Miracle-dz/UniFAR-FTUs huggingface dataset:
RF-WTI: Wood Types Identification based on Commodity RFID Devices

The identification of wood is an important problem both in industrial manufacturing and in people’s daily life. Traditional methods based on experts are laborious. New technologies based on computer visions rely on high-quality cross section images. In this paper, we take the first attempt to identify the wood type based on commodity RFID devices. A system named RF-WTI is proposed. The main idea of RF-WTI is that different wood types result in different signal changes when RF signals pass through the wood. Specifically, after collecting the changes of Received Signal Strength (RSS) and phase when RFID signals pass through the wood, a feature that is unique for the wood is derived. Then RF-WTI applies a Bayesian neural network to identify wood types. Experimental results show that RF-WTI achieves 92.33% average accuracy for identifying 12 different types of wood.
Inhomogeneous Stepped-Impedance Corrugated Waveguide Low-Pass Filters

Previous work on homogeneous stepped-impedance corrugated filters has been extended to the general inhomogeneous structure, where the waveguide broad dimension is varied in addition to the narrow dimension. It is then possible to design waveguide lowpass filters with specified stopbands free of spurious mode propagation to give rejection levels as high as 100 dB. Additionally, control over the lower cut-off frequency and the ability to match down almost to this frequency facilitates the design of compact waveguide bandpass filters having very broad bandwidths.
Online Computation Offloading and Resource Scheduling in Mobile-Edge Computing

With the explosion of mobile smart devices, many computation intensive applications have emerged, such as interactive gaming and augmented reality. Mobile-edge computing (EC) is put forward, as an extension of cloud computing, to meet the low-latency requirements of the applications. In this article, we consider an EC system built in an ultradense network with numerous base stations. Heterogeneous computation tasks are successively generated on a smart device moving in the network. An optimal task offloading strategy, as well as optimal CPU frequency and transmit power scheduling, is desired by the device user to minimize both task completion latency and energy consumption in a long term. However, due to the stochastic task generation and dynamic network conditions, the problem is particularly difficult to solve. Inspired by reinforcement learning, we transform the problem into a Markov decision process. Then, we propose an attention-based double deep $Q$ network (DDQN) approach, in which two neural networks are employed to estimate the cumulative latency and energy rewards achieved by each action. Moreover, a context-aware attention mechanism is designed to adaptively assign different weights to the values of each action. We also conduct extensive simulations to compare the performance of our proposed approach with several heuristic and DDQN-based baselines.
Hierarchical Context-Aware Recurrent Network for Session-Based Recommendation

Recently, session-based recommendations are becoming popular to explore the temporal characteristics of customers’ interactive behaviors. The user’s behavior in the session not only contains the item sequence but also rich context information such as how the user navigates to the item, the operation type, and the dwell time on the item, which may impact users’ next actions. How to incorporate these contextual features effectively for session-based recommendations remains a great challenge. In this paper, we divide them into two hierarchies: session-context at the macro-level and event-context at the micro-level, and then propose a Hierarchical Context-aware Recurrent Network (HiCAR) by incorporating both users’ micro-interaction with the item and the two-hierarchy contexts, wherein a Session Context Learning module with the n-way hybrid strategy is adopted to model multi-feature interactions in the session-context. Moreover, an Event Context Learning module consists of TIME-LSTM with a time gate is designed to model the sequential behavior with event-context. By experimenting on two real-world datasets, we find that our HiCAR model outperforms state-of-the-art baselines on both datasets, which demonstrates its advantages in modeling users’ sequential behaviors and contexts simultaneously.
Making Deep Learning-Based Predictions for Credit Scoring Explainable

Credit scoring has become an important risk management tool for money lending institutions. Over the years, statistical and classical machine learning models have been the most researched risk management tools in credit scoring literature, and recently the focus has turned to deep learning models. This transition is due to better performances that are shown by deep learning models in different domains. Despite deep learning models’ superior performances, there is still a need for explaining how these models make their predictions. The non-transparency nature of deep learning models has created a bottleneck for their use in credit scoring. Explanations of decisions are important for lending institutions since it is a requirement for automated decisions that are generated by non-transparent models to be explained. The other issue in using deep learning models, specifically 2D Convolutional Neural Networks (CNNs), in credit scoring is the need to have the data in image format. We propose an explainable deep learning model for credit scoring which can harness the performance benefits offered by deep learning and yet comply with the legislation requirements for the automated decision-making processes. The proposed method converts tabular datasets into images and thus allowing the application of 2D CNNs in credit scoring. Each pixel of the image corresponds to a feature bin of the tabular dataset. The predictions from the 2D CNNs were explained using state-of-the-art explanation methods. Furthermore, explanations were evaluated using a sanity check methodology and also performances of the explanation methods were compared quantitatively. The proposed explainable deep learning model outperforms the other credit scoring methods on publicly available credit scoring datasets.
User Interest Communities Influence Maximization in a Competitive Environment

In the field of social computing, influence-based propagation only studies the maximized propagation of a single piece of information. However, in the actual network environment, there are more than one piece of competing information spreading in the network, and the information will influence each other in the process of spreading. This paper focuses on the problem of competitive propagation of multiple similar information, which considers the influence of communities on information propagation, and establishes overlapping interest communities based on label propagation. Based on users' interests and preferences, the influence probability between nodes of different types of information is calculated, and combining the characteristics of the community structure, the influence calculation method of nodes is proposed. Specifically, aiming at the shortcomings of strong randomness in existing overlapping community detection methods that are based on label propagation, this paper proposes the User Interest Overlapping Community Detection Algorithm based on Label Propagation (UICDLP). Furthermore, when the seed node set of competition information is known, this paper proposes the Influence Maximization Algorithm of Node Avoidance (IMNA). Finally, the experimental results verified that the proposed algorithms are effective and feasible.
Forecasting of electricity production from Solar Energy in India for the next decade

With the end goal of moving towards a sustainable economy; India has started the production of electricity with the help of renewable source of energy – Solar Energy, following the target of the National Solar Mission 2022. To achieve this, a rigorous analysis of the pattern of solar energy supply for domestic electricity production is done. There is a possibility of a supply-demand gap, which is expected to increase over the next decade and hence, immediate policy intervention is required. In this research paper, the possible electricity production from solar energy is forecasted up to the year 2030 using Exponential Smoothing & Regression models as short-term forecast. This is done for minimum possible error in error types namely; RMSE, MAD & MSE. The 4th degree regression model shows the least error with RMSE = 0.759, MAD = 0.457 and MSE = 0.577. The gap between the target and the forecast creates an opportunity for investors and policymakers to invest and foster the use of renewable resources to move towards a sustainable economy.
A Formation Control Strategy of Quadrotor UAV Study Based on MIQP Cluster Track Method

In this paper, obstacle avoidance and obstacle avoidance trajectories of four UAVs are calculated based on the four-rotor UAV cluster planning theory based on MIQP (mixed integral quadratic planning). Articles from QP (quadratic programming), MIQP theory is introduced to obtain, in turn, this paper introduces the single frame four rotor unmanned aerial vehicle (uav) tracks, single frame four rotor unmanned aerial vehicle (uav) obstacle avoidance, double unmanned aerial vehicle (uav) obstacle avoidance path, the obstacle avoidance path between pairs of unmanned aerial vehicle (uav) machine, as well as multiple unmanned aerial vehicle (uav) between machine and calculation theory of obstacle avoidance algorithm verification, verify the algorithm validity in four rotor unmanned aerial vehicle (uav) trajectory planning problems. Based on the default physical parameters of common quadrotor assembly configuration in PIXHAWK flight control system, this paper ensures the effectiveness of cluster trajectory and lays a foundation for the next real flight and larger cluster control.
Classification of American Sign Language by Applying a Transfer Learned Deep Convolutional Neural Network

Having the community with more than 500,000 deaf and mute English speaking people, sign alphabet detection has become a domain of interest among the researchers for the last decade. Previously, a lot of signs of progress have been made in accurate recognition of the American Sign Language. Both convolutional neural networks and traditional machine learning classifiers have been applied formerly for the recognition process. In this study, we’ve considered an American Sign Language dataset having 36 classes of English characters and digits. The latest research on this real-world dataset achieved an accuracy of 90%. However, in our research, we introduced modified inceptionV3 architecture for the detection of American Sign characters and obtained overall correctness of 98.81% which outperformed all the previous studies by a notable margin.
Admittance Control With Unknown Location of Interaction

Admittance control is commonly used for controlling robots that physically interact with the environment or objects. Its goal is to realize a desired admittance property between the interaction wrench and robot motion. The original admittance control requires the interaction wrench and motion be converted to/from the space in which the desired admittance property is defined. In this letter, we first present the end-to-end admittance control formulation that represents the direct relationship between the generalized velocity and interaction wrench measurement when they satisfy the desired admittance property. Based on this formulation, we then develop a sampling-based algorithm for directly computing the robot reference motion without using the actual location of the interaction or the additional inverse kinematics step that is typically required for task-space admittance control. The algorithm is demonstrated in a number of simulation examples including collisions with a moving object and fixed obstacle.
SpiNet - A FrameNet-like Schema for Automatic Information Extraction about Spine from Scientific Papers

New medical research concerning the spine and its diseases are incrementally made available through biomedical literature repositories. Several Natural Language Processing (NLP) tasks, like Semantic Role Labelling (SRL) and Information Extraction (IE), can offer support for, automatically, extracting relevant information about spine, from scientific papers. This paper presents a domain-specific FrameNet, called SpiNet, for automatic information extraction about spine concepts and their semantic types. For this, we use the frame semantic and the MeSH ontology in order to extract the relevant information about a disease, a treatment, a medication, a sign or symptom, related to spine medical domain. The differential of this work is the enrichment of SpiNet's base with the MeSH ontology, whose terms, concepts, descriptors and semantic types enable automatic semantic annotation. We use the SpiNet framework in order to annotate one hundred of scientific papers and the F1-score metric, calculated between the classification of relevant sentences performed by the system and the human physiotherapists, achieved the result of 0.83.
Negative Emotion Management Using a Smart Shirt and a Robot Assistant

Negative affects such as anger, fear, nervousness, depression, etc., may increase human's susceptibility to illness. In this letter, we propose a negative emotion management system that is able to recognize negative emotions through ECG signal and perform emotion regulation through a robot assistant, which has a potential for reducing health risks. A smart shirt is developed to collect the ECG signal from the human body. The robot assistant has the ability to engage in verbal conversations with humans. Recurrence Quantitative Analysis (RQA) is used to extract ECG features for emotion classification purpose. Along with our own dataset, two other public datasets, RECOLA and DECAF, are also used to evaluate our methodology. The detection of negative emotion can trigger the robot assistant to help the user get out of such situations through interactive conversations. We tested and evaluated the proposed framework through experiments. We also assessed the effectiveness of the interactions with the robot on the emotional well-being of older adults.
A New Framework for Automatic Detection of Motor and Mental Imagery EEG Signals for Robust BCI Systems

Nonstationary signal decomposition (SD) is a primary procedure to extract monotonic components or modes from electroencephalogram (EEG) signals for the development of robust brain–computer interface (BCI) systems. This study proposes a novel automated computerized framework for proficient identification of motor and mental imagery (MeI) EEG tasks by employing empirical Fourier decomposition (EFD) and improved EFD (IEFD) methods. Specifically, the multiscale principal component analysis (MSPCA) is rendered to denoise EEG data first, and then, EFD is utilized to decompose nonstationary EEG into subsequent modes, while the IEFD criterion is proposed for a single conspicuous mode selection. Finally, the time- and frequency-domain features are extracted and classified with a feedforward neural network (FFNN) classifier. Extensive experiments are conducted on four multichannel motor and MeI data sets from BCI competitions II and III using a tenfold cross-validation strategy. Results compared with the other existing methods demonstrated that the highest classification accuracies of 99.82% (data set IV-a), 93.33% (data set IV-b), 91.96% (data set III), and 88.08% (data set V) in subject-specific scenarios, while 82.70% (data set IV-a) in the subject-independent framework are achieved for IEFD with FFNN classifiers collectively. The overall exploratory results authenticate that the proposed IEFD-based automated computerized framework not only outperforms the conventional SD methods but is also robust and computationally efficient for the development of subject-dependent and subject-independent BCI systems.
A Novel IP-ICN Coexistence Deployable Frame-work Based SDN

Information-Centric Network (ICN) emerged as a new Internet paradigm to address the problems arising from the mismatch between the TCP/IP host-to-host transmission pattern and content-oriented access mode. However, the current network infrastructure does not support ICN. ICN needs to be deployed incrementally on existing networks, and its coexistence with IP is inevitable. Software-Defined Network (SDN) is an effective solution for achieving flexible and controlled deployment. In this paper, we design a compatible format for IP packets and NDN (Named Data Networking, Typical representatives of ICN) packets based on MPLS. On this basis, a network architecture for IP and NDN coexistence, called SDNDN (Software-Defined Named Data Network), is designed in combination with SDN. With data plane programmable capabilities, functionally extended switches enable parsing, processing, and forwarding of NDN packets; algorithms such as route optimization and cache management are implemented based on a global view. Experimental results indicate that SDNDN can realize the coexistence of ICN and IP networks and has performance advantages such as reduced content retrieval time. Besides, IP and NDN in the coexistence network are suited to different traffic models, which facilitates flexible network slicing.
Methods for the localization of supporting slats of laser cutting machines in single images

The supporting slats of laser ﬂatbed machines cause process reliability problems, such as tilted parts colliding with the cutting head. In order to mitigate these problems the position of the supporting points for a part to be cut must be known, before the machines numerical control program can be changed accordingly. Being able to detect the position of supporting slats accurately is necessary to do that. This work compares image processing methods to localize the supporting slats in single images. The best features are based on ﬁlters in the frequency domain and can have accuracies above 96 %.
DEMETRIUS CANTEMIR. FAMILY RESEMBLANCES OF AUCTORITAS IN EARLY MODERN EUROPE

The present paper stakes out the destiny of certain ideas on scientific methods and epistemic and ontological representations that spread in 17 century Europe like a cultural epidemiology of representations against a deist, theosophical, empiricist and occult maze-like background. Our intellectual history study evaluates the family resemblances of auctoritas of three polymaths: Francis Bacon, Jan Baptist Van Helmont and Demetrius Cantemir along the cultural corridors of knowledge. If Francis Bacon was a theoretical founder of doctrines and Jan Baptist Van Helmont was a complex experimenting spirit, Demetrius Cantemir was an able disseminator of philosophy in South Eastern Europe and a creative synthetic spirit bridging the Divan ideas of Western and Eastern minds caught up in the busy exchange of ideas of the Republic of Letters.
Efficacy of therapeutic physical culture and hypoxic respiratory techniques in patients with chronic obstructive pulmonary disease

Horbata R.M. Efficacy of therapeutic physical culture and hypoxic respiratory techniques in patients with chronic obstructive pulmonary disease Aim: to determine the effect of therapeutic physical culture in combination with hypoxic respiratory techniques on the level of somatic health of patients with chronic obstructive pulmonary disease. Material and methods. The level of somatic health was assessed according to the method [4]. The results obtained during the initial survey were compared with tabular data with the assignment of a certain number of conditional points for each of the registered indicators. The assessment of the integrated indicator the level of somatic health was carried out taking into account the total number of points obtained and the gradation of the level of somatic health to the following functional classes: "low", "below average", "medium", "above average", "high". Results. To achieve this goal, a study of the level of somatic health in patients who were selected for the experiment. The examination was performed in 30 patients. The examination was performed twice at the beginning of the study and after the use of therapeutic physical culture in combination with respiratory techniques. Conclusion. After the application of physical therapy, which included: general developmental exercises, therapeutic gymnastics, morning hygienic gymnastics, as well as breathing exercises the number of cases of patients with chronic obstructive pulmonary disease decreased. As a result of the obtained results of rehabilitation in patients of the first degree of severity, we observe a significant improvement.
Phenotype Concept Set Construction from Concept Pair Likelihoods

Phenotyping algorithms are essential tools for conducting clinical research on observational data. Manually devel- oped phenotyping algorithms, such as those curated within the eMERGE (electronic Medical Records and Genomics) Network, represent the gold standard but are time consuming to create. In this work, we propose a framework for learning from the structure of eMERGE phenotype concept sets to assist construction of novel phenotype definitions. We use eMERGE phenotypes as a source of reference concept sets and engineer rich features characterizing the con- cept pairs within each set. We treat these pairwise relationships as edges in a concept graph, train models to perform edge prediction, and identify candidate phenotype concept sets as highly connected subgraphs. Candidate concept sets may then be interrogated and composed to construct novel phenotype definitions.
A Study of Credit Card Fraud Detection using Adaptive Pattern Matching with Optimize Itemset

-Credit card fraud events take place frequently and then result in huge financial losses. The number of online transactions has grown in large quantities and online credit card transactions hold a huge share of these transactions. Therefore, banks and financial institutions offer credit card fraud detection applications much value and demand. Fraudulent transactions can occur in various ways and can be put into different categories. This work focuses on four main fraud occasions in real-world transactions. Each fraud is addressed using a series of machine learning models and the best method is selected via an evaluation. This evaluation provides a comprehensive guide to selecting an optimal algorithm with respect to the type of the frauds and we illustrate the evaluation with an appropriate performance measure. Another major key area that we address in our project is real-time credit card fraud detection. For this, we take the use of predictive analytics done by the implemented machine learning models and an API module to decide if a particular transaction is genuine or fraudulent. We also assess a novel strategy that effectively addresses the skewed distribution of data. The data used in our experiments come from a financial institution according to a confidential disclosure agreement. As the developed machine learning models APM-OI (Adaptive Pattern Matching with Optimize Itemset) present an average level of accuracy, we hope to focus on improving the prediction levels to acquire a better prediction. Precision improve upto 21.32% during characterization process, hence maximum fraud detection may be relevant with train data. Recall improves upto 14.1% during arrangement process, hence maximum relevant train data to be classified as fraud detection. Accuracy improves upto 23.4%, hence high inspecting unpredictable examine. F1-Score improves upto 18.05%, Uncertainty of characterization becomes reduce.