Reviewerly/exHarmony huggingface dataset:
Deep Neural Architectures for Highly Imbalanced Data in Bioinformatics

In the postgenome era, many problems in bioinformatics have arisen due to the generation of large amounts of imbalanced data. In particular, the computational classification of precursor microRNA (pre-miRNA) involves a high imbalance in the classes. For this task, a classifier is trained to identify RNA sequences having the highest chance of being miRNA precursors. The big issue is that well-known pre-miRNAs are usually just a few in comparison to the hundreds of thousands of candidate sequences in a genome, which results in highly imbalanced data. This imbalance has a strong influence on most standard classifiers and, if not properly addressed, the classifier is not able to work properly in a real-life scenario. This work provides a comparative assessment of recent deep neural architectures for dealing with the large imbalanced data issue in the classification of pre-miRNAs. We present and analyze recent architectures in a benchmark framework with genomes of animals and plants, with increasing imbalance ratios up to 1:2000. We also propose a new graphical way for comparing classifiers performance in the context of high-class imbalance. The comparative results obtained show that, at a very high imbalance, deep belief neural networks can provide the best performance.
An Outlier Detection Approach Based on Improved Self-Organizing Feature Map Clustering Algorithm

Local Outlier Factor (LOF) outlier detecting algorithm has good accuracy in detecting global and local outliers. However, the algorithm needs to traverse the entire dataset when calculating the local outlier factor of each data point, which adds extra time overhead and makes the algorithm execution inefficient. In addition, if the K-distance neighborhood of an outlier point P contains some outliers that are incorrectly judged by the algorithm as normal points, then P may be misidentified as normal point. To solve the above problems, this paper proposes a Neighbor Entropy Local Outlier Factor (NELOF) outlier detecting algorithm. Firstly, we improve the Self-Organizing Feature Map (SOFM) algorithm and use the optimized SOFM clustering algorithm to cluster the dataset. Therefore, the calculation of each data point's local outlier factor only needs to be performed inside the small cluster. Secondly, this paper replaces the K-distance neighborhood with relative K-distance neighborhood and utilizes the entropy of relative K neighborhood to redefine the local outlier factor, which improves the accuracy of outlier detection. Experiments results confirm that our optimized SOFM algorithm can avoid the random selection of neurons, and improve clustering effect of traditional SOFM algorithm. In addition, the proposed NELOF algorithm outperforms LOF algorithm in both accuracy and execution time of outlier detection.
Comparing Spatial and Mobile Augmented Reality for Guiding Assembling Procedures with Task Validation

Assembly tasks are a common situation in many industrial applications. These tasks are often presented on paper or digital manuals containing instructions, photos or diagrams to guide an assembly sequence. While some Augmented Reality (AR) systems have also been proposed to support these processes, only a few track the state of the assembling procedure, validating the process in real-time. In this work, we propose two different AR-based (mobile and spatial AR) methods with real-time validation to provide assistance to users during the execution of an assembly process. The validation process uses computer vision techniques to keep track of the state of the assembly sequence, verifying the completion of each stage and providing information at the end of the assembly. A controlled experiment was used to compare the performance, ease of use, and acceptance of the two AR-based methods proposed. Participants were significantly faster and made fewer errors using the Spatial AR condition. Besides, participants also preferred this condition. In addition, Nasa TLX rating showed that the Spatial AR condition had a slightly lower cognitive load on the participants.
Quality-aware strategies for optimizing ABR video streaming QoE and reducing data usage

Streaming videos over cellular networks is highly challenging. Since cellular data is a relatively scarce resource, many video and network providers offer options for users to exercise control over the amount of data consumed by video streaming. Our study shows that existing data saving practices for Adaptive Bitrate (ABR) videos are suboptimal: they often lead to highly variable video quality and do not make the most effective use of the network bandwidth. We identify underlying causes for this and propose two novel approaches to achieve better tradeoffs between video quality and data usage. The first approach is Chunk-Based Filtering (CBF), which can be retrofitted to any existing ABR scheme. The second approach is QUality-Aware Data-efficient streaming (QUAD), a holistic rate adaptation algorithm that is designed ground up. We implement and integrate our solutions into two video player platforms (dash.js and ExoPlayer), and conduct thorough evaluations over emulated/commercial cellular networks using real videos. Our evaluations demonstrate that compared to the state of the art, the two proposed schemes achieve consistent video quality that is much closer to the user-specified target, lead to far more efficient data usage, and incur lower stalls.
Scaling Up Subgraph Query Processing with Efficient Subgraph Matching

A subgraph query finds all data graphs in a graph database each of which contains the given query graph. Existing work takes the indexing-filtering-verification (IFV) approach to first index all data graphs, then filter out some of them based on the index, and finally test subgraph isomorphism on each of the remaining data graphs. This final test of subgraph isomorphism is a sub-problem of subgraph matching, which finds all subgraph isomorphisms from a query graph to a data graph. As such, in this paper, we study whether, and if so, how to utilize efficient subgraph matching to improve subgraph query processing. Specifically, we modify leading subgraph matching algorithms and integrate them with top-performing subgraph querying algorithms. Our results show that (1) the slow verification method in existing IFV algorithms can lead us to over-estimate the gain of filtering; and (2) our modified subgraph querying algorithms with efficient subgraph matching are competitive in time performance and can scale to hundreds of thousands of data graphs and graphs of thousands of vertices.
Group-based whale optimization algorithm

Meta-heuristic algorithms are divided into two categories: biological and non-biological. Biological algorithms are divided into evolutionary and swarm-based intelligence, where the latter is divided into imitation based and sign based. The whale algorithm is a meta-heuristic biological swarm-based intelligence algorithm (based on imitation). This algorithm suffers from the early convergence problem which means the population convergences early to an unfavorable optimum point. Usually, the early convergence occurs because of the weakness in exploration capability (global search). In this study, an optimized version of the whale algorithm is proposed that introduces a new idea in grouping of whales (called GWOA) to overcome the early convergence problem. The proposed whale optimization algorithm is compared with the standard whale algorithm (WOA), CWOA improved whale algorithm, particle swarm optimization, and BAT algorithms applying CEC2017 functions. The results of the experiments show that the proposed method applying Friedman’s test on 30 standard benchmark functions has a better performance than the other baseline algorithms.
Near support-free multi-directional 3D printing via global-optimal decomposition

In 3D printing, it is critical to use as few as possible supporting materials for efficiency and material saving. Multiple model decomposition methods and multi-DOF (degrees of freedom) 3D printers have been developed to address this issue. However, most systems utilize model decomposition and multi-DOF independently. Only a few existing approaches combine the two, i.e. partitioning the models for multi-DOF printing. In this paper, we present a novel model decomposition method for multi-directional 3D printing, allowing consistent printing with the least cost of supporting materials. Our method is based on a global optimization that minimizes the surface area to be supported for a 3D model. The printing sequence is determined inherently by minimizing a single global objective function. Experiments on various complex 3D models using a five-DOF 3D printer have demonstrated the effectiveness of our approach.
Online Visual Tracking of Weighted Multiple Instance Learning via Neutrosophic Similarity-Based Objectness Estimation

An online neutrosophic similarity-based objectness tracking with a weighted multiple instance learning algorithm (NeutWMIL) is proposed. Each training sample is extracted surrounding the object location, and the distribution of these samples is symmetric. To provide a more robust weight for each sample in the positive bag, the asymmetry of the importance of the samples is considered. The neutrosophic similarity-based objectness estimation with object properties (super straddling) is applied. The neutrosophic theory is a new branch of philosophy for dealing with incomplete, indeterminate, and inconsistent information. By considering the surrounding information of the object, a single valued neutrosophic set (SVNS)-based segmentation parameter selection method is proposed, to produce a well-built set of superpixels which can better explain the object area at each frame. Then, the intersection and shape-distance criteria are proposed for weighting each superpixel in the SVNS domain, mainly via three membership functions, T (truth), I (indeterminacy), and F (falsity), for each criterion. After filtering out the superpixels with low response, the newly defined neutrosophic weights are utilized for weighting each sample. Furthermore, the objectness estimation information is also applied for estimating and alleviating the problem of tracking drift. Experimental results on challenging benchmark video sequences reveal the superior performance of our algorithm when confronting appearance changes and background clutters.
T-Gaming: A Cost-Efficient Cloud Gaming System at Scale

Cloud gaming (CG) system could pursue both high-quality gaming experience via intensive computing, and ultimate convenience anywhere at anytime through any energy-constrained mobile devices. Despite the abundance of efforts devoted, state-of-the-art CG systems still suffer from multiple key limitations: expensive deployment cost, high bandwidth consumption and unsatisfied quality of experience (QoE). As a result, existing works are not widely adopted in reality. This paper proposes a Transparent Gaming framework called T-Gaming that allows users to play any popular high-end desktop/console games on-the-fly over the Internet. T-Gaming utilizes the off-the-shelf consumer GPUs without resorting to the expensive proprietary GPU virtualization (vGPU) technology to reduce the deployment cost. Moreover, it enables prioritized video encoding based on the human visual feature to reduce the bandwidth consumption without noticeable visual quality degradation. Last but not least, T-Gaming adopts adaptive real-time streaming based on deep reinforcement learning (RL) to improve user's QoE. To evaluate the performance of T-Gaming, we implement and test a prototype system in the real world. Compared with the existing cloud gaming systems, T-Gaming not only reduces the expense per user by 75 percent hardware cost reduction and 14.3 percent network cost reduction, but also improves the normalized average QoE by 3.6-27.9 percent.
Chatbot Assisted Marketing in Financial Service Industry

The rise of chatbots in the finance sector is the latest disruptive force that has change the way customers interact. The adoption of Artificial Intelligence powered chatbots particularly in the banking industry has changed the face of communication interface between bank and customers. This paper explores the effectiveness of the current use of chatbot in Singapore’s banking industry. The banking sector in Singapore play a significant role in Singapore economy. It also investigates the current chatbot functionality to determine if it can meet the ever-changing expectation of customers.
Match Me if You Can: Matchmaking Encryption and Its Applications

We introduce a new form of encryption that we name matchmaking encryption (ME). Using ME, sender S and receiver R (each with its own attributes) can both specify policies the other party must satisfy in order for the message to be revealed. The main security guarantee is that of privacy-preserving policy matching: During decryption nothing is leaked beyond the fact that a match occurred/did not occur. ME opens up new ways of secretly communicating, and enables several new applications where both participants can specify fine-grained access policies to encrypted data. For instance, in social matchmaking, S can encrypt a file containing his/her personal details and specify a policy so that the file can be decrypted only by his/her ideal partner. On the other end, a receiver R will be able to decrypt the file only if S corresponds to his/her ideal partner defined through a policy. On the theoretical side, we define security for ME, as well as provide generic frameworks for constructing ME from functional encryption. These constructions need to face the technical challenge of simultaneously checking the policies chosen by S and R, to avoid any leakage. On the practical side, we construct an efficient identity-based scheme for equality policies, with provable security in the random oracle model under the standard BDH assumption. We implement and evaluate our scheme and provide experimental evidence that our construction is practical. We also apply identity-based ME to a concrete use case, in particular for creating an anonymous bulletin board over a Tor network.
General models for estimating daily and monthly mean daily diffuse solar radiation in China's subtropical monsoon climatic zone

Solar energy is the most prominent kind of renewable energy resource. Accessibility to accurate information on diffuse solar radiation is critical to solar energy applications. This study primarily aims to present a general approach for estimating diffuse solar radiation in China's subtropical monsoon climatic zone. For this purpose, 46 existing empirical models in eight sub-categories were investigated for estimating diffuse solar radiation. Long-term, continuously measured solar radiation and other meteorological data were used for calibration and validation models. General models on both daily and monthly mean daily bases were established from the best-performing models. Among the daily basis models, FCS3 exhibited superiority as compared to the other models with the lowest average MAE, RMSE, U95, MAPE, and the largest average R2 of 0.854 MJ/m2, 1.376 MJ/m2, 3.658, 10.924%, and 0.888, respectively. The best overall performance on a monthly mean daily basis was obtained by CCS3 with corresponding statistical indicators of 0.569 MJ/m2, 0.723 MJ/m2, 1.866, 8.870%, and 0.938. Moreover, the percentage gap between the general and best site-dependent model performances was lower than 20% for most of the statistical indicators. Overall, the general models could be used to estimate the diffuse solar radiation in areas without diffuse solar radiation records.
Collecting Health Lifelog Data From Smartwatch Users in a Privacy-Preserving Manner

With the development of easily wearable devices for humans, monitoring, and collecting lifelog data related to personal health status is easier than ever before. The advances in such wearable devices allow healthcare service providers to easily collect a vast amount of health lifelogs from diverse users for the purpose of data analysis. However, collecting health information of individual users indiscriminately may lead to serious privacy issues, because health lifelog data usually contain sensitive information. Thus, in this paper, we develop methods capable of collecting sensitive health lifelogs from a smartwatch, which is the most popular wearable device, while protecting the data privacy of smartwatch users. Experimental results show that the proposed approach can achieve an effective tradeoff between the degree of privacy protection and the accuracy in aggregate statistics. A correlation coefficient with an absolute value ranging from 0.808 to 0.945 between the degree of privacy protection and the accuracy in aggregate statistics can be accomplished using the proposed methods.
A parameter-free particle swarm optimization algorithm using performance classifiers

This paper presents an investigation into the short-term versus long-term performance of various particle swarm optimization (PSO) control parameter configurations. While evidence suggests that the best PSO parameter values to employ are time-dependent, this paper provides an in-depth examination of a small set of parameter values to provide a more concrete quantification of the performance degradation observed with specific control parameter configurations over time. Given that the short-term performance is not necessarily indicative of long-term performance, this paper proposes that machine learning techniques be used to build predictive models based on two easily-observable landscape characteristics. Finally, using the predictive models as a basis, this paper also proposes a parameter-free PSO algorithm, which performs on par with other top-performing PSO variants, namely the three best performing static PSO configurations, particle swarm optimization with time-varying acceleration coefficients (PSO-TVAC), and particle swarm optimization with improved random constants (PSO-iRC).
Pattern Mining from big IoT Data with fog Computing: Models, Issues, and Research Perspectives

As we are living in the era of big data, huge volumes of a wide variety of complex data-which can be of different levels of veracity-are generated or collected at a high velocity from rich sources of data in various real-life applications. A rich source of these big data sources is the Internet of Things (IoT), which include a collection of sensors, smartphones and other mobile devices, wearable devices, as well as other "things" that are capable to operate within the existing Internet infrastructure. Embedded in these big data are valuable knowledge and useful information. Hence, the research problem of data mining from big IoT data have drawn attention of many researchers as it aims to discover implicit, previously unknown and potentially useful information and knowledge from the data. For instance, frequent pattern mining finds sets of frequently co-occurring items in the IoT domains. Associative classification discovers rules revealing relationships among items within the frequent patterns and their associations with the corresponding class labels. Induction based classification uses decision tree or random forest to learn from old big IoT for classifying or making predictions on new data. Over the past quarter of a century, many serial, distributed, parallel, and MapReduce-based (Hadook-based and Spark-based) big data mining algorithms have been proposed. These algorithms are run in local computers, distributed and parallel environments, clusters, grids, clouds and/or data centers. In this paper, we review some of these algorithms, discuss issues and research prospective in mining classification patterns from these big IoT data in fog. Our case study on a real-life application shows the feasibility on classifying real-life big IoT data over fog for urban analytics.
On the importance of standardising the process of generating digital forensic reports

The ISO/IEC 27043:2015 international standard provides new standardised guidelines for common investigation processes across various investigation scenarios that mostly involve digital evidence. The reporting process is one of the many investigative processes described in the ISO/IEC 27043:2015 standard, but the manner in which the reporting process is presented does not constitute or cover the specificity of the presentation of the entire processes covered in the standard. In this paper, we posit the importance of having the report generation process covering details obtained from all other classes of the digital investigation processes in a standardised format, as well as the need to standardise the process of generating digital forensic reports. Such a standardised process can facilitate future automation and text analytics, sharing of reports and knowledge across jurisdictions, etc. We also identify a number of key factors, such as the use of Blockchain, which should be added to the ISO/IEC 27043 international standard in order to support a standardised digital forensic report generation process.
Accurate and efficient 3D hand pose regression for robot hand teleoperation using a monocular RGB camera

In this paper, we present a novel deep learning-based architecture, which is under the scope of expert and intelligent systems, to perform accurate real-time tridimensional hand pose estimation using a single RGB frame as an input, so there is no need to use multiple cameras or points of view, or RGB-D devices. The proposed pipeline is composed of two convolutional neural network architectures. The first one is in charge of detecting the hand in the image. The second one is able to accurately infer the tridimensional position of the joints retrieving, thus, the full hand pose. To do this, we captured our own large-scale dataset composed of images of hands and the corresponding 3D joints annotations. The proposal achieved a 3D hand pose mean error of below 5 mm on both the proposed dataset and Stereo Hand Pose Tracking Benchmark, which is a public dataset. Our method also outperforms the state-of-the-art methods. We also demonstrate in this paper the application of the proposal to perform a robotic hand teleoperation with high success.
Implicit meteorological parameter-based empirical models for estimating back temperature solar modules under varying tilt-angles in Lagos, Nigeria

In this study, measured back temperature of solar modules tilted at angles 6.7°, 16.8°, 26.8° and 0° respectively were used to develop requisite models for estimating this entity for solar modules used in Lagos (Latitude 6.6080oN and Longitude 3.6218oE) Nigeria. Overall solar radiation received by the solar modules showed an appreciable energy gain of 8.74%, 20.85% and 19.49% respectively over that generated by the horizontally placed module. A thorough statistical performance analysis of the obtained data yielded twelve models for each of the solar modules, a total of forty-eight models in all. These models were tested to know which that gave the most accurate regression model for estimating the back temperature in the environment. Application of statistical indicators such as MBE, MPE, RMSE, RRMSE, R2 and GPI showed that model 8 for the module tilted at 16.8° registered the most performance followed by model 19 for module tilted at 6.7°, model 28 for module tilted at 26.8° and lastly, model 44 for module oriented at 0°. Similar analysis carried out on the established models for the four modules in comparison with existing models, showed that model 44 performed excellently in terms of accuracy compared to other models.