Reviewerly/exHarmony huggingface dataset:
Blockchain Based Share Economy Trust Point

Blockchain is attracting attention from academia and Industry as well due to its amalgamation of various characteristics; cryptography, public key infrastructure, and economic modeling, that is applied to peer-to-peer networking and decentralized consensus to achieve distributed database synchronization. Among other applications of Blockchain, secure and decentralized way of sharing economy is one of the most emergent ones. In this paper, we have proposed a Blockchain based share economy model. Blockchain Share Economy Trust point (SE-TP) mechanism is at the core of the idea for the lender and renter communication. Using SE_TP, the sharing economy communication environment provide effective data management along with data security and reliability. Also, the detail of every shared item can be accessed ubiquitously. A case study to validate the proposed idea is also presented.
An Improved K2 Algorithm for Symptom-Disease Bayesian Network

Abstract This paper establishes a Bayesian network for pre-diagnosis based on symptom-disease knowledge from clinical data. In order to solve the local optimal problem, the idea of simulated annealing algorithm is introduced into an improved K2 algorithm, and the BDE scoring function is used to determine the optimal network structure of the Bayesian network. performance evaluation indexes are used to evaluate the performance of the network. The experimental results show that the improved K2 algorithm outperformed the K2 algorithm.
Secure and Fast Decision Tree Evaluation on Outsourced Cloud Data

Decision trees are famous machine learning classifiers which have been widely used in many areas, such as healthcare, text classification and remote diagnostics, etc. The service providers usually host a decision tree model on the cloud server and provide some classification service for clients to use such a model remotely. In such a scenario, the model is a valuable asset to the cloud which should not be disclosed to the clients, while the query data and classification results are private to the client. To solve such a problem, we propose several building blocks, i.e., secure comparison and secure polynomial calculation, in a two-cloud model. Based on these building blocks, we design a privacy-preserving decision tree evaluation scheme. Compared with the most recent works, our scheme can fully protect the tree model and clients’ data privacy simultaneously. Besides, our scheme also supports offline service users which is essential to the system’s scalability. Moreover, through theoretical analysis and real-world experimental test, it is oblivious that our scheme is quite efficient.
An insight into the sale of prescription drugs and medicine on the AlphaBay cryptomarket

Internet access has provided new ways to trade goods. Unlike conventional legal sale sites, cryptomarkets facilitate exchanges in a context where the anonymity of participants is warranted. The aim of this article was to obtain a better understanding of the trafficking of prescription drugs and medicine on the AlphaBay cryptomarket. The results showed that alprazolam, oxycodone, and Adderall were the most offered prescription drugs while alprazolam, diazepam, and oxycodone were the most sold substances. The sale was dominated by North America, Australia, and Western European countries. The revenue of prescription drugs was estimated to be more than US$65 million since the creation of AlphaBay, a small market in comparison with the worldwide legal pharmaceutical market’s estimate of US$1.3 trillion in 2020. Digital traces offer a complementary way to understand the trafficking of prescription drugs and medicine and to identify the most prolific vendors and their implication in this trafficking.
Dual algorithm for truncated fractional variation based image denoising

Fractional-order derivative is attracting more and more attention of researchers in image processing because of its better property in restoring more texture than the total variation. To improve the performance of fractional-order variation model in image restoration, a truncated fractional-order variation model was proposed in Chan and Liang [Truncated fractional-order variation model for image restoration, J. Oper. Res. Soc. China]. In this paper, we propose a dual approach to solve this truncated fractional-order variation model on noise removal. The proposed algorithm is based on the dual approach proposed by Chambolle [An algorithm for total variation minimisation and applications, J. Math Imaging Vis. 20 (2004), pp. 89–97]. Conversely, the Chambolle's dual approach can be treated as a special case of the proposed algorithm with fractional order α=1. The work of this paper modifies the result in Zhang et al. [Adaptive fractional-order multi-scale method for image denoising, J. Math. Imaging Vis. 43(1) (2012), pp. 39–49. Springer Netherlands 0924–9907, Computer Science, pp. 1–11, 2011], where the convergence is not analysed. Based on the truncation, the convergence of the proposed dual method can be analysed and the convergence criteria can be provided. In addition, the accuracy of the reconstruction is improved after the truncation is taken.
Sparse Representation Face Recognition Based on Gabor and CSLDP Feature Fusion

Aiming at the problem of low recognition rate caused by local deformations such as pose, angle, expression change and occlusion, especially small local deformations in face recognition. A face recognition algorithm based on Gabor wavelet transform and central symmetric local direction pattern (CSLDP) is proposed. Gabor is used to extract multi-dimensional features, and CSLDP is used to reduce the computational complexity. Then histogram feature information is divided into blocks and counted in series as feature vectors of face images. Finally, sparse representation classifier (SRC) is used for classification and recognition. The experimental results on ORL, YALE and AR face databases show that the proposed method can adapt to the changes of fine expressions and light, and has strong robustness.
Trajectory Planning for Ground Service Robot

The aim of this paper is to present a local trajectory planning method that can generate feasible and comfortable trajectory for ground service robot, meanwhile considering several stationary obstacles and the curvature limit of the path, and a curvature estimation method and its accuracy budget was proposed. The proposed algorithm was evaluated under the MATLAB using wheeled vehicle with nonholonomic dynamics, and results verified the effectiveness of the proposed algorithm.
A Reservoir Computing Framework for Continuous Gesture Recognition

We present a novel gesture recognition system for the application of continuous gestures in mobile devices. We explain how meaningful gesture data can be extracted from the inertial measurement unit of a mobile phone and introduce a segmentation scheme to distinguish between different gesture classes. The continuous sequences are fed into an Echo State Network, which learns sequential data fast and with good performance. We evaluated our system on crucial network parameters and on our established metric to compute the number of successfully recognized gestures and the number of misclassifications. On a total of ten gesture classes, our framework achieved an average accuracy of 85%.
Continuous Integration Using Gitlab

The method of Continuous Integration (CI) enables developers to use fast-paced development environments, such as Agile, without the quality of code being compromised. The use of stable repositories on which developers frequently make small changes, running tests with each modification, results in code that is highly tested and readily deployable. GitLab, like other tools of its kind, offers CI features along with code versioning, and several team organizational features that correctly map with Agile artifacts. Before discussing GitLab, this report contains a literature review explaining existing CI practices, and it identifies fundamental aspects that are necessary to achieving the industry standards of CI. This report also evaluates GitLab’s effectiveness in maintaining CI standards using an available open-source project (OpenMCT), while also offering insight on the implications of the gathered findings. The paper concludes with possible directions of future inquiry, and also provides guidance on how one might expand on the work initiated by the team.
Detecting Everyday Scenarios in Narrative Texts

Script knowledge consists of detailed information on everyday activities.Such information is often taken for granted in text and needs to be inferred by readers.Therefore, script knowledge is a central component to language comprehension.Previous work on representing scripts is mostly based on extensive manual work or limited to scenarios that can be found with sufficient redundancy in large corpora.We introduce the task of scenario detection, in which we identify references to scripts.In this task, we address a wide range of different scripts (200 scenarios) and we attempt to identify all references to them in a collection of narrative texts.We present a first benchmark data set and a baseline model that tackles scenario detection using techniques from topic segmentation and text classification.
Detecting Design Violations in Django-based Web Applications

If on one hand frameworks allow programmers to reuse well-known architectural solutions, on the other hand they can make programmers unaware of important design decisions that should be followed during software construction, maintenance and evolution. And if programmers are unaware of these design decisions, there is a high risk of introducing design violations in the source code, and the accumulation of these violations might hinder software maintainability and evolvability. The use of static analysis tools might be employed to mitigate these problems by assisting the detection of recurring design violations in a given architectural pattern. In this work, we present MTV-Checker, a tool to assist the automatic detection of 5 design violations in Django-based web applications. We also conducted an empirical study in the context of the SUAP system, a large-scale Django-based information system with more than 175.000 lines of Python code currently deployed in more than 30 Brazilian institutions. Our results present the most recurrent violations, how they evolve along software evolution, and the opinions and experiences of software architects regarding these violations.
Semi-Supervised Information Extraction for Cancer Pathology Reports

Pathology reports are a main source of data for cancer surveillance programs. Manual coding of pathology reports is labor-intensive but necessary for obtaining labeled data to train automated information extraction systems. In this study, we investigated semi-supervised deep learning, improving the performance of a multitask information extraction system for automated annotation of pathology reports. We used a set of over 374,000 pathology reports from the Louisiana Tumor Registry and a novel convolutional attention-based auto-encoder. We performed a set of experiments comparing supervised training augmented with unlabeled data at 1%, 5%, 10%, and 50% of the original data size. We also compared the impact of extending text processing to include unlabeled tokens. We find that semi-supervised training consistently improved individual performance with increased micro-averaged F-scores between 0.012 and 0.064 and increased macro-averaged F-scores of up to 0.158. This demonstrates that semantic information learned via unsupervised learning can be used to improve supervised clinical task performance.
A method to recognize eye movements based on uplift movement of skin

Movements of human eyes provide useful insights for understanding human's physical and mental condition and ability, which has high attention in various industrial and academic fields. Although many methods for sensing eye movements have been proposed, each method has some problems in sensing eye movements constantly in all situations. In this research, in order to realize a device which constantly recognizes eye movements in every scene, we adopted a sensing technology using uplift movements of a skin accompanying eye movements. The proposed method attached a infrared distance sensor to an inside of glasses so as to sense eye movements based on the change in a distance between eyes and a distance sensor. Then, necessary parameters such as eye movements directions are recognized based on an extracted feature quantity using a machine learning. We conducted two kinds of experiments at early-stage and confirmed the feasibility of our proposed method on eye movements recognition.
DCT: Differential Combination Testing of Deep Learning Systems

Deep learning (DL) systems are increasingly used in security-related fields, where the accuracy and predictability of DL systems are critical. However the DL models are difficult to test and existing DL testing relies heavily on manually labeled data and often fails to expose erroneous behavior for corner inputs. In this paper, we propose Differential Combination Testing (DCT), an automated DL testing tool for systematically detecting the erroneous behavior of more corner cases without relying on manually labeled input data or manually checking the correctness of the output behavior. Our tool aims at automatically generating test cases, that is, applying image combination transformations to seed images to systematically generate synthetic images that can achieve high neuron coverage and trigger inconsistencies between multiple similar DL models. In addition, DCT utilizes multiple DL models with similar functions as cross-references, so that input data no longer must be manually marked and the correctness of output behavior can be automatically checked. The results show that DCT can find thousands of erroneous corner behaviors in the most commonly used DL models effectively and quickly, which can better detect the reliability and robustness of DL systems.
Adaptively Accelerating Map-Reduce/Spark with GPUs: A Case Study

In this paper, we propose and evaluate a simple mechanism to accelerate iterative machine learning algorithms implemented in Hadoop map-reduce (stock), and Apache Spark. In particular, we describe a technique that enables data parallel tasks in map-reduce and Spark to be dynamically and adaptively scheduled on CPU or GPU, based on availability and load. We examine the extent of performance improvements, and correlate them to various parameters of the algorithms studied. We focus on end-to-end performance impact, including overheads associated with transferring data into and out of the GPU, and conversion between data representations in the JVM and on GPU. We also present three optimizations that, in our analysis, can be generalized across many iterative machine learning applications. We present a case study where we accelerate four iterative machine learning applications - multinomial logistic regression, multiple linear regression, K-Means clustering and principal components analysis using singular value decomposition, implemented in three data analytics frameworks - Hadoop Map-Reduce (HMR), IBM Main-Memory Map-Reduce (M3R) and Spark. We observe that the use of GPGPUs decreases the execution time of these applications on HMR by up to 8X, M3R by up to 18X, and Spark by up to 25X. Through our empirical analysis, we offer several insights that can be helpful in designing middleware and cluster managers to accelerate map-reduce and Spark applications using GPUs.
Predicting online performance of job recommender systems with offline evaluation

At Indeed, recommender systems are used to recommend jobs. In this context, implicit and explicit feedback signals we can collect are rare events, making the task of evaluation more complex. Online evaluation (A/B testing) is usually the most reliable way to measure the results from our experiments, but it is a slow process. In contrast, the offline evaluation process is faster, but it is critical to make it reliable as it informs our decision to roll out new improvements in production. In this paper, we review the comparative offline and online performances of three recommendations models, we describe the evaluation metrics we use and analyze how the offline performance metrics correlate with online metrics to understand how an offline evaluation process can be leveraged to inform the decisions.
Capsule Generative Models

Neuroscience studies inspire that structures are needed in the hidden space of deep learning models. In this paper, we propose a capsule restricted Boltzmann machine and a capsule Helmholtz machine by replacing individual hidden variables with encapsulated groups of hidden variables. Our preliminary experiments show that capsule activities in both models can be dynamically determined in context, and these activity spectra exhibit between-class patterns and within-class variations. Our models offer a novel approach to visualizing and understanding the hidden states.
On the Origins and Basic Aspects of User-Centered Design and User Experience

Over a decade after the iPhone was first commercialized, screen-based tactile interfaces have become our primary means for interacting with computational technologies; although thanks to recent developments in Machine Learning (ML), gesture and voice control mechanisms will become more common. Smart technologies and what we end up defining and recognizing as robots will determine the practical principles, and the type of experience designers will be able to shape. To understand where the future of User-Centered Design and User Experience will take us, we need to understand how we got to where we are. The main goal of this chapter is clarifying what may be understood by User-Centered Design and User Experience. To do so, it will look at the period between the late 1960s and early 1980s, when Personal Computing and the ensuing democratization of technology forced designers to think about their users honestly.
PRECEPT

With the increasing number of low-cost sensing modalities, bulk amount of spatial and temporal data is collected and accumulated from building systems. Substantial information could be extracted about occupant behavior and actions from the data gathered. Understanding the data provides an opportunity to decode movement patterns, circulation-flow i.e. how an occupant tends to move inside the building and extract occupant presence impressions. Occupant Presence can be defined as digital traces of spatial coordinates (x,y) of an occupant at a particular instant that moves within the monitored space and is represented by a chronologically ordered sequence of those position coordinates. This study analyzes the occupant presence inside a building and makes predictions on the next location, i.e., where an occupant possibly could be in the future. This paper introduces a predictive model for occupancy presence prediction using the data collected from an instrumented commercial building spanning for over 30 days - May 2019 to June 2019. The proposed prediction model named PRECEPT - is a variant of Recurrent Neural Network known as Gated Recurrent Unit (GRU) Network. PRECEPT is capable of learning mobility patterns and predict presence impressions based on the occupant's past spatial coordinates. We evaluate the performance of PRECEPT on a dataset using metrics such as Mean Squared Error (MSE) and Mean Absolute Error (MAE) for each training epoch. The model results in a Root Mean Squared Error (RMSE) value of 4.79 centimeters for a single occupant. We also illustrate how the prediction model can be used for the task of identifying important zones and extract unique space-usage patterns. This could further assist the Building Management System (BMS) authorities to reduce energy wastage and perform efficient HVAC control and intelligent building operations.
From Sensor-Space to Eigenspace – A Novel Real-Time Obstacle Avoidance Method for Mobile Robots

This paper presents a novel real-time obstacle avoidance and navigation technique called as “Free-configuration Eigenspace” (FCE). The FCE enables an autonomous robot to detect unknown obstacles and avoid collisions while simultaneously steering the robot towards the target. The methodology utilizes a two-dimensional (2D) Cartesian Eigenspace as a world model. 2D way points (in world model) are extracted by computing (through FCE model) stacked Eigenvectors of laser data at discrete time scans which manifest desired robotic trajectory. The world model is updated continuously upon obtaining discrete time laser scans sampled by on-board Light Detection and Ranging (LiDAR) sensors. The FCE technique has been implemented on Robotic Operating System and real-time robotics simulator Gazebo®. Encouraging results are obtained and shown in this paper. The FCE has also been tested in real-time with laser scans obtained from VL6180X LiDAR sensor mounted on a custom autonomous robot. The results obtained are further compared with the state-of-the-art obstacle avoidance and path planning method called as vector field histogram (VFH). The FCE results (shown in the paper) are comparable, encouraging, and outperform VFH in path length and safe distance maintenance.
FreeD∗: a mechanism for finding a short and collision free path

Path planning is extensively used in different fields not only in robotics but also in games, manufacturing, auto-motive applications, and so on. Robot path planning is one of the major research issues in the area of autonomous mobile robot. The critical step in path planning problem is to find the shortest path from the start position to a defined goal position through a known, unknown, or partially known environment. Hazardous events that may devastate some parts of the intended area convert those areas to untraversable areas. These events introduce topological constraints for the robot motion because of information discrepancy about the environment before and after the damage. In this study, the authors propose a novel method, FreeD∗, to find the shortest path by exploiting the benefits of D∗, Dijkstra, and artificial potential field (APF) algorithms. The generated path using D∗ is optimised using Dijkstra by combining D∗ sub-paths into a single diagonal path if there is no known obstacle between them. Then, APF is used in unknown obstacle avoidance. The simulation results using Webots simulator demonstrate the effectiveness of FreeD∗ in avoiding unknown obstacles with shortest path.
Comparing General and Locally-Learned Word Embeddings for Clinical Text Mining

Dense vector-representations of words, referred to as word embeddings, capture word relations differently depending on the type and the size of the corpus they are trained on. Choosing which word embeddings to use poses a problem when applying machine learning on clinical text which contains many specialized words. In this work, we explore the effects of different embedding sources for clinical text classification using various cohort sizes on three medical prediction tasks: Clostridium Difficile infections, MRSA infections, and in-hospital mortality. We compare three embedding sources: pre-trained embeddings from large and general corpora, pre-trained embeddings from large and domain-related corpora, and locally-learned embeddings trained on the task-specific training data. We experiment with several cohort sizes ranging from 20 patients to 2,500 patients. Our results indicate that pre-trained domain-related embeddings are superior for medium-sized cohorts greater than 150 patients, while locally-learned embeddings become increasingly competitive as cohort size grows.