Reviewerly/exHarmony huggingface dataset:
Online sequential extreme learning machine in river water quality (turbidity) prediction: a comparative study on different data mining approaches

Abstract As a measure of water quality, water turbidity might be a source of water pollution in drinking water resources. Henceforth, having a reliable tool for predicting turbidity values based on common water quantity/quality measured parameters is of great importance. In the present paper, the performance of the online sequential extreme learning machine (OS‐ELM) in predicting daily values of turbidity in Brandywine Creek, Pennsylvania, is evaluated. For this purpose, in addition to the developed OS‐ELM, several data‐driven models, that is, multilayer perceptron neural network (MLPANN), the classification and regression tree (CART), the group method of data handling (GMDH) and the response surface method (RSM) have been applied. The general findings of the study confirm the superiority of the OS‐ELM model over the other applied models so that the OS‐ELM improved the averaged RMSE of the predicted values 9.1, 11.7, 20.5 and 29.3% over the MLPANN, GMDH, RSM and CART models, respectively.
SASL: Saliency-Adaptive Sparsity Learning for Neural Network Acceleration

Accelerating the inference of CNNs is critical to their deployment in real-world applications. Among all pruning approaches, the methods of implementing a sparsity learning framework have shown effectiveness as they learn and prune the models in an end-to-end data-driven manner. However, these works impose the same sparsity regularization on all filters indiscriminately, which can hardly result in an optimal structure-sparse network. In this paper, we propose a Saliency-Adaptive Sparsity Learning (SASL) approach for further optimization. A novel and effective estimation of each filter, i.e., saliency, is designed, which is measured from two aspects: the importance for prediction performance and the consumed computational resources. During sparsity learning, the regularization strength is adjusted according to the saliency, so our optimized format can better preserve the prediction performance while zeroing out more computation-heavy filters. The calculation for saliency introduces minimum overhead to the training process, which means our SASL is very efficient. During the pruning phase, in order to optimize the proposed data-dependent criterion, a hard sample mining strategy is utilized, which shows higher effectiveness and efficiency. Extensive experiments demonstrate the superior performance of our method. Notably, on ILSVRC-2012 dataset, our approach can reduce 49.7% FLOPs of ResNet-50 with very negligible 0.39% top-1 and 0.05% top-5 accuracy degradation.
Feature-Level Rating System Using Customer Reviews and Review Votes

This work studies how we can obtain feature-level ratings of the mobile products from the customer reviews and review votes to influence decision-making, both for new customers and manufacturers. Such a rating system gives a more comprehensive picture of the product than what a product-level rating system offers. While product-level ratings are too generic, feature-level ratings are particular; we exactly know what is good or bad about the product. There has always been a need to know which features fall short or are doing well according to the customer’s perception. It keeps both the manufacturer and the customer well-informed in the decisions to make in improving the product and buying, respectively. Different customers are interested in different features. Thus, feature-level ratings can make buying decisions personalized. We analyze the customer reviews collected on an online shopping site (Amazon) about various mobile products and the review votes. Explicitly, we carry out a feature-focused sentiment analysis for this purpose. Eventually, our analysis yields ratings to 108 features for 4000+ mobiles sold online. It helps in decision-making on how to improve the product (from the manufacturer’s perspective) and in making the personalized buying decisions (from the buyer’s perspective) a possibility. Our analysis has applications in recommender systems, consumer research, and so on.
Designing Leakage-Resilient Password Entry on Head-Mounted Smart Wearable Glass Devices

With the boom of Augmented Reality (AR) and Virtual Reality (VR) applications, head-mounted smart wearable glass devices are becoming popular to help users access various services like E-mail freely. However, most existing password entry schemes on smart glasses rely on additional computers or mobile devices connected to smart glasses, which require users to switch between different systems and devices. This may greatly lower the practicability and usability of smart glasses. In this paper, we focus on this challenge and design three practical anti-eavesdropping password entry schemes on stand-alone smart glasses, named gTapper, gRotator and gTalker. The main idea is to break the correlation between the underlying password and the interaction observable to adversaries. In our IRB-approved user study, these schemes are found to be easy-to-use without additional hardware under various test conditions, where the participants can enter their passwords within moderate time, at high accuracy, and in various situations.
Application of Imbalanced Data Classification Quality Metrics as Weighting Methods of the Ensemble Data Stream Classification Algorithms

In the era of a large number of tools and applications that constantly produce massive amounts of data, their processing and proper classification is becoming both increasingly hard and important. This task is hindered by changing the distribution of data over time, called the concept drift, and the emergence of a problem of disproportion between classes-such as in the detection of network attacks or fraud detection problems. In the following work, we propose methods to modify existing stream processing solutions-Accuracy Weighted Ensemble (AWE) and Accuracy Updated Ensemble (AUE), which have demonstrated their effectiveness in adapting to time-varying class distribution. The introduced changes are aimed at increasing their quality on binary classification of imbalanced data. The proposed modifications contain the inclusion of aggregate metrics, such as F1-score, G-mean and balanced accuracy score in calculation of the member classifiers weights, which affects their composition and final prediction. Moreover, the impact of data sampling on the algorithm's effectiveness was also checked. Complex experiments were conducted to define the most promising modification type, as well as to compare proposed methods with existing solutions. Experimental evaluation shows an improvement in the quality of classification compared to the underlying algorithms and other solutions for processing imbalanced data streams.
A new path planning method of mobile robot based on adaptive dynamic firefly algorithm

Path planning has always been a hot topic in the field of mobile robot research. At present, the mainstream issues of the mobile robot path planning are combined with the swarm intelligence algorithms. Among them, the firefly algorithm is more typical. The firefly algorithm has the advantages of simple concepts and easy implementation, but it also has the disadvantages of being easily trapped into a local optimal solution, with slow convergence speed and low accuracy. To better combine the path planning of mobile robot with firefly algorithm, this paper studies the optimization firefly algorithm for the path planning of mobile robot. By using the strategies of optimizing the adaptive parameters in the firefly algorithm, an adaptive firefly algorithm is designed to solve the problem that the firefly algorithm is easy to get into the local optimal solution and improves the performance of firefly algorithm. The optimized algorithm with high performance can improve the computing ability and reaction speed of the mobile robot in the path planning. Finally, the theoretical and experimental results have verified the effectiveness and superiority of the proposed algorithm, which can meet the requirements of the mobile robot path planning.
Importance of Big Data in Precision and Personalized Medicine

The rapidly increasing adaptation of Big data technologies in biomedicine, has introduced a revolution in and medical research practice. Trending high-throughput data analysis techniques, have converted the appearance of the biological system to acquire idolization methods for complicated diseases. Majority of the acquired Big-data models govern the materialization of illustrating medicine. This transformation aims at quantification of the period of P4 medicine that will then progressively be more predictive, personalized, pre-emptive, and participatory. It layouts a track to modernize antiseptic methods for the patient's concern center. P4 medicine besides being a scientific face of systems medicine has two highlighted purposes first of which is to evaluate wellness, while the other is, to identify and expose disease. Patients are major operators in the cognizance of P4 medicine as they directly get engaged with a medically familiar network that helps them boost their health. This article will discuss the maturity in big data planning and correlated challenges in biomedicine.
Haze Removal: Push DCP at the Edge

In the task of haze scene restoration, dark channel prior is the most convincing theory. Its biggest contribution is to introduce the concept of dark channel map, which is convenient for acquiring initial transmission. Minimum filter is the basic operation for dark channel map. Nevertheless, the disadvantage of minimum filter is to cause edge information jump, which further brings Halo effect. Besides, dark channel prior is invalid for highlight areas such as white objects, and it also causes distortion. In this letter, we design a delicate function to replace minimum filter operation, and its purpose is to fundamentally avoid the errors of edge assignment. The function includes two pieces: power-law transform compresses low-luminance regions, and linear attenuation retains original characteristics of bright parts in the image. Additionally, we use logarithmic compression to simulate dark channel map of haze-free image and receive a polishing transmission. Compared with other state-of-the-art algorithms on both synthetic datasets and real-world images, our algorithm shows favorable performance for haze removal.
GPU-Enabled Shadow Casting for Solar Potential Estimation in Large Urban Areas. Application to the Solar Cadaster of Greater Geneva

In the context of encouraging the development of renewable energy, this paper deals with the description of a software solution for mapping out solar potential in a large scale and in high resolution. We leverage the performance provided by Graphics Processing Units (GPUs) to accelerate shadow casting procedures (used both for direct sunlight exposure and the sky view factor), as well as use off-the-shelf components to compute an average weather pattern for a given area. Application of the approach is presented in the context of the solar cadaster of Greater Geneva (2000 km2). The results show that doing the analysis on a square tile of 3.4 km at a resolution of 0.5 m takes up to two hours, which is better than what we were achieving with the previous work. This shows that GPU-based calculations are highly competitive in the field of solar potential modeling.
Apps, maps and war rooms: on the modes of existence of “COVtech” in India

This paper on the SARS-CoV-2 crisis in India examines the emergence of ”COVtech” as a range of public sector-led technologies of surveillance, management and containment of the virus. COVtech lends itself to examination of what Simondon calls ”concretisation” of technical objects. We argue that COVtech initiatives have different modes of existence in India – first as the redeployment of existing technologies assembled across urban, regional and federal scales of governance; second, as current technologies curated within translocally situated networks which are mobilised to provide relief and support to vulnerable groups locked out/in Indian cities. We conclude by proposing that a progressive mode of existence of COVtech rests not with the ‘what’ and ‘where’ of the disease monitored by the apps, maps and War rooms of the State, but in addressing the ‘who’ and ‘how’ of its impacts mobilised by civil society and non-state actors.
Unbalanced data processing using oversampling: Machine Learning

Nowadays, the DL algorithms show good results when used in the solution of different problems which present similar characteristics as the great amount of data and high dimensionality. However, one of the main challenges that currently arises is the classification of high dimensionality databases, with very few samples and high-class imbalance. Biomedical databases of gene expression microarrays present the characteristics mentioned above, presenting problems of class imbalance, with few samples and high dimensionality. The problem of class imbalance arises when the set of samples belonging to one class is much larger than the set of samples of the other class or classes. This problem has been identified as one of the main challenges of the algorithms applied in the context of Big Data. The objective of this research is the study of genetic expression databases, using conventional methods of sub and oversampling for the balance of classes such as RUS, ROS and SMOTE. The databases were modified by applying an increase in their imbalance and in another case generating artificial noise.
SketchFlow: Per-Flow Systematic Sampling Using Sketch Saturation Event

Sampling is a powerful tool to reduce the processing overhead in various systems. NetFlow uses a local table for counting records per flow, and sFlow sends out the collected packet headers periodically to a collecting server over the network. Any measurement system falls into either one of these two models. To reduce the overhead, as in sFlow, simple random sampling (SRS) has been widely used in practice because of its simplicity. However, SRS provides non-uniform sampling rates for different fine-grained flows (defined by 5-tuple), because it samples packets over an aggregated data flow (defined by switch port or VLAN). Consequently, some flows are sampled more than the designated sampling rate (resulting in over-estimation), and others are sampled fewer (resulting in under-estimation). Starting with a simple idea that "independent per-flow packet sampling provides the most accurate estimation of each flow", we introduce a new concept of per-flow systematic sampling, aiming to provide the same sampling rate across all flows. In addition, we provide a concrete sampling method called SketchFlow, which approximates the idea of the per-flow systematic sampling using a sketch saturation event. We demonstrate SketchFlow's performance in terms of accuracy, sampling rate, and overhead using real-world datasets, including a backbone network trace, I/O trace, and Twitter dataset. Experimental results show that SketchFlow outperforms SRS (i.e., sFlow) and the non-linear sampling method while requiring a small CPU overhead to measure high-speed traffic in real-time.
Deeper Exercise Monitoring for Smart Gym using Fused RFID and CV Data

Individual activity recognition is crucial for Human-Computer Interaction (HCI) applications, especially in multi-person scenarios. Current approaches, based on wearable sensors or wireless signals (e.g., WiFi and RFID), however, are often focused on single person scenario only, due to the limitation of existing wireless sensing technologies. In order to address the issue, we design a DEeper Exercise Monitoring system, called DEEM, in which we introduce computer vision techniques to facilitate RFID devices to provide exercise estimation support, as well as identifying the users and the objects users hold. We implement this design with COTS Kinect camera and RFID devices in a smart gym application. To the best of our knowledge, it is the first system for estimating multiple people behavior in a complicated gym environment. We conduct extensive experiments to evaluate the performance of the DEEM system. The experimental results show that the matching accuracy can reach 95%, and the exercise estimation accuracy can reach 94% on average.
No one is perfect: Analysing the performance of question answering components over the DBpedia knowledge graph

Question answering (QA) over knowledge graphs has gained significant momentum over the past five years due to the increasing availability of large knowledge graphs and the rising importance of Question Answering for user interaction. Existing QA systems have been extensively evaluated as black boxes and their performance has been characterised in terms of average results over all the questions of benchmarking datasets (i.e. macro evaluation). Albeit informative, macro evaluation studies do not provide evidence about QA components' strengths and concrete weaknesses. Therefore, the objective of this article is to analyse and micro evaluate available QA components in order to comprehend which question characteristics impact on their performance. For this, we measure at question level and with respect to different question features the accuracy of 29 components reused in QA frameworks for the DBpedia knowledge graph using state-of-the-art benchmarks. As a result, we provide a perspective on collective failure cases, study the similarities and synergies among QA components for different component types and suggest their characteristics preventing them from effectively solving the corresponding QA tasks. Finally, based on these extensive results, we present conclusive insights for future challenges and research directions in the field of Question Answering over knowledge graphs.
Evaluating the quality of linked open data in digital libraries

Cultural heritage institutions have recently started to share their metadata as Linked Open Data (LOD) in order to disseminate and enrich them. The publication of large bibliographic data sets as LOD is a challenge that requires the design and implementation of custom methods for the transformation, management, querying and enrichment of the data. In this report, the methodology defined by previous research for the evaluation of the quality of LOD is analysed and adapted to the specific case of Resource Description Framework (RDF) triples containing standard bibliographic information. The specified quality measures are reported in the case of four highly relevant libraries.
Work-in-Progress—Learning about Virtual Worlds in Virtual Worlds: How Remote Learning in a Pandemic Can Inform Future Teaching

In response to the COVID-19 pandemic, many university classes shifted to remote learning. In some cases, this requirement intersected in productive ways with course content, especially when the class subject matter included virtual environments. This paper describes teacher and student experiences in an upper-level communication course on virtual worlds at a large United States university. As regular lectures moved to video conferencing and asynchronous communication, "field trips" and guest lectures became opportunities for students and teachers to connect remotely using desktops and headsets. We discuss how this will inform the design of future courses that combine face-to-face and virtual instruction.
A Systematic Study of Tiny YOLO3 Inference: Toward Compact Brainware Processor With Less Memory and Logic Gate

The emerging of deep neural networks, especially the convolutional neural network (CNN), substantially promotes the fast development of brainware processors in object detection. However, the vast network architecture brings severe challenges to the design of brainware processor, which requires a large number of logic gates and memories. Therefore, a compact brainware processor with less memory and logic gate has a high demand in object detection. Typically, the object detection involves single-shot and multi-shot detectors in accordance with different detection principle. In the early stage, the multi-shot detector has a leading role in solving object detection issues, such as region-based convolutional neural networks (R-CNNs), faster R-CNNs etc. However, the multi-shot detector suffers from a low detection rate comparing with the single-shot detector. The you only look once (YOLO) algorithm, as the state-of-the-art real-time object detection algorithm, receives extensive attention from the academics and industry. Particularly, the lightweight YOLO algorithm, tiny YOLO3, has excellent potential for circuit design of compact brainware processor. Nonetheless, systematic studies of tiny YOLO3 are still missing up to the present. This paper offers a thorough review of the tiny YOLO3 algorithm, which can fill the gap in the field of object detection. Furthermore, the open solutions of compressing the tiny YOLO3 algorithm are proposed from the aspects of algorithm, hardware and emerging technology. The comprehensive study presented in this paper can not only enhance understanding of the tiny YOLO3 algorithm for researchers or engineers but also make a significant contribution to accelerating the development of compact brainware processor.
Wasmachine: Bring IoT up to Speed with A WebAssembly OS

WebAssembly is a new-generation low-level byte-code format and gaining wide adoption in browser-centric applications. Nevertheless, WebAssembly is originally designed as a general approach for running binaries on any runtime environments more than the web. This paper presents Wasmachine, an OS aiming to efficiently and securely execute WebAssembly applications in IoT and Fog devices with constrained resources. Wasmachine achieves more efficient execution than conventional OSs by compiling WebAssembly ahead of time to native binary and executing it in kernel mode for zero-cost system calls. Wasmachine maintains high security by not only exploiting many sandboxing features of WebAssembly but also implementing the OS kernel in Rust to ensure memory safety. We benchmark commonly-used IoT and fog applications and the results show that Wasmachine is up to 11% faster than Linux.