Reviewerly/exHarmony huggingface dataset:
An information fusion framework for person localization via body pose in spectator crowds

Person localization or segmentation in low resolution crowded scenes is important for person tracking and recognition, action detection and anomaly identification. Due to occlusion and lack of inter-person space, person localization becomes a difficult task. In this work, we propose a novel information fusion framework to integrate a Deep Head Detector and a body pose detector. A more accurate body pose showing limb positions will result in more accurate person localization. We propose a novel Deep Head Detector (DHD) to detect person heads in crowds. The proposed DHD is a fully convolutional neural network and it has shown improved head detection performance in crowds. We modify Deformable Parts Model (DPM) pose detector to detect multiple upper body poses in crowds. We efficiently fuse the information obtained by the proposed DHD and the modified DPM to obtain a more accurate person pose detector. The proposed framework is named as Fusion DPM (FDPM) and it has exhibited improved body pose detection performance on spectator crowds. The detected body poses are then used for more accurate person localization by segmenting each person in the crowd.
Constructing Test Collections using Multi-armed Bandits and Active Learning

While test collections provide the cornerstone of system-based evaluation in information retrieval, human relevance judging has become prohibitively expensive as collections have grown ever larger. Consequently, intelligently deciding which documents to judge has become increasingly important. We propose a two-phase approach to intelligent judging across topics which does not require document rankings from a shared task. In the first phase, we dynamically select the next topic to judge via a multi-armed bandit method. In the second phase, we employ active learning to select which document to judge next for that topic. Experiments on three TREC collections (varying scarcity of relevant documents) achieve t 0.90 correlation for [email protected] ranking and find 90% of the relevant documents at 48% of the original budget. To support reproducibility and follow-on work, we have shared our code online1.
Beyond Digital Inventions—Diffusion of Technology and Organizational Capabilities to Change

DigitalizationDigitalization is currently creating numerous opportunities for value creation in intellectual industries. In the legal industryLegal industry however, the vast majority of law firmsLaw firms have remained the same without responding to the rising opportunities. The reluctance in the mainstream legal industryLegal industry to adopt new digital technologiesNew digital technologies has created a duality to the field (with legal techLegal Tech enthusiasts on one side and traditional sceptics on the other). This chapter explores why and discusses the connection between industrial diffusionDiffusion and capabilities to change residing in individual firms. We relate digitalizationDigitalization to technological shifts in the past and explore the specific challenges and barriers for a digital transformation among law firmsLaw firms . We find that most law firmsLaw firms neither have the technological capabilities nor the economical motivation to change, why digitalizationDigitalization has, instead, become a source of fear. However, in order to seize digital opportunities and adapt to the constantly, and rapidly, changing environment, law firmsLaw firms need to overcome this fear and develop organizational capabilities to change. We conclude that for a true industrial transformation—beyond digital inventions—we cannot only focus on the presence of new technologies but we also need to address the diffusionDiffusion of them.
Bayesian mixtures of Hidden Tree Markov Models for structured data clustering

The paper deals with the problem of unsupervised learning with structured data, proposing a mixture model approach to cluster tree samples. First, we discuss how to use the Switching-Parent Hidden Tree Markov Model, a compositional model for learning tree distributions, to define a finite mixture model where the number of components is fixed by a hyperparameter. Then, we show how to relax such an assumption by introducing a Bayesian non-parametric mixture model where the number of necessary hidden tree components is learned from data. Experimental validation on synthetic and real datasets show the benefit of mixture models over simple hidden tree models in clustering applications. Further, we provide a characterization of the behaviour of the two mixture models for different choices of their hyperparameters.
Is Virtual Reality Product Development different?

With the rise of Virtual Reality (VR) footprint in many organizations, it was unclear if traditional software engineering practices are still exercised during VR product development. As part of our research, we conducted a year-long multi-level exploratory study to understand the various software development practices within VR product development teams. An empirical study on VR practitioners from 6 different countries was done to examine their development strategies, methods, and models adopted along with the various challenges faced during the course of VR product release. We found that VR practitioners adopted hybrid Software Engineering approaches in VR product development. In this paper, we present our insights from the empirical study and stress on the need for a diverse software development model for VR products.
Bigdata fog based cyber physical system for classifying, identifying and prevention of SARS disease

The Severe acute respiratory syndrome coronavirus (SARS-CoV) are deadly infectious disease which can easily transmit and causes severe problems in humans. It is known as a coronavirus and referred as a common form of virus that naturally causes upper-respiratory tract illnesses and the symptoms ar e hard to identify. It is important to recognize the patient and providing them with suitable action with constant intensive care. Healthcare amenities is constructed on fog and big-data based system and it is integrated with cyber-physical system. The role of Cyber physical system in health care domain is to fetch deep insights about the nature of disease and carry the monitoring process with early detection of infected users. The objective is to identify occurrence of SARS at initial stage. In proposed system, resemblance factor is evaluated from the extracted keywords. In order to identify the difference between SARS affected and others, the proposed scheme fetches the inputs from user's displayed in the form of text. It is passed to deep recurrent neural network (RNN) model. It extracts useful information from the raw information given by the user. The J48graft algorithm is used to carry the classification based on the type of infection and symptoms of each user. The data is stored in the bigdata layer (mongoDB) and it detects the infected area by using the geospatial feature in mongo dB. The methodology is framed in the proposed model to prevent the spread of disease to other users. In case of any abnormality the generation of alert process is done instantaneously and directed on user's mobile from fog layer. The final experimental outcome reveals information about the performance of proposed system in terms of Success rate, failure rate, latency and accuracy %. It shows that the proposed algorithm gives high level of accuracy when it is compared with other primitive methods.
Deep Feature Fusion by Competitive Attention for Pedestrian Detection

Pedestrian detection is a key problem for automatic driving, and the results have been improved significantly via deep convolutional networks. However, there is still room to improve the performance of pedestrian detection by carefully dealing with some critical issues. To take advantages of more discriminative information for pedestrian detection, we propose a novel architecture to auto-choose semantic as well as specific information among the feature maps at different levels and integrate valuable information among the feature maps in multi-scales. Particularly, our architecture consists of feature maps concatenating in different levels and feature maps integrating with multi-scales. Both the operations are equipped with a competitive attention block. The architecture has the ability to obtain more efficient and discriminating features for pedestrian detection. In comparison with the other prevailing models, our architecture provides superior performance. The promising results achieved through experimentation with this architecture achieve a new state-of-the-art on Caltech dataset.
Content-based image retrieval using local texture features in distributed environment

In this paper, we propose novel content-based image retrieval (CBIR) algorithms using Local Octa Patterns (LOtP), Local Hexadeca Patterns (LHdP) and Direction Encoded Local Binary Pattern (DELBP). LOtP and LHdP encode the relationship between center pixel and its neighbors based on the pixels’ direction obtained by considering the horizontal, vertical and diagonal pixels for derivative calculations. In DELBP, direction of a referenced pixel is determined by considering every neighboring pixel for derivative calculations which results in 256 directions. For this resultant direction encoded image, we have obtained LBP which is considered as feature vector. The proposed method’s performance is compared to that of Local Tetra Patterns (LTrP) using benchmark image databases viz., Corel 1000 (DB1) and Brodatz textures (DB2). Performance analysis shows that LOtP improves the average precision from 59.31% to 64.36% on DB1, and from 83.24% to 85.95% on DB2, LHdP improves it to 65.82% on DB1 and to 87.49% on DB2 and DELBP improves it to 60.35% on DB1 and to 86.12% on DB2 as compared to that of LTrP. Also, DELBP reduces the feature vector length by 66.62% as compared to that of LTrP. To reduce the retrieval time, the proposed algorithms are implemented on a Hadoop cluster consisting of 116 nodes and tested using Corel 10K (DB3), Mirflickr 100,000 (DB4) and ImageNet 511,380 (DB5) databases.
A scalable correlation‐based approach for outlier detection in wireless body sensor networks

Summary Outlier detection is one of the prominent research domain in the field of data mining and big data analytics. Nowadays, most of the data in healthcare centers are remotely monitored and are generated from different wireless sensors. The core objective of outlier detection in this domain is the recognition of the true physiologically anomalous data and the anomalies because of faulty sensors. In real healthcare monitoring scenario, various sensors are related to each other. So, while detecting outliers in wireless body sensor networks (WBSNs), correlation among different sensor nodes is of major concern. Most of the existing outlier detection techniques consider the sensors to be linearly correlated, which may not always be the case in real life applications. The traditional techniques for outlier detection are also not scalable to big data. To address the above issues, in this paper, we propose an approach for outlier detection that is scalable to big data and also handles the nonlinearly correlated attributes efficiently. The proposed approach is implemented on Hadoop map reduce framework for the rapid processing of big data. The evaluation results are validated using the simulated dataset of WBSNs taken from the Physionet library. The results are compared with various existing outlier detection approaches and demonstrated that the proposed approach is more effective in spotting the physiological outliers and sensor anomalies accurately.
Faster Training by Selecting Samples Using Embeddings

Long training times have increasingly become a burden for researchers by slowing down the pace of innovation, with some models taking days or weeks to train. In this paper, a new, general technique is presented that aims to speed up the training process by using a thinned-down training dataset. By leveraging autoencoders and the unique properties of embedding spaces, we are able to filter training datasets to only include only the samples that matter the most. Through evaluation on a standard CIFAR-10 image classification task, this technique is shown to be effective. With this technique, training times can be reduced with a minimal loss in accuracy. Conversely, given a fixed training time budget, the technique was shown to improve accuracy by over 50%. This intelligent dataset sampling technique is a practical tool for achieving better results with large datasets and limited computational budgets.
Seamless Writing: How the Digitisation of Writing Transforms Thinking, Communication, and Student Learning

This chapter extends the scope of the term 'seamless learning' to include writing as one of the most prominent learning activities in education. The seamless learning concept is used to analyse the ongoing flow of digital innovation with respect to the changing instructional and intellectual positioning of writing in its various contexts. The term 'seams' is interpreted as frictions or barriers in the writer's experience, and the concept of 'seamlessness' refers to coherence in the practice and learning of writing. In order to understand the impact of digitisation on writing, this chapter recaps on the major development steps of digital writing technology, before offering an example of an integrated learning platform for the writing of academic theses which intends to offer students a seamless writing experience. The results indicate that the term seamlessness in the context of writing should not be directly associated with mobile learning or to the ubiquitous learning opportunities which are qualities that writing technologies have already offered for some time. Other more recent technological changes have provided considerably greater impact on what the authors term as 'seamless writing'.
Optimal Multi-View Surface Normal Estimation Using Affine Correspondences

An optimal, in the least squares sense, method is proposed to estimate surface normals in both stereo and multi-view cases. The proposed algorithm exploits exclusively photometric information via affine correspondences and estimates the normal for each correspondence independently. The normal is obtained as a root of a quartic polynomial. Therefore, the processing time is negligible. Eliminating the outliers, we propose a robust extension of the algorithm that combines maximum likelihood estimation and iteratively re-weighted least squares. The method has been validated on both synthetic and publicly available real-world datasets. It is superior to the state of the art in terms of accuracy and processing time. Besides, we demonstrate two possible applications: 1) using our algorithm as the seed-point generation step of patch-based multi-view stereo method, the obtained reconstruction is more accurate, and the error of the 3D points is reduced by 30% on average and 2) multi-plane fitting becomes more accurate applied to the resulting oriented point cloud.
Crawling in <i>Rogue</i>'s Dungeons With Deep Reinforcement Techniques

This paper is a report of our extensive experimentation, during the last two years, of deep reinforcement techniques for training an agent to move in the dungeons of the famous Rogue video game. The challenging nature of the problem is tightly related to the procedural, random generation of new dungeon maps at each level, which forbids any form of level-specific learning and forces us to address the navigation problem in its full generality. Other interesting aspects of the game from the point of view of automatic learning are the partially observable nature of the problem since maps are initially not visible and get discovered during exploration, and the problem of sparse rewards, requiring the acquisition of complex, nonreactive behaviors involving memory and planning. In this paper, we develop on previous works to make a more systematic comparison of different learning techniques, focusing in particular on Asynchronous Advantage Actor-Critic and Actor-Critic with Experience Replay (ACER). In a game like Rogue, sparsity of rewards is mitigated by the variability of the dungeon configurations (sometimes, by luck, exit is at hand); if this variability can be tamed-as ACER, better than other algorithms, seems able to do-the problem of sparse rewards can be overcome without any need of intrinsic motivations.
Search Mindsets

Music listening is a commonplace activity that has transformed as users engage with online streaming platforms. When presented with anytime, anywhere access to a vast catalog of music, users face challenges in searching for what they want to hear. We propose that users who engage in domain-specific search (e.g., music search) have different information-seeking needs than in general search. Using a mixed-method approach that combines a large-scale user survey with behavior data analyses, we describe the construct of search mindset on a leading online streaming music platform and then investigate two types of search mindsets: focused, where a user is looking for one thing in particular, and non-focused, where a user is open to different results. Our results reveal that searches in the music domain are more likely to be focused than non-focused. In addition, users' behavior (e.g., clicks, streams, querying, etc.) on a music search system is influenced by their search mindset. Finally, we propose design implications for music search systems to best support their users.
Bayesian networks for the interpretation of biological evidence

In court, it is typical for biological evidence to be reported at a level that only addresses how likely the DNA evidence is if it originated from a particular individual, or individuals. However, there are other questions that could be considered that would be of value in enabling the court, including the jury, to make better informed decisions. For example, although answers to specific questions such as: “Which type of bodily fluid has the DNA originated from?” or, “How was the DNA deposited at the scene?” would be probabilistic in nature, they can be crucial to the outcome of a case. The relationship between the DNA evidence, the source of the DNA and the activity that took place is described in a term called the “hierarchy of propositions.” Currently, such questions are usually answered by scientists subjectively with little to no logical framework to assist them. Bayesian networks have proven to be beneficial in providing logical reasoning by way of a likelihood ratio to help combine subjective, yet, experience‐based, opinions of experts with experimental data when answering questions which can be both complex and uncertain. These networks offer a framework that provides balance, transparency, and robustness in the evaluation of evidence. A current limitation of the use of Bayesian networks includes a lack of understanding of the underlying concepts from both forensic scientists and the courts and consequently a reduced recognition of the potential strengths. This article is categorized under: Forensic Biology &gt; Interpretation of Biological Evidence
Mine with it or sell it

In proof of work blockchain systems, there are strong incentives towards designing hardware that can mine faster and/or with less power consumption. There are two ways of taking advantage of such devices: one can use them to mine more coins with less power, or he can sell it to other miners. The two strategies are not independent, of course: if everybody has the boosting technology, the difficulty will rise, and it won't be an advantage anymore. On the other hand, if the boost is above a certain threshold, being used only by a small subset of miners might mean centralizing the system, with potentially dangerous consequences on the platform credibility. In this paper we analyse the impact of different strategies to exploit a significant increase in mining hardware efficiency. To do so, we developed a multi-agent based simulator, that mimics the relevant mechanics of the mining ecosystem, as well as some features of the miners as economic actors. We then characterized different significant sell-it-or-mine-with-it strategies, and observed the simulated outcome.
The Evolutionary Buffet Method

Within the field of Genetic Algorithms (GA) and Artificial Intelligence (AI) a variety computational substrates with the power to find solutions to a large variety of problems have been described. Research has specialized on different computational substrates that each excel in different problem domains. For example, Artificial Neural Networks (ANN) (Russell et al., Artificial intelligence: a modern approach, vol 2. Prentice Hall, Upper Saddle River, 2003) have proven effective at classification, Genetic Programs (by which we mean mathematical tree-based genetic programming and will abbreviate with GP) (Koza, Stat Comput 4:87–112, 1994) are often used to find complex equations to fit data, Neuro Evolution of Augmenting Topologies (NEAT) (Stanley and Miikkulainen, Evolut Comput 10:99–127, 2002) is good at robotics control problems (Cully et al., Nature 521:503, 2015), and Markov Brains (MB) (Edlund et al., PLoS Comput Biol 7:e1002,236, 2011; Marstaller et al., Neural Comput 25:2079–2107, 2013; Hintze et al., Markov brains: a technical introduction. arXiv:1709.05601, 2017) are used to test hypotheses about evolutionary behavior (Olson et al., J R Soc Interf 10:20130,305, 2013) (among many other examples). Given the wide range of problems and vast number of computational substrates practitioners of GA and AI face the difficulty that every new problem requires an assessment to find an appropriate computational substrates and specific parameter tuning to achieve optimal results. Artificial intelligence NEAT
Provably Secure Generative Steganography Based on Autoregressive Model

Synthetic data and generative models have been more and more popular with the rapid development of machine learning and artificial intelligence (AI). Consequently, generative steganography, a novel steganographic method finishing the operation of steganography directly in the process of image generation, tends to get more attention. However, most of the existing generative steganographic methods have more or less shortcomings, such as low security, small capacity or limited to certain images. In this paper, we propose a novel framework for generative steganography based on autoregressive model, or rather, PixelCNN. Theoretical derivation has been taken to prove the security of the framework. A simplified version is also proposed for binary embedding with lower complexity, for which the experiments show that the proposed method can resist the existing steganalytic methods.
How to Measure Energy Consumption in Machine Learning Algorithms

Machine learning algorithms are responsible for a significant amount of computations. These computations are increasing with the advancements in different machine learning fields. For example, fields such as deep learning require algorithms to run during weeks consuming vast amounts of energy. While there is a trend in optimizing machine learning algorithms for performance and energy consumption, still there is little knowledge on how to estimate an algorithm’s energy consumption. Currently, a straightforward cross-platform approach to estimate energy consumption for different types of algorithms does not exist. For that reason, well-known researchers in computer architecture have published extensive works on approaches to estimate the energy consumption. This study presents a survey of methods to estimate energy consumption, and maps them to specific machine learning scenarios. Finally, we illustrate our mapping suggestions with a case study, where we measure energy consumption in a big data stream mining scenario. Our ultimate goal is to bridge the current gap that exists to estimate energy consumption in machine learning scenarios.