Miracle-dz/UniFAR-FTUs huggingface dataset:
Practical Limits and Challenges for Powering of Wireless Systems in Implantable and Lab on a Chip Biomedical Devices and Review of the Low Power Design Techniques

There is a high demand for research into innovation and development of miniaturized electronic devices for biomedical applications such as lab on a chip device, implantable medical devices (IMD), and wireless biosensor systems (WBS), etc. These electronic systems must be wireless as wires penetrating through human skin increase the risk of infections as they act as conduits for viruses and bacteria and they also limit the flexibility of movement for patients. Ultralow power transceivers are essential because wireless communication subsystems consume most of the power in wireless biomedical implants and implanted batteries are undesirable due to their limited lifespan and the risk of infection they pose. Also, a limited amount of power can be transferred through the wireless power link system. The design requirements for wireless communication and power source subsystems for wireless biomedical implants can be determined based on the specific application of the wireless biomedical implants. Practical limits and challenges in low power and low voltage design of wireless systems in lab-on-a-chip devices and implantable biomedical devices need to be considered for investigating various techniques for low power design with the advantages and the trade-offs of each design technique. This paper outlines the practical limits and challenges for powering wireless systems in implantable and lab-on-a-chip biomedical devices and reviews low power design techniques.
Wireless Virtual Reality in Beyond 5G Systems with the Internet of Intelligence

Virtual reality (VR) over wireless has promising applications in healthcare, education, entertainment, and industrial production. However, it is difficult for the existing wireless systems to meet the needs of massive content transmission, ultra-low latency, and high computation in wireless VR. In this article, with the recent advances of edge intelligence and the Internet of Intelligence, we propose a novel framework that can jointly provide computation, storage, and communication resources for wireless VR in beyond 5G systems. In this framework, intelligence can be fully exploited to coordinate the computing, caching, and transmission systems to enable ubiquitous deployments of wireless VR. We present some key techniques and propose specific methods to support wireless VR. In addition, we propose a novel quantum-inspired RL reinforcement learning (QRL) algorithm for the multidimensional resource provisioning issue in wireless VR. In the simulations, some essential performance metrics are evaluated and some interesting results are presented, showing the effectiveness of the proposed strategy.
Using Fuzzy Inference Systems for the Creation of Forex Market Predictive Models

This paper presents a method for creating Forex market predictive models using multi-agent and fuzzy systems, which have the objective of simulating the interactions that provoke changes in the price. Agents in the system represent traders performing buy and sell orders in a market, and fuzzy systems are used to model the rules followed by traders performing trades in a live market and intuitionistic fuzzy logic to model their decisions’ indeterminacy. We use functions to restrict the agents’ decisions, which make the agents become specialized at particular market conditions. These “specialization” functions use the grades of membership obtained from an agent’s fuzzy system and thresholds obtained from training data sets, to determine if that agent is specialized enough to handle a market’s current conditions. We have performed experiments and compared against the state of the art. Results demonstrate that our method obtains predictive errors (using mean absolute error) that are in the same order of magnitude than those errors obtained by models generated using deep learning and models generated by random forest, AdaBoost, XGBoost, and support-vector machines. Furthermore, we performed experiments that show that identifying specialized agents yields better results.
Write-Optimized and Consistent Skiplists for Non-Volatile Memory

Skiplist as an in-memory index performs pretty well on rapid insertions because there are no rotations or reallocations for rebalancing. The emerging non-volatile memory (NVM) technologies have spurred a deep interest in designing efficient NVM-based skiplist. Because the data written to NVM may be partially updated or reordered by the memory controller, write operations in NVM-based persistent skiplist may suffer data inconsistency in the face of system failures. Moreover, a traditional Redo-Logging-based consistent Skiplist (RLS for short) guarantees data consistency but introduces double NVM writes, which can significantly degrade the lifetime of NVM. In this paper, we propose two write-optimized and consistent skiplists for NVM, called Atomic Skiplist (AS for short) and Atomic and Selective Consistency Skiplist (ASCS for short). AS exploits log-free failure-atomic writes for both the 0th and internal levels of skiplist to avoid double NVM writes of redo-logging. ASCS leverages selective consistency and log-free failure-atomic writes to further reduce NVM writes from index pointers. Compared with RLS, experimental results show that AS and ASCS reduce the number of cache-line flushes by 67.5% and 75%, decrease the insertion latency by 32.3% - 40.9% and 36.2% - 54.2%, degrade the deletion latency by 38.7% - 47.1% and 44.9% - 57.8%, as well as increase insertion throughput by 49.1% and 65.0% and deletion throughput by 65.1% and 80.5%, respectively.
Identifying Phasic dopamine releases using DarkNet-19 Convolutional Neural Network

Understanding the role of neurotransmitter dopamine in brain function under normal or pathological states is one of the most active areas of research in neurosciences. Failures in dopamine neurotransmission affects tremendous amount of brain abilities including movement, mental, and motivation and reward systems of the brain. Ability to measure phasic release of dopamine in specific locations of the brain will lead to a powerful tool for the neuroscientists, However, the tremendous amount of image-formed data as produced from different locations of the brain makes the manual analysis of these data cumbersome. Luckily, image processing techniques will help in solving these problems effortlessly to ease and speed the analysis for neuro-physicians. In this paper, we propose a deep-learning based identification scheme to identify the release case of phasic dopamine by examining the dopamine analysis (DA) imaging attributes using a convolutional neural network (CNN). More precisely, the proposed scheme exploits the transfer learning based DarkNet-19 network to train and identify the phasic dopamine release-2019 (PDR19) dataset into two-classes; namely, “release images,” or “non-release images” The experimental outcomes demonstrated the distinction of our identification scheme, recording an identification accuracy of 99.1% with a cross entropy loss of 0.022 attained after 25 epochs each with 100 iterations (i.e., 2500 iterations) for the 2-class classifier. Besides, our identification scheme was assessed using many other assessment factors, such as the identification precision percentage (IPP), the identification sensitivity percentage (ISnP), the identification specificity percentage (ISpP), and the identification weighted average percentage (F1P). Consequently, the performance of the proposed scheme surpassed several existing dopamine identification schemes.
A Travel Recommendation Method Based on User Personalized Characteristics with Collaborative Fusion Matrix

With the improvement of people’s living standards, people’s spiritual needs continue to increase, which makes personalized travel recommendation more and more popular. However, the complicated and overloaded information in the internet have caused troubles for people to choose travel scenic spots. Although collaborative filtering is widely used for recommendation algorithms, neither user-based nor item-based collaborative filtering take the users’ personalized characteristics into account. In order to recommend better travel scenic spots to users, we propose a personalized collaborative fusion algorithm combining user-based original collaborative filtering matrix with users’ personalized preference matrix to make the recommendation more accurate and attractive. We build our experiments on the data of Qunaer, the results show that our proposed algorithms can not only recommend similar users’ travel scenic spots, but also consider users’ own personalized characteristics.
Nachweis von natürlichen und synthetischen Östrogenen in Kläranlagen und Fliessgewässern

A method for trace analysis of steroids in water with lower limits of detection down to I ng/l was developed. Using this method natural and synthetic estrogen as well as the phytoestrogen β-sitosterol can be detected even in water samples which are extremely contaminated by organic matter. The occurrance of steroids in influents and effluents of sewage plants, river water and tap water was investigated. It was shown that the steroids are eliminated effectively but not completely in sewage plants (58 to 91 %). In effluents of different sewage plants, estradiol (up to 20 ng/l), mestranol (up to 21 ng/l), ethinyl estradiol (up to 62 ng/l) and β-sitosterol (up to 402 ng/l) were detected. In river water the synthetic estrogens mestranol and ethinyl estradiol were occasionally detected with concentrations below 5 ng/l. β-sitosterol was determined in river water in a concentration range up to 56 ng/l. The investigated natural and synthetic estrogens were not found in tap water. Only the phytoestrogen β-sitosterol was detectable in some samples with concentrations in the range from 20 to 60 ng/l.
Two Novel Algorithms for Low-Rank Matrix Completion Problem

Low-rank matrix completion has been exploited in many signal processing problems over the past few years. It has been shown that nuclear norm minimization can retrieve such low-rank matrices via uniform sampling under coherence conditions. However, in many applications such as the Netflix problem, the low-rank matrix is not coherent, hence nuclear norm minimization is unable to solve the problem because of uniform sampling. To overcome this issue, we propose two algorithms to recover both coherent and incoherent low-rank matrices. In the first algorithm, it is assumed that we have a limited sampling budget that is associated with the available number of samples. Using a portion of the sampling budget, we propose an Uncertainty Information (UI) metric to quantify the uncertainty that we have for each element and consider the elements with the most uncertainty as the most informative elements to sample. Finally, the simulations results are performed to investigate the performance of the proposed algorithms and show their superiority to conventional approaches such as nuclear norm minimization Candès and Recht, 2009 and two-phase sampling algorithm Chen et al. 2015.
The Simulation Analysis of DOA Estimation Based on MUSIC Algorithm

Array signal processing is a very important content in the field of signal processing and it's developing very rapidly in recent years. Direction of arrival (DOA) estimation is a key research. It has a broad prospect of application in radar, communication, sonar, seismology and other fields. Now the high resolution DOA estimation algorithm has achieved many important results, In this paper, we mainly studied and analyzed the classical multiple signal classification(MUSIC) algorithm, and the various factors which affect the estimation performance of the simulation, this provide the reference for the further study of MUSIC algorithm.
A Comprehensive Review of Signal Processing and Machine Learning Technologies for UHF PD Detection and Diagnosis (I): Preprocessing and Localization Approaches

Partial discharge (PD) detection and diagnosis based on the ultra-high frequency (UHF) signals is one of the most widely adopted methods to evaluate the internal insulation status of high voltage equipment. Benefit from the rapid development of computing hardware and data processing algorithms, the intelligent PD fault diagnosis method based on the UHF data has made considerable progress in the past two decades. This two-part paper aims to give a comprehensive review about the application of signal processing and machine learning technologies in UHF PD detection and diagnosis. These technologies are divided into three categories according to their respective purpose, which are the preprocessing technology, source localization technology and pattern recognition technology. As the first one of the two-part review, we focus on the preprocessing and localization approaches in this paper. Specifically, for the preprocessing topic, the methods for signal denoising, multi-source separation, and pulse segmentation are included. While for the localization topic, the time difference of arrival (TDOA) method, direction of arrival (DOA) method, received signal strength indicator (RSSI) method, and other latest methods are reviewed. For each topic, the basic ideas, recent research progresses, advantages and limitations are discussed in detail. Before the conclusion, we also make a discussion about the application effects of the above technologies and prospect some future directions accordingly. In the second paper, the pattern recognition problems based on the UHF PD data will be concentrated, especially the application of deep learning algorithms.
Aplicando Redes Neurais sem Peso em Mineração de Opinião no Twitter

Resumo—Redes Sociais online, como por exemplo o Twitter, são amplamente utilizadas pelos seus usuários para expressar opiniões sobre questões que os cercam diariamente. Por meio da aplicação de algoritmos de classificação é possı́vel a identificação de padrões capazes de categorizar as mensagens oriundas de mı́dias sociais sob diversos pontos de vista. Os classificadores WiSARD podem ser utilizados para classificação de documentos textuais no modelo bag-of-words. O presente trabalho visa comparar a aplicação do classificador WiSARD com outros classificadores amplamente utilizados na mineração de opinião no contexto de documentos textuais oriundos de mı́dias sociais online. O classificador WiSARD apresentou acurácia próxima a do SVM e superior a do Naive Bayes nos dois datasets de teste utilizados.
Prevalence of Periodontitis and Associated Factors among Pregnant Women: A cross sectional survey in Southern Province, Rwanda

Background The literature has shown the relationship between maternal periodontitis and complications associated with pregnancy. Thus, prevalence estimates and risk factor identification for periodontitis during pregnancy in Rwanda are paramount. Aim The aim of the current study was to determine the prevalence of periodontitis and identify related risk factors among pregnant women in Rwanda. Methods A cross sectional study was conducted to determine the prevalence of periodontal diseases in a convenience sample of 400 pregnant women in the Southern Province of Rwanda. A logistic regression analysis using a hierarchical approach was performed to assess the risk factors for periodontal disease. Socio demographic factors were put in the regression model first followed by a second step for other potential factors. Rwanda Journal of Medicine and Health Sciences Vol.4 No.1, March 2021 https://dx.doi. org/10.4314/rjmhs.v4i1. 10 ___________________________________________________________________________________ 132 Results The overall prevalence of periodontitis was 60.5%. Multivariable logistic regression showed that age OR=2.48 (95% CI. 1.18-5.22), education level OR=82.15 (95% CI. 8.21-822.11), socio economic status OR=2.28 (95% CI. 1.496.62), employment status OR=7.3 (95% CI. 1.38-38.74, and tobacco use OR=6.89 (95% CI. 1.78-60.65) were significantly associated with periodontitis. Conclusion Periodontitis appears to be a common problem among pregnant women in Rwanda. Risk factor screening could help identify pregnant women at higher risk of periodontal disease. Rwanda J Med Health Sci 2021;4(1):131-150 ______________________________________________________________________
The Historical Development Of Civil Aviation Security With Applications Of Time Series Modelling

The examination of civil aviation security has started to be examined in more detail after the first successful sabotage event in 1971. The purpose of the study is to determine the affecting factors to the number of sabotage events between the years 1979-2018. The Vector Autoregressive Model was used to analyze this relationship. In the time series model, the series was examined beginning from the year 1979, the beginning period of the crew resource management concept. In this year, human factors also started to be examined in civil aviation accidents. While the number of casualties and the number of total passengers in the events negatively affect the number of sabotage in the short term, their effects disappear in the long term. It was found that the number of commercial passengers had a positive effect on sabotage in the short term. This situation suggests that sabotage events trigger terrorist acts against commercial passengers in order to make an impression in the world. For this reason, the rules should be structured in the most effective way to prevent sabotage events.
Portrait of Ms. Diaz: Empirical study of patient journey mapping instruction for medical professional students

An interdisciplinary team of educators from medicine, design, and informatics piloted an online journey map (JM) exercise targeting 48 medical students and physicians assistants students. The JM exercise was designed to teach about patient empathy skills, person-centred care, and the socio-ecologic determinants of health. Prior to the exercise, the students were given a sample patient archetype introducing Ms. Diaz, a person with diabetes visiting a virtual clinic. Students worked in small groups to create a JM from Ms. Diaz’ perspective about, and experiences with, a telemedicine clinic. Our preliminary qualitative analysis of the JMs from the exercise showed that learners were able to create JMs that included all key sections including process phases, user perceptions, pain points, and design opportunities. Almost half of the responses focused upon socio-cultural and socio-technical issues as opposed to strictly clinical concerns. We believe this pilot shows the potential for journey maps to be used in health professional education to empathize with patients, identify societal problems in healthcare delivery, and design responsive solutions. Furthermore, the virtual classroom format highlights the scalability and extensibility of this strategy to a broad range of educational goals.
Learning Reasoning Mechanisms for Unbiased Question-based Counting

Biases allow deep learning models to reach impressive performances on regular datasets but can be harmful if exploited in the real-world [10, 17]. It is critical to create benchmarks that reflect these failures [2, 8]. We tackle this issue for the visual counting task [1, 7, 18], a subset of VQA [4, 11] which also requires high level reasoning abilities and displays similar biases. The appearance of certain words in the question or objects in the image is predictive of the count label. An example of bias from the TallyQA counting dataset [1] is displayed in Figure 1. First, we introduce two counting datasets meant to evaluate a model’s ability to avoid biases. Both datasets are built on with the idea of changing distributions, meaning the training and testing distributions are different, similarly to VQA-CP. Second, we introduce the Spatial Counting Network (SCN), a model that better avoids learning the biases and instead relies on more suited mechanisms for counting, and we show its superior performance on our evaluation procedure.
Accuracy and Generality of Trained Models for Lift Planning Using Deep Reinforcement Learning - Optimization of the Crane Hook Movement Between Two Points -

– An optimization system of a lifting plan must have generality to manage different design conditions and real-time changes at a construction site. Furthermore, it must optimize the construction planning and scheduling. In a previous study, we trained a two-point locomotion model for crane hook movement using deep reinforcement learning to generate and optimize lifting plans automatically. However, we did not test the accuracy and generality of the model. In this study, we test (1) the accuracy and (2) the generality of the trained model using a new environment. To evaluate the accuracy of the optimal solution, we examined the locus of the movement of each frame between two points. To verify the generality of the trained model, we solved an optimization problem of the crane hook movement under different conditions of the crane’s learning environment using the trained model. From the results, we found that the movement path was 3.6 times the shortest path and the crane hook initially moved vertical. Furthermore, the agent solved the optimization problem of the crane hook movement when the size of the crane changed. Therefore, the corresponding range increased with increasing size of the crane. However, the agent did not solve the problem when the slewing angle in the target position was larger than the slewing angle in training. Based on these results, we believe that the limited vertical movement range and rotation range of the crane reduces the accuracy and generality of the trained model.
Biomedical Data Mining

Temporal information extraction is important to understand the text in clinical documents. Extracting temporal courses of clinical events from a patient’s electronic health records may present structured information of the patient’s symptoms, diagnosis, and treatments etc. in the actually occurring order. We have developed the clinic temporal information extractor, an easy-to-use standalone application, which can automatically extract such information and mitigate the difficulty of analyzing tremendous clinical data. This extractor is able to extract temporal expressions and events with their attributes and relations. The system has been tested on the colon cancer data and brain cancer data of Semeval 2017 Clinical TempEval data and achieves the top performance comparable to the state of the art. It achieves 0.62 F-measure for the temporal expression extraction, 0.73 for event extraction and 0.59 for document time relation extraction.
An Asset Pricing Model for Cryptocurrencies

Based on stock pricing literature, this research examines if the five factors market, size, momentum, liquidity, and volatility show significant excess returns. Despite the short sample size of less than six years, a significant momentum effect is found. The factors size, liquidity, and volatility are not significant, but do show the expected sign. Then, an asset pricing model is researched to price the cryptocurrency cross-section. I find that the four-factor model containing factors for capturing the market, momentum, liquidity, and volatility contains the smallest pricing error and strongly outperforms the CAPM. The size factor is found to be redundant.