Miracle-dz/UniFAR-FTUs huggingface dataset:
Bagging and Fusion of Multiple Feature Extraction Models for Early Diagnosis of Alzheimer's disease

In this work, a new algorithm is proposed using a combination of different point detection feature extraction methods like SURF, FAST, BRISK, Harris and Min Eigen for early diagnosis and prediction of various stages of Alzheimer's disease. This new method is integrated with different classifiers like Decision Tree and Naive Bayes for the classification of different stages of Alzheimer's disease. The classification accuracy and performance parameters are determined and analyzed for both classifiers. The new proposed method provides an accuracy rate of 98.13% when Decision tree classifier is used and gives an accuracy rate of 97.31% when Naïve Bayes classifier is used. The accuracy rate and performance of the proposed method is found to be very high when compared to the existing feature extraction methods. From the analysis results it's been observed that the new proposed method is found to be more accurate and sensitive than the existing algorithms since it uses multiple combined feature extraction techniques, when compared to the techniques which uses single feature extraction method.
A learning-assisted dynamic security enabled operational planning with transferable load

Towards penetrating increasing renewable energies, demand side has been gradually incorporated into power system dispatch due to its immense potential. However, extensive load shifts only for accommodating clean energies may jeopardize system security. To address this issue, a secure demand response strategy for operational dispatch is proposed based on learning assisted solving scheme. Firstly, backpropagation neural network (BPNN) is used to construct the security rule quantified by total transfer capability. This rule serves as a surrogate, is then embedded into demand side involved dispatch for implication of dynamic security margin. An efficient solving scheme is applied to solve the surrogate-assisted dispatch at the end. The modified IEEE 39-bus system is carried out to testify the proposed scheme. The results indicate that, our method enables efficient decision making to allow demand side to help balance production and demand without compromising the dynamic security.
FaStER: Fast, Stable, Expendable and Reliable Radio Map for Indoor Localization

At present, fingerprinting based localization methods gain a massive attraction for numerous indoor location-based services. However, creation of a radio map is a laborious and time-consuming process that limits the applicability of this method. In this study, we propose FaStER, a Fast, Stable, Expendable and Reliable radio map creation system for fingerprinting based indoor localization applications. Data from a chest mounted IMU with ZigBee protocol based wireless network technology and indoor floor plan are used as inputs to the system. The proposed system ensures high accuracy both in terms of location estimation and RSSI acquisition by correcting errors generated from the IMU and from the presence of human body. The stability of a generated radio map is guaranteed by exploiting the concept of RSSI similarity index. The system is especially proposed for facilitating indoor location-based services with chest mounted wearable devices towards IoT connected wearable health and environmental monitoring applications. Results demonstrate that the proposed system can create a fast, stable, expendable, and reliable radio map that can help to improve the accuracy for location fingerprinting.
Security Issues in Fog Computing

Fog computing (FC) is complementary to cloud, and widely used for communication, computation, and storage in real time IoT devices and applications. Fog nodes are present in FC that is connected to many IoT devices through different gateways that makes FC prone to attacks. Due to vulnerable nature of FC, security has been always a prominent issue and that needs to be talk about and resolved efficiently. In this paper, different security issues of FC are discussed along with the causes. The presented paper mainly focuses on the solutions of the FC security issues with blockchain (BC) technology and categorizes the solutions in detailed table form. BC-FC integration is an important factor for understanding security solutions of FC, which is also discussed in a well-organized manner.
Inside Touch: Presentation of Tactile Feeling Inside Virtual Object Using Finger-Mounted Pin-Array Display

Previously, tactile displays focused on the tactile presentation of the surface of a virtual object. In contrast, this study attempts to provide a tactile feeling on the space inside a virtual object when fingers penetrate it. Our previous study developed a finger-mounted pin-array display with the smallest pin pitch, representing the highest spatial resolution. Using the display, we attempt to present the tactile impression of “Rough,” “Grainy,” and “Sparse” which correspond to Japanese onomatopoeia of “Zara-Zara,” “Tsubu-Tsubu,” or “Chiri-Chiri” with simple patterned stimuli arranged in 3D space. A series of experiments were conducted in this study to determine the following: (1) the stimuli that can provide users with different tactile impressions inside objects and (2) the effect of tactile display spatial resolution on the recognition of tactile impressions inside objects. The results clarified that we could provide three different tactile impressions in space with certain stimuli configurations. Besides, the results demonstrated that participants recognized the tactile impression better with a larger spatial resolution configuration of the device. This study reveals a new field of tactile presentation, that is, tactile presentation inside an object.
A Survey on Artificial Neural Network based Stock Price Prediction Using Various Methods

Stock price prediction has been a big challenge since many years due to the unpredictable nature of the market with around 200 transactions per second, making it hard to predict the stock price. Stock market also known as share market or equity market is a cluster of sellers and buyers of stock. Big and private companies sell a part of their company to investors in order to generate crowdfunding. Since the stock market is quite sensitive to economic sentiments and rapid changes, the main objective of the stock market is to develop new techniques to forecast higher profits for stock prices. This paper aims to examine different deep learning methods by which the future stock prices can be accurately predicted.
Multi-Task Learning For Thyroid Nodule Segmentation With Thyroid Region Prior

Thyroid nodule segmentation in ultrasound images is a valuable and challenging task, and it is of great significance for the diagnosis of thyroid cancer. Due to the lack of the prior knowledge of thyroid region perception, the inherent low contrast of ultrasound images and the complex appearance changes between different frames of ultrasound video, existing automatic segmentation algorithms for thyroid nodules that directly apply semantic segmentation techniques can easily mistake non-thyroid areas as nodules. In this work, we propose a thyroid region prior guided feature enhancement network (TRFE-Net) for thyroid nodule segmentation. In order to facilitate the development of thyroid nodule segmentation, we have contributed TN3k: an open-access dataset of thyroid nodule images with high-quality nodule masks labeling. Our proposed method is evaluated on TN3k and shows outstanding performance compared with existing state-of the-art algorithms. Source code and data are available1.
Towards EEG-based Performance Assessment in Dataset Annotation Tasks

Artificial intelligence is more and more adopted to complement human activity in solving complex tasks in several domains, including healthcare, security, finance, and automation. In order to be effective, several artificial intelligence tools rely on large training sets of carefully annotated data. Since labeling is mostly performed manually, it is a costly and error-prone process. Hence, there is increasing interest in devising innovative tools to support the annotation task. In this paper, we report an initial investigation on the application of EEG data mining for evaluating the performance of humans carrying out image annotation tasks. Our approach relies on a cheap portable EEG sensor and on supervised learning methods. We collected a dataset from five volunteers, and performed an initial evaluation of our technique. The achieved results are promising, and pave the way to several research directions. To the best of our knowledge, our work is the first one applying EEG data mining for assessing the performance of labelers.
Tree Localization and Monitoring on Autonomous Drones employing Deep Learning

Forest management relies on the analysis of satellite imagery and time intensive physical on-site inspections. Both methods are costly and time consuming. Satellite based images are often not updated in a sufficient frequency to react to infestations or other occurring problems. Forest management benefits greatly from accurate and recent information about the local forest areas. In order to react appropriately and in time to incidents such as areas damaged by storms, areas infested by bark beetles and decaying ground water level, this information can be extracted from high resolution imagery. In this work, we propose UAVs to meet this demand and demonstrate that they are fully capable of gathering this information in a cost efficient way. Our work focuses on the cartography of trees to optimize forest-operation. We apply deep learning for image processing as a method to identify and isolate individual trees for GPS tagging and add some additional information such as height and diameter.
Information Flow Through U-Nets

Deep Neural Networks (DNNs) have become ubiquitous in medical image processing and analysis. Among them, U-Nets are very popular in various image segmentation tasks. Yet, little is known about how information flows through these networks and whether they are indeed properly designed for the tasks they are being proposed for. In this paper, we employ information-theoretic tools in order to gain insight into information flow through U-Nets. In particular, we show how mutual information between input/output and an intermediate layer can be a useful tool to understand information flow through various portions of a U-Net, assess its architectural efficiency, and even propose more efficient designs.
Anomaly Detection Using Autoencoder With Feature Vector Frequency Map

Anomaly detection uses various machine learning techniques to identify and classify defective data on the production line. The autoencoder-based anomaly detection method is an unsupervised method that classifies abnormal samples using an autoencoder trained only from normal samples and is useful in environments where it is difficult to obtain abnormal samples. This method uses an abnormal score based on the reconstruction loss function, making it difficult to detect defects, such as stains, having a similar texture to a normal sample. To solve this problem, we propose an anomaly detection method using a vector quantized variational autoencoder and a feature vector frequency map. We use the prototype vector histogram and its frequency for anomaly detection instead of the reconstruction loss function. The prototype vector histogram is obtained from the vector quantized variational autoencoder’s codebook in the training stage. The feature vector frequency map of the input image is generated using the prototype vector histogram in the inference stage. We calculated the abnormal score using the generated frequency map and classified the abnormal samples. The experimental results showed that the proposed method has a higher Area Under Receiver Operating Characteristics (AUROC) than the previous method in stain and scratch defects.
Visualization of Topic Transitions in SNSs Using Document Embedding and Dimensionality Reduction

Social networking services (SNSs) have become the main avenue, where people speak their thoughts. Accordingly, we can explore people’s thoughts by analyzing topics in SNS. When do topics change? Do they ever come back? What do people mainly talk about? In this study, we design and propose a novel visual analytics system to answer these interesting questions. We abstract the topic per unit time as a point in a two-dimensional space through document embedding and dimensionality reduction techniques and provide supplemented charts that represent words appearing at a certain time and the time-series change of word occurrence over the entire period. We employ a novel text visualization technique, called semantic preserving word bubbles, to visualize words at a certain time. In addition, we demonstrate the effectiveness of our system using Twitter data about early COVID-19 trends in Japan. We propose our system to help users to explore and understand transitions in posted contents on SNS.
Compact Low-Profile Differential Filtering Microstrip Patch Antenna With High Selectivity and Deep Rejection Using Single-Layer Substrate

A compact low-profile differential filtering microstrip patch antenna with high selectivity and deep rejection using single-layer substrate is proposed. The presented antenna mainly consists of the quasi-H-shaped patch (QHSP) and the meandering quasi-T-shaped resonator (QTSR) which is composed of two open-end meandering arms in opposite direction and an inset coupling structure. To the best of our knowledge, a novel differential feeding structure is adopted for the first time that a pair of differential inset coupling feeding structures are placed in opposite direction to drive the QHSP with insetting-capacitive-coupling, simultaneously. Its equivalent lumped circuit is further analyzed to study the characteristics. In the point of views of filter and antenna design, the meandering QTSR not only acts as the first stage of resonator, but also a feed structure, and the QHSP can be treated as the last order of resonator as well as the radiator. A prototype of the proposed antenna is designed, analyzed, simulated and measured. Two controllable radiation nulls (RNs) locate at the both sides of the passband, a sharp skirt selectivity with the sideband roll-off rates of 667/182 dB/GHz and out-of-band suppressions of −40.1/−39 dB in lower and upper stopbands are achieved, respectively. The measured flat realized gain can be obtained in the passband with the average gain more than 6 dBi and the low cross-polarization level can be achieved. The dimension of the proposed antenna is only $0.361\lambda _{0} \times 0.426\lambda _{0} \times 0.013\lambda _{0}$ . Good agreement between the simulated and measured results is obtained, which verifies the proposed antenna with the differential feeding structure has excellent performance only using single layer low profile substrate. The proposed antenna is a good candidate for WLAN and power harvest application.
Convergence Issues in Sequential Partial-Update LMS for Cyclostationary White Gaussian Input Signals

The computational complexity of adaptive filtering algorithms increases with the number of filter coefficients. In applications where a long adaptive filter is required, the complexity can become prohibitively large due to scarce digital signal processing (DSP) resources such as the available DSP slices on a field programmable gate array (FPGA). Several partial-update least mean square (PU-LMS) algorithms have been proposed to address this problem. Among these algorithms, the sequential PU-LMS alleviates the computational complexity by updating a small subset of adaptive filter coefficients in a round-robin fashion at each iteration. Being data independent, it has no complexity overheads associated with coefficient selection. However, a major problem with the sequential PU-LMS is its vulnerability to poor convergence for cyclostationary input signals encountered in many practical applications. This letter presents a detailed analysis of the convergence issues with the sequential PU-LMS algorithm for cyclostationary white Gaussian input signals. The convergence analysis predicts when poor convergence is likely to occur based on a relationship between partial update and cyclostationary input signal parameters. The insights gained through this analysis can be used to adjust the sequential partial update parameters in order to avoid convergence difficulties. Simulation examples are presented to confirm the validity of the convergence analysis conducted.
Some of Biological and HistopathologicalEffects of Cooked Red and White Beans (Phaseolus Vulgaris L.)Consumption on Obese Rats

The present study was conducted to investigate some biological effects of cooked red and white beans (Phaseolus Vulgaris L.) on obese rats.The obtained results showed that obese-hyperlipidemic rats have significant (p<0.05) increase in body weight, % change of body weight, serum levels of total lipids, triglycerides, total cholesterol, LDL-c, blood glucose, leptin hormone (LH), malondialdehyde (MDA)andactivities ofaspartate aminotransferase (AST), alanine aminotransferase (ALT) andalkaline phosphatase (ALP) enzymesas well asvalue of atherogenic index,and have significant (p<0.05) decrease inserum HDL-c, insulin, reduced glutathione levels and serum activities of glutathione peroxidase (GPx), superoxide dismutase (SOD) and catalase (CAT) enzymes, compared to those of the normal rats. However, administration of both cooked red and white beans to obese-hyperlipidemic rats significantly ameliorated all of the above parameters, compared to that of untreated obese-hyperlipidemic rats. In addition, the obtained result of histopathological examination showed that obese-hyperlipidemic rats have dilated blood vessel with thick muscle wall and intramuscular hemorrhages in heart. While, vasculitis with thick inflamed wall and perivasculitis with dilated and congested blood vessel in were observed in aorta. However, administration of red or white beans caused partially Journal of Home Economics,Volume 27, number(1), 2017 192 improvement in hear and aorta sections, which more detectable in red beans, compared to those in untreated-obese rats.This study concluded that the white or red beans have antiobesity, antihyperlipidemic and antihyperglycemic potential and ameliorate the antioxidant-defense system in rats fed on highfat diet.
Analyzing the Direction of Emotional Influence in Nonverbal Dyadic Communication: A Facial-Expression Study

Identifying the direction of emotional influence in a dyadic dialogue is of increasing interest in the psychological sciences with applications in psychotherapy, analysis of political interactions, or interpersonal conflict behavior. Facial expressions are widely described as being automatic and thus hard to be overtly influenced. As such, they are a perfect measure for a better understanding of unintentional behavior cues about socio-emotional cognitive processes. With this view, this study is concerned with the analysis of the direction of emotional influence in dyadic dialogues based on facial expressions only. We exploit computer vision capabilities along with causal inference theory for quantitative verification of hypotheses on the direction of emotional influence, i.e., cause-effect relationships, in dyadic dialogues. We address two main issues. First, in a dyadic dialogue, emotional influence occurs over transient time intervals and with intensity and direction that are variant over time. To this end, we propose a relevant interval selection approach that we use prior to causal inference to identify those transient intervals where causal inference should be applied. Second, we propose to use fine-grained facial expressions that are present when strong distinct facial emotions are not visible. To specify the direction of influence, we apply the concept of Granger causality to the time-series of facial expressions over selected relevant intervals. We tested our approach on newly, experimentally obtained data. Based on quantitative verification of hypotheses on the direction of emotional influence, we were able to show that the proposed approach is promising to reveal the cause-effect pattern in various instructed interaction conditions.
The Bottleneck of Distribution in Supply Chain's Manufacturers

Distribution is a form of supply and demand management. Suppliers send the raw materials to producers, where they are transformed into finished products. Likewise, producers distribute their products to consumers. This situation shows that distribution is an important activity in trade or business, and any obstacles to this risk disrupting the performance of the supply chain; one of its stakeholders are the producers. However, previous research has not examined the distribution problems experienced by producers. For this reason, this study aims to identify the obstacles that occur for producers in their distribution activities. This research uses a case study approach, with three respondents from electronics, textile, and food manufacturing companies. Referring to the results of the interviews, the loading and unloading problems at ports implies additional costs. The result shows that distribution activities are inefficient, which results in much higher logistics costs. This research concludes that bottlenecks at ports have negative consequences for supply chain networks. Barriers to the distribution process in ports disrupt the activities of organizations in the supply chain's network.
Ultra-fast Machine Learning Classifier Execution on IoT Devices without SRAM Consumption

With the introduction of edge analytics, IoT devices are becoming smart and ready for AI applications. A few modern ML frameworks are focusing on the generation of small-size ML models (often in kBs) that can directly be flashed and executed on tiny IoT devices, particularly the embedded systems. Edge analytics eliminates expensive device-to-cloud communications, thereby producing intelligent devices that can perform energy-efficient real-time offline analytics. Any increase in the training data results in a linear increase in the size and space complexity of the trained ML models, making them unable to be deployed on IoT devices with limited memory. To alleviate the memory issue, a few studies have focused on optimizing and fine-tuning existing ML algorithms to reduce their complexity and size. However, such optimization is usually dependent on the nature of IoT data being trained. In this paper, we presented an approach that protects model quality without requiring any alteration to the existing ML algorithms. We propose SRAM-optimized implementation and efficient deployment of widely used standard/stable ML-frameworks classifier versions (e.g., from Python scikit-learn). Our initial evaluation results have demonstrated that ours is the most resource-friendly approach, having a very limited memory footprint while executing large and complex ML models on MCU-based IoT devices, and can perform ultra-fast classifications while consuming 0 bytes of SRAM. When we tested our approach by executing it on a variety of MCU-based devices, the majority of models ported and executed produced 1-4x times faster inference results in comparison with the models ported by the sklearn-porter, m2cgen, and emlearn libraries.