Miracle-dz/UniFAR-FTUs huggingface dataset:
Regularized ESN Based on TSVD

Echo state networks (ESNs) are a novel kind of recurrent neural networks. , with the idea of using a large randomly and sparsely connected recurrent layer. Pseudoinverse is an effective method for training ESNs. However, the pseudoinverse method is liable to produce ill-posed problems. To avoid these problems, truncated singular value decomposition (TSVD) is introduced to calculate the output weights of ESNs. First, an oversize echo state network is used to map the inputs into a large reservoir. Then, the smaller singular values as well as the corresponding eigenvectors are removed. Finally, the output weights are calculated using the remainder singular values based on the pseudoinverse method. The experimental results on benchmark problems shows the effectiveness of the proposed method.
A Learning Effect by Presenting Machine Prediction as a Reference Answer in Self-correction

Can people learn from machines behavior in microtask based crowdsourcing? Can we train the machines as our mentor even without domain expertise? In this paper, we investigate how the task results improve concerning quality during and after presenting machine prediction as a reference answer in self-correction. Four reference types were examined in the experiment; Correct, Random, Machine prediction trained by correct answers, and that trained by human answers. Learning effects were observed only in presenting machine prediction, although those accuracy rates were far from correct (100%). Moreover, there were no learning effects in "Correct" and "Random". This suggests the following hypothesis: Since machine learners make some "models" for the problem, it is easier for humans to interpret the outputs of machine learners than the results without via them; it is more difficult to interpret not only random answers but also the correct answers in a case where the perfect interpretation of the problem is difficult. Furthermore, some workers answered with higher accuracy rate than machines in the post-test. Therefore, this strategy can be expected to be useful for bootstrapping solutions in the situation where unknown problems occur without expertise or at a low cost.
Mechanism Design of a Novel Multi - Functional Assistant Robot for Rehabilitation Training

The number of hemiplegic patients increase rapidly as the emerging elderly society. Since the motor nerve can be reconnected along the motion exercise, the method to assist hemiplegic survivor rehabilitation through physical training is valuable. Compared to manual operation by physical therapist (PT), robotic assistance benefits both PT and patients on precise training, low workload and quantitative exercise. This paper presents the mechanism design of a novel multi-functional rehabilitation robot. Distinguish with the existing rehabilitation robot, the design concept of the multi-functional assistant robot is to mimic the manual techniques, which hold patient pelvic for gait rehabilitation. In this paper, the detail design of the mechanism is introduced, and the working space of the robot arm based on the actual mechanism is also calculated. Finally, we present the future research direction of Multi - functional rehabilitation assistant robot.
On Network Embedding for Machine Learning on Road Networks: A Case Study on the Danish Road Network

Road networks are a type of spatial network, where edges may be associated with qualitative information such as road type and speed limit. Unfortunately, such information is often incomplete; for instance, OpenStreetMap only has speed limits for 13% of all Danish road segments. This is problematic for analysis tasks that rely on such information for machine learning. To enable machine learning in such circumstances, one may consider the application of network embedding methods to extract structural information from the network. However, these methods have so far mostly been used in the context of social networks, which differ significantly from road networks in terms of, e.g., node degree and level of homophily (which are key to the performance of many network embedding methods). We analyze the use of network embedding methods, specifi-cally node2vec, for learning road segment embeddings in road networks. Due to the often limited availability of information on other relevant road characteristics, the analysis focuses on leveraging the spatial network structure. Our results suggest that network embedding methods can indeed be used for deriving relevant network features (that may, e.g, be used for predicting speed limits), but that the qualities of the embeddings differ from embeddings for social networks.
PER: A Probabilistic Attentional Model for Personalized Text Recommendations

In many recommendation domains, items to be recommended are associated with text. We observe that for an item, customers are usually attracted by parts of its associated text rather than the whole one. For example, a researcher may decide to read a paper if some of its words or sentences are matched with his or her own interests. However, previous methods fail to attentively focus on different parts of text according to users’ personal interests.In this paper, we first introduce a novel Personalized Attentional Network (PAN) to capture parts of text matched with a user’s personal interests. The network is able to adapt to a user’s personal interests and capture relevant parts of text for the user. Then, we propose a probabilistic attentional model for PErsonalized text Recommendation (PER). PER further integrates PAN into a probabilistic framework, which leads to a better generalization.In the experiments, we validate the effectiveness of the proposed model (PER) and show that on average, PER improves the strongest baseline by 18.2% and 14.2% in terms of Recall and Mean Average Precision (MAP), respectively.
INTEGRO: an algorithm for data-integration and disease-gene association

Biological information are continuously analyzed and collected by health-informatics tools for knowledge extraction. For instance, Next Generation Sequencing (NGS) and Micro Array technologies produce a large amount of genomic and biological data that are analyzed in clinical research trials to evaluate novel treatments. In bioinformatics many solutions has been designed using data -mining and -integration techniques to collect heterogeneous data useful to correlate human diseases and genomic data.In this paper, a python algorithm for data-integration and disease-gene association is presented. It integrates a set of curated sources (DO, OMIM, SNOMED-CT, NCBI-MedGen, and DisGeNET) establishing new relations in order to acquire as much information as possible for a specific disease in input, as well as the disease-gene associations.
Determination of the Effects of Penalty Coefficient on the Meta-Heuristic Optimization Process

In the case of unconstrained continuous value problems, a cost is minimized or a gain is maximized. While in the case of constrained optimization problems, the cost function is optimized and the conditions related to the constraints must be met. Moreover, the constraints are typically nonlinear. Constraints are often violated while attempting to improve the objective function. In this case the penalty procedure is applied. Penalties prevent violations of the constraints but often cause the optimum solution also to be missed. The most important reason why the optimal solution is missed is that the penalty coefficient is randomly determined by the researchers. In this process, researchers use very large number to prevent the solution candidates from violating the constraints. For all these reasons, unlike unconstrained continuous value optimization problems, exploring the global solution of constrained optimization problems is a challenge task. Besides, in the majority of the publications on which the meta-heuristic algorithms are introduced, the search performance of the algorithms is only tested on unconstrained continuous optimization problems. There are two problems for researchers in this case. The first of these is that there is no accepted solution for determining the penalty coefficient. Second, there is insufficient information about the search performance of meta-heuristic search algorithms for constrained optimization problems. One purpose of this study is to research the effect of penalty coefficients used for constraint violations through the optimization process. Another goal of the study is to measure the performance of the commonly used and modern meta-heuristic search algorithms on constrained optimization problems. Important information for researchers has been obtained from experimental studies.
Improved cubic B-spline curve method for path optimization of manipulator obstacle avoidance

Aiming at the special path points problem and the jitter problem caused by the first-order discontinuity in the straight-line connection of the generated route points during path planning, this paper proposes an Artificial Potential Field (APF) method and an improved cubic B-spline optimization method to avoid obstacle during the manipulator motion. The APF method is used to generate a series of intermediate path points, while the traditional cubic B-spline method is used to conquer the problem of jitter and connect these path points smoothly. Later, the cubic B-spline curve is improved to pass through some important specified path points when required. The simulation diagram verifies the feasibility of the proposed algorithm.
Feedback Feed-Forward Control of Piezoelectric Positioning Stages and Experimental Evaluation

This study addresses the problem of precise tracking control for a piezoelectric positioning stage (PZT). In order to make full use of the advantages of feedback and feed-forward control, in this paper, the feedback and feed-forward iterative learning control with inverse hysteresis compensation (FFILCWIH) has been proposed to control of the PZT. First, the architecture of the PZT platform is introduced, then, the model of PZT is identified with experimental data, furthermore, five control algorithms are performed on the PZT experimental platform. The results show that the FFILCWIH method is especially suitable for tracking the triangular waveform signal.
A System for Learning Atoms Based on Long Short-Term Memory Recurrent Neural Networks

In recent years, researchers in the fields of bioinformatics and cheminformatics have attempted to utilize machine learning methods for molecule modeling, bioactivity prediction, chemical property prediction, biology analysis, etc. In this paper, we present a system that merges the merits of various techniques such as long short-term memory (LSTM) recurrent neural networks, and is designed for learning atoms and solving the classic problems such as single task classification in the field of drug discovery. We have implemented our approach and conducted extensive experiments based on several widely used datasets such as SIDER and Tox21. The experimental results consistently demonstrate the feasibility and superiority of our proposed approach.
Image and Text Fusion for Character-Based Breast Cancer Classification

According to the World Health Organization (WHO), Breast cancer remains one of the leading causes of mortality in female premature death. Over the last decade, the increasing world-wide demand for early detection of breast cancer has resulted in necessity of new research avenues. The current work on the early detection of breast cancer, which mainly focus on the respective (independent) analysis of clinical document and medical images, ignoring the internal relevance internal interactions (relations) of images and reports, and has been proved to be somewhat effective in the experiment but not practical in the real world. In this paper, we propose fusion convolutional neural networks (f-CNNs) to extract and mine the semantic interactions of radiology images and reports. Our f-CNNs provides an end-to-end framework with convolutional architectures to exploit image representation, text composition, and the matching relations between the two modalities. More specifically, it consists of one image CNN encoding the image content and one matching CNN modeling the joint representation of image and sentence. The matching CNN composes different semantic fragments from words based on character-level representation and learns the inter-modal relations between image and the composed fragments at different levels, thus fully exploit the relations between image and sentence. Experimental results demonstrate that the proposed f-CNNs can effectively capture the information necessary for image and sentence matching. More specifically, our proposed f-CNNs significantly outperform the state-of-the-art approaches for disease classification on the datasets.
Binary Classification of Criminal Tools from the Images of the Case Using CNN

Recent advances in object detection and classification has accelerated by virtue of Convolutional Neural Networks (CNN) and large-scale image datasets like ImageNet. In this study, we aim to classify objects such as knives and weapons on the image data contained in electronic evidence, which may be criminal objects in judicial cases. To this end, we make this process autonomous by contributing to the examination of electronic evidence via artificial intelligence. For training of the CNN model presented in this paper, we use 10000 training data handled from ImageNet. The model with best parameter settings has been achieved 94.37% classification accuracy on 2500 test samples. We also investigate the effect of different parameters used in the network to increase the classification accuracy and try to achieve a set of optimal hyper parameters by tuning best possible parameters. As a result of the work, we present a detailed comparative study on binary object classification.
An Energy-Aware Selection Cooperation Protocol for Energy-Constrained Sensor Networks

Selection cooperation is an efficient and low complexity way to improve transmission reliability for wireless sensor networks. However, it is difficult to accomplish cooperation when the nodes run out of battery. How the remaining energy of nodes affects the implementation of selection cooperation is interesting. In this paper, the energy-constrained sensor networks is considered, where the relay nodes are battery-powered and energy-aware. Comprehensively considering channel quality and residual energy of relay nodes, an energy-aware selection cooperation protocol which employs feedback from the destination is proposed to enhance the reliability of data transmission and reduce energy consumption. And in order to balance the energy and extend the network lifetime, three energy-based relay selection strategies are presented. In which, the relay node with insufficient energy or bad channel quality is excluded first to ensure the transmission. The simulation results provide practical insights into the effects of the energy on the outage probability with various system parameters. In particular, our approach can slow down energy consumption of relay nodes and prolong the networks lifetime.
The Readiness of Palm Oil Industry in Enterprise Resource Planning

The palm oil industry is one of the sources of non-oil and gas foreign exchange of Indonesia. It contributes to National Gross Domestic Product at 13.96% in 2017. This study aims to examine the readiness of palm oil industry in the implementation of Enterprise Resources Planning (ERP). The readiness of palm oil industry in implementing ERP, is not only about a large cost, but also the ERP has required great resources including human respurces, technology, management, and process and data. Thus, it should fit the needs of the industry. Using the Critical Success Factors (CSF) method, this study employs five dimensions of the ERP readiness, that is management, process, technology, data, and personnel. A survey research method is employed to acquire the data and there are 19 items for those five dimensions. A gap analysis is employed to analyse the data and this method is used to determinethe disparity between existing conditions and future expectations. The findings reveal that the palm oil industry is well prepared to implement the ERP from four dimensions of management, technology, process and personnel.The management factor is the one of an factor of readiness in the ready category together with other factors including process, and human resources. Technology is the best factor which has led to increasing demands for building a strong information technology infrastructure to integrate business processes. The company needs to complete the master data onto the database and change the database system integrated into a single database that causes data is lack of ready data in ERP implementation.
Improved dielectric and electrical properties of PANI achieved by using low cost mineral additive

In the present study, polyaniline (PANI) conducting polymer was mechanically doped with volcanic basalt rock (VBR), sodium metasilicate (Na2SiO3), and sodium tetraborate (borax) minerals at 10 to 50 wt%, to improve the dielectric response. The influence of these mineral additives on the complex dielectric, electrical modulus, and AC impedance spectra of the PANI was investigated. It was revealed that among the mineral additives, the highest s' is achieved for 50 wt % sodium metasilicate doped PANI composite. In addition, the appearance of peaks in the imaginary component of the electric modulus plots, for especially borax and sodium metasilicate doped samples, indicates the existence of a relaxation process. In addition, the Cole-Cole plots in the complex electrical modulus plane suggests a non-Debye type of relaxation. The single semi-circle arcs observed in Nyquist plots for PANI/Na2SiO3 composites indicates a dominant grain (bulk) contribution. Moreover, the bulk resistance and capacitance values for PANI/Na2SiO3 composites were found to be in the order of MΩ-GΩ and pF, respectively. While the AC conductivity spectrum of the PANI/VBR composites obeys the Nearly Constant Loss model, the PANI/Borax and PANI/Na2SiO3 composites were found to obey the Super Linear Power Law model. In conclusion, the low cost Na2SiO3 additive with different weight percentages for PANI shows remarkable effects on the dielectric and electrical properties.
Performance Specifications guaranteed Variable Speed Control of Wind Turbines Subject to Input Quantization and Modeling Uncertainties

This paper investigates the problem of maximum-power-point-tracking for variable-speed wind turbines (VSWT), where the input signal is quantized by a sector-bounded quantiz-er. This quantizer has the advantages of relaxing the transmission bandwidth requirement and reducing the load of the drive system. Compared with existing results in adaptive control for VSWT, an adaptive neural-network unit is designed to account for modeling uncertainties of the wind turbine, thus, removing the requirement of availability of precise aerodynamics. The proposed control algorithm consists of a supervisory part and robust neuroadaptive part based on prescribed performance bound (PPB) connected by a soft linking module, which is able to ensure the continuity of the controller at the moment of switching. The effectiveness of the proposed control strategy is confirmed by systematic stability analysis and simulation.
Digital Economy Planning in Kuwait

The aim of this paper is to provide a case study of digital economy planning in Kuwait, a country that is attempting to match the developments of neighboring middle eastern countries, but constrained by policy and cultural limitations. The Gulf Cooperation Council (GCC), including Kuwait, push to be involved in digital and smart cities is not surprising. Dubai has no oil economy and those GCC countries reliant on oil wish to diversify their economies.
Real-Time Inspection of MDF Fiber Spread Uniformity

Wood pieces are turned into very thin fibers for the production of the Medium Density Fiberboard (MDF). These fibers are than spread on a conveyor belt before going into the hot press. These fibers must be distributed uniformly on the conveyor belt in order to produce high quality boards. When the mechanical spreader clogs up, the fiber is spread unevenly, creating trench like vertical craters. This article describes the methods and the algorithms to detect those craters in real-time. The method consists of special lighting to create shadows of those craters and algorithms to detect the shadows and the change in the texture. This article introduces a new method; direct input of Unser’s difference histograms to Neural Networks to detect the patterns caused by the uneven spread. The article then shows how existing feature elimination methods can be modified specific to this method to achieve %35 reduction in processing power compared to the conventional methods.
Quality Assurance of Concept Roles in the National Cancer Institute thesaurus

Ontologies are integral components of many health information processing environments. Hence, quality assurance (QA) of the conceptual content of any ontology is essential. A methodology for facilitating the identification and correction of a kind of error called “missing-role error” for ontology concepts is presented. Specifically, being a member of the “null-role set” is used as an indicator that missing-role errors are likely. Our QA methodology was applied to the Biological Process hierarchy, one of the hierarchies of the National Cancer Institute thesaurus (NCIt), a prominent ontology in the biomedical domain. Two hypotheses pertaining to the effectiveness of our QA approach were investigated. Many missing-role errors were discovered and confirmed in our analysis. In comparison to a control sample, our QA approach yielded a statistically significant number of concepts with missing-role errors, confirming our two hypotheses. QA is a critical part of an ontology's life-cycle, and automated or semi-automated tools for supporting this process are invaluable. The presented approach is a useful addition to the arsenal of tools available to QA personnel.
An Application of Adaptive-Capacitor-Converter in Improving the Stability of the Cascade System in DC-Microgrid

A problem about the stability of the cascade system exists in distributed power system because of the impedance characteristic curve intersection between the source converter and the load converter. In this paper, an Adaptive-Capacitor-Converter(ACC) is proposed to adjust the impedance curves with the aim of preventing intersection, which can assist the unstable cascade system to satisfy the impedance rule to make the system stable. This proposed application has these following advantages: the circuit is simple, a method of nonelectrolytic capacitor is achieved. The topology of this converter, the operation modes analysis and the control strategy are proposed. Finally the simulation result is listed to verify the correctness of the analysis and the feasibility of ACC.
Examining the Impact of Feature Selection on Classification of User Reviews in Web Pages

The user reviews in web pages can provide useful information about the content of the web page for text processing applications. Automatically extracting data from a web page is a crucial process for these applications. One of the used methods in this process is to construct a learning model with an appropriate classification method using features that are derived from data. However, some features can be either redundant or irrelevant for this model. In this study, an imbalanced dataset including 47 shallow text features obtained from web pages is utilized for extracting of the user reviews. Then, various well-known feature selection techniques are applied to reduce the number of these features. The effects of this reduction on the classification methods are also examined. The experimental results indicate that approximately half of the features are sufficient for the classification task. Additionally, the AdaBoost classifier gives the best results concerning precision of about 0.930 for the review layout prediction.