long Short-Term Memory (LSTM) and Negative Selection technologies that anticipates the occurrence of anomalies in power consumption, and to provide useful information for energy efficiency. Using the proposed method it is possible to anticipate the occurrence of anomalies in power consumption. When applied to the power consumption recorded during 20 weeks of a building the method yielded promising results. Finally, the effectiveness and advantages of this method is demonstrated which it could be directly used for real-time electricity monitoring and anomaly prediction.
Zero-Shot Learning Based on Multitask Extended Attribute Groups

Since the learning of attribute classifiers is independent of the learning of object classifier in zero-shot learning, it is difficult to guarantee that the learned attribute classifiers are optimal for the subsequent object recognition tasks. Therefore, a novel zero-shot learning method based on multitask extended attribute groups (MTEAGs) is proposed by using the multitask learning framework and grouping idea. First, we used an unsupervised clustering method to group the attributes and object classes of training images. Then, based on the obtained attribute and class groups, we constructed the group-based attribute/object classifier collaborative learning model where the class groups are viewed as the extension of attribute groups. In order to explore the shared features within a group as well restrict the feature sharing between groups, we applied the structured sparse method to constrain the model parameter matrix. At last, a hybrid zero-shot classifying model is designed by simultaneously considering the class-class and class-attribute matrices to predict the class labels of testing images, where the class-class relationship is measured by the Jaccard similarity coefficient. Experiments on two popular attribute datasets show that, MTEAG can yield higher zero-shot image classification accuracy compared with several baselines.
Location Correlated Differential Privacy Protection Based on Mobile Feature Analysis

Recently, with the popularity of smartphones and other GPS embedded devices, location-based service applications are being rapidly developed. In addition, individual privacy protection is also receiving increasing attention. Currently, most studies assume that individual location records are independent. However, the records are mostly interrelated in the real world. If the information is protected without considering the location-correlated information between users, an attacker can use a background knowledge attack to obtain the user's private information. Therefore, this paper proposes a method to protect multiuser location-correlated information under a strict privacy budget. First, a method for group movement analysis based on adaptive time segmentation is proposed in this paper. In addition, based on the time dimension, time-continuous hotspot areas are constructed by adaptively segmenting and merging the stay areas, which are established for subsequent location-correlated privacy protection. Second, a data publishing mechanism is proposed to resist inferred attacks and to adaptively protect user-correlated location information. In addition, this paper also proposes the individual user correlation sensitivity concept and extends differential privacy by building an individual sensitivity matrix to correct noise. The experiments on real datasets show that under the same conditions, compared with the existing methods, the heat value of the hotspot areas formed by the method is increased by 10.11% under the same time slice length. In addition, the method reduces the similarity of 26.98% of group users.
Impact of Internet Adoption and Mobile Phone Penetration on Corruption: Evidence from Selected Asia-Paciﬁc Countries

This study uses the mobile phone penetration as a tool of accountability against corruption and promotes transparency and good governance among selected Asia-Pacific countries. The consensus regarding fast internet speed plays a vital role to eliminate the corruption through modern informational and communication technology system. This study empirically investigates the causal relationship between internet adoption and mobile phone penetration to overcome the issue of corruption. This study adopted the Granger causality approach to analyse the panel data (2006–2016) for 12 countries. The results of Granger causality test did not find any evidence for bidirectional causality from corruption to mobile phone penetration and internet adoption. This shows that an increase in mobile phone penetration and internet adoption leads to decrease in corruption. The good governance and rule of law also found the powerful instruments to reduce the perceived corruption. This study is the first ever attempt to examine this issue for Asian Pacific countries, while other studies include only developing and advanced economies in their sample.
Generalizing to Unseen Head Poses in Facial Expression Recognition and Action Unit Intensity Estimation

image with landmarks input in-plane normalization source domain prediction of correspondence points and visibilities source domain target domain texture warping output
Continuous Pedestrian Orientation Estimation using Human Keypoints

Pedestrian orientation is essential for trajectory prediction, intention and behavior analysis. Existing methods using low-level features result in imperfect performance. Aiming at continuous pedestrian orientation estimation from a single frame, we propose a novel approach based on high-level semantic features extracted from human keypoints locations. The proposed method first predicts human keypoints locations by pose estimation algorithm, based on human pose, body motion limitations and body parts occlusion. High-level semantic features are then extracted from keypoints locations. Finally, this method applies a soft classifier and interpolation to produce continuous results. Experiments conducted on public datasets show that the proposed method outperforms the state-of-the-art by a large margin, demonstrating the critical role of high-level semantic feature representation for the task of continuous pedestrian orientation estimation.
A Feasible FPGA Weightless Neural Accelerator

AI applications have recently driven the computer architecture industry towards novel and more efficient dedicated hardware accelerators and tools. Weightless Neural Networks (WNNs) is a class of Artificial Neural Networks (ANNs) often applied to pattern recognition problems. It uses a set of Random Access Memories (RAMs) as the main mechanism for training and classifying information regarding a given input pattern. Due to its memory-based architecture, it can be easily mapped onto hardware and also greatly accelerated by means of a dedicated Register Transfer-Level (RTL) architecture designed to enable multiple memory accesses in parallel. On the other hand, a straightforward WNN hardware implementation requires too much memory resources for both ASIC and FPGA variants. This work aims at designing and evaluating a Weightless Neural accelerator designed in High-Level Synthesis (HLS). Our WNN accelerator implements Hash Tables, instead of regular RAMs, to substantially reduce its memory requirements, so that it can be implemented in a fairly small-sized Xilinx FPGA. Performance, circuit-area and power consumption results show that our accelerator can efficiently learn and classify the MNIST dataset in about 8 times faster than the system's embedded ARM processor.