Miracle-dz/UniFAR-FTUs huggingface dataset:
A Prior Knowledge Intergrated Scheme for Detection and Segmentation of Lymphomas in 3D PET Images based on DBSCAN and GAs

Lymphoma detection and segmentation from PET images are critical tasks for cancer staging and treatment monitoring. However, it is still a challenge owing to the complexities of lymphoma PET data themselves, and the huge memory requirements for 3D volume data. In this work, a prior knowledge integrated scheme based on DBSCAN and GAs is proposed to detect and segment lymphomas in 3D PET images. To reduce memory requirements and add more feature information, billions of voxels in 3D volume data are first aggregated into supervoxels. Then, such supervoxels serve as basic data units for further clustering the supervoxels by using DBSCAN algorithm, in which a new similarity measure based on prior knowledge is proposed. Meanwhile, a genetic algorithm is used to search the most appropriate parameters of DBSCAN to obtain the optimal clustering results. Finally, a series of comparison experiments among various feature attributes and various similarity functions are performed, and the results are encouraging, and show that the proposed scheme by intergrating the prior knowledge of organ distributions can achieve better performance than traditional methods.
Linear and Sensitive Detection Electronics for Capacitive Sensors

This paper presents a linear, sensitive and simple signal conditioning circuit for capacitive sensors. The proposed two stage circuit comprises a capacitance to voltage conversion circuit then voltage to frequency converter. The capacitance to voltage conversion circuit is designed to compensate initial offset sensor capacitance. It is a challenge to measure small capacitance change in capacitive sensor with large initial offset capacitance. Therefore, compensation of initial offset capacitance of capacitive sensor based instrumentation system is demanded. The proposed linear and sensitive signal conditioning circuit effectively compensates initial offset capacitance of the capacitive sensor. The circuit is tested for a wide range of capacitance (57.74 pF-1050.9 pF) which gives adjusted R-squared equal to 0.99901 for a linear fit response characteristic. The sensitivity of the circuit is 71.24 Hz/pF. However, there is a trade-off between sensitivity and working range of the circuit.
Intelligent Diagnostic System of KAMAZ Robotic Vehicle Units

The article is devoted to the development of an intelligent system using artificial neural networks to monitor and diagnose the technical states of a robotic vehicle. The main task is the continuous monitoring of the main unit states during operation. The absence of a driver makes it necessary to use smart technology to analyze information about the technical condition for the assessment and management decisions. In this paper two approaches to the diagnosis of aggregates are considered. The first approach consists in remote control units for their thermal conditions using comparative infrared thermography. The neural network program analyzer is proposed. The neural network's training is conducted both on real thermograms of aggregates and using model thermograms. During the operation of a robotic vehicle, continuous remote monitoring of the units is carried out using one or several thermal imagers. According to the measured thermograms, the neural network classifies states and determines the operability or failures in the aggregates. The second approach is related to the analysis of the history of aggregate parameters measurement, including thermography. The artificial neural network during car operation predicts the behavior of the monitored units based on the evaluation of the parameter time series. This makes it possible to prevent failure of the most important units. The developed neural networks are focused on the control of the following systems and components of a robotic vehicle: engine, transmission and the elements of the internal control system. The proposed solutions using neural networks provide a continuous and remote control of the KAMAZ car, which increases the reliability of agricultural robotic system.
Hole transport layer free stable perovskite solar cell with low temperature processed carbon electrodes

Perovskite solar cells, despite showing promising efficiency of more than 22%, is still far from realizing its full prospect on commercial scale, majorly owing to the cost associated with gold or silver counter electrode as well as hole transport material. Herein we report, low temperature processed carbon paste for hole transport layer free perovskite solar cell (PSC) fabricated in ambient conditions. Conductive (11.46 Ω □-1) and adherent carbon paste were prepared with acrylic and ethyl cellulose based binders. We have optimized the amount of binder required for good adherence as well as conductivity. Perovskite solar cell with the FTO/TiO2 compact layer/TiO2 mesoporous layer/MAPbI3/carbon architecture was fabricated with 7.41% power conversion efficiency. The prepared devices show promising stability for more than 500 hours.
Combined Self-attention Mechanism For Biomedical Event Trigger Identification

As a basic task of natural language processing, the performance of biomedical event trigger identification is lower than that of named entity recognition(NER), which is mainly limited by complex events and the scale of annotated corpus. In recent years, researches on the identification of biomedical event trigger mainly focus on the small size of corpus, and tend to use external knowledge or joint training to improve the final performance. However, there are few researches on the insufficient mining of the inherent features of texts caused by complex events. This article focuses on biomedical event trigger identification in text articles using a neural network model that combines bidirectional gated Recurrent Unit with a self-attention mechanism. In this method, context-related information is captured by bidirectional gated recurrent unit to better understand and express sentence structure, self-attention mechanism is adopted to fully explore the features of text itself, and finally to improve the performance of trigger identification of complex events. A series of comparative experiments were carried out on MLEE corpus, and the experimental results verified the effectiveness of the method.
Predicting Missing and Spurious Protein-Protein Interactions Using Graph Embeddings on GO Annotation Graph

Protein-protein interaction (PPI) prediction is a key step towards many bioinformatics applications including prediction of protein functions and drug-disease interactions. However, previous research on PPI prediction rarely considered missing and spurious interactions in PPI networks. To address these two issues, we define two corresponding tasks, namely missing PPI prediction and spurious PPI prediction, and propose a novel method that employs graph embeddings to learn vector representations from constructed Gene Ontology (GO) annotation graphs. Our method leverages the information from both term-term relations among GO terms and term-protein annotations between GO terms and proteins, and preserves properties of both local and global structural information of the GO annotation graph. We compare our method with methods based on information content and on word embeddings, using three PPI datasets from STRING database. Experimental results demonstrate that our method is more effective than those compared methods.
Implementation Fixed Order Interval Method for Developing Inventory Control System Application

Most companies engaged in the yarn spinning industry have problems that occur in the current system, namely the processing of incoming and outgoing goods data and yarn sales data that is still recorded in the ledger that contains how many items enter and exit and inventory data recorded inventory manually in the book and then copied back to the computer by the company administration. So companies have difficulty in counting the goods and in getting information about the stock of goods to have difficulty, sometimes the information provided is not following the availability of goods. The purpose of this research is to design an inventory control system application that can manage incoming and outgoing goods, stock items, and yarn sales using the Fixed Order Interval (FOI) method to plan stock. The results of this study are the design of Inventory Control Applications and inventory planning that have used the Fixed Order Interval (FOI) method.
Scalable Efficient Byzantine Fault Tolerance

With the development of BlockChain [1], more research attention is placed on Byzantine Fault Tolerance. This paper proposes Scalable Efficient BFT protocol (SeBFT) based on MinZyzzyva [4]. Replicas in SeBFT can join or exit freely in groups. SeBFT also inherits MinZyzzyva’s excellent performance with the communication complexity is O(n) and replicas number required within group is 2f+1.Moreover, this paper also proposes HSeBFT which applies SeBFT to hierarchical network and further expands the maximum network scale and provides a BFT solution for multi-level network. Compared with the existing scalable solution, SeBFT has great advantages in algorithm complexity, delay, scalability and fault tolerance.
Energy Efficient Robust Pre-Charge Sense Amplifier

The pre-charge sense amplifier is essential for the design of hybrid circuits based on the combination of CMOS and spin devices like magnetic tunnel junction (MTJ). The double tail sense amplifier (DTSA) offers better reliability, power efficiency and good performance. This paper offered a novel modified double-tail sense amplifier' (MDTSA) and compared its performance in terms of delay, power and sensing error with the existing DTSA.
Research on Multi-level Offloading Scheduling Algorithm for Mobile Edge Computing

With the rapid development of wireless sensor networks, existing sensor devices are difficult to meet the needs of low latency and high reliability applications. In order to improve the efficiency of the program, some tasks are migrated from the local device to the edge cloud or edge server through edge computing technology to reduce the running time of the application and the power consumption of the device. This paper proposes a multi-level offloading model for edge computing, and studies the offloading scheduling algorithm to minimize the power consumption of the sensor device under the specified delay. This paper proposes a fast algorithm based on greedy strategy, which optimizes the particle swarm optimization algorithm combined with greedy strategy. The experimental results show that the total cost of the HGA algorithm in multi-level offloading is reduced by 23.6% and 8.4%, respectively, compared to the traditional single-level offloading. The proposed HPSO algorithm reduces the total cost of the HGA algorithm by 12.1%.
The Key Role of Ontology Alignment and Enrichment Methodologies for Aligning and Enriching Dwipa Ontology with the Weather Concept on the Tourism Domain

The Dwipa Ontology III is a one of many sources of knowledge about the tourism domain in Indonesia. It stores information and its relations mainly about accommodation, attraction, activity, and amenity. However, this particular ontology is lack to present information about weather which highly needed by tourists during traveling to the tourism destinations. Firstly, this research aims to align two sources of weather ontologies: the weather ontology and the weather ontology for smart city in order to create a new concept of weather ontology. This new weather ontology concept is generated by measuring its linguistic similarity. Secondly, this research focus on enriching the concept of initial ontology named the Dwipa Ontology III with the concept generated by ontology alignment approaches. The ontology alignment method has been successfully mapped two weather ontologies into a new concept of weather that consist of 14 literal data: WeatherCondition, WeatherReport, dewPointAtmospere, unit, gustingWind, Celsius, Humidity, AtmosperePressure, Speed, Temperature, Interval, hasDate, hasSpeedValue, and hasUnit. This new concept has been enriched to the Dwipa Ontology III by adding one new concept/ class named weatherReport that consist of four sub classes: Unit, Interval, WeatherCondition and Temperature and a total of 255 instances. This latest version of aligned and enriched ontology is given name as Dwipa Ontology III+.
PV-TEG- WiFi Multiple Sources Design Energy Harvesting System for WSN Application

Ambient energy harvesting is becoming an essential factor for wireless sensor nodes applications. Harvested energy from ambient sources, such as solar, indoor light, thermal energy, vibration, and radio frequencies (RF) provide a potential power capability that may supplement batteries to provide longevity to the sensor nodes. In an indoor setting, the intermittent availability of these sources will likely lessen the energy densities. Using only a single ambient-energy harvested source may not sustain the power requirement for the design of a battery-less wireless sensor node. Thus, this work investigates and design a power combiner circuit of the indoor light, thermal. The design utilized a cross-coupled charge pump operation to combined the same DC ambient sources, the PV cell, and TEG energy transducers. The system design is simulated using the 65 nm CMOS process technology. The simulation shows that the system is able to supply peak power of 1.69 mW at $\pmb{500}\ \Omega$ load. Peak power efficiency is 69% with 919 μA ILOAD.
A Compact Memory Structure based on 2T1R Against Single-Event Upset in RRAM Arrays

The single-event upset (SEU) has significant influence on the reliability of 1-transistor-1-RRAM (1T1R) array due to heavy ion strikes. This paper proposes a compact memory structure based on 2-transistor-1-RRAM (2T1R), which exploits the signal space between adjacent cells in the RRAM array structure. Results show that the proposed structure can successfully alleviate the SEU effect in RRAM array - it eliminates the overhead of 1ns delay and 400pJ power consumption of READ operation, as well as 2ns delay of RESET operation, at the cost of only 1.9% area overhead compared to the 1T2R structure.
Sequential Detection of Flying Small Target in Infrared Images Based on Generalized Low-Rank Background Estimation

Addressing the problems of flying small target detection in infrared image sequences, a new approach is proposed based on generalized low-rank background estimation. Firstly, the generalized low rank approximations is introduced to model infrared background image over sequential infrared images. Subsequently, the foreground target image is achieved by background subtraction with the generalized low-rank background estimation. Finally, flying small target detection is achieved over separated target image by threshold segmentation. The experiment results on two infrared image sequences of flying plane demonstrate that the proposed method have effective detection performance and outperform the baseline methods in precision and recall evaluation.
Aspect Based Sentiment Analysis in E-Commerce User Reviews Using Latent Dirichlet Allocation (LDA) and Sentiment Lexicon

User ratings on products sold bye-commerce greatly influence the number of purchases. Positive ratings will encourage other buyers to participate in buying the product. While negative ratings given by users will reduce the interest in purchasing products. Nonconformities between rating and user reviews sometimes provide a wrong assessment of a product. This happens because buyers also provide reviews on the quality of delivery services from e-commerce. Based on that issue, the utilization of the Latent Dirichlet Allocation (LDA) could be used on sentiment analysis of the user reviews. Sentiment analysis of the user reviews aims to facilitate e-commerce in informing the product quality as rating supporters that have been given by users. This research aims to determine the classification performance of sentiment analysis on e-commerce user reviews using the LDA algorithm with input data in the form of e-commerce user reviews. Then, compare the application of sentiment analysis of the user reviews with the use of general training data and per category training data. The result of this research showed that in the first iteration the best architecture was produced by the application of LDA with a combination of parameters of alpha 0.001, beta 0.001, and number of topics 15. The architecture had 67,5% accuracy level. From the best architecture then training data input is given based on each product review category. The result showed that the combination of the usage of general data and per category data indicate an increase in the average accuracy of 0,82 % from the three-test data. Therefore, in order to produce the best performance of building a classification model of sentiment analysis of the user reviews, it should be performed by applying LDA with a combination of general data and per category data usage
A Neural Network-Based Approach for Cryptographic Function Detection in Malware

Cryptographic technology has been commonly used in malware for hiding their static characteristics and malicious behaviors to avoid the detection of anti-virus engines and counter the reverse analysis from security researchers. The detection of cryptographic functions in an effective way in malware has vital significance for malicious code detection and deep analysis. Many efforts have been made to solve this issue, while existing methods suffer from some issues, such as unable to achieve promising results in accuracy, limited by prior knowledge, and have a high overhead. In this paper, we draw on the idea of text classification in the field of natural language processing and propose a novel neural network to detect the type of cryptographic functions. The new network is an end-2-end model which includes two important modules: Instruction-2-vec and K-Max-CNN-Attention. The Instruction-2-vec model extracts the “words” of assembly instructions and transfers them into continuous vectors. The K-Max-CNN-Attention is used to encode the instruction vectors and generate the representation of the function. And we designed a softmax classifier to predict the categories of the functions. Extensive experiments were conducted on a collected dataset which contains 15 common types of cryptographic functions extracted from malware, to assess the validity of the proposed approach. The experiment results showed that the proposed approach archives a better performance than the recent embedding network SAFE with the Precision, Recall and F1-score of 0.9349, 0.8933 and 0.9020, respectively. We also compared it with four widely-used tools, the results demonstrated that our approach is much better in accuracy and effectiveness than all of them.
Control and State of Charge Balancing Technique of a Modular Multilevel STATCOM Integrated with Battery Energy Storage System

Power generation based on wind energy has seen significant growth over the last decade around the world. But this has shown negative impact on the performance of the existing grid due to injection of fluctuating power by these systems. This has necessitated to employ STATCOM and an Energy Storage System (ESS) for a grid connected Wind Energy Generating System (WEGS) at its Point of Common Coupling (PCC). This paper presents a configuration of STATCOM with integrated energy storage system which has the ability to provide active and reactive power support simultaneously for high-power/high-voltage applications. This is formed by distributing battery energy storage system over the dc-link of a Modular Multilevel Converter(MMC), which has higher redundancy and modularity compared to the two/three level based topologies. The control methodology to operate the configuration and an algorithm to balance the SoC of distributed battery modules are discussed in this paper. The complete system is simulated in PSCAD /EMTDC and the results are presented to show its effectiveness for WEGS.
Functionally-Predefined Kernel: a Way to Reduce CNN Computation

Convolutional Neural Networks (CNNs) have achieved high classification accuracy in image recognition, and now, they are widely used for numerous applications. For higher accuracy or more advanced applications, CNNs need to consume tremendous computational resources and time. Hence, many studies for reducing the computational cost of CNNs are actively being conducted. However, many previous methods for reducing the computational cost lead to a non-negligible loss in output accuracy. Therefore, it is still a challenge to reduce the computational cost of CNNs with keeping the output accuracy high. In this paper, we propose a novel concept "Functionally-Predefined Kernel" to reduce the computational cost for CNN training and discuss the potential of computation reuse to reduce the computational cost for CNN inference. Our experimental results show that the number of parameters to be trained can be significantly reduced by utilizing Functionally-Predefined Kernels without accuracy loss. In addition, we revealed that CNN’s inference process includes many convolution operations with the same inputs and computation reuse, therefore, has high affinity to CNN computation.
Social inequalities and prevalence of depressive symptoms: a cross-sectional study of women in a Mexican border city, 2014

ABSTRACT Objective. To assess the association between intersectional disadvantage and clinically significant depressive symptoms (CSDS), describing the magnitude of social inequalities in the prevalence of symptoms among adult women in Tijuana, Mexico. Methods. This was a cross-sectional study. CSDS were assessed using the Centers for Epidemiological Studies Depression Scale among a probability sample of 2 345 women from 18 – 65 years of age in 2014. CSDS prevalence was calculated according to categories of three social stratifiers: socioeconomic status (SES), educational attainment, and fertility (number of children). Social inequality was measured with the slope index of inequality (SII) and the concentration index (CIx). Intersectionality among stratifiers was explored descriptively and with multivariable regression analysis. Results. CSDS prevalence was 17.7% (95%CI: 15.1% – 21.0%). The SII and CIx showed inequity in all social stratifiers. The absolute difference in CSDS prevalence between the lowest and highest ends of the SES gradient was 21.9% (95%CI: 21.5% – 22.4%). Among the most disadvantaged women, i.e., those at the intersection of lowest SES, lowest educational attainment, and highest fertility, the CSDS prevalence was 39.5% (95% CI: 26.0% – 52.9%). Conclusions. Disadvantage along multiple axes was associated with CSDS. Efforts to improve the mental health of women should include equity-oriented policies that address its social determinants.