Reviewerly/exHarmony huggingface dataset:
Scalable Detection of Crowd Motion Patterns

Studying the movements of crowds is important for understanding and predicting the behavior of large groups of people. When analyzing crowds, one is often interested in the long-term macro-level motions of the crowd as a whole, as opposed to the micro-level short-term movements of individuals. A high-level representation of these motions is thus desirable. In this work, we present a scalable method for detection of crowd motion patterns, i.e., spatial areas describing the dominant motions within crowds. For measuring crowd movements, we propose a fast, scalable, and low-cost method based on proximity graphs. For analyzing crowd movements, we utilize a three-stage pipeline: (1) represents the behavior of each person at each moment in time as a low-dimensional data point, (2) cluster these data points based on spatial relations, and (3) concatenate these clusters based on temporal relations. Experiments on synthetic datasets reveals our method can handle various scenarios including curved lanes and diverging flows. Evaluation on real-world datasets shows our method is able to extract useful motion patterns which could not be properly detected by existing methods. Overall, we see our work as an initial step towards rich pattern recognition.
HDT crypt : Compression and encryption of RDF datasets

The publication and interchange of RDF datasets online has experienced significant growth in recent years, promoted by different but complementary efforts, such as Linked Open Data, the Web of Things and RDF stream processing systems. However, the current Linked Data infrastructure does not cater f or the storage and exchange of sensitive or private data. On the one hand, data publishers need means to limit access to confidential data (e.g. health, financial, personal, or other sensitive data). On the other hand, the infrastructure needs to compress RDF graphs in a manner that minimises the amount of data that is both stored and transferred over the wire. In this paper, we demonstrate how HDT – a compressed serialization format for RDF – can be extended to cater for supporting encryption. We propose a number of different graph partitioning strategies and discuss the benefits and tradeoffs of each approach.
On the Influence of Tools on Collaboration in Participative Enterprise Modeling—An Experimental Comparison Between Whiteboard and Multi-touch Table

The paper presents an experiment about the influence of the modeling tool on group work in the context of enterprise modeling. A goal modeling task was set where three groups of three persons worked with a whiteboard, and three groups of three persons worked with a multi-touch table. Comparisons of working styles between the two tools indicate that multi-touch tables promote parallel working and that a team member's position plays a role in taking on certain tasks. Whiteboard users may more easily lose track of what teammates are doing.
Learning Energy Based Inpainting for Optical Flow

Modern optical flow methods are often composed of a cascade of many independent steps or formulated as a black box neural network that is hard to interpret and analyze. In this work we seek for a plain, interpretable, but learnable solution. We propose a novel inpainting based algorithm that approaches the problem in three steps: feature selection and matching, selection of supporting points and energy based inpainting. To facilitate the inference we propose an optimization layer that allows to backpropagate through 10K iterations of a first-order method without any numerical or memory problems. Compared to recent state-of-the-art networks, our modular CNN is very lightweight and competitive with other, more involved, inpainting based methods.
Multi-kernel Gaussian process latent variable regression model for high-dimensional sequential data modeling

Modeling sequential data has been a hot research field for decades. One of the most challenge problems in this field is modeling real-world high-dimensional sequential data with limited training samples. This is mainly due to the following two reasons. First, if the dimension of the data is significantly greater then the number of the data, it may result in the over-fitting problem. Second, the dynamic behavior of the real-world data is very complex and difficult to approximate. To overcome these two problems, we propose a multi-kernel Gaussian process latent variable regression model for high-dimensional sequential data modeling and prediction. In our model, we design a regression model based on the Gaussian process latent variable model. Furthermore, a multi-kernel learning model is designed to automatically construct suitable nonlinear kernel for various types of sequential data. We evaluate the effectiveness of our method using two types of real-world high-dimensional sequential data, including the human motion data and the motion texture video data. In addition, our method is compared with several representative sequential data modeling methods. Experimental results show that our method achieves promising modeling capability and is capable of predict human motion and texture video with higher quality.
TSFS: An Integrated Approach for Event Segmentation and ADL Detection in IoT Enabled Smarthomes

With the recent advances in Internet of Things, smarthomes containing a variety of sensors are expected to monitor and react to the activities of daily living (ADL) of the residents with the goal of improving convenience, comfort, and safety. However, a single human activity may trigger multiple sensors, and each sensor is triggered by multiple activities. Also, the very human nature of interleaving activities along with the presence of multiple inhabitants makes ADL detection a complex problem. This paper tackles the problem with a data driven de-multiplexing approach termed TSFS (Temporal-Sensor-Frequency-Stitch) that disentangles each individual activity from the sensor stream and thus simplifies the ADL recognition problem. TSFS leverages temporal(T), sensory(S), and frequency(F) information to discriminate between two consecutive/parallel activities, followed by a stitch(S) to enhance segmentation. The multi-faceted approach helps us in correctly identifying segments; extensive evaluation on 10 public datasets reveals that TSFS is better by 185 percent when compared to a simple temporal segmentation approach. The activity detection accuracy yields within 93 percent of the best possible (an oracle) approach.
Multimodal open-domain conversations with robotic platforms

The chapter discusses how to move from closed-domain dialogs to open-domain dialogs, and from speech-based dialogs to multimodal dialogs with speech, gestures, and gaze, using robot agents. We briefly describe the Constructive Dialog Model, the foundation for our work. Management of topic shifts is one of the challenges for open-domain dialogs, and we describe how Wikipedia can be used for topic shifts as well as an open-domain knowledge source. Multimodal issues are illustrated by our multimodal WikiTalk open-domain robot dialog system. Two future research directions are discussed: the use of domain ontologies in dialog systems and the need to integrate robots with the Internet of Things.
Hardware-Efficient Post-Processing Architectures for True Random Number Generators

In this brief we present novel post-processing modules for use in true random number generators (TRNGs). These modules are based on mathematical constructs called strong blenders, which provide theoretical guarantees for the randomness of the output. We start by pointing out problems with current post-processing methods used in state-of-the-art TRNG designs. We present three novel hardware-efficient architectures and provide guidelines for choosing the design parameters.
Label aided deep ranking for the automatic diagnosis of Parkinsonian syndromes

Parkinsonism is the second most common neurodegenerative disease in the world. Its diagnosis usually relies on visual analysis of Emission Computed Tomography (SPECT) images acquired using 123I−ioflupane radiotracer. This aims to detect a deficit of dopamine transporters at the striatum. The use of Computer Aided tools for diagnosis based on statistical data processing and machine learning methods have significantly improved the diagnosis accuracy. In this paper we propose a classification method based on Deep Ranking which learns an embedding function that projects the source images into a new space in which samples belonging to the same class are closer to each other, while samples from different classes are moved apart. Moreover, the proposed approach introduces a new cost-sensitive loss function to avoid overfitting due to class imbalance (an usual issue in practical biomedical applications), along with label information to produce sparser embedding spaces. The experiments carried out in this work demonstrate the superiority of the proposed method, improving the diagnosis accuracy achieved by previous methodologies and validate our approach as an efficient way to construct linear classifiers.
A Novel Classifier based on Firefly Algorithm

During the past decade, assigning objects to one of several predefined classes with higher accuracy has received considerable focus among researchers. Hence, many algorithms such as statistical algorithms have been developed to solve classification problem. Recently, considerable literature has grown up around swarm intelligence algorithms. Firefly algorithm is a swarm intelligence algorithm that imitates the behavior of the firefly in nature. In this research, a novel classifier approach based on firefly algorithm is introduced as a supervised learning algorithm. Classification based on firefly algorithm is processed by simulating the behavior of firefly in attracting other mates based on intensity and distances. The full process of the proposed algorithm comprises three phases. Feature selection phase, which is responsible for reducing features and picking the most informative features, model construction phase responsible for picking firefly class presenters, and model usage/prediction phase responsible for allocating testing or unseen samples in their relative classes using class presenters. Seven different datasets have been used to test classifier performance. Some of the datasets results were compared with ant-miner algorithm. Results proved that firefly classifier algorithm is a promising and competitive classifier.
Multitask Policy Adversarial Learning for Human-Level Control With Large State Spaces

The sequential decision-making problem with large-scale state spaces is an important and challenging topic for multitask reinforcement learning (MTRL). Training near-optimality policies across tasks suffers from prior knowledge deficiency in discrete-time nonlinear environment, especially for continuous task variations, requiring scalability approaches to transfer prior knowledge among new tasks when considering large number of tasks. This paper proposes a multitask policy adversarial learning (MTPAL) method for learning a nonlinear feedback policy that generalizes across multiple tasks, making cognizance ability of robot much closer to human-level decision making. The key idea is to construct a parametrized policy model directly from large high-dimensional observations by deep function approximators, and then train optimal of sequential decision policy for each new task by an adversarial process, in which simultaneously two models are trained: a multitask policy generator transforms samples drawn from a prior distribution into samples from a complex data distribution with higher dimensionality, and a multitask policy discriminator decides whether the given sample is prior distribution from human-level empirically derived or from the generator. All the related human-level empirically derived are integrated into the sequential decision policy, transferring human-level policy at every layer in a deep policy network. Extensive experimental testing result of four different WeiChai Power manufacturing data sets shows that our approach can surpass human performance simultaneously from cart-pole to production assembly control.
The long-wave infrared (8-12 μm) spectral features of selected rare earth element—Bearing carbonate, phosphate and silicate minerals

Rare earth elements (REEs) are a group of metals essential to high technology industries. This high demand, combined with a high supply risk, has led to an understanding that REEs are critical to society. Despite the potential that hyperspectral imaging (HSI) data offers for a fast and non-invasive characterization of the REEs, it is still poorly understood whether REEs have some information in the long-wave infrared (LWIR; 8–12 μm) wavelength range that can be used for their identification. To partially fill this gap, we have investigated the spectroscopy of twelve REE-bearing mineral samples using relatively high spatial and spectral resolution LWIR hyperspectral imaging data. These samples were formerly characterized using electron probe microanalysis (EPMA), scanning electron microscopy (SEM), and hyperspectral imaging data acquired in the 0.4–2.5 μm wavelength range. Results from these analyses were compared to and used to guide the analysis of the HSI data recorded in the LWIR range. This information was further compared to a reference spectral library of rare earth oxides. Our findings suggest that the spectral features of the samples can generally be traced to the asymmetric degenerate stretching and bending modes of the X-O (X = C, Si, P) groups. Moreover and contrary to what has been observed in the shorter wavelengths, there are no definitive spectral features in the LWIR wavelength region that could be assigned to any specific REE.
The order-preserving pattern matching problem in practice

Given a pattern x of length m and a text y of length n, both over a totally ordered alphabet, the order-preserving pattern matching (OPPM) problem consists in finding all substrings of the text with the same relative order as the pattern. The OPPM problem, which might be viewed as an approximate variant of the well-known exact pattern matching problem, has gained attention in recent years. This interesting problem finds applications in such diverse fields as time series analysis (as share prices on stock markets or weather data analysis) and musical melody matching, just to mention a few. In this paper we present two new filtering approaches which turn out to be much more effective in practice than the previously presented methods, reducing the number of false positives up to 99%. We also present a new efficient approach inspired by the well-known Skip Search algorithm for the exact string matching problem. It makes use of efficient SIMD SSE instructions for speeding up the searching phase. Experimental results show that our proposed algorithms are up to twice faster than previous solutions.
A Hybrid Fake Banknote Detection Model using OCR, Face Recognition and Hough Features

Currency duplication remains an emergent concern among nations due to the advancement of printing and scanning technology. Many note detection systems are present in banks but they are very costly and often inaccurate. The fields of image processing, neural network and machine vision have potential to significantly overcome this issue. This paper proposes a model comprised of Optical Character recognition (OCR), Face Recognition and Hough transformation algorithm. The microprinting, water-mark, and ultraviolet lines features of Bangladeshi notes are extracted for testing of genuine notes. The experimental results of the proposed model give accuracy as high as 93.33% which makes it suitable for deployment on a mobile application. Moreover, the obtained results are compared with the output from individual algorithm of OCR, Face Recognition and Hough transformation, to show that the proposed algorithm gives the highest accuracy.
Activity recognition from visual lifelogs: State of the art and future challenges

Activity recognition is a very active area of research in computer vision. During the last decade, several efforts have focused on recognizing human activities in the wild from unedited videos posted in Youtube or captured by video-surveillance cameras. More recently, following the success of wearable cameras, a lot of interest has shifted towards the analysis of activities captured from a first-person point of view. In this chapter, we focus on the problem of activity recognition from visual lifelogs, that is, a set of images captured at regular intervals from a wearable photo-camera, as opposite to egocentric video shots. After describing the problem and the challenges it involves, we provide a detailed picture of the state of the art, also supported by an experimental survey of existing methods. Finally, we highlight limitations of the current approaches for real world applications and, in view of our analysis, we outline future challenges and research lines.
Remote monitoring and control smart floor for detecting falls and wandering patterns in people with dementia

The rapid rates of industrialization and urbanization have induced considerable changes in family structures, increasing the number of older adults who voluntarily or involuntarily live alone. The rapid increase in the population of older adults living alone has raised many safety concerns, with fa ll-induced injuries and dementia presenting immediate dangers to older adults. Falls are prevalent in older adults, and not only cause injuries for the individuals, but also impose an extremely heavy burden on family members and caregivers. Furthermore, dementia is common among older adults in aging societies and is usually accompanied by dysfunctions in daily living activities, causing considerable difficulties for family members. The objective of this study was to develop a remote monitoring and control (M&C) smart floor system for detecting falls and wandering patterns in older adults with dementia in order to provide comprehensive care assistance. The proposed system integrates a floor detection sensor and Wi-Fi technology to analyze and determine the occurrence of falls and wandering in older adults with dementia. Conventionally, detection processes for falls and wandering in older adults with dementia are conducted using visual monitoring or wearable detectors, which may reduce the privacy, comfort, and convenience of older adults. By contrast, the proposed system maintains users' privacy and eliminates the inconvenience associated with wearing detectors. The system determines fall behaviors and wandering patterns in older adults with dementia; when an accident occurs, the system can issue a warning and notify medical care units or relatives for immediate attention, thus reducing the occurrence of further accidents. The remote M&C and warning functions of the proposed system were verified through experiments.
Memoized zipper-based attribute grammars and their higher order extension

Attribute grammars are a powerfull, well-known formalism to implement and reason about programs which, by design, are conveniently modular. In this work we focus on a state of the art zipper-based embedding of classic attribute grammars and higher-order attribute grammars. We improve their execution performance through controlling attribute (re)evaluation by means of memoization techniques. We present the results of our optimizations by comparing their impact in various implementations of different, well-studied, attribute grammars and their Higher-Order extensions.
Secure and reversible data hiding scheme for healthcare system using magic rectangle and a new interpolation technique

In this chapter a very high capacity and secure medical image information embedding scheme capable of hiding an Electronic Patient Record (EPR) imperceptibly in grayscale medical images for healthcare applications has been proposed. To ensure the reversibility of medical images, a new image interpolation technique has been used to scale up the original image to obtain the cover image. The cover image pixels have been divided into two types: Seed pixels and nonseed pixels. The Electronic Patient Record (EPR) has been embedded only in nonseed pixels while no embedding has been carried out in seed pixels, to facilitate reversibility. A fragile watermark has been embedded in addition to the EPR, to make the proposed scheme capable of detecting any tampering of EPR during its transit from transmitter to receiver. The fragile watermark is also used for content authentication at the receiver. At the receiving end, first we extract the fragile watermark; if it is the same as the one embedded, signaling that the EPR has not been attacked during transit, the EPR is extracted. Otherwise, the system uses a retransmission request instead of wasting time to extract the compromised Electronic Patient Record (EPR). For ensuring the security of the EPR, we encrypt it using a magic rectangle. Embedding has been carried out using least significant bit (LSB) substitution. The scheme has been evaluated for perceptual imperceptibility and content authentication by subjecting it to various image processing and geometric attacks. Experimental results reveal that the proposed system, besides being completely reversible, is capable of providing high-quality stego-images (term used to refer to the image containing the embedded data) for fairly high payload. Further, it has been observed that the proposed technique is able to detect tampering. A comparison of the observed results with those of some state-of-the-art schemes show that our scheme performs better.
Siamese adversarial network for object tracking

In this Letter, a Siamese adversarial network tracker (SANT) is proposed. Recently, in computer vision field, generative adversarial network (GAN) has been widely used for image and video generation. Using the GAN, the proposed method constructs a Siamese adversarial network (SAN) for object tracking. Unlike existing GANs, the proposed SAN uses similarity learning with SAN discriminator. To show the effectiveness of the proposed SAN, the same structure as the residual long-short-term memory tracker is used. Experimental results show that the proposed SANT achieves the highest performance among existing Siamese trackers.
An adaptive sequential wavelet-based algorithm developed for dynamic optimization problems

In this paper we present an adaptive wavelet algorithm (WAA) tailored for dynamic optimization problems (DOP). The main feature of the WAA is the automatic computation of time-domain discretization, generating a self-adapting control parameterization, which depends on the nonlinear characteristics of the mathematical model. For this, the control variables are analyzed and treated at different wavelet levels. First, we have demonstrated the advantages of WAA over heuristic adaptive procedures, proposed in the last years. Second, the results of the proposed strategy are illustrated through the solution of ten case studies. According to the results, the computation cost could be reduced by about 56% on average. Besides, the average NLP size reduction was approximately 49.94%, showing that one of the most considerable advantages of the algorithm is the adaptive discretization without prior information of the control profile.