Reviewerly/exHarmony huggingface dataset:
iFlowGAN: An Invertible Flow-based Generative Adversarial Network For Unsupervised Image-to-Image Translation

We propose iFlowGAN that learns an invertible flow (a sequence of invertible mappings) via adversarial learning and exploit it to transform a source distribution into a target distribution for unsupervised image-to-image translation. Existing GAN-based generative model such as CycleGAN [1], StarGAN [2], AGGAN [3] and CyCADA [4] needs to learn a highly under-constraint forward mapping F: X → Y from a source domain X to a target domain Y. Researchers do this by assuming there is a backward mapping B: Y → X such that x and y are fixed points of the composite functions B °F and F °B. Inspired by zero-order reverse filtering [5], we (1) understand F via contraction mappings on a metric space; (2) provide a simple yet effective algorithm to present B via the parameters of F in light of Banach fixed point theorem; (3) provide a Lipschitz-regularized network which indicates a general approach to compose the inverse for arbitrary Lipschitz-regularized networks via Banach fixed point theorem. This network is useful for image-to-image translation tasks because it could save the memory for the weights of B. Although memory can also be saved by directly coupling the weights of the forward and backward mappings, the performance of the image-to-image translation network degrades significantly. This explains why current GAN-based generative models including CycleGAN must take different parameters to compose the forward and backward mappings instead of employing the same weights to build both mappings. Taking advantage of the Lipschitz-regularized network, we not only build iFlowGAN to solve the redundancy shortcoming of CycleGAN but also assemble the corresponding iFlowGAN versions of StarGAN, AGGAN and CyCADA without breaking their network architectures. Extensive experiments show that the iFlowGAN version could produce comparable results of the original implementation while saving half parameters.
A Blockchain framework for reverse logistics of used medical equipment

Reverse logistics (RL) activities of medical equipment play a crucial role in properly managing durable medical devices at the end of their life. However, refurbishing medical equipment is not a straightforward process, as there exist many challenges associated to their proper re-positioning into the market. In this paper the use of blockchain technology is proposed as a viable solution for establishing sound refurbishing channels for medical equipment while considering relevant regulatory, security and operational prerequisites. In particular, we propose a highly robust traceability mechanism for medical equipment refurbishing activities based on blockchain and smart contracts. Our approach offers multiple and disparate stakeholders (device manufacturers, hospitals, refurbishers, retailers etc.) to share critical RL information in a timely, verifiable and secure fashion. The system provides forensics-by-design functionalities and safeguards the chain-of-custody for all the refurbishing processes taking place. A proof-of-concept implementation is provided as well as a discussion of the various benefits of the proposed RL system.
Deep Recurrent Q Reinforcement Learning model to Predict the Alzheimer Disease using Smart Home Sensor Data

Abstract Early detection of alzheimer disease is an essential process of the elderly persons when they are with the mild cognitive impairments. This work adapts the smart home based alzheimer disease detection by recording the daily activities of the resident’s equipped with the sensor devices in their appliances as smart home. Using sensor data based clinical assessment of alzheimer patient is very complex as it is uncertain to predict such vague monitoring and understanding its deep characteristics. To acquire the deep characteristics of sensor dataset unlike the existing conventional supervised learning paradigm, this paper constructs an optimized self-learning model known as reinforcement learning process. This proposed model Deep Recurrent Q Learning based Reinforcement Model (DRQLRM) Process comprised of two stages they are pretraining using recurrent neural network to overcome the overfitting and fine tuning of the parameters involved in deep neural network and Deep Q learning process is used as reinforcement approach which uses the agents rewards and penalties to determine the optimal agents and their experience to predict the unknown pattern in the uncertain condition in effective way. The proposed model produced better result in CASA smart home test bed for investigating the presence of alzheimer
PcmNet: Position-sensitive context modeling network for temporal action localization

Temporal action localization, which aims to locate temporal regions where actions take place and recognize their corresponding classes in untrimmed real-world videos, is a challenging task. As a critical cue to video understanding, exploiting the video context has become an important strategy to boost the localization performance. However, previous methods mainly focus on exploring semantic context which captures the feature similarity among frames or proposals. The temporal position context which is also vital for temporal action localization is less explored. In this paper, we propose a position-sensitive context modeling approach to fuse both semantic and position context for more precise action localization. Specifically, we first propose a position encoding method tailored for temporal action localization on both frame-level and proposal-level, which ensures that the generated position representations can model the distance and chronological relationships among frames or proposals. Then we conduct attention-based context aggregation to produce discriminative features and help with precise boundary detection and proposal evaluation. Our method achieves state-of-the-art performance on two widely used datasets, THUMOS-14 and ActivityNet-1.3, demonstrating the effectiveness and generalizability of our method.
Do Google Trends forecast bitcoins? Stylized facts and statistical evidence

In early 2018 Bitcoin prices peaked at US$ 20,000 and, almost two years later, we still continue debating if cryptocurrencies can actually become a currency for the everyday life or not. From the economic point of view, and playing in the field of behavioral finance, this paper analyses the relation between Bitcoin price and the search interest on Bitcoin since 2014. We questioned the forecasting ability of Google Bitcoin Trends for the behavior of Bitcoin price by performing linear and nonlinear dependency tests, and exploring performance of ARIMA and Neural Network models enhanced with this social sentiment indicator. Our analyses and models are founded upon a set of statistical properties common to financial returns that we establish for Bitcoin, Ethereum, Ripple and Litecoin.
A Body Part Embedding Model With Datasets for Measuring 2D Human Motion Similarity

Human motion similarity is practiced in many fields, including action recognition, anomaly detection, and human performance evaluation.While many computer vision tasks have benefited from deep learning, measuring motion similarity has attracted less attention, particularly due to the lack of large datasets.To address this problem, we introduce two datasets: a synthetic motion dataset for model training and a dataset containing human annotations of real-world video clip pairs for motion similarity evaluation.Furthermore, in order to compute the motion similarity from these datasets, we propose a deep learning model that produces motion embeddings suitable for measuring the similarity between different motions of each human body part.The network is trained with the proposed motion variation loss to robustly distinguish even subtly different motions.The proposed approach outperforms the other baselines considered in terms of correlations between motion similarity predictions and human annotations while being suitable for real-time action analysis.Both datasets and codes are released to the public.
Category Recognition in E-Commerce using Sequence-to-Sequence Hierarchical Classification

E-commerce platforms often use a predefined structured hierarchy of product categories. Apart from helping buyers sort between different product types, listing categorization is also critical for multiple downstream tasks, including the platform's main listing search. Traditionally, when creating a new listing, sellers need to assign the product they sell to a single category. However, the high diversity of product types in the platform, along with the hierarchy's low level of granularity result in tens of thousands of different possible categories that sellers need to pick from. This, in turn, creates a unique classification challenge, especially for sellers with a large number of listings. Moreover, the expected cost of making a category classification error is high, as it can impact the likelihood that their listing will get discovered by relevant buyers, and eventually sold.
Reservoir computing with swarms

We study swarms as dynamical systems for reservoir computing (RC). By example of a modified Reynolds boids model, the specific symmetries and dynamical properties of a swarm are explored with respect to a nonlinear time-series prediction task. Specifically, we seek to extract meaningful information about a predator-like driving signal from the swarm’s response to that signal. We find that the naïve implementation of a swarm for computation is very inefficient, as permutation symmetry of the individual agents reduces the computational capacity. To circumvent this, we distinguish between the computational substrate of the swarm and a separate observation layer, in which the swarm’s response is measured for use in the task. We demonstrate the implementation of a radial basis-localized observation layer for this task. The behavior of the swarm is characterized by order parameters and measures of consistency and related to the performance of the swarm as a reservoir. The relationship between RC performance and swarm behavior demonstrates that optimal computational properties are obtained near a phase transition regime.
Optimal design subsampling from Big Datasets

Big Data are huge amounts of digital information that rarely result from properly planned surveys; as a consequence they often contain redundant observations.When the aim is to answer particular questions of interest, we suggest selecting a subsample of units that contains the majority of the information to achieve this goal.Selection methods driven by the theory of optimal design incorporate the inferential purposes and thus perform better than standard sampling schemes.
Content Based Image Retrieval Using Deep Learning Convolutional Neural Network

Abstract Content-based image retrieval (CBIR) is a widely used method for image retrieval from large and unlabeled image collections. However, users are not satisfied with the traditional methods of retrieving information. Moreover the abundance of online networks for production and distribution, as well as the quantity of images accessible to consumers, continues to expand. Therefore, in many areas, permanent as well as widespread digital image processing takes place. Therefore, the rapid access to these large image databases as well as the extraction of identical images from this large set of images from a given image (Query) pose significant challenges as well as involves efficient techniques. A CBIR system’s efficiency depends fundamentally on the calculation of feature representation as well as similarity. For this purpose, they present a basic but powerful deep learning system focused on Convolutional Neural Networks (CNN) and composed of feature extraction and classification for fast image retrieval. We get some promising findings from many detailed observational studies for a number of CBIR tasks using image database, which reveals some valuable lessons for improving the efficiency of CBIR. CBIR systems allow another image dataset to locate related images to such a query image. The search per picture function of Google search has to be the most popular CBIR method.
WITHDRAWN: A framework to manage smart educational certificates and thwart forgery on a permissioned blockchain

We humans deal with a vast number of documents in our day-to-day activities. The varying list of documents we deal with in our daily life include land registration, vehicle registration, marriage registration, health prescriptions and voter ID. For example, a person’s capabilities and achievements are generally showcased in terms of his educational, professional and extracurricular certificates. In today’s computer era, we humans are trying to make our life easy by developing mechanisms to preserve and manage these certificates in terms of softcopies. Maintenance of Soft copy of all these certificates has become the primary motto and the hardcopy maintenance has become obsolete. These certificates help us to showcase our personal abilities and career growth. There is a possibility of these certificates being forged or being lost. So utmost importance is given to secure these certificates against losing or to make them tamper proof. Numerous methods exist to preserve these certificates as well as to thwart forgery. Conventional method of storing revolves around a centralized certificate storage system which is vulnerable to data breach. Many Blockchain based frameworks and implementations are proposed. We will discuss various certificate management approaches based on blockchain, their drawbacks and propose a new and effective framework.
An Image Contrast Measure Based on Retinex Principles

The image contrast is a feature capturing the variation of the image signal across the space. Such a feature is very useful to describe the local image structure at different scales and thus it is relevant to many computer vision applications, like image/texture retrieval and object recognition. In this work, we present MiRCo, a novel measure of image contrast derived from the Retinex theory. MiRCo is robust against in-plane rotations and light changes at multiple scales. Thanks to these properties, MiRCo enables an accurate and robust description of the local image structure. Here we describe and discuss the mathematical insights of MiRCo also in comparison with other popular contrast measures.
Pre‐training of gated convolution neural network for remote sensing image super‐resolution

Many very deep neural networks are proposed to obtain accurate super-resolution reconstruction of remote sensing images. However, the deeper the network for image SR is, the more difficult it is to train. The low-resolution inputs and features contain abundant low-frequency information and noise, which are treated equally as the high-frequency information to across the network. To solve these problems, a novel single-image super-resolution algorithm named pre-training of gated convolution neural network (PGCNN) is proposed for remote sensing images. The proposed PGCNN consists of several residual blocks with long skip connections. Each residual block contains an additional well-designed gated convolution unit, which provides different weights to high-frequency information and low-frequency information to control the transmission of information, making the main network focus on learning high-frequency information. Compared with several state-of-the-art methods, experimental results on the remote sensing datasets (SIRI-WHU, NWPU-RESISC45, RSSCN7 and UC-Merced-Land-Use) show that the proposed PGCNN has the accuracy and visual improvements.
Initial coin offerings and the cryptocurrency hype - the moderating role of exogenous and endogenous signals

Abstract Initial coin offerings (ICOs) have recently emerged as a new financing instrument for entrepreneurial ventures, spurring economic and academic interest. Nevertheless, the impact of exogenous and endogenous signals on the performance of ICOs as well as the effects of the cryptocurrency hype and subsequent downfall of Bitcoin between 2016 and 2019 remain underexplored. We applied ordinary least squares (OLS) regressions based on a dataset containing 1597 ICOs that covers almost 2.5 years. The results show that exogenous and endogenous signals have a significant effect on the funds raised in ICOs. We also find that the Bitcoin price heavily drives the performance of ICOs. However, this hype effect is moderated, as high-quality ICOs are not pegged to these price developments. Revealing the interplay between hypes and signals in the ICO’s asset class should broaden the discussion of this emerging digital phenomenon.
Touchless controls for passenger elevator

Control panels play important role in providing interaction with machines. Centuries ago, it started with mechanical switches and has now reached to capacitive touchscreens after a long journey. All these require a contact or touch and in case of capacitive touchscreen it requires some specific type of material through which the contact is made at any point on the touchscreen surface. During the pandemic, the rise of new normal created the need of touchless. Example of passenger elevator can explain the need. Passenger elevators have switches installed outside the elevator cabin, are known as Landing operating panel and inside the elevator cabin are known as COP Cabin operating panel which are touched by lot of people and people are using them fearfully during pandemic. We have developed control panels with which elevators can be operated without the need of touching any surface by fingers and it does not require any special kind of material, stylus or device. Our design is based on Infrared light field which is invisible, low cost and light weight. The design is also easy to install.
Analysis of the Scalability of a Deep-Learning Network for Steganography “Into the Wild”

Since the emergence of deep learning and its adoption in steganalysis fields, most of the reference articles kept using small to medium size CNN, and learn them on relatively small databases.
A multi-instance networks with multiple views for classification of mammograms

Breast cancer is the most common malignant disease in women, and early screening of breast cancer is crucial for improving the survival rate. Mammography is one of the most popular imaging methods for breast cancer screening with the characteristics of practicality, effectiveness, and low cost. However, the classification of mammograms suffers from large image sizes, indistinct image characteristics of lesions, small proportion of abnormalities, and class imbalance. To address these difficulties, the multi-view input and weighted multi-instance learning (MIL) methods are proposed. A novel model called the weighted MIL DenseNet with multi-view input (WMDNet) is presented that integrates the two methods above. The multi-view inputs method is used to enhance the abnormalities of mammograms and obtain more potential features from mammograms with different views, simultaneously. The weighted MIL is designed to extract the most suspicious lesions from mammograms to resolve the problems of small proportion of abnormalities and class imbalance. To verify the effectiveness of the proposed methods, three binary classification models are evaluated on two public datasets, the INbreast and MIAS datasets. The experimental results demonstrate that the proposed methods can achieve preferably results compared with several other state-of-the-art approaches, especially the proposed WMDNet model gains the best classification results on both datasets.
Motion planning approach for car-like robots in unstructured scenario

When a car-like robot travels in an unstructured scenario, real-time motion planning encounters the problem of unstable motion state in obstacle avoidance planning. This paper presents a hybrid motion planning approach based on the timed-elastic-band (TEB) approach and artificial potential field. Different potential fields in an unstructured scenario are established, and the real-time velocity of the car-like robot is planned by using the conversion function of the virtual potential energy of the superimposed potential field and the virtual kinetic energy of the robot. The optimized TEB approach plans the local optimal path and solves the problems related to the local minimum region and non-reachable targets. The safety area of the dynamic obstacle is constructed to realize turning or emergency stop obstacle avoidance, thereby effectively ensuring the safety of the car-like robot in emergency situations. The simulation experiments show that the proposed approach has superior kinematic characteristics and satisfactory obstacle avoidance planning effects and can improve the motion comfort and safety of the car-like robot. In the practical test, the car-like robot moves stably in a dynamic scenario, and the proposed approach satisfies the actual application requirements.
Mapping study using the unsupervised learning clustering approach

Abstract The aim of this research is to analyze the number of criminal cases in Indonesia by utilizing unsupervised learning techniques. The unsupervised learning technique used is data mining by mapping clusters of regions in Indonesia. Sources of data were obtained from the Operations Control Bureau, National Police Headquarters of the Republic of Indonesia through processed data from the Central Statistics Agency (abbreviated as BPS) with data url: https://www.bps.go.id. The data mining method used to map the form of calcter is k-medoid. The data used is data on the number of crimes according to the regional police (2017-2019) which consists of 34 records. The attribute used is the number of crimes in the past three years based on the regional police for each province. The mapping label used is the high cluster (D1) and the low cluster (D2) on the number of criminal acts in Indonesia. The mapping analysis process uses the help of Rapid Miner software. In determining the amount of calcter (k = 2) is done using the Davies Bouldin Index (DBI) parameter with a value of 0.876 (the smaller the better). The results showed that six provinces were in the high cluster (D1) and twenty-eight provinces were in the low cluster (D2). The final centroid in each cluster is 16,008; 21,498; 21,616 (cluster_0 / D1) and 6,994; 7,311; 6,785 (cluster_1 / D2). The six provinces in the high cluster of criminal cases are North Sumatra, South Sumatra, Metro Jaya, West Java, East Java and South Sulawesi. The results of the research are expected to provide information for the government to reduce the number of criminal acts in Indonesia based on the number of clusters that exist.
Quality Assessment оf Defocused Image Recovery Algorithms

Camera shake during video recording is a major source of image defocus. Restoring these defocuses is one of the most important tasks in image processing. This article examines and evaluates the most commonly used algorithms in practice, namely: Wiener filtration, Richardson-Lucy algorithm, Tikhonov regularization, and Blind deconvolution. The comparisons were made using structural similarity and root mean square error.