Miracle-dz/UniFAR-FTUs huggingface dataset:
Research on Time Series Problem Model Based on Dynamic Network NAR and Multiple Regression

With the escalation of the trade war between China and the United States and the outbreak of the new coronavirus epidemic in early 2020, China’s economy has been seriously affected. Accurate prediction of future economic development is a necessary means to formulate economic development strategies. For this reason, taking Guangdong province, the province with the strongest economic vitality, as an example, this paper uses multiple regression method to calculate the correlation degree and influence weight of population number, number of enterprises and per capita disposable income with economic development, and obtains multiple regression linear equation. In addition, this paper also uses the NAR dynamic neural network model in machine learning algorithms to predict the future trends of the three factors and economic aggregates, and analyzes the feedback results of network training errors, autocorrelation values, and partial correlation values. Compared with the multiple regression method, it is found that the final results of the two are very similar, with small errors and high correlation.
Superconducting NbTiN Nanowire Single Photon Detectors with Low Kinetic Inductance

We report on the development of superconducting nanowire single photon detectors (SNSPDs) by using NbTiN thin films on single crystal MgO substrates. NbTiN thin films showed fine crystal structure with closer lattice constant to MgO substrates than that of NbN thin films. The transition temperature of 3.5-nm-thick NbTiN films was comparable to, and the resistivity at 20 K was lower than NbN thin films. The kinetic inductance of NbTiN-SNSPDs was ∼25% lower than that of NbN-SNSPD, and system detection efficiency was 1.4% at a 1550 nm wavelength, which is comparable to NbN-SNSPDs.
Characterization of Corrosion Products Formed on Copper in Urban, Rural/Coastal, and Hot Spring Areas

Corrosion products formed on copper plates exposed in urban, rural/coastal, and hot spring areas in Japan were characterized by X-ray photoelectron spectroscopy. X-ray diffraction (XRD), and coulometric reduction. The exposure tests were carried out for a month in summer. XRD patterns showed that hydrated copper sulfate [Cu 4 SO 4 (OH) 6 . H 2 O, posnjakite] and cuprite Cu 2 O were formed on copper surfaces exposed in urban and rural/coastal areas. The existence of sulfate salt agrees well with the XPS spectra. Sulfur dioxide and particulate sulfate such as ammonium sulfate and sea salt are both possible origins of this sulfate. In contrast. XRD revealed that the only corrosion product formed on the copper exposed in the hot spring area was cuprite Cu 2 O, although the amount of sulfur on the surface of this sample was the highest. Coulometric reduction analysis showed that there was copper sulfide Cu 2 S in corrosion products formed on copper exposed in the hot spring area. Posnjakite Cu 4 SO 4 (OH) 6 . H 2 O is initially formed in addition to cuprite Cu 2 O when copper is exposed in urban and rural/coastal atmospheres. This crystalline phase is located at and near the copper surface. Initially formed posnjakite Cu 4 SO 4 (OH) 6 . H 2 O may change to brochantite Cu 4 SO 4 (OH) 6 , which is a typical patina component that appears after further exposure.
Advance Machine Learning Methods for Dyslexia Biomarker Detection: A Review of Implementation Details and Challenges

Dyslexia is a neurological disorder that is characterized by imprecise comprehension of words and generally poor reading performance. It affects a significant population of school-age children, with more occurrences in males, thus, putting them at risk of poor academic performance and low self-esteem for a lifetime. The long-term hope is to have a dyslexia diagnostic method that is informed by neural-biomarkers. In this regard, large numbers of machine learning methods and, more recently, deep learning methods have been implemented across various types of dataset with the above-chance classification accuracy. However, attainment of clinical acceptability of these state-of-the-art methods is bedeviled by certain challenges including lack of biologically-interpretable biomarkers, privacy of dataset and classifiers, hyper-parameter selection/optimization, and overfitting problem among others. This review paper critically analyzes recent machine learning methods for detecting dyslexia and its biomarkers and discusses challenges that require proper attentions from the users of deep learning methods in order to enable them to attain clinically relevance and acceptable level. The review is conducted within the premise of implementation and experimental outcomes for each of the 22 selected articles using the Preferred Reporting Items for Systematic review and Meta-Analyses (PRISMA) protocol, with a view to outlining some critical challenges for achieving high accuracy and reliability of the state-of-the-art machine learning methods. As an evidence-based protocol for reporting in systematic reviews and meta-analyses, PRISMA helps to ensure clarity and transparency of this paper by showing a four-phase flow diagram of the selection process for articles used in this review. It is therefore, envisaged that higher classification performance of clinical relevance can be achieved using deep learning models for dyslexia and its biomarkers by addressing identified potential challenges.
Implementation and Analytics of the Distributed Eco-Driving Simulation iCO2

We describe iCO2, a simulation platform for collecting driving behavior data. It is designed as the first massively multiplayer online game for mobile devices to practice eco-friendly driving. It facilitates the collection of large-scale data on driving behavior to better understand compliance and incentive mechanisms for eco-driving and users’ preferences. We present the results of a campaign with iCO2 that used a game promoter to attract 2455 users. The results are described from three angles: (1) types of drivers are identified by clustering driving behavior; (2) types of players are identified by relating players’ interaction with game elements and their driving behavior; (3) by looking at longer sessions, we demonstrate that players who show eco-unfriendly behavior at the beginning of the session improve their eco-driving behavior throughout their playtime.
Tra2Tra: Trajectory-to-Trajectory Prediction With a Global Social Spatial-Temporal Attentive Neural Network

Accurate trajectory prediction plays a key role in robot navigation. It is beneficial for planning a collision-free and appropriate path for the autonomous robots, especially in crowded scenes. However, it is a particularly challenging task because there are complex and subtle interactions among pedestrians. There have been many studies focusing on how to model this spatial interactions but most of them neglected the temporal characteristic. Towards this end, we propose a novel Global Social Spatial-Temporal Attentive Neural Network for trajectory-to-trajectory prediction (Tra2Tra). In this model, we first extract features of spatial interactions with decentralization operation and attention mechanism, and then iteratively extract its temporal dependency through the Long Short-Term Memory network for obtaining the global spatial-temporal feature representation. We further aggregate this global spatial-temporal feature representation and velocity features into our encoder-decoder module for prediction. In order to make multi-modality predictions, we introduce a random noise perturbation while decoding, which enhances the robustness and the generalization ability of our model. Experimental results demonstrate that our Tra2Tra model can achieve better performance than the state-of-the-art methods not only on two pedestrian-walking datasets, i.e. ETH and UCY, but also on three other complex trajectory datasets, i.e. Collisions, NGsim and Charges.
Delay-Dependent Stability Analysis of Modern Shipboard Microgrids

This study proposes a new application for delay-dependent stability analysis of a shipboard microgrid system. Gain and phase margin values are taken into consideration in delay dependent stability analysis. Since such systems are prone to unwanted frequency oscillations against load disturbances and randomness of renewable resources, a virtual gain and phase margin tester has been incorporated into the system to achieve the desired stabilization specification. In this way, it is considered that the system provides the desired dynamic characteristics (e.g. less oscillation, early damping, etc.) in determining the time delay margin. Firstly, the time delay margin values are obtained and their accuracy in the terms of desired gain and phase margin values are investigated. Then, the accuracy of the time delay margin values obtained by using the real data of renewable energy sources and loads in the shipboard microgrid system is shown in the study. Finally, a real-time hardware-in-the-loop (HIL) simulation based on OPAL-RT is accomplished to affirm the applicability of the suggested method, from a systemic perspective, for the load frequency control problem in the shipboard microgrid.
A Design of Differentially Encoded LDPC Coding Based on Multi-Edge Framework

We propose to optimize the design of a system of which the transmitter is composed of a low-density parity-check (LDPC) code followed by differential encoding (DE). The DE is included to remove the phase ambiguity in channel estimates. To achieve low complexity and/or high efficiency in channel estimation and also an acceptable performance in transmission, we optimize the outer LDPC code by considering the DE-LDPC code as a multi-edge type LDPC (MET-LDPC) code. The advantage of the proposed approach is verified by both the asymptotic behaviour and the finite-length simulation.
Cloud Computing security classifications and taxonomies: a comprehensive study and comparison

Cloud Computing is an evolving term that is subject to security threats, vulnerabilities, and attacks. Latterly, various classifications and taxonomies have been suggested to characterize and classify cloud security issues. Some of them are based on general security factors, such as the CIA triad (confidentiality, integrity, and availability), while others specify cloud security classes. Most of these classes are determined by the cloud’s attributes, such as Cloud service models, cloud deployment models, and cloud actors. In this paper, we explore the already existing criteria and dimensions considered in the development of cloud computing security classification/taxonomy. Then, we study and compare their strengths and characteristics. Thereafter, our objective is to provide and develop exhaustive cloud security taxonomy and push researchers to better comprehend the nature of any newly introduced threat or attack, categorize them, and explain the relationship between threats and other categories or subcategories.
Impact of Visibility Classification Schemes on Traffic Efficiency in Intelligent Freeway Management Systems A Mechanism for all-Weather Transportation Services with Vehicle-Road Coordination Technology

Visibility often has large impact on driving safety and speed, thus is an important factor to be considered in freeway management. In this article, we propose a methodology to evaluate the visibility and its impact on freeway transportation. We carefully examined the concept of visibility, and propose classification schemes and controlling mechanisms designed accordingly. Furthermore, we evaluate traffic efficiency as outcome of the schemes and likelihood of congestion by defining a Delay Index, modelling and simulating a few scenarios and benchmarking to big data statistics from China major cities. Results from our study show that with proper designed mechanisms, the overall efficiency and safety of the freeway transportation will be improved. Once applied to commercial systems and become fully operative in Vehicle-Road Coordination systems, transportation in all-weather conditions can be gradually achieved and will bring significant social and economic benefits.
Probabilistic Forecasting Model for Non-normally Distributed EV Charging Demand

A method for probabilistic electric vehicle (EV) demand forecasting is proposed in this paper. The EV demand in a certain area is forecasted by an ensemble forecasting model. The forecast result includes a deterministic forecasting and prediction interval that indicates the probability of deviation from deterministic forecasting. In the case study, an actual observed dataset from the UK is used to verify the proposed algorithm. The results show that the target EV charging demand to be forecasted shows 80% prediction interval coverage for 27 days out of 30 simulated days.
Multi-Faceted Hierarchical Image Segmentation Taxonomy (MFHIST)

An abundance of various segmentation techniques are available in the literature, that cater to wide range of image understanding applications. The paper proposes a unified way of systematic categorization of the research work on image segmentation called Multi-Faceted Hierarchical Image Segmentation Taxonomy (MFHIST), which consist of six facets presented in a hierarchical manner - scope, requirement, control, feature, image representation and approach specifications. Every scope is exemplified with research works from the literature for better understanding. The paper gives an illustration of populating MFHIST, to provide the reader a quick grasp of few important state-of-art image segmentation research works and their adaptations. As a case study, the illustrations display a limited version to uncover the journey of basic to modern adaptations in the areas region based segmentation approach, such as Markov Random Fields, Spectral Clustering, Active Contour Model, Mean Shift Clustering. The other segmentation approaches have not been considered here, owing to the enormous volume of works in the past four to five decades and limitation in articulating all of them using MFHIST. The performance analysis of the algorithms using quantitative metrics is not in the present scope and will be considered in future version of MFHIST.
Robust Scheduling of Electric Vehicle Charging for PV Integrated Parking-lots

This paper proposes a robust charging scheduling scheme for Electric Vehicle (EV) in a commercial PV integrated parking lot. There are uncertainties in information of parking lot EV charging management problem, like arrival and departure time of EVs, PV power generation and cost of electricity. An event triggered approach can tackle the charging demand uncertainty of EVs. For PV power generation, a short term forecast model based on XGBoost algorithm is used for mitigating renewable energy uncertainty. Finally a real time EV charging scheduling problem is developed that can minimize the operating cost of the parking lot operator at the same time the charging demands of all the connected EVs are satisfied. From the simulation results we can infer that the proposed algorithm guarantees the stability and robustness of system uncertainties in particular, irregular EV arrival and departure and power generation from PV system.
Graph Database using Data Crawling

Data on the internet is growing nonstop. Every platform has millions of active users, searching for something specific. LinkedIn is one such platform, known for career opportunities, company and employee information and more. In this paper, we make a graph database system, collecting data from LinkedIn, IBM new feed, DNB. Using data crawling the data is gathered followed by data cleaning and, building APIs for graph database. A graph database is made of nodes and relationships, Cypher query language is used to store and retrieve the data from graph database. Neo4j and cypher query language are used for visual representation, with Neovis library. The system shows company details, employee details such as skills, experience, education background, contact information, certifications, licenses and more. The system is resourceful for companies and employees, provides easy and quick relevant information about the company and a person, such as a company’s employees, it’s blogs and articles, and further about the employee’s details. The project holds great future scope, with bigger, multiple sources.
Research on an Arm Rehabilitation System Based on Artificial Intelligence

After the damage of arm muscle, especially for stroke recoveries, patients are not able to use their muscles to perform daily tasks normally. Also, the traditional rehabilitation process requires high intensity of repetitive work, making this a dull and stressful process. Therefore, this device focuses on the rehabilitation for arm muscles using an interactive user interface accommodated with an artificial intelligence system. The user interface includes programs such as flappy bird and space shooting in order to offer patients a more interesting and more interactive rehabilitation process while the artificial intelligence system based on SVM, NN, KNN to make sure they do the correct task. The artificial intelligence system has a high accuracy of identifying and classifying the users’ arm movements, ensuring the accuracy of the rehabilitation process.
SYNTHG: Mimicking RDF Graphs Using Tensor Factorization

There is a need for synthetic graphs to help benchmarking efforts. Synthetic graphs that mimic real-world graphs can be used to avoid sending sensitive information to third parties while preserving topological characteristics of the input original graph. They can also be used to evaluate the scalability of different algorithms since the size of synthetic graphs can be scaled. In view of these applications, we introduce a novel approach to mimik RDF graphs. Our approach introduces a random rotation in the tensor factorization of the input RDF graph. By combining this matrix with the core tensor computed by the factorization, our approach can generate a graph which maintains the querying characteristics of the input graph, while not permitting a reconstruction of the input graph. We use Semantic Web Dog Food and DBpedia 2016 to evaluate our approach and compare the original, reconstructed and synthetic graphs by using them to benchmark five triple stores. The results show that the Pearson correlation between the performance of the triple stores under original and synthetic graphs is 0.91, 0.64 for Semantic Web Dog Food and DBpedia respectively. Our results also suggest that the synthetic graphs inherit the main graph characteristics of the original graphs. SynthG is open-source and is available at: https://github.com/dice-group/SynthG
Acoustical Characteristics of the Cantonese Vowels and Tones Produced by Hearing Impaired Speakers

The paper investigates the acoustical characteristics of the Cantonese vowels and tones produced by three male and three female hearing impaired (HI) speakers with cochlear implants or hearing aids. The vowels and tones produced by two speakers of normal hearing (NH) are also analysed for comparison purposes. Results show all the six HI speakers differentiate the durations of (a) long, (b) medium-long and (c) short vowels in Cantonese. While the HI speakers differ from the NH speakers in absolute vowel durations, the ratios of (a):(b) and (b):(c) for the HI speakers are comparable to those for the NH speakers. The vowel loop data show the HI speakers with cochlear implants (CI) perform almost as equally well as the NH speaker and outperform the HI speakers with hearing aids (HA) in the production of the Cantonese vowels. The HA speakers produce the vowel loops of reduced sizes compared to those produced by the NH speakers. The F0 curves show the HA speakers outperform the CI speakers in the production of the Cantonese tones. It appears there is a correlation between the type of HI speakers and the production performance of vowels or tones.
NAFAS: Non-Rigid Air Flow Active Sensor, a Cost-Effective, Wearable, and Ubiquitous Respiratory Bio-Sensor

A Non-rigid Air Flow Active Sensor (NAFAS) to detect the respiratory patterns is introduced in this paper. The main part of NAFAS is an ionic electroactive polymer-based (<inline-formula> <tex-math notation="LaTeX">${i}$ </tex-math></inline-formula>-EAP) soft sensor. It can accurately detect breathing patterns and has promising features to develop low cost, wearable, and ubiquitous spirometers. Patients with respiratory diseases need to take pulmonary function tests consistently. Hence, it is time-consuming and expensive. To address these problems, NAFAS presents point-of-care testing (POCT) of the respiratory system’s function. The proposed <inline-formula> <tex-math notation="LaTeX">${i}$ </tex-math></inline-formula>-EAP based sensor is a small strip of Ionic Polymer Metal Composite (IPMC) that has been fabricated using a Nafion membrane and two Pt electrodes. A test, including different breathing patterns, is designed and is performed by several male and female subjects. Results show that the NAFAS can detect delicate breathing patterns with various frequencies in an appropriate manner.
CTNet: Conversational Transformer Network for Emotion Recognition

Emotion recognition in conversation is a crucial topic for its widespread applications in the field of human-computer interactions. Unlike vanilla emotion recognition of individual utterances, conversational emotion recognition requires modeling both context-sensitive and speaker-sensitive dependencies. Despite the promising results of recent works, they generally do not leverage advanced fusion techniques to generate the multimodal representations of an utterance. In this way, they have limitations in modeling the intra-modal and cross-modal interactions. In order to address these problems, we propose a multimodal learning framework for conversational emotion recognition, called conversational transformer network (CTNet). Specifically, we propose to use the transformer-based structure to model intra-modal and cross-modal interactions among multimodal features. Meanwhile, we utilize word-level lexical features and segment-level acoustic features as the inputs, thus enabling us to capture temporal information in the utterance. Additionally, to model context-sensitive and speaker-sensitive dependencies, we propose to use the multi-head attention based bi-directional GRU component and speaker embeddings. Experimental results on the IEMOCAP and MELD datasets demonstrate the effectiveness of the proposed method. Our method shows an absolute 2.1$\sim$6.2% performance improvement on weighted average F1 over state-of-the-art strategies.