Reviewerly/exHarmony huggingface dataset:
Fractioning of the polycyclic aromatic hydrocarbons in the components of the non-equilibrium geochemical systems (thermodynamic analysis)

The paper investigates the properties of geochemical systems components (GCS) within the thermodynamic framework. In a departure from previous approaches, the maximum possible number of interconnected media in holistic GCS is analyzed using hierarchical classification procedures. The studied sample covers the maximum possible selection of media, which are interconnected in natural systems in real conditions. The paper reports that complex organic compounds, such as polycyclic aromatic hydrocarbons (PAHs) are highly informative indicators of the properties of substances migration in media. Being geochemical markers, these compounds demonstrate the ability to accumulate on geochemical barriers and selectively move in interacting media, which makes them key objects of study. In addition, the marker role of these compounds draws on their “confinement” to several geochemical processes (natural or industrial) and ability to ultimately identify the source of the pollution. The existing accounts fail to describe these mechanisms of mass transfer. The proposed phenomenological models of migration, transformation, and accumulation of PAHs are based on quantitative estimates, which provide more consistent results compared with the descriptive representations of the behavior of substances. This allowed us to identify the role of studied physicochemical characteristics of PAHs in their accumulation in the environmental media. The paper demonstrates that the most active evolution of GCS occurs in case plant media and, in general, living matter is included in their composition.
Detection of the electric vehicle using thermal characteristics

The main aim of this paper is to compare the thermal features of electric and conventional (equipped with combustion engines) vehicle exteriors. The thermal cameras can complement or substitute visible spectrum video cameras and other conventional sensors and provide rich classification data linked with vehicle type recognition. The thermal images, obtained through an infrared thermography camera, were evaluated. The important differences between thermal features of these types of vehicle exteriors were found out. The results confirmed the hypothesis that it is possible to use an infrared thermography for vehicle drive categorization, collection of traffic data and eventually for traffic management, Smart city implementation and telematics systems in the towns in future as well.
Adaptive Multi-Task Dual-Structured Learning with Its Application on Alzheimer’s Disease Study

Multi-task learning has been widely applied to Alzheimer’s Disease (AD) studies due to its capability of simultaneously rating the disease severity (classification) and predicting corresponding clinical scores (regression). In this article, we propose a novel technique of Adaptive Multi-task Dual-Structured Learning, named AMDSL, by mutually exploring the dual manifold structure for the label and regression score of the disease data under joint classification and regression tasks, while learning an adaptive shared similarity measure and corresponding feature mapping among these two tasks. We encode both the reconstructed label representation and regression score adaptive to the ideal similarity measure on disease data to achieve the ideal performance on these two joint tasks. The alternating algorithm is proposed to optimize the above objective. We theoretically prove the convergence of the optimization algorithm. The superiority of AMDSL is experimentally validated under joint classification and regression as per various evaluation metrics against the most authoritative Alzheimer’s disease data.
Eliciting User Preferences for Personalized Explanations for Video Summaries

Video summaries or highlights are a compelling alternative for exploring and contextualizing unprecedented amounts of video material. However, the summarization process is commonly automatic, non-transparent and potentially biased towards particular aspects depicted in the original video. Therefore, our aim is to help users like archivists or collection managers to quickly understand which summaries are the most representative for an original video. In this paper, we present empirical results on the utility of different types of visual explanations to achieve transparency for end users on how representative video summaries are, with respect to the original video. We consider four types of video summary explanations, which use in different ways the concepts extracted from the original video subtitles and the video stream, and their prominence. The explanations are generated to meet target user preferences and express different dimensions of transparency: concept prominence, semantic coverage, distance and quantity of coverage. In two user studies we evaluate the utility of the visual explanations for achieving transparency for end users. Our results show that explanations representing all of the dimensions have the highest utility for transparency, and consequently, for understanding the representativeness of video summaries.
Introducing a forensics data type taxonomy of acquirable artefacts from programmable logic controllers

The understanding of available data artefacts is fundamental to performing digital forensics. There is good understanding of what data artefacts are acquirable from common information technology (IT) systems such as a Windows operating system and what their potential forensic value could be. As a result, IT forensic investigators can make clear predictions about what information the acquired data would yield. The same level of understanding for programmable logic controllers (PLCs) found within industrial control systems (ICS) is limited. Previous research has restricted the discussion of PLC data to generic and common data formats. This makes it challenging to prepare for incidents proactively, develop new forensic capabilities and prioritise the collection of data should an incident occur. Examples of previous cyber incidents such as Stuxnet and Triton have employed ad-hoc methods for forensic the investigation, highlighting the lack of a systematic approach. This paper examines the specific data types stored on a PLC and describes a forensic artefact taxonomy based on the acquirable data. Data acquisition tests were performed primarily using third-party communication libraries that utilise the PLC's proprietary industrial communications protocol to leverage data stored within memory structures of each of the tested PLCs. Three PLCs, from two different manufacturers were examined. Potential PLC attack scenarios, identified from the literature, were used to guide the evaluation of the acquirable data, categorised into high-level data types, to highlight the potential benefits of acquiring each form of data.
Machine learning for human movement understanding

Main purpose of this project is to develop fundamental technology for assist robots to recover and maintain human motor skill and to extend scope of human activity. Our goal is to provide a system that adapts to its user’s personal behavior patterns in real-time. We aim to develop a continuous collaboration system between the assist robots and the user where both alternatively adjust to each other to maximize the system’s utility. To understand human movement, we recorded motion sequence of several tasks for different subjects using motion capture system. Through analysis of human motion data, we extracted a general model by rule-based approach. On the other hand, since such tasks are not feasible with static models, we investigate the potential benefit of supervised online learning in the task of online action classiﬁcation and Deep Learning in the task of acquiring human motion. Finally, developed system was extended to show its potential effect in ergonomics and in assist robotics.
Real-time Gender Identification from Face Images using you only look once (yolo)

Gender identification is an important area under which the researches are still going on. There are many gender prediction systems made using different architectures. This paper presents a real-time system which can be used for gender prediction from face images. The technique used is You Only Look Once (YOLO) v3 object detection algorithm. Darknet is used for training. Keras and OpenCV are used for testing. The dataset used is a combination of IMDb, Google Indian images and some Indian custom images taken using mobile camera. The test image accuracy was found to be 84.69%. Labelimg is the software used for labeling the face images. The aim of the proposed work is to use the system in case of monitoring, security concerned areas etc.
Heritage Education for Primary Age Through an Immersive Serious Game

Heritage education is an activity that is increasingly present in the educational paths of schools and museums. Cognitive mechanisms and representative devices must be thoroughly analysed and designed in order to promote the creation of didactic paths to foster effective learning experiences. Immersive visualization technologies are well suited for gamification applications and the technological and economic accessibility of VR HMD (head-mounted display) viewers makes these technologies particularly attractive for the development of potentially more widespread methodologies. The present contribution will describe an educational path, and its related experimentation, on the cultural heritage about the production of the typical bread of the Val Pusteria area—and the rural life around it. The project was aimed at primary school children and was based on a serious game in Virtual Immersive Reality.
Scalable build service system with smart scheduling service

Build automation is critical for developers to check if their code compiles, passes all tests and is safe to deploy to the server. Many companies adopt Continuous Integration (CI) services to make sure that the code changes from multiple developers can be safely merged at the head of the project. Internally, CI triggers builds to make sure that the new code change compiles and passes the tests. For any large company which has a monolithic code repository and thousands of developers, it is hard to make sure that all code changes are safe to submit in a timely manner. The reason is that each code change may involve multiple builds, and the company needs to run millions of builds every day to guarantee developers’ productivity.
A Hybrid Feature Selection Approach Based on LSI for Classification of Urdu Text

The feature selection method plays a crucial role in text classification to minimizing the dimensionality of the features and accelerating the learning process of the classifier. Text classification is the process of dividing a text into different categories based on their content and subject. Text classification techniques have been applied to various domains such as medical, political, news, and legal domains, which show that the adaptation of domain-relevant features could improve the classification performance. Despite the existence of plenty of research work in the area of classification in several languages across the world, there is a lack of such work in Urdu due to the shortage of existing resources. In this paper, First, we present a proposed hybrid feature selection approach (HFSA) for text classification of Urdu news articles. Second, we incorporate widely used filter selection approaches along with Latent Semantic Indexing (LSI) to extract essential features of Urdu documents. The hybrid approach tested on the Support Vector Machine (SVM) classifier on Urdu “ROSHNI” dataset. The evaluated results were used to compare with the results obtained by individual filter feature selection methods. Also, the approach is compared to the baseline feature selection method. The proposed approach results show a better classification with promising accuracy and better efficiency.
Traffic Sign Recognition with Neural Networks in the Frequency Domain

Abstract In this paper we describe traffic sign recognition with neural networks in the frequency domain. Traffic signs exist in all countries to regulate the traffic of vehicles and pedestrians. Each country has its own set of traffic signs that are more or less similar. They consist of a set of abstract forms, symbols, numbers and letters, which are combined into different signs. Automatic traffic sign recognition is important for driver assistance systems and for autonomous driving. Traffic sign recognition is a subtype of image recognition. The traffic signs are usually recorded by a camera and must be recognized in real time, i.e. assigned to a class. We use neural networks for traffic sign recognition. The special feature of our method is that the traffic sign recognition does not take place in the spatial domain but in the frequency domain. This has advantages because it is possible to significantly reduce the number of neurons and thus the computing effort of the neural network compared to a conventional neural network.
Towards Situation-Adaptive In-Vehicle Voice Output

Human-machine interaction is increasingly speech-based, with a trend away from the earlier command-based style towards natural, intuitive dialogues based on the human model. A prerequisite is the ability of a Spoken Dialogue System to flexibly react according to individual requirements, e.g., by means of adaptive voice output. The necessity to maximize the efficiency of language interaction through alignment at all linguistic levels becomes particularly relevant in dual-task situations. Here speech represents a secondary task in parallel to a prioritized primary task, such as driving a car. In addition to the individual requirements of a user, the demands of the interaction context need to be considered. For this purpose, it is beneficial to examine the particular characteristics of user language during the performance of a primary task.
Drinking from a Firehose: Continual Learning with Web-scale Natural Language

Continual learning systems will interact with humans, with each other, and with the physical world through time - and continue to learn and adapt as they do. An important open problem for continual learning is a large-scale benchmark which enables realistic evaluation of algorithms. In this paper, we study a natural setting for continual learning on a massive scale. We introduce the problem of personalized online language learning (POLL), which involves fitting personalized language models to a population of users that evolves over time. To facilitate research on POLL, we collect massive datasets of Twitter posts. These datasets, Firehose10 M and Firehose100 M, comprise 100 million tweets, posted by one million users over six years. Enabled by the Firehose datasets, we present a rigorous evaluation of continual learning algorithms on an unprecedented scale. Based on this analysis, we develop a simple algorithm for continual gradient descent (ConGraD) that outperforms prior continual learning methods on the Firehose datasets as well as earlier benchmarks. Collectively, the POLL problem setting, the Firehose datasets, and the ConGraD algorithm enable a complete benchmark for reproducible research on web-scale continual learning.
Subspace Clustering Based on Latent Low Rank Representation with Schatten-p Norm

Clustering analysis is one of the key technologies in the field of data mining. Among them, high-dimensional data clustering is the core and the most challenging task in clustering analysis. Subspace clustering is an effective clustering method for high-dimensional data. Subspace clustering with Latent Low-Rank Representation (LatLRR) is promising because it can solves the problem of Low-Rank Representation (LRR) of insufficient samples. However, the nuclear norm is usually used to approximate the rank in LatLRR since finding the low rank solution is NP-hard. In order to obtain a better low rank matrix and take into account the insufficiency of samples, this paper proposes a LatLRR model based on Schatten-p norm which introduces the Schatten-p norm to approximate the rank function and an Lp norm constraint error term to improve the robustness. Experimental results show that the algorithm can effectively improve the subspace clustering performance.
Cascaded Hierarchical CNN for RGB-Based 3D Hand Pose Estimation

3D hand pose estimation can provide basic information about gestures, which has an important significance in the fields of Human-Machine Interaction (HMI) and Virtual Reality (VR). In recent years, 3D hand pose estimation from a single depth image has made great research achievements due to the development of depth cameras. However, 3D hand pose estimation from a single RGB image is still a highly challenging problem. In this work, we propose a novel four-stage cascaded hierarchical CNN (4CHNet), which leverages hierarchical network to decompose hand pose estimation into finger pose estimation and palm pose estimation, extracts separately finger features and palm features, and finally fuses them to estimate 3D hand pose. Compared with direct estimation methods, the hand feature information extracted by the hierarchical network is more representative. Furthermore, concatenating various stages of the network for end-to-end training can make each stage mutually beneficial and progress. The experimental results on two public datasets demonstrate that our 4CHNet can significantly improve the accuracy of 3D hand pose estimation from a single RGB image.
E2PAMEA: A fast evolutionary algorithm for extracting fuzzy emerging patterns in big data environments

In this paper, a cooperative-competitive multi-objective evolutionary fuzzy system called E2PAMEA is presented for the extraction of emerging patterns in big data environments. E2PAMEA follows an adaptive schema to automatically employ different genetic operators according to the learning needs, which avoid the tuning of some parameters. It also employs a token-competition-based procedure for updating an elite population where the best set of patterns found so far is stored. In addition, a novel MapReduce procedure for an efficient computation of the evaluation function employed for guiding the search process is proposed. The method, called Bit-LUT employs a pre-evaluation stage where data is represented as a look-up table made of bit sets. This look-up table can be employed later in the chromosome evaluation by means of bitwise operations, reducing the computational complexity of the process. The experimental study carried out shows that E2PAMEA is a promising alternative for the extraction of high-quality emerging patterns in big data. In addition, the proposed Bit-LUT evaluation shows a significant improvement on efficiency with a great scalability capacity on both dimensions of data, which enables the processing of massive datasets faster than other alternatives.
An Accurate and Efficient 1-D Barcode Detector for Medium of Deployment in IoT Systems

Camera-based 1-D barcode detectors have a lot of applications in Internet-of-Things (IoT) systems (e.g., retail, air travel, post and parcel services, and manufacturing). Based on the observation that 1-D barcodes always come with a 12-digit product code, this article proposes an end-to-end trainable and fully convoluted model that can detect and output accurate localization results of 1-D barcode and product code simultaneously. Our method uses dilated convolutions-based feature extractors which are then combined with systematic feature merging layers to create a U-shaped network. It predicts multichannel feature maps which later yield localization results after thresholding with a confidence map generated by the model and nonmaximum suppression. Furthermore, we use a Taylor series expansion-based criterion to rank and eliminate a subset of least important convolutional filters of the model, which further increases the inference speed to a great extent. This model can act as a preprocessing module for camera-based barcode decoders. Experimental results on the data set combined from public data sets and self-collected retail products data set obtained in more challenging environment conditions demonstrate that our strategy is effective in increasing the decoding rate of existing commercial barcode decoders efficiently.
High Accuracy Short Reads Alignment Using Multiple Hash Index Tables on FPGA Platform

Next-generation sequencing can preliminarily solve many problems in the biological area and it plays a more and more important role in bioinformatics. An important step of next-generation sequencing is mapping the query reads to a long reference DNA sequence. Nevertheless, many existing read alignment tools do not have high mapping accuracy. BWA-MEM is one of the most popular tools used for mapping short reads to reference sequences and it can be considered a good read alignment tool with high accuracy. In this paper, we focus on designing a high accuracy short reads alignment system utilizing the read mapping algorithm based upon seed-and-extend type hash tables. To replace the BWA-MEM, we present a heterogeneous computing platform to construct our hash based mapping system and put forward new algorithms to replace corresponding BWA-MEM algorithms. Our system consists of a host processor to execute logical operations, and a coprocessor FPGA to accelerate computation speed. In addition, we experiment on our proposed method and compare the precision with related BWA-MEM methods. The experiment results show that we achieve more than 99 percent similarity of the intermediate results and 99.90 percent similarity of the final results with BWA-MEM. Finally, the comparison demonstrates that both BWA-MEM and our hash based mapping system can get the same output without the random selection.
“Loving you”: Use of metadiscourse for relational acts in WeChat public account advertisements

Social media advertising, with its interactivity, has become a critical site for brand-consumer relationship building. Recent work on its use for relational functions is primarily confined to the field of marketing, yet little attention has been paid to the linguistic devices that support relationship building on social media plarforms. In this paper, an empirical study is conducted on the use of metadiscourse in WeChat public account advertisements (hereafter WPA ads). Specifically, it examines how three interactional metadiscourse subcategories (engagement markers, self-mentions, and attitude markers) are used for relational acts in Chinese WPA ads. Analysis was conducted on 50 selected advertisements published in 2018 on the public WeChat accounts of five brands in different industries (mobile phone, laptop, tourism, electric appliance, and motorcar). The results indicate that engagement markers, self-mentions, and attitude markers, along with their distinctive linguistic devices, are used to perform the relational acts of directing, addressing, and expressing in WPA ads. This study extends the scope of metadiscourse research by investigating the use of metadiscourse in WPA ads while contributing to a more nuanced understanding of the interpersonal function of metadiscourse in WPA ads. It has implications for the technical writing of WPA ads, raising advertisers’ awareness of how to employ metadiscourse to build solidarity with readers and manage their corporate image in an era of e-commerce. It also evidences the usefulness of applying a pragmatic concept to metadisocurse research, providing insight into how metadiscourse might be approached by drawing on notions from fields other than discourse analysis.