Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards wearable blood pressure measurement systems from biosignals: a review

Blood pressure is the pressure by the blood to the vein wall. High blood pressure, which is called silent death, is the cause of nearly 13% of mortality all over the world. Blood pressure is not only measured in the medical environment, but the blood pressure measurement is also a need for people in their daily life. Blood pressure estimation systems with low error rates have been developed besides the new technologies and algorithms. Blood pressure measurements are differentiated as invasive blood pressure (IBP) measurement and noninvasive blood pressure (NIBP) measurement methods. Although IBP measurement provides the most accurate results, it cannot be used in daily life because it can only be performed by qualified medical staff with specialized medical equipment. NIBP measurement is based on measuring physiological signals taken from the body and producing results with decision mechanisms. Oscillometric, pulse transit time (PTT), pulse wave velocity, and feature extraction methods are mentioned in the literature as NIBP. In the oscillometric method of the sphygmomanometer, an electrocardiogram is used in PTT methods as a result of the comparison of signals such as electrocardiography, photoplethysmography, ballistocardiography, and seismocardiography. The increase in the human population and worldwide deaths due to the highly elevated blood pressure makes the need for precise measurements and technological devices more clear. Today, wearable technologies and sensors have been frequently used in the health sector. In this review article, the invasive and noninvasive blood pressure methods, including various biosignals, have been investigated and then compared with each other concerning the measurement of comfort and robust estimation.
Unsupervised learning for brain–computer interfaces based on event-related potentials

Machine learning has become a core component in brain-computer interfaces (BCIs). Unfortunately, the use of machine learning typically requires the collection of subject specific labelled data. This process is time-consuming and not productive from a user's point of view, as during calibration the user has to follow given instructions and cannot make own decisions. Only after calibration, the user is able to use the BCI freely. In this chapter, we describe how the supervised calibration process can be circumvented by unsupervised learning in which the decoder is trained while the user is utilising the system. We discuss three variations. First, expectation-maximisation (EM)-based training, which works wells empirically but can sometimes be unstable. Learning from label proportions (LLP)-based training, which is guaranteed to converge to the optimal solution, but learns more slowly. Third, a hybrid approach combining the stability of LLP with the speed of learning of EM in a highly efficient and effective approach that can readily replace supervised decoders for event-related potential BCI.
Context-Aware Improved Experiences in Smart Environments

Paradigms such as Smart Cities, Smart Environments, and Internet of Things have been highly debated and used in the last few years. Many researches in smart environments are focused on detection, location, and identi cation of people, to make di erent environments adapt to the needs and preferences of citizens. To make this possible, it is necessary to combine di erent sensors, actuators, mathematical models, and machine learning techniques. Although some works have been performed in this direction, there is still room for contribution, and this research is focused on that. The objective of this thesis is to improve users' experience in smart environments based on information gathered from indoor spaces following a non-intrusive occupancy monitoring approach. Thus, the main objective is to know if someone is in a room and how many occupants are there, using information gathered from sensors. In the rst stage of the project, the objective is to design and build a device to measure environmental data using di erent sensors, such as temperature, light intensity, noise, and carbon dioxide monitoring, to estimate the presence of occupants through these environmental features. In the second stage, machine learning and pattern recognition mechanisms should be used to evaluate the performance of the proposed solution. The results obtained during this research show that the system designed using a Raspberry Pi and a selection of sensors is able to collect, process, and store environmental data. Additionally, the analysis performed over the gathered data using machine learning and pattern recognition mechanisms depicts that it is possible to determine the occupancy of indoor environments. Thus this information could be taken into consideration by a third-party application to adjust the level of comfort, for example, in a room.
A Malware and Variant Detection Method Using Function Call Graph Isomorphism

The huge influx of malware variants are generated using packing and obfuscating techniques. Current antivirus software use byte signature to identify known malware, and this method is easy to be deceived and generally ineffective for identifying malware variants. Antivirus experts use hash signature to verify if captured sample is one of the malware databases, and this method cannot recognize malware variants whose hash signatures have changed completely. Function call graph is a high-level abstraction representation of a program and more stable and resilient than byte or hash signature. In this paper, function call graph is used as signature of a program, and two kinds of graph isomorphism algorithms are employed to identify known malware and its variants. Four experiments are designed to evaluate the performance of the proposed method. Experimental results indicate that the proposed method is effective and efficient for identifying known malware and a portion of their variants. The proposed method can also be used to index and locate a large-scale malware database and group malware to the corresponding family.
Learning Gestures From WiFi: A Siamese Recurrent Convolutional Architecture

We propose a gesture recognition system that leverages existing WiFi infrastructures and learns gestures from channel state information (CSI) measurements. Having developed an innovative OpenWrt-based platform for commercial WiFi devices to extract CSI data, we propose a novel deep Siamese representation learning architecture for one-shot gesture recognition. Technically, our model extends the capacity of spatio-temporal patterns learning for the standard Siamese structure by incorporating convolutional and bidirectional recurrent neural networks. More importantly, the representation learning is ameliorated by our Siamese framework and transferable pairwise loss which helps to remove structured noise, such as individual heterogeneity and various measurement conditions during domain-different training. Meanwhile, our Siamese model also enables one-shot learning for higher availability in reality. We prototype our system on commercial WiFi routers. The experiments demonstrate that our model outperforms state-of-the-art solutions for temporal–spatial representation learning and achieves satisfactory results under one-shot conditions.
LAND USE CLASSIFICATION BASED ON MULTI-STRUCTURE CONVOLUTION NEURAL NETWORK FEATURES CASCADING

Abstract. Learning efficient image representations is at the core of the classification task of remote sensing imagery. The existing methods for solving image classification task, based on either feature coding approaches extracted from convolution neural networks(CNNs) or training new CNNs, can only generate image features with limited representative ability, which essentially prevents them from achieving better performance. In this paper, we investigate how to transfer features from these successfully pre-trained CNNs for classification. We propose a scenario for generating image features via cascading features extracted from different CNNs. First, pre-trained CNNs, like CaffeNet, VGG-S and VGG-F, are used as feature extractor since their different structures help extract richer information of images. Then the fully-connected layers of the pre-trained CNNs are fine-tuned with UC Merced land use dataset. Finally, the image features generating from cascading the outputs of three networks above, are fed into multi-class Optimal Margin Distribution Machine (mcODM) to obtain the final classification results. Extensive experiments on public land use classification dataset demonstrates that the image features obtained by the proposed scenario can result in remarkable performance and improve the state-of-the-art by a significant margin. The results reveal that the features from pre-trained CNNs generalize well to land use dataset and are more expressive than features from single CNN.
An Online Task Assignment Based on Quality Constraint for Spatio-Temporal Crowdsourcing

Crowdsourcing is the perfect embodiment of group wisdom. With the rapid development of mobile network and the sharing economy model, spatio-temporal crowdsourcing technology has been research hotspot. Task assignment is one of the core issues of spatio-temporal crowdsourcing technology. There are three algorithms: Random Algorithm, Random-Threshold-Based Algorithm (RT) and Adaptive random-threshold-based Algorithm (Adaptive RT) for maximizing the total utility in the online task assignment of three types of objects, tasks, workers and workplaces. But these algorithms ignore the distance cost and fairness between task requester and workers. Unfairness means that higher task’s reward with lower worker’s success ratio or lower task’s reward with higher worker’s success ratio in a match. Therefore, this paper proposes Quality Constraint Algorithm (QCA), which quantifies fairness between task requester and workers as match quality and adopts a matching strategy of automatic negotiation on task’s reward to improve the average match quality. QCA not only has higher average match quality and higher total utility, but also optimizes the average distance cost. Compared with Adaptive RT, QCA has an average increment of 11% on total utility, an average increment of 19% on average match quality and an average decrease of 17% on distance cost. In term of time cost, QCA is only 8% of Adaptive RT.
Tracking and Control of Soft, Self-Folding Miniaturized Agent using Ultrasound Images

The usage of micro-sized agents in magnetic based drug delivery systems could be made a lot more affordable if it is possible to control these agents wirelessly based on feedback obtained from the ultrasound clinical images. However, since the image is of ultrasound type, the most common edge detection techniques used in digital image processing has to be modified and adapted in such a way, the control of the micro agent is made possible. The main reason is due to absence of sharp edges with the obtained image. The thesis mainly focuses on solving this problem. Also Control algorithms were written to control and drive the robot across various scenarios and motion profiles for performing pick and move operations. Thus the goal of this assignment is to track and control hydrogel micro-grippers using two-dimensional ultrasound images. In the initial step a tracker is designed which is evaluated with the collected datasets. The tracker is then implemented to detect the miniaturized gripper and to track it in the presence of occlusions. Following it, an experimental validation of the proposed technique is performed. After the evaluation, the proposed algorithm is used in the experimental setup to control the motion of the miniaturized agent along a defined path. Various motion experiments were performed in order to evaluate the performance of the proposed technique. Finally, with the help of proper motion profiles and control algorithms, the hydrogel gripper is made to reach a bead, grasp it and transport it to a specified target area. Also in order to evaluate clinically relevant scenarios, algorithms and advanced computer vision techniques were selected and compared for exploring the best possible features from the ultrasound images and eventually based on the results, a specialized tracker was formulated with aim of tracking the micro gripper in real tissue type of environment.
Exploiting the Remote Server Access Support of CoAP Protocol

The constrained application protocol (CoAP) is a specially designed Web transfer protocol for use with constrained nodes and low-power networks. The widely available CoAP implementations have failed to validate the remote CoAP clients. Each CoAP client generates a random source port number when communicating with the CoAP server. However, we observe that in such implementations it is difficult to distinguish the regular packet and the malicious packet, opening a door for a potential off-path attack. The off-path attack is considered a weak attack on a constrained network and has received a less attention from the research community. However, the consequences resulting from such an attack cannot be ignored in practice. In this article, we exploit the combination of IP spoofing vulnerability and the remote server access support of CoAP is to be launch an off-path attack. The attacker injects a fake request message to change the credentials of the 6LoWPAN smart door keypad lock system. This creates a request spoofing vulnerability in CoAP, and the attacker exploits this vulnerability to gain full access to the system. Through our implementation, we demonstrated the feasibility of the attack scenario on the 6LoWPAN-CoAP network using smart door keypad lock. We proposed a machine learning (ML)-based approach to mitigate such attacks. To the best of our knowledge, we believe that this is the first article to analyze the remote CoAP server access support and request spoofing vulnerability of CoAP to launch an off-path attack and demonstrate how an ML-based approach can be deployed to prevent such attacks.
Modulation Strategy and Controller for Grid-Tied Trinary Hybrid Multilevel Inverter

This article deals with the modulation and control of a trinary hybrid multilevel inverter (THMI), which is a cascade multilevel converter that maximizes the number of levels per full-bridge. The modulation presented defines the duty cycle of switches as piecewise functions of the desired output voltage, minimizing the processing time. In order to control this converter, it is modeled as a linear system with bounded disturbance. A quasi-sliding mode control (QSMC) is designed, simulated, and experimentally verified for the THMI operating in grid-tie mode obtaining better results when compared to a classic proportional–integral (PI) controller.
Optimizing web search engines with interactions

Web search engines are used by many people everyday, serving as one of the primary gateways to information stored online. Optimizing search engines is challenging because it requires large datasets annotated by human judges. Such datasets are expensive to create and are often not reliable because there can be a mismatch between what human judges and real users find relevant. This motivates using observed interactions between the user and the web search engine. These interactions are readily available in massive quantities and have been shown to correlate with user satisfaction with the search engine. In this thesis we study how to use these interactions to optimize web search engines.
A Case Study on Gamified Interventions for Team Cohesion in Factory Work

In this article, we aim to provide insights into the design and implementation of game elements for teamwork on the work floor and to study their effect. Inventing games to break monotonous jobs is a long-standing practice, yet conscious implementation of motivational elements of games at work is a recent phenomenon. Generally, gamification is used to enhance individual performance; it may be effective in enhancing teamwork as well. We developed game elements aimed at team cohesion and examined the effect of two gamified interventions (team performance feedback and personal profiles) on team cohesion in a factory. Results suggest that the interventions mainly raised attention toward the aspects of the work that were explicitly addressed. Team performance feedback led to increased task commitment and perceived team performance, while personal profiles increased nonwork-related conversations among team members. We conclude with lessons learned regarding the development and study of gamified interventions for teams on the work floor.
Sensor-less control of a novel stepped hydraulic flow control valve

© 15th International Conference on Condition Monitoring and Machinery Failure Prevention Technologies, CM 2018/MFPT 2018. All rights reserved. This paper aims to create a sensor-less feedback position detection for a flow control orifice actuated by a stepper motor. Nowadays, many applications such as hydraulic and pneumatic systems use a stepper motor as an actuator, instead of traditional mechanical or solenoid. In nonlinear environment such as hydraulic systems, a stepper motor may suffer from step losing and leads to poor controllability of the system. Mechanical sensors are usually used as a feedback of the position and speed, but at the same time the harsh environment of hydraulic applications prevents implementing this kind of sensing. On the other hand, a sensor-less technique based on Kalman filter was used to control a stepper motor in different applications. This research represents a primarily investigation of the performance of Kalman filter in these for the valve based on modelling and simulation.
Real-Time Crop Recognition in Transplanted Fields With Prominent Weed Growth: A Visual-Attention-Based Approach

Crop recognition is one of the key processes for robotic weeding in precision agriculture, which remains an open problem due to the unstructured field environment and the wide variety of plant species. It becomes especially challenging when the weeds are prominent and overlap with the crop plants. This paper presents a novel method for recognizing crop plants of field images with a high weed presence. This method segments crop plants from overlapped weeds based on the visual attention mechanism of the human visual system using a convolutional neural network. The network utilizes ResNet-10 as backbone, while introducing side outputs and short connections for multi-scale feature fusion. The Adaptive Affinity Fields method is adopted to improve the segmentation at object boundaries and for fine structures. To train and test the network, a field image dataset has been created which consists of 788 color images with manually segmented annotations. The images are captured under challenging conditions with extremely high weed pressure. The experimental results show that the proposed method can accurately segment crops from weeds and soil, with mean absolute errors less than 0.005 and F-measure scores exceeding 97%. In terms of efficiency, the proposed method can process up to 169 images per second when accelerated by a NVIDIA RTX 2080Ti graphics processing unit (GPU), and operate at approximately 5.6 Hz in a Jetson TX2 embedded computer. The results indicate that the proposed method has the potential to provide an efficient solution for recognizing crop plants, even in the presence of severe weed growth. The code and the dataset are available at https://github.com/ZhangXG001/Real-Time-Crop-Recognition.
Consumer Behaviour through the Eyes of Neurophysiological Measures: State-of-the-Art and Future Trends

The new technological advances achieved during the last decade allowed the scientific community to investigate and employ neurophysiological measures not only for research purposes but also for the study of human behaviour in real and daily life situations. The aim of this review is to understand how and whether neuroscientific technologies can be effectively employed to better understand the human behaviour in real decision-making contexts. To do so, firstly, we will describe the historical development of neuromarketing and its main applications in assessing the sensory perceptions of some marketing and advertising stimuli. Then, we will describe the main neuroscientific tools available for such kind of investigations (e.g., measuring the cerebral electrical or hemodynamic activity, the eye movements, and the psychometric responses). Also, this review will present different brain measurement techniques, along with their pros and cons, and the main cerebral indexes linked to the specific mental states of interest (used in most of the neuromarketing research). Such indexes have been supported by adequate validations from the scientific community and are largely employed in neuromarketing research. This review will also discuss a series of papers that present different neuromarketing applications, such us in-store choices and retail, services, pricing, brand perception, web usability, neuropolitics, evaluation of the food and wine taste, and aesthetic perception of artworks. Furthermore, this work will face the ethical issues arisen on the use of these tools for the evaluation of the human behaviour during decision-making tasks. In conclusion, the main challenges that neuromarketing is going to face, as well as future directions and possible scenarios that could be derived by the use of neuroscience in the marketing field, will be identified and discussed.
A Utility Model for Photo Selection in Mobile Crowdsensing

Existing mobile photo crowdsensing approaches focus on the participant-to-server photo pre-selection, i.e., reducing the photo redundancy from participants to a server. The server may still receive plenty of photos for a target area. Yet, another important problem is to select a proper photo subset of an area from the server to a requester. This is a challenging problem because the selected subset with a small size should attain both coverage on the PoIs - Points of Interest (i.e., photo coverage of the area) and quality on the views (i.e., view quality). In this paper, we propose a novel and generic server-to-requester photo selection approach even when there are neither photo shooting direction information nor reference photos. A utility model is designed to measure photo merits of coverage and quality by exploiting photos’ spatial distribution and visual representativeness. We present two photo selection schemes, basic and PoI number-aware, to maximize the photo selection utility with multiple levels of granularity. Experimental results on real-world datasets show that our basic scheme outperforms the baselines by an average of <inline-formula><tex-math notation="LaTeX">$33\%$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>33</mml:mn><mml:mo>%</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="zhou-ieq1-2941927.gif"/></alternatives></inline-formula> and <inline-formula><tex-math notation="LaTeX">$18.7\%$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>18</mml:mn><mml:mo>. </mml:mo><mml:mn>7</mml:mn><mml:mo>%</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="zhou-ieq2-2941927.gif"/></alternatives></inline-formula> on photo coverage and view quality, respectively. Our PoI number-aware scheme can yield an additionally 44.8 percent improvement on the photo coverage performance.
Named Entity Recognition and Aspect based Sentiment Analysis

Twitter is a microblogging website where people can share their feelings quickly and spontaneously by sending a tweets limited by 280 characters. You can directly address a tweet to someone by adding the target sign “@” or participate to a topic by adding an hashtag “#” to your tweet. Here specific hashtag (#) based tweets are downloaded using tweepy and they are cleansed for removal of irrelevant data then Entity Recognition is performed using the NER Algorithm which specifies different entities belonging to that tweet eg person, place, organization, etc. and finally sentiment analysis is performed where we analyze the general sentiment that can either be positive, negative or neutral at the entity level.