Reviewerly/exHarmony huggingface dataset:
High-Definition Map Combined Local Motion Planning and Obstacle Avoidance for Autonomous Driving

Local motion planning plays an important role in an autonomous driving system. And applying mature local motion planning methods to real traffic scenarios with regular constraints is one of the keys to the applications of autonomous vehicles. In this paper, we present a local motion planning method combined with High-Definition (HD) maps. Through the HD map defined by OpenStreetMap, the local motion planner can obtain the prior knowledge of traffic scenarios and achieve path planning and optimization accordingly. In order to improve the safety and comfort of the obstacle avoiding process, we also propose an inertia-like path selection algorithm based on this planning method. We evaluated the proposed method on our designed autonomous driving experimental platform `Pioneer' and participated in the 2018 Intelligent Vehicles Future Challenge. In the competition, the `Pioneer' successfully completed all the races and won the championship without any manual intervention.
Gender Profiling From a Single Snapshot of Apps Installed on a Smartphone: An Empirical Study

The integration of the fifth generation (5G) networks and artificial intelligence (AI) benefits to create a more holistic and better connected ecosystem for industries. User profiling has become an important issue for industries to improve company profit. In the 5G era, smartphone applications have become an indispensable part in our everyday lives. Users determine what apps to install based on their personal needs, interests, and tastes, which is likely shaped by their genders-the behavioral, cultural, or psychological traits typically associated with their sex. It is possible to profile users' gender based simply on a single snapshot of apps installed on their smartphones. With this inference based on easy to access data, we can make smartphone systems more user-friendly, and provide better personalized products and services. In this article, we explore such possibilities through an empirical study on a large-scale dataset of installed app lists from 15 000 Android users. More specifically, we investigate the following research questions: 1) What differences between females and males can be explored from installed app lists? 2) Can user gender be reliably inferred from a snapshot of apps installed? Which snapshot feature(s) are the most predictive? What is the best combination of features for building the gender prediction model? 3) What are the limitations of a gender prediction model based solely on a snapshot of apps installed on a smartphone? We find significant gender differences in app type, function, and icon design. We then extract the corresponding features from a snapshot of apps installed to infer the gender of each user. We assess the gender predictive ability of individual features and combinations of different features. We achieve an accuracy of 76.62% and area under the curve of 84.23% with the best set of features, outperforming the existing work by around 5% and 10%, respectively. Finally, we perform an error analysis on misclassified users and discussed the implications and limitations of this article.
Robot Workspace Monitoring Using a Blockchain-Based 3D Vision Approach

Blockchain has been used extensively for financial purposes, but this technology can also be beneficial in other contexts where multi-party cooperation, security and decentralization of the data is essential. Properties such as immutability, accessibility and non-repudiation and the existence of smart-contracts make blockchain technology very interesting in robotic contexts that require event registration or integration with Artificial Intelligence. In this paper, we propose a system that leverages blockchain as a ledger to register events and information to be processed by Oracles and uses smart-contracts to control robots by adjusting their velocity, or stopping them, if a person enters the robot working space without permission. We show how blockchain can be used in computer vision problems by interacting with multiple external parties, Oracles, that perform image analysis and how it is possible to use multiple smart-contracts for different tasks. The method proposed is shown in a scenario representing a factory environment, but since it is modular, it can be easily adapted and extended for other contexts, allowing for simple integration and maintenance.
An efficient lossy cartoon image compression method

This paper introduces a new lossy approach for compression of cartoon images. The image is firstly partitioned into regions of roughly the same colour. The chain codes are then determined of all regions. The sequence of the obtained chain code symbols is transformed with the Burrows-Wheeler Transform, Move-To-Front transform, and compressed with Run-Length Encoding. In the final step, an arithmetic encoder may be used to compress the obtained binary stream additionally. The proposed algorithm is asymmetric, meaning that the decompression does not reverse all the steps of the compression procedure. The experimental results have shown that the described method produces considerably better compression ratios than JPEG, JPEG2000, WebP, SPIHT, PNG, and two of the algorithms specialised in compression of cartoon images: the algorithm using quad-tree, and RS-LZ algorithm.
The bitcoin: a sparkling bubble or price discovery?

The phenomenal rise in the price of bitcoin, prior to the trend reversal of early 2018, resembles a bubble as spectacular as any other bubble. A mere observation of the price rise and a comparison with the common characteristics of a bubble provide anecdotal evidence for the bitcoin bubble. Based on price and volume data up to the end of November 2017, formal empirical evidence is presented by using procedures that do not require the estimation of a fundamental value for bitcoin. The empirical evidence shows that (i) the volume of trading can be explained predominantly in terms of price dynamics; (ii) trading in bitcoin is based exclusively on technical considerations pertaining to past price movements, particularly positive price changes; and (iii) the price of bitcoin is an explosive process. These findings are interpreted to imply a price bubble.
Combining density peaks clustering and gravitational search method to enhance data clustering

Data clustering is a valuable field for extracting effective information and hidden patterns from datasets. In this paper we propose a clustering approach based on density peaks clustering (DPC) and a modified gravitational search algorithm (GSA), called GSA-DPC. To take advantage of the distance measure and nearest neighbor rule among the data points, our method simultaneously combines the distance and density mechanisms. Based on the optimized cluster center set selected by DPC working with density measure, the best clustering distribution is achieved according to the distance criterion of GSA. We compare the performance of GSA-DPC with other well-known clustering approaches, including density-based spatial clustering of applications with noise (DBSCAN), density peaks clustering, K-Means, spectral clustering (SC), grey wolf optimizer for clustering (GWO-C), gravitational search algorithm for clustering (GSA-C) and data clustering algorithm based on GSA and K-Means (GSA-KM). The experimental results indicate that GSA-DPC outperforms these competing approaches.
Person Re-Identification Using Spatial and Layer-Wise Attention

Person re-identification requires extraction of discriminative features to ensure a correct match; this must be done independent of challenges, such as occlusion, view, or lighting changes. While occlusion can be eliminated by changing the camera setup from a horizontal to a vertical (overhead) viewpoint, other challenges arise as the total visible surface area of persons is decreased. As a result, methods that focus on the most discriminative regions of persons must be applied, while different domains should also be considered to extract different semantics. To further increase feature discriminability, complementary features extracted at different abstraction levels should be fused. To emphasize features at certain abstraction levels depending on the input, fusion should be done intelligently. This work considers multiple domains and feature discrimination, where a multimodal convolution neural network is applied to fuse RGB and depth information. To extract multi-local discriminative features, two different attention modules are proposed: (1) a spatial attention module, which is able to capture local information at different abstraction levels, and (2) a layer-wise attention module, which works as a dynamic weighting scheme to assign weights and fuse local abstraction-level features intelligently, depending on the input image. By fusing local and global features in a multimodal context, we show state-of-the-art accuracies on two publicly available datasets, DPI-T and TVPR, while increasing the state-of-the-art accuracy on a third dataset, OPR. Finally, through both visual and quantitative analysis, we show the ability of the proposed system to leverage multiple frames, by adapting feature weighting depending on the input.
Designing Meaningful Products in the Digital Age

Devices such as phones, laptops and tablets have become central to the ways in which many people communicate with others, conduct business and spend their leisure time. This type of product uniquely contains both physical and digital components that affect how they are perceived and valued by users. This article investigates the nature of attachment in the context of technological possessions to better understand ways in which designers can create devices that are meaningful and kept for longer. Findings from our study of the self-reported associations and meaningfulness of technological possessions revealed that the digital contents of these possessions were often the primary source of meaning. Technological possessions were frequently perceived as systems of products rather than as singular devices. We identified several design opportunities for materialising the associations ascribed to the digital information contained within technological products to more meaningfully integrate their physical and digital components.
Learning reciprocal actions for cooperative collision avoidance in quadrotor unmanned aerial vehicles

The ability to avoid collisions with each other is one of the fundamental requirements for autonomous unmanned aerial vehicles (UAVs) to be safely integrated into the civilian airspace, and for the viability of multi-UAV operations. This paper introduces a new approach for online cooperative collision avoidance between quadcopters, involving reciprocal maneuvers, i.e., coherent maneuvers without requiring any real-time consensus. Two maneuver strategies are presented, where UAVs respectively change their speed or heading to avoid a collision. A learning-based framework that trains these reciprocal actions for collision evasion (called TRACE) is developed. The primary elements of this framework include: 1) designing simulated experiments that cover a variety of UAV–UAV approach scenarios; 2) performing optimization to identify speed/heading change actions that satisfy safety constraints while minimizing the energy cost of the maneuver; and 3) using the offline optimization outcomes to train classifier (via ensemble bagged tree) and function approximation (via neural networks and Kriging) models for respectively selecting and encoding the avoidance actions. Trajectory generation and dynamics/controls are incorporated in the simulation environment used for training and testing. Over 90% accuracy in action prediction and over 95% success in avoiding collisions is observed when the trained models are applied to simulated unseen test scenarios.
Study of Shadow and Reflection based Image Forgery Detection

The technical evolution the world is conquering, the trust on the digital imaging technology is grinding down. In daily life, peoples come across the tampered or forged images from the tabloid magazines to the business industry. Furthermore in media outlets, scientific journals, political campaigns, courtrooms, and photo hoaxes that land in our email boxes, forged images are appearing more frequently in a unique way unable to identify the fake image with the needed sophistication. The nominal advancement from the film photography to digital photography is feasible boon but it is not trustworthy. This paper mainly focuses on the image forgery detection based on reflection and shadow based images.
Analysis of PROMETHEE II Method on Selection of Lecturer Community Service Grant Proposals

Abstract This study aims to determine the recipient lecturers of the service to the internal community at STIKOM Tunas Bangsa. The data were obtained from the archive of the Institute for Research and Service to the Tunas Bangsa STIKOM Community in 2017 and conducted interviews with the Chairperson of the Institute. The alternatives used are 6, namely Situation Analysis (C1), Partner prioritization problems and solutions offered (C2), output targets (C3), accuracy of approach methods to overcome problems, activity plans, partner participation contributions (C4), college feasibility (C5), the cost of work for the feasibility of the proposed cost (C6). The method used in the research of the PROMETHEE II Method. From the results of the study obtained 5 alternatives selected lecturers who received internal service grants, namely: A1 (Net flow 13.79), A5 (Net flow 7.23), A2 (Net flow 7.16), A6 (Net flow 6.29), A3 (Net flow 2.08) of 9 Alternatives. The implementation of the PROMETHEE II method is expected to provide an objective assessment and to compare the results of the reviewer’s assessment in determining the recipients of internal service grants. So that it is expected that the decisions given can maintain the quality and quality of the assessment.
Single Image Numerical Iterative Dehazing Method Based on Local Physical Features

To address the hazy image degradation problem, we introduce a single image numerical iterative dehazing method based on local physical features. The method involves three components: region division based on haze density, local atmospheric light estimation and transmission map estimation, and recovery of hazy image scene radiance by using an iterative algorithm. Because of the nonuniform haze density within an image, we first employ the affinity propagation (AP) clustering algorithm to divide a hazy image into different haze density regions. Second, to reflect the difference in atmospheric light among regions and avoid the generation of halo artifacts in recovered images, we estimate the local atmospheric light in each region to replace the global atmospheric light and then estimate the transmission via a dark channel prior. Finally, an iterative dehazing algorithm, which can be used to not only further optimize local atmospheric light and transmission but also remove haze completely, is developed based on a physical model. Experimental results illustrate that our method can effectively improve the quality of a foggy image without sacrificing color fidelity and can retain image details sufficiently.
An Incremental Construction of Deep Neuro Fuzzy System for Continual Learning of Non-stationary Data Streams

Existing fuzzy neural networks (FNNs) are mostly developed under a shallow network configuration having lower generalization power than those of deep structures. This article proposes a novel self-organizing deep FNN, namely deep evolving fuzzy neural network (DEVFNN). Fuzzy rules can be automatically extracted from data streams or removed if they play limited role during their lifespan. The structure of the network can be deepened on demand by stacking additional layers using a drift detection method, which not only detects the covariate drift, variations of input space, but also accurately identifies the real drift, dynamic changes of both feature space and target space. The DEVFNN is developed under the stacked generalization principle via the feature augmentation concept, where a recently developed algorithm, namely generic classifier, drives the hidden layer. It is equipped by an automatic feature selection method, which controls activation and deactivation of input attributes to induce varying subsets of input features. A deep network simplification procedure is put forward using the concept of hidden layer merging to prevent the uncontrollable growth of dimensionality of input space due to the nature of the feature augmentation approach in building a deep network structure. The DEVFNN works in the samplewise fashion and is compatible for data stream applications. The efficacy of the DEVFNN has been thoroughly evaluated using seven datasets with nonstationary properties under the prequential test-then-train protocol. It has been compared with four popular continual learning algorithms and its shallow counterpart, where the DEVFNN demonstrates improvement of classification accuracy. Moreover, it is also shown that the concept of the drift detection method is an effective tool to control the depth of the network structure, while the hidden layer merging scenario is capable of simplifying the network complexity of a deep network with negligible compromise of generalization performance.
Dynamic Memory Management for GPU-Based Training of Deep Neural Networks

Deep learning has been widely adopted for different applications of artificial intelligence-speech recognition, natural language processing, computer vision etc. The growing size of Deep Neural Networks (DNNs) has compelled the researchers to design memory efficient and performance optimal algorithms. Apart from algorithmic improvements, specialized hardware like Graphics Processing Units (GPUs) are being widely employed to accelerate the training and inference phases of deep networks. However, the limited GPU memory capacity limits the upper bound on the size of networks that can be offloaded to and trained using GPUs. vDNN addresses the GPU memory bottleneck issue and provides a solution which enables training of deep networks that are larger than GPU memory. In our work, we characterize and identify multiple bottlenecks with vDNN like delayed computation start, high pinned memory requirements and GPU memory fragmentation. We present vDNN++ which extends vDNN and resolves the identified issues. Our results show that the performance of vDNN++ is comparable or better (up to 60% relative improvement) than vDNN. We propose different heuristics and order for memory allocation, and empirically evaluate the extent of memory fragmentation with them. We are also able to reduce the pinned memory requirement by up to 60%.
Ratings vs. Reviews in Recommender Systems: A Case Study on the Amazon Movies Dataset

Together with the prevalence of e-commerce and online shopping, recommender systems have been playing an increasingly important role in people's daily lives in terms of discovering their potential preferences. Therein, users' preferences are mostly reflected by their online behaviors, specially their evaluation towards particular items, e.g., numeric ratings and textual reviews. Many existing recommender systems focus on using item ratings to determine users' preferences, while others provide approaches using textual reviews instead. In this work, via a case study on the Amazon movies data, we compare the recommendation results when using ratings or reviews, as well as that of combining both.
1-D DCT Domain Analysis for JPEG Double Compression Detection

The authenticity of an evidence is a necessary and crucial requirement in forensic investigations and trials, expecially when it comes to digital evidences. In order to demonstrate the authenticity of a (digital) image, different techniques have been proposed in the last years which take advantage of some kinds of detectable anomalies on double or multiple compressed JPEG images. In this article, we present an in-depth analysis in the DCT Domain of an image exploiting the "first-digits" statistics after 1D-DCT transformation. The insights thus obtained were used to build a simple and explainable classifier able to detect doubly compressed JPEG images; this approach seems to outperform much more complex techniques in the state-of-the-art.
Learning Pedestrian Detection from Virtual Worlds

In this paper, we present a real-time pedestrian detection system that has been trained using a virtual environment. This is a very popular topic of research having endless practical applications and recently, there was an increasing interest in deep learning architectures for performing such a task. However, the availability of large labeled datasets is a key point for an effective train of such algorithms. For this reason, in this work, we introduced ViPeD, a new synthetically generated set of images extracted from a realistic 3D video game where the labels can be automatically generated exploiting 2D pedestrian positions extracted from the graphics engine. We exploited this new synthetic dataset fine-tuning a state-of-the-art computationally efficient Convolutional Neural Network (CNN). A preliminary experimental evaluation, compared to the performance of other existing approaches trained on real-world images, shows encouraging results.
Storage capacity of hyperbolic Hopfield neural networks

A complex-valued Hopfield neural network (CHNN) is a multistate model of Hopfield neural network, and has been applied to the storage of multi-level data. A hyperbolic Hopfield neural network (HHNN) is another multistate model. The storage capacity is an important issue of Hopfield neural networks. In this work, the storage capacity of HHNN is analyzed, and is compared with that of CHNN.
Classification and Clustering Algorithms of Machine Learning with their Applications

In order to minimize human effort and increase efficiency, we use machines. But nowadays, advancements have been done to such an extent that machines can learn from experience and make decisions by itself substituting humans. Machine learning is basically a subfield of Artificial Intelligence, which is based on the principal of a machine being able to analyze patterns, learn from data and thereby make decisions itself with minimal or none explicit assistance. This is an introductory chapter to machine learning containing supervised, unsupervised, semi-supervised, and reinforcement algorithms and applications of machine learning. This chapter covered four classification techniques (Logistic Regression, Decision Tree, K-Nearest Neighbors, and Naive Bayes) and K means, and Hierarchical clustering algorithms considering two well-known datasets (Iris and tennis) using Python.
G-Fall: Device-free and Training-free Fall Detection with Geophones

The inevitable aging trend of the world's population brings a lot of challenges to the health care for the elderly. For example, it is difficult to guarantee timely rescue for a single-resided elder who falls at home. Under this circumstance, a reliable automatic fall detection machine is in great need for emergent rescue. However, the state-of-the-art fall detection systems are suffering from serious privacy concerns, having a high false alarm or being cumbersome for users. In this paper, we propose a device-free fall detection system, namely G-Fall, based on geophones. We first decompose the falling mode and characterize it with time-dependent floor vibration features. By leveraging Hidden Markov Model (HMM), our system is able to recognize the fall event precisely and achieve training-free recognition. It requires no training from the elderly but only an HMM template learned in advance through a small number of training samples. To reduce the false alarm rate, we propose a novel reconfirmation mechanism, namely Energy-of-Arrival (EoA) positioning to assist in recognizing a human's fall. Extensive experiments have been conducted on 12 human subjects. The results demonstrate that G-Fall achieves a 95.74% recognition precision with a false alarm rate of 5.30% on average. Furthermore, with the assistance of EoA, the false alarm rate is reduced to nearly 0%.