Miracle-dz/UniFAR-FTUs huggingface dataset:
LDA Based model for mining textual features from financial news articles

The exorbitant amount of textual information circulated every day in the form of news articles, blog posts, etc., is used by humans to predict future trends. However, using this textual information to predict future drifts (crash/boom) in stocks or stock market indices in a realistic manner is very difficult through the pure use of algorithms. Also, it is quite difficult to make predictions about stock prices or market trends purely based on historical data. This paper proposes a variation of Latent Dirichlet Allocation (LDA) on a dataset of financial news items and historical data of the S&P 500 to effectively mine out words that can later be used as important features for various machine learning models with an aim to predict market trends and more specifically crash/boom in the stock market. It can be used as a topic modelling tool. In the end, a table with the top words per topic generated is illustrated which gives an insight into the result of the implementation of the proposed variation.
A Survey of Powertrain Technologies for Energy-Efficient Heavy-Duty Machinery

This article presents a comprehensive, multidisciplinary overview of the development of powertrain technologies for energy-efficient heavy-duty earthmoving machines. The heavy-duty earthmoving equipment industry has been among the biggest contributors to emissions globally. However, due to high power demand and multidisciplinary powertrain structures, improving the energy efficiency of heavy-duty mobile machines has been a pressing and challenging task in the industry. To cope with this challenge, hydraulics and power electronics (PE) have been the key driving forces. As such, the relative developments in both fields are covered in this article. For hydraulics, developments of efficient hydraulic circuits will be overviewed in detail along with the introduction of hydraulic energy recovery technologies. In addition, developments of PE architectures in hybrid and electrified machines will be introduced. Furthermore, potential medium-voltage dc mining site power distribution and the valves of wide bandgap devices will also be discussed with the hope to open up new research opportunities in PE. Moreover, emerging hybrid electrohydraulic drive technology is introduced. Based on the overview in this article, it is anticipated that electrohydraulic hybridization will be the future trend in the earthmoving machine industry. Deeper collaboration between the two areas is desirable.
A Fault-Tolerant Distributed Framework for Asynchronous Iterative Computations

Asynchronous iterative computations (AIC) are common in machine learning and data mining systems. However, the lack of synchronization barriers in asynchronous processing brings challenges for continuous processing while workers might fail. There is no global synchronization point that all workers can roll back to. In this article, we propose a fault-tolerant framework for asynchronous iterative computations (FAIC). Our framework takes a virtual snapshot of the AIC system without halting the computation of any worker. We prove that the virtual snapshot capture by FAIC can recover the AIC system correctly. We evaluate our FAIC framework on two existing AIC systems, Maiter and NOMAD. Our experiment result shows that the checkpoint overhead of FAIC is more than 50 percent shorter than the synchronous checkpoint method. FAIC is around 10 percent faster than other asynchronous snapshot algorithms, such as the Chandy-Lamport algorithm. Our experiments on a large cluster demonstrate that FAIC scales with the number of workers.
Deep Neural Network-Based Heterogeneous Domain Adaptation Using Ensemble Decision Making in Land Cover Classification

A domain adaptation (DA) problem is investigated for land cover classification by utilizing the ensemble decision approach of deep neural networks to address the extra and missing class problem. Two different pretrained models followed by two autoencoders are used to extract the two sets of cross-domain features for samples in the reduced shape. Thereafter, the primary labels and probability scores of target samples are obtained using two different classifiers trained over the source samples with these different features. Moreover, unlike other open set DA techniques, the proposed framework is capable of identifying extra classes separately by exploring the agreement between these two classifiers. Experiments were carried out using three aerial image datasets, and the results are found to be encouraging for the proposed scheme in comparison with other state-of-the-art techniques.
Miniaturized Dual-Polarized Metasurface Antenna With High Isolation

A dual-polarized (DP) metasurface (MTS) antenna with miniaturized radiating aperture is proposed for polarimetric synthetic aperture radar. The antenna consists of an MTS radiator and a cross aperture antenna. Characteristic mode analysis (CMA) is utilized to analyze the working mechanism of the MTS radiator and to guide the miniaturization of the radiating aperture. Then, the CMA is used for the optimization of the DP cross aperture antenna to achieve better isolation. A reduced radiating aperture of <inline-formula><tex-math notation="LaTeX">$0.37{\lambda _0} \times 0.36{\lambda _0}$</tex-math></inline-formula> is realized with a thickness of <inline-formula><tex-math notation="LaTeX">$0.13{\lambda _0}$</tex-math></inline-formula>(<inline-formula><tex-math notation="LaTeX">${\lambda _0}$</tex-math></inline-formula> is the free-space wavelength of central frequency). The simulated and measured results show that the DP antenna yields 10 dB fractional bandwidths of 43.72% for Port H and 38.65% for Port V. The cross-polarization levels are better than –23 dB at 9.6 GHz, and the isolation between the two ports remains above 35 dB over the whole operating bandwidth.
User Fast Authentication Method Based on Microservices

Aiming to the characteristics and requirements of high concurrent user authentication request and high demand of real-time system response in the microservices, this paper proposes a kind of user fast authentication method based on microservices, which is a storage model applying binary system to mark abnormal user status. Combining with present system architecture, this method rebuilds the system authentication process of microservices and solves the technical difficulties of frequent user authentication and high demand of server performance during the using of microservices architecture. The authors first analyze and state the authentication bottleneck and security issues that the system faces after the emergence of microservices from the basic situation of user authentication; second, come up with a general authentication process on the basis of microservices system, and design a system authentication model; then, design system simulation test method, analyze and compare the simulation test results; in the end, verify the feasibility of the method proposed in this paper based on test results and analyze the value and significance of the existence of this system.
IoT Assisted Real Time PPG Monitoring System for Health Care Application

Photoplethysmography is an important area that measures heart rate and its variability for clinical diagnosis of cardiac illness and oxygen saturation level in blood. Nowadays biomedical signal transmission through IoT cloud provides an additional benefit in health monitoring especially for ailing senior citizens who are remotely located. The process of bioelectric signal transmission takes place at a very slow sampling rate. In the present work, a prototype system is proposed for PPG monitoring using Internet-of-Things (IoT). PPG data are captured by a reflectance-type PPG sensor with an embedded controller over a measured interval of time. PPG waveforms are then modeled using either Fourier or Gaussian method, the model parameters thus obtained are truly representing the sampled PPG Data. The computed model coefficients are then transmitted to the IoT cloud server (e.g. Dropbox) with WiFi connectivity. At the remote end, provision is made to access these model parameters from the cloud server and reconstructing the PPG waveform. The performance of the reconstruction process is evaluated by calculating mean square error (MSE) and percentage root mean squared difference (PRD). Experiments were performed on ten volunteers of different ages in order to assess the reliability of the entire method. Experimental results reveal the ruggedness of the proposed method, which can supplement the clinical diagnosis in cardiac ailments and facilitate the treatment of rural patients from any urban location through expert physicians.
28.6 A 22.6µ W Biopotential Amplifier with Adaptive Common-Mode Interference Cancelation Achieving Total-CMRR of 104dB and CMI Tolerance of 15Vpp in 0.18µm CMOS

Improving robustness to common-mode interference (CMI) is imperative for reliable two-electrode ECG recording. CMI degrades the signal quality in two ways. First, it appears as a differential-mode signal together with the ECG, because of common-mode to differential-mode (CM-DM) conversion. This is often due to the electrode impedance mismatch [1], as contacts inevitably lose their properties differently during long-term recording. Second, CMI that exceeds the supply voltage saturates the amplifier and distorts the recording when the subject is near power lines [4]. To address both of these issues, the recoding IC must withstand large CMI and also have a large total-CMRR (T-CMRR) that includes the effect of electrode mismatch. Unfortunately, prior works provide solutions to only one of them, and thus there is a lack of reliable long-term ECG recording in a practical setting. In [2, 3], CM-DM conversion is addressed by increasing the input impedance in common-mode. However, the IA fails when there is large CMI. In addition, one of the main issues of the input impedance boosting method is that chopping, despite its benefit in 1/f noise reduction, cannot be used, as it reduces the input impedance. Hence, a CM-DM suppression technique that is effective for low input impedance IA is desired. In [4, 5], tolerance to CMI is improved by absorbing the CMI current. However, their T-CMRRs are poor $(\sim65$ dB). In this work, we propose a CMI-tolerant amplifier with an adaptive CMI-canceling technique. It ensures $\gt100$ dB of T-CMRR when there is an electrode mismatch of $152k \Omega$ and CMI of 15V, even when the input capacitance is as large as 120pF.
Optimal Design of a Score-based Incentive Mechanism for Promoting Demand Response Participations of Residential Users

Demand response (DR) can effectively promote the users to adjust their power demands independently, so as to optimize the overall load curve, relieve the pressure of peak load growth, and ensure the reliable operation of the power system concerned. Aiming at the fact that residential users do not have the access to the market quotation and the non-real-time dynamic variability of electricity price under the current electricity market mechanism in China, a new strategy to promote active participations of users in demand-side response, i.e., a score-based incentive mechanism for attaining demand response (SIDR) from residential users, is proposed. First, the load models of electrical appliances in residential users are established, with the comfort degrees of the users taken into account. Then, the cost-benefit analysis is carried out from two market entities: the power grid company and the residential users. On this basis, a bi-level optimization model of score-based incentive mechanism for promoting demand response participations of residential users is established. It is shown by case studies that the proposed SIDR can effectively encourage the residential users to participate in DR and hence enhance the economics of power system operation.
Research on Target tracking Based on Improved KCF Algorithm

Aiming at the problem of tracking instability caused by imperfect feature expression and variable scale, an improved KCF algorithm is proposed in this paper. Firstly, HOG and MB-LBP features are extracted to form H-L fusion feature, and Hue color features are extracted too. Then the output response values of extracted features are calculated respectively by position filter. Next, the fusion is carried out by linear weighting at the feature level, to obtain the target’s predicted location. And in the tracking process, the scale changes of targets are estimated and adaptively adjusted by the standard deviation of the feature matching corner points of the two frames before and after. Finally the difference of the adjacent two frames is judged by the corner matching condition, and the position filter is updated, to achieve better target tracking effect. The experiments on OTB-50 and OTB100 show that the algorithm not only improves the tracking accuracy by 8.3% and the success rate by 10.5%, achieves the real-time tracking effect.
Twitter Network Mimicking for Data Storage Benchmarking

A significant portion of the textual data available on the Web comes from microblogging services such as Twitter. A considerable body of research has hence investigated methods for processing streams of short texts from such services as well as benchmarking online social networks. However, the costs connected with the acquisition of real microblogs is prohibitive for researchers with limited resources. We address this challenge by proposing TWIG, a benchmark generator for microblogging services similar to Twitter. It is a collection of algorithms to: 1) serialize users and their tweets from Twitter in RDF, 2) analyze such serialized data in RDF to approximate distributions over the underlying social network and 3) mimic a social network by generating synthetic users and tweets based on the approximated distributions. By using TWIG generated data, researchers can carry out preliminary evaluations of social network analysis and NLP approaches at low cost. Experimental and human evaluation results suggest that the synthetic tweets generated by TWIG are hardly distinguishable from human-generated tweets. Moreover, our results also underpin the scalability of our approach. Our Java implementation of TWIG is open-source and can be found at: https://github.com/dice-group/TWIG.
Advanced Grid Integration Test Platform for Increased Distributed Renewable Energy Penetration in Smart Grids

Renewable energy-based generators are constantly being deployed to future grids. It is expected that their share in overall generation will increase in the future. These novel devices have unknown characteristics and cause novel issues in power system operation. Traditional distribution networks have been operated as passive networks. These devices, such as smart inverters, change this paradigm completely. Due to these considerations, grid operators insist on enforcing strict grid-integration requirements. These rules are developed to ensure the impact of the connected devices is minimized and their behavior can be accounted for, at least to some extent. Testing different devices for different grid codes is a daunting task. Since such tests are undertaken in lab environment with manual control and data collection, they are prone to errors, time-consuming and inefficient. A solution is required to standardize and automate such tests. This will provide consistent testing ability and minimize testing times and errors due to human-intervention. This article presents the design and implementation of an integrated testing platform. Steps of lab equipment integration and associated challenges are presented along with their solutions. Several smart inverter behavior tests are executed, and results are presented. The test durations are compared with traditional test durations and the benefits are reported. It is discovered that use of such platform can increase the system testing efficiency by 85 % while minimizing human-errors, inconsistencies and man-hours required to run the tests.
OPTICAL-PROPERTIES OF PECVD ERBIUM-DOPED SILICON-RICH SILICA - EVIDENCE FOR ENERGY-TRANSFER BETWEEN SILICON MICROCLUSTERS AND ERBIUM IONS

We report the fabrication by PECVD of silicon-rich erbium-doped silica films that exhibit both 1535 nm fluorescence and visible photoluminescence. Fluorescence spectra are presented along with absorption spectra that display a strong band edge in the blue, which we ascribe to the presence of Si microclusters. We are unable to observe characteristic Er3+ absorption bands and propose that excitation of the rare earth is via an energy transfer process from Si microclusters.
Formalizing Ontologies for AI Models Validation: from OWL to Event-B

Quality data is of decisive importance for controlling critical cyber-physical systems. Most common real-life systems are driven by unstructured, decentralized, and growing amounts of data, while validation requires coherent data, that is well structured, consistent, and without ambiguities. Often, ontologies are well-suited for capturing domain knowledge data, deriving requirements, providing analysis, and developing applications. Still, ontological representations fall short when it comes to formal verification and validation, especially for large complex systems. In this research, we suggest a fully automated approach to transform ontology axioms, expressed in the Web Ontology Language (OWL), to Event-B predicates. We show, in a practical example, how bridging OWL to Event-B can scale the validation of ontology-driven AI systems.
A cloud-based foundational infrastructure for water management ecosystem

Water monitoring is one of the critical battles of sustainability for a better future of humanity. 44 countries are considered at high risk of the water crisis, 28 of which are developing countries and have limited capacity to deploy a national scale solution. As a response to the United Nation’s sustainability goals and initiatives, this paper proposes an intelligent water monitoring service which acts as a foundational infrastructure for all future water management systems. It also provides municipalities, Non-Governmental Organization and other private initiatives with the tools needed to establish local water monitoring in the scale of villages or rural areas with a very small initial investment.
Short- Term Probabilistic Analysis of Australian Wind Power Generation

With the significant number of renewable generators being connected to power grids, the existing operating mode is challenged due to the intermittent nature of these generators such as wind farms. Therefore, it is important to properly consider the short-term variation in power generation through suitable probabilistic distribution (PD) models to better analyses and predict the wind generation. In this paper, a short-term probabilistic analysis and modelling is performed using the three -year generation data of Australian wind farms. The study has considered Normal distribution to represent the probabilistic variations in generation of wind farms and the standard deviations (SD) are calculated accordingly for various cases. It has been observed that the long-term data may lead to small SDs which may not accurately represent the probability of the short-term changes in the wind power generation. The study has also shown that continuous zero-generation conditions can adversely affect the probabilistic representation of short-term wind generation, which can lead to an incorrect prediction of wind-generation and subsequent system failures. This study provides a platform for more accurate analysis and forecast of wind generation to maintain the stability of the system.
Microwave Metagratings for Generation of Vortex beams Carrying OAM modes

In this paper, a new method to generate vortex beams carrying OAM modes is proposed based on microwave metagratings. Such metagratings can introduce phase shift by modulating the displacement of the meta-atoms instead of varying the parameters or rotating the units. The principle of the detour phase is proven in detail. Two kinds of unit cells are designed for linear-polarized wave and circularly polarized wave. An extra degree of freedom is introduced by the appropriate incident angle to suppress +1 diffraction order. Finally, two metagratings are built to demonstrate the validity of the proposed method.
The procedural syntax of fake modification constructions in Chinese

Abstract The clitic morpheme de in Mandarin Chinese has various uses. Typically, it is cliticized to a phrase whether the phrase is nominal or adjectival; it can also occur between two noun phrases when there is no relation of semantic modification. The constructions that involve the latter use of de, known as fake modification constructions, have been theoretically characterized many a time. In the existing characterizations, the morpheme is treated either as a mysteriously inserted lexical item, a modification marker, or a genitive morpheme. The existing accounts suffer from a variety of theoretical and empirical problems. Evidence is presented that in some other constructions and in fake modification constructions, de, while having no lexical semantic content of its own, occupies a position that is otherwise occupied by a two-place predicate. Based on this observation, a partially unitary theoretical account of fake modification constructions is formulated from a parsing perspective in the framework of Dynamic Syntax. In this account, four de-morphemes in fake modification constructions are recognized with different syntactic distributions; however, they all contribute a semantically underspecified predicate that is updated by syntactically constrained or context-based inference.
Reactivity of calcium phosphate cements

Since their discovery in 1983, calcium phosphate cements (CPCs) have been increasingly used as injectable bone substitutes. Moreover, most recent research studies have shown that CPCs have a very large potential for the synthesis of granular or macroporous calcium phosphate bone substitutes with enhanced biological properties. In all these applications, the control of the CPC reaction rate is of great importance. Despite numerous studies on the topic, there is still a relatively poor understanding of the mechanisms involved. This paper attempts to change this situation by reviewing the most relevant experimental and phenomenological approaches that can be used to control CPC setting rate.
Preparation and properties of conductive polyimide nanocomposites with assistance of a metallo-organic compound

Highly reflective and electrical conductive polyimide films have been developed by incorporation of surface modified silver flakes and a metallo-organic compound, silver 2-ethylhexanoate, into a polyimide matrix. The surface conductivity can be obtained during the imidization process of polyimide, because the metallo-organic compound can decompose into silver atoms at a low temperature of 150 °C. The optimum weight ratio of silver 2-ethylhexanoate to silver flakes was 2/8, and the surface resistivity of the conductive polyimide film, which contained 30 wt% of silver fillers, was 10−2 ohm sq−1. The metallized films had excellent thermal properties. Their glass transition temperatures were above 276 °C, and the thermal decomposition temperatures were over 512 °C. The optical reflectivity of the films was up to 90%.