Miracle-dz/UniFAR-FTUs huggingface dataset:
An Optimal Pulse Heating Strategy for Lithium-ion Battery Considering both Capacity Fade and Heating Time

The driving performance of electric vehicles seriously degrades due to the deterioration of lithium-ion batteries at low temperatures. Preheating lithium-ion batteries can effectively improve the driving range of electric vehicles at subzero temperatures. In this paper, an optimal pulse heating strategy is proposed for low-temperature heating of lithiumion battery. Firstly, this paper establishes a coupling model to describe the electro-thermal-aging behavior of battery. Secondly, the heating time and capacity loss jointly form a multi-objective optimization problem with the current constraint. The optimization problem is solved by using the particle swarm optimization(PSO) algorithm and the effect of weighting coefficient on heating performance is discussed to obtain the optimal pulse current. The results show that the proposed strategy can effectively reduce heating time without causing serious capacity reduction.
Performance of 1D-CNNs for EEG-Based Mental State Classification: Effects of Domain, Window Size and Electrode Montage

Deep learning paradigms have revolutionized the field of brain-computer interfacing and enabled the use of complex, nuanced methods for recognizing mental states. Prior work has demonstrated that these models can recognize the mental state in a variety of tasks, but few have specifically explored their performance concerning human factors that come into play with the use of brain-computer interfaces in day-to-day applications. For this research, we explored the use of 1D-convolutional neural networks to recognize two mental states–mental arithmetic and rest–from electroencephalograph signals. We focused our analysis on three parameters that affect the design and usability of brain-computer interfaces: input data representation (i.e. domain), window size (i.e. latency), and electrode montage (i.e. form-factor). In line with prior work, we found a clear bias in performance towards the frequency domain representation. We also found that training our model with short windows of time (i.e. 0.25s) provided close to peak accuracy. Furthermore, high accuracy was maintained with sparse electrode subsets of the full 10-20 system. We discuss these findings and how they can contribute to ongoing work to bring deep learning enabled brain-computer interfaces into day-to-day applications.
Digital Solutions for Smart Food Supply Chain

Food waste is a major problem in the current economy, equaling 20% of all food produced in the EU. The European Commission reported, in 2019, that the EU-27 generated 89 million tons of food waste, while the food service and hospitality sector accounts for 12.5 million tons of food waste (14% of total food waste). Catering and Food Services in Healthcare institutions face even more significant challenges: plate waste in healthcare facilities ranges between 6% - 65%; hospitalized patients continue to suffer from the risk of malnutrition resulting from inappropriate food distribution and consumption. In this paper we introduce ADCATER project, which proposes an advanced ICT platform based on innovative technologies integrated in a Smart Food Catering Supply Chain platform. ADCATER aims for economic efficiencies to farmers and suppliers; personalized nutrition accuracy to diners/kitchens, wholesalers, and caterers; also, verification of personal, organizational, and global nutritional policies. This will be achieved by harnessing IoT, computer vision and deep learning technologies to identify and decode images of prepared food "served to plate before a meal" and "left in plate after a meal", applying advanced analytics to derive valuable information such as served meal ingredients, the degree of adjustment and dietary gaps to the diner profile, batch traceability data, effective and up-to-date nutritional supervision, food waste, and correlation between consumer consumption and health.
Dynamic Time-Constrained Path Recommendation with User’s Preference

Path recommendation is an essential application in people’s daily life. However, drivers’ experience hidden in their driving history and their personal preferences are less considered in path planning. In this paper we propose a dynamic time-constrained path recommendation method, utilizing the historical GPS trajectory information and user preferences. First, based on graph entropy, critical intersections are extracted from trajectory information to simplify the road network structure. Then the time-dependent mostly chosen path (TDMCP) is obtained by trajectories processing to determine the time-varying paths between critical intersections. Thus the original complex road network is abstracted as a search subnet. Finally we propose an improved dynamic A* search (IDAS) on the search sub-net to find the candidate paths satisfying the time-constraints, and further allow users to set personal preferences to select the final optimal path. The proposed method is validated on the real-world data set, and the result shows that the proposed method surpasses the competing ones especially on long distance path recommendation.
Real-time Prediction of Highway Equipment Faults Based on GCN and GRU Algorithms

The maintenance and renewal of the highway equipment requires a large amount of financial investment, and it is extremely expensive to directly inspect all the equipment in the highway equipment. Therefore, a cost-effective equipment failure prediction technology is needed, such as a failure prediction model, which can detect highway equipment failures in time and reduce maintenance costs. In this paper, knowledge map technology is introduced to construct a highway equipment map, and a highway equipment fault prediction model based on the combination of semi-supervised Graph Convolution Network (GCN) and Gated Recurrent Unit (GRU) is proposed. This model is based on the GCN’s device network feature perception process, with the fault prediction of highway equipment, which uses GRU to extract the real-time features of the device. The initial values of parameters in GCN and GRU adopt Gaussian distribution and uniform distribution respectively. We apply our model to the problem of active maintenance of highway equipment, and have an experiment with the model using a real data set from a Chinese highway company. The results show that our model can effectively use equipment map information and real-time information, and effectively predict the fault of highway equipment only by partial samples, thereby saving fault response time and maintenance costs.
Reference Point Specification for Greedy Hypervolume Subset Selection

Hypervolume subset selection (HSS) aims to select a subset with a fixed size from a candidate solution set so that the hypervolume of the subset is maximized. The greedy HSS (GHSS) is the most efficient way for solving the HSS problem. When we use GHSS, we implicitly assume that well-distributed solutions over the entire Pareto front will be selected. However, the distribution of selected solutions by GHSS has not been studied. In this paper, we investigate this issue by examining selected solution subsets for different reference point specifications in GHSS. First, we show that a sufficiently large reference point is a good choice for GHSS to select a well-distributed subset on a triangular Pareto front. However, it is not easy to properly specify a reference point for an inverted triangular Pareto front. Then, we propose a dynamic reference point specification method for GHSS to select a well-distributed subset for various types of Pareto fronts. Static and dynamic reference point specifications are compared through computational experiments using 3- and 5-objective candidate solution sets from various types of Pareto front shapes. The experimental results demonstrate the effect of different reference point specifications on the subsets selected by GHSS and the usefulness of the dynamic reference point specification for GHSS.
Diagnosability Analysis of Discrete Event Systems Using a Compact State Space Representation

This work addresses the problem of diagnosability of discrete event systems using labeled Petri nets. The possible faults in a system are modeled as faulty transitions (a subset of unobservable transitions) of a Petri net system. Moreover, the observable transitions are indistinguishable (i.e., two or more transitions may share the same label). We first construct two compact diagnosers, called a redundant basis reachability diagnoser and a basis reachability diagnoser, of a net system involving faulty transitions. Then, a necessary and sufficient condition for diagnosability is provided by checking whether each cycle in the two diagnosers satisfies particular conditions. The approach is efficient since the exhaustive enumeration of the reachability space is avoided thanks to a compact representation of the state space of a discrete event system.
Skill-level-based Hybrid Shared Control for Human-Automation Systems*

In this paper, a hybrid shared controller is proposed for assisting human novice users to emulate human expert users within a human-automation interaction framework. This work is motivated to let human novice users learn the skills of human expert users using automation as a medium. Automation interacts with human users in two folds: it learns how to optimally control the system from the experts demonstrations by offline computation, and assists the novice in real time without excess amount of intervention based on the inference of the novice’s skill-level within our properly designed shared controller. Automation takes more control authority when the novices skill-level is poor, or it allows the novice to have more control authority when his/her skill-level is close to that of the expert to let the novice learn from his/her own control experience. The proposed scheme is shown to be able to improve the system performance while minimizing the intervention from the automation, which is demonstrated via an illustrative human-in-the-loop application example.
Short Term Traffic Flow Prediction with Neighbor Selecting Gated Recurrent Unit

Traffic flow prediction is an important component of a modern intelligent transport system. Building an effective model for short term traffic flow prediction model is challenging. Traffic is spatial temporal in nature. A traffic flow prediction model should consider an appropriate scope of neighbourhood of traffic. To address the needs of a dynamic scope of neighbourhood. We introduce a novel gated recurrent unit variant call Neighbor Selecting Gated Recurrent Unit(NSGRU). NSGRU feature a learn-able spatial kernel with distance based K-nearest neighbor trimming scheme. Embedded external traffic knowledge are used to aid with the learning of spatial kernel. The NSGRU was evaluated with a quantized real world dataset and observed consistent improvement over baseline models.
Indian Buffet Process-Based on Nonnegative Matrix Factorization with Single Binary Component

Nonnegative matrix factorization seeks to find a basic matrix and a weight matrix to approximate the nonnegative matrix. It has proven to be a powerful low-rank decomposition technique for nonnegative multivariate data. However, its performance largely depends on the assumption of a fixed number of features. In this work, we propose a new probabilistic nonnegative matrix factorization which factorizes a nonnegative matrix into a low-rank factor matrix with {0,1} constraints and a nonnegative weight matrix. In order to automatically learn the potential binary features and feature number. A deterministic Indian buffet process variational inference is introduced to obtain the binary factor matrix. And the weight matrix is set to satisfy the exponential prior. In order to obtain the real posterior distribution of the two factor matrices, a variational Bayesian exponential Gaussian inference model is established. The comparative experiments on both the synthetic and real-world data sets show the efficacy of the proposed method.
Online Hand Motor Imagery Direction Decoding using Brain Computer Interface

Brain-Computer Interface (BCI) systems decode specific neuronal signatures from the brain and can provide an alternative communication pathway for patients suffering from neuromuscular disorders. Decoding complex neuronal signatures such as direction, speed, orientation from Electroencephalogram (EEG) is challenging due to complex network connectivity in the brain. In the existing work, phase and amplitude-related features are used to decode the direction information during motor execution task. In this paper, we aim to decode the binary direction (left v/s right direction) during hand motor imagination task. We use the phase-related feature known as Wavelet-Phase Locking Value (W-PLV) to decode the hand motor imagery direction on calibration session data of 20 healthy subjects. Further, an online session is conducted on 13 subjects that use the trained model from the calibration session to decode imagined movement direction. The average classification accuracy achieved on calibration and online session data using phase related features (W-PLV) is 64. 39 ± 4. 23% and 58. 67 ± 4. 06%, respectively. The phase related features (W-PLV) achieve an improvement of 4. 82% and 3. 04% on calibration and online sessions, respectively compared to the amplitude related features alone. Online decoding of user’s intentions such as direction can aid in achieving more natural control of a BCI system.
Broadband Low-Loss On-Body UHF to Millimeter-Wave Surface Wave Links Using Flexible Textile Single Wire Transmission Lines

On-body transmission represents a challenge due to human body shadowing. This paper proposes a Sommerfeld-Goubau single-wire transmission line (SWTL) implemented using electronic textiles for low-loss on-body links up to millimeter-wave frequencies, overcoming the spherical spreading loss and on-body absorption. The SWTL is fabricated using a conductive thread suitable for embroidery on textiles. A compact tapered launcher is implemented on a flexible polyimide substrate to excite the surface mode along the SWTL. In space, a 3 m-long line maintains a forward transmission over −10 dB between 1 and 3 GHz. The SWTL link is characterized for different body parts showing under 20 dB insertion loss with a 1 cm air gap. Across the torso, a forward transmission over −20 dB is maintained from 0.5 to 2.5 GHz, which represents at least 20 dB improvement over two antennas, of larger dimensions, over-the-air. Directly on-skin, the SWTL can be used around 1 GHz with an $S_{21}$ over −25 dB, over 50 dB improvement over two on-skin antennas. At 50 GHz, the shielded SWTL exhibits an ultra-low on-body attenuation around 0.11 dB/mm, a four-fold improvement over a microstrip line on the same substrate. It is concluded that SWTLs can enable ultra high-speed future body area networks.
Multi-Stage Optimization of Deep Learning Model to Detect Thoracic Complications

The diagnosis of thoracic diseases, many of which are easily treatable, is mainly done with the use of chest X-rays and other chest imaging techniques and making sense of these require expert radiologists, who aren’t always accessible. Using Deep Neural Networks, patterns corresponding to different thoracic diseases can be detected from these chest X-rays with the aid of machines. In this study, we have proposed such a model which can detect the presence of 14 different thoracic diseases from a chest X-ray alone. Our novel dense convolutional neural network model works in 2 stages, first training on images from disease-ridden patients alone, and then training the entire network on the whole dataset which includes X-rays from both healthy and unhealthy patients. Given a chest X-ray alone, our model can give accurate predictions, with an AUROC mean score of 82.9% competing with the existing state-of-the-art models in this field.
Multi-robot Information Sampling Using Deep Mean Field Reinforcement Learning

We study the problem of information sampling of an ambient phenomenon using a group of mobile robots. Autonomous robots are being deployed for various applications such as precision agriculture, search-and-rescue, among others. These robots are usually equipped with sensors and tasked with collecting maximal information for further data processing and decision making. The studied problem is proved to be NP-Hard in the literature. To solve the stated problem approximately, we employ a multi-agent deep reinforcement learning framework and use the concepts of mean field games to potentially scale the solution to larger multi-robot systems. Simulation results show that our presented technique easily scales to 10 robots in a 19 × 19 grid environment, while consistently sampling useful information.
Towards Context-based Model Selection for Improved Crop Price Forecasting

Accuracy of crop price forecasting techniques plays an important role in enabling the supply chain planners and government bodies to take necessary actions by estimating market factors such as demand and supply. In emerging economies such as India, the crop prices at marketplaces are manually entered, which can be prone to human-induced errors like entry of incorrect data or entry of no data for many days. In addition to such human prone errors, the fluctuations in the prices itself make the creation of stable and robust forecasting solution a challenging task. Considering such complexities in crop price forecasting, in this paper, we present techniques to build robust crop price prediction models considering various features such as (i) historical price and market arrival quantity of crops, (ii) historical weather data that influence crop production and transportation, (iii) data quality-related features obtained by performing statistical analysis. Furthermore, we propose a crop-specific context-based model selection strategy using trend analysis to deal with high fluctuations in crop prices. To show the efficacy of the proposed approach, we show experimental results using two different time-series feature representations on two crops - Tomato and Maize for 14 marketplaces in India and demonstrate that the proposed approach provides improved accuracy metrics when compared to standard forecasting techniques.
Chemistry Inspired Molecular Representation for Property Prediction

Prediction of molecular properties using molecular structures is an important problem in chem-/bioinformatics. In this work, we propose a molecular representation approach using the concept of functional groups in chemistry. The proposed molecular representation is then used for predicting molecular properties. The approach is also compared with the state-of-the art deep learning model.
Solving TC-type AWPs using external knowledge & learning

Arithmetic Word Problems(AWPs) are mathematical numerical problems expressed in natural language like English, and they provide a natural representation for many quantitative reasoning situations, such as understanding epidemic facts, finance & sports news, etc. Transfer-Case(TC) problems are specific AWPs involving the transfer of objects from one agent to another. In the current modeling, we assume that TC-type AWPs involve a single transfer of a quantity. A TC-type AWP consists of one or more sentences and they essentially contain the information of four types: beforetransfer(BT), transfer(TR), after-transfer(AT), question(QS). The state-of-the-art approaches adopt either statistical, treebased, or template-based modelings. Tree-based approaches[1, 2, 4, 7] leverage the idea of transforming an arithmetic expression to an equivalent tree-structure whereas template-based approach[6] focuses on predicting the appropriate template for the given AWP.We try solving the TC-type AWPs using existing approaches and experiment with number-of-sentences, question-template, complexity-ofthe-sentences, etc; and observe the followings: previous approaches are less efficient in processing the compound or longer sentences of the problem-text, and are less robust to even small template variations. Additionally, they rely on extensive manually-given annotations. Moreover, existing approaches are missing relevant background knowledge(humans solve AWPs efficiently, as they have required knowledge). Knowledge-based systems are considered imperative for building intelligent AI systems. They have a wide variety of applications in various domains like program analysis, natural language understanding, decision making, search & analysis, and knowledge-infused learning, etc. Therefore, in the proposed work1, we focus on solving TC-type AWPs using external knowledge and learning. In Figure 1, we present an example TC-type AWP. SystemOverview(Figure 2): Broadly, the proposed framework has three components; classifier, TC-Ontology, and SWRL module.
Extraction of Product Specifications from the Web - Going Beyond Tables and Lists

E-commerce product pages on the web often present product specification data in structured tabular blocks. Extraction of these product attribute-value specifications has benefited applications like product catalogue curation, search, question answering, and others. However, across different Websites, there is a wide variety of HTML elements (like , , , , etc.) typically used to render these blocks that makes their automatic extraction a challenge. Most of the current research has focused on extracting product specifications from tables and lists and, therefore, suffers from recall when applied to a large-scale extraction setting. In this paper, we present a product specification extraction approach that goes beyond tables or lists and generalizes across the diverse HTML elements used for rendering specification blocks. Using a combination of hand-coded features and deep learned spatial and token features, we first identify the specification blocks on a product page. We then extract the product attribute-value pairs from these blocks following an approach inspired by wrapper induction. We created a labeled dataset of product specifications extracted from 14,111 diverse specification blocks taken from a range of different product websites. Our experiments show the efficacy of our approach compared to the current specification extraction models and support our claim about its application to large-scale product specification extraction. ,
swMETIS: A high performance graph partitioning library on Sunway manycore architecture

The graph structure and graph partitioning algorithms are widely used in many fields of scientific computing and artificial intelligence. With the development of big data, the magnitude of graph is increasing rapidly, which makes high performance graph partitioning library extremely important. In this paper, we proposed swMETIS, a high performance graph partitioning library on Sunway manycore architecture. To exploit more parallelism and performance on Sunway manycore architecture, we introduce the concept of hierarchical scheme and propose an instruction-level hierarchical scheme for the multilevel k-way partitioning algorithm. This scheme allows us to exploit two levels of parallelism, which developed more parallelism than traditional one-level parallel schemes. Moreover, we propose a dropping strategy with threshold and a continuous cycle layout which effectively balances the processor load, improved the data access efficiency and exactly offload the processor load to more powerful CPE array. Our experimental results show that swMETIS achieving 9.52x speedup on average over the state-of-the-art ParMETIS library.