Miracle-dz/UniFAR-FTUs huggingface dataset:
Improvement of Skin Cancer Detection Performance Using Deep Learning Technique

— Determination of cancer on time and accurately could increase the probability of successful treatment of it. Inside the cancer types malignant skin cancer is the most dangerous cancer type which is seen widely in the world and frequency of seeing it increases with time. Computer aided diagnosis applications are used for the classification of skin cancers. In this study, dermoscopic images acquired from ISIC archieve are used to create a dataset which has two classes, and this dataset is used to classify benign and malignant cancer types. And as a result of early diagnosis, the classification score is aimed to be increased. To do this, some image preprocessing operations like color clarification, edge detection, and noise extraction on dermoscopic images obtained from dataset are applied. After this processing operation, InceptionV2 deep learning network is used to classify these processed images. As a result of this study, it is seen that the preprocessing operation increases the accuracy ratio by 3.33 point, and an accuracy rate of 88.66% is get.
Do They Want to Cross? Understanding Pedestrian Intention for Behavior Prediction

Driving in urban traffic requires making quick and safe decisions while interacting with multiple pedestrians and other road users. Early anticipation of others' intentions is especially important for predicting their future behavior. In this work, we explore the human ability to estimate intentions of pedestrians in typical urban traffic conditions. Towards this goal, we analyze the results of our large-scale experiment that involved over 700 subjects to establish a human reference point for the task of pedestrian intention estimation. We determine what visual features correlate with human decisions and the relative difficulty of scenarios and validate our conclusions using a linear logistic model. Furthermore, we propose two models to demonstrate the benefits of using intention for pedestrian trajectory and future crossing action prediction. Our experiments show that an improvement of up to 5 % can be achieved on both tasks.
Enhancement Of Multiple Routing Configurations Based on Graph Augmentation

—The seamless transmission of real-time trafﬁc ﬂows in IP networks in case of the failures of network components is of great importance in order to meet the timing requirements of network services. Multiple Routing Conﬁgurations (MRC) is a mechanism which efﬁciently provides alternate paths in case of link/node failures in a network. In this paper, a graph augmentation approach enhancing the graph connectivity by intelligently adding new links to the physical topology graph, and hence signiﬁcantly improving the performance of MRC is proposed
Unsupervised Scene Categorization, Path Segmentation and Landmark Extraction while Traveling Path

Segmenting the movement path is an essential requirement of intelligent mobile robots. It assists intelligent systems in gaining a better understanding of the scene and identifying re-visited spaces. Moreover, it helps intelligent robots quantize the wide scene into sub-spaces that visually represent the same content-for instance, distinguishing rooms and kitchen in an indoor environment. This paper proposes an unsupervised approach to understand the transition of the scene while a robot is moving and helps to extract sub-spaces that visually represent a similar environment. The proposed approach benefits from a pre-trained deep network architecture to extract a description (feature representation) for the mobile robot's visual information at each time step. Then, based on the pairwise distance of the feature representations, sub-spaces of the scene and transition points are extracted.
Selenite interactions with some particulate organic and mineral fractions isolated from a natural grassland soil

The nature of the main selenium (Se) carrier phases in soil is in debate, with some authors emphasizing the role of minerals, and others the role of organic material. In this study, we address the role of the different soil components in Se sorption by determining its interaction with easily separable and workable fractions of bulk soil. Thus four separated fractions (two organic and two mineral) isolated by size density fractionation from a grassland soil were taken as models for soil Se carrier phases. Two concentrations of selenite were used to estimate its reactivity during experiments. In addition, SEM‐TEM analyses (coupled with EDS) were used to determine the chemical environment of Se sorbed onto the different fractions. The results showed that for the smaller selenite concentration nearly all the fractions gave the same selenite sorption (approximately 99%). For the larger concentration, selenite sorption ranged between 44% (mineral fractions) and 93% (organic fractions), indicating that the sorption capacities of organic fractions were greater than those of mineral fractions. In all soil fractions, elementary analyses and SEM/EDS and TEM/EDS observations revealed that Se sorption and the detection of Se hot spots on the soil particles correlated with the presence of Fe and Al. Although a direct association between Se and organic material could not be excluded, we suggest that Se sorption on organic particles is indirect, mainly resulting from association with surface Fe oxides or clays. These findings question the current view of a direct correlation between organic carbon and Se in natural soil samples.
Spam Filtering with KNN: Investigation of the Effect of k Value on Classification Performance

In this study, it is aimed to filter spam e-mails by using machine learning and text mining techniques. K-Nearest Neighbor (KNN) algorithm which is one of the techniques of machine learning is used. KNN algorithm is an easy to use and high performance classification algorithm. But the main problem of this algorithm is what will be the k value at the beginning. The performance of the algorithm changes according to the selected k value. In this study, three different data sets are discussed. These are Enron, Ling-Spam and SMSSpam-Collection data sets. Firstly, basic text mining techniques and term frequency–inverse document frequency (TF-IDF) term weighting method are applied to all data sets. By, according to the Chi-Square feature selection method, the best 500 attributes are selected and given to KNN algorithm. Finally, extensive experiments are carried out by giving the values of 1, 3, 5, 7 and 9 to the k value of the algorithm. In all three data sets, the most successful result is obtained when k is 1. The most successful results obtained from Ling-Spam, Enron and SMSSpam-Collection data sets according to F-measure are 0:9324, 0:9215 and 0:9196 respectively.
Prediction of the rate of dust fall in Quetta city, Pakistan using seasonal ARIMA (SARIMA) modeling

Department of Chemistry, Government Degree College, Khuzdar, Pakistan. Department of Chemistry, COMSATS Institute of Information Technology, Abbottabad-22060, Pakistan. Department of Statistics, University of Baluchistan, Quetta-87300, Pakistan. Department of Chemistry, University of Balochistan, Quetta-87300, Pakistan. Department of Pharmaceutical Sciences, COMSATS Institute of Information Technology, Abbottabad-22060, Pakistan.
Detection of Tremor Associated with Rest and Effort Activity Using Machine Learning

Tremor or handshaking can be provoked by neurological diseases like Parkinson's, physical efforts, stress, medicine, etc. To investigate the tremor characteristics, we asked volunteers to make three kinds of exercises: postural, rest, and effort. Accelerometer data were collected using sensors positioned on the wrist and finger of the participant's dominant hand. In the study, models for estimation and prediction of the tremor was generated. Machine learning was applied to detect and classify rest, effort, and postural tasks according to two scenarios. In the first scenario, we separated the tasks into three classes, while in the second one, two classes (effort and rest) were used. To compute the statistical features as maximum, minimum, and mean amplitude, number of peaks above the mean, standard deviation (STD), root mean square (RMS), and Pearson correlation of the finger and wrist acceleration, the accelerometer data were divided on windows with different length (128, 256, 320, and 512 samples per window). The following algorithms were used to build the models for the events' classification: decision tree, support vector machine, k-nearest neighbor, and ensemble bagging classifier. A cross-validation method was applied to train and test the models. The achieved performance of the models was from 85.0% to 94.1% for the 3-classes scenario and 86.5% to 94.9% for the two-classes scenario.
Supervisor Synthesis for Networked Discrete Event Systems with Delays against Non-FIFO Communication Channels

In this work, we study the problem of supervisory synthesis for networked discrete event systems against non-FIFO communication channels with bounded delays. Both the observation and control communication channels are represented by finite state automata under the assumption that all communication delays are bounded, the resilient networked supervisor synthesis problem is then reduced to supervisor synthesis for non-deterministic automata. We firstly analyze the structure of networked discrete-event systems with delays, and then describe the message transmission process through the communication channels which connect the plant and the supervisor. The assumption of the networked discrete event systems: 1)The buffer size of the communication channels is limited; 2)There is a maximum delay boundary for each event in the communication channel; 3)The number of multiple copies of the same event in the communication channel is limited. The content of the observation and control channel will be represented by the timed finite state automata. Finally, an example will illustrate how the networked resilient supervisor will be synthesized.
Deep Feature Extraction Based Fine-Tuning

In this study, an optimal deep feature set is determined for the classification of brain tumor tissue types Glioma, Meningioma and Pituitary. It is a good alternative to use a pre-trained network that is trained with millions of data in areas such as medical imaging where the amount of available data is not sufficient to train a CNN. Another method is to train the last layers of the network known as fine-tuning by configuring the problem at hand. In this study, AlexNet is used. Then, an SVM model is trained using features extracted from pre-trained and fine-tuned networks. Experimental results show that the 3D feature vectors extracted from the fine-tuned network yield superior results in a different test set, compared to the feature vectors extracted directly from the pre-trained one and increase the generalization ability of the model.
End-to-End Joint Intention Estimation for Shared Control Personal Mobility Navigation

Advancements in technology propose a future where systems work collaboratively sharing the same workspace as humans. Navigation is one such crucial aspect of daily life where collaborative technologies can offer major assistance. Ageing population dictates a likely increase in personal mobility devices (PMDs), whilst autonomous cars are bringing intelligent vehicles to the road today. However, in such scenarios the expected assistance can only be given if the device is aware of its user's intention, so that controls can be applied in a tightly collaborative manner. Moreover, they should be robust to different environments, users and mobile platforms. A user driven navigation framework is proposed in this work to complement end-to-end sensing-only solutions to estimate controls as joint intention from vehicle states and user inputs. The solution is proven to be an improvement over similar strategies that rely on exteroceptive data and omit inputs from the driving agent. Furthermore, the developed framework is proven capable of transferring the learning into different environments and mobility platforms using a small amount of training data. Data from the autonomous driving community (Udacity dataset) and other obtained in-house with an instrumented power wheelchair are given to demonstrate the validity of the proposed approach.
Brain Activity to Study Physical Pain: A Survey of Tools and Methods

Pain is a problem that has a significant effect on the quality of life, both personal and social, and in the knowledge of the authors. To date, there is no practical device or method that allows us to generate a quantitative pain index. In recent years, studies related to pain and its measurement have been reported, which have used brain activity as a biological marker of pain based on various methodologies. Therefore, the purpose of this survey article is to concentrate the tools and methods that use brain activity to study two types of physical pain: 1) chronic, as a result of a clinical condition; and, 2) acute physical induced by a painful stimulus. The survey analyzes the elements involved in evaluating these types of pain, considering the number of subjects, the EEG setting, the stimulus applied, the pain perception test used, the software for analysis and processing, and additional resources. The results present a systematic classification of the information; it contains the techniques and technologies that have been used for the study of pain. Finally, the article concludes identifying opportunity areas as quantitative pain measurement tools based on brain activity analysis to understand, adapt, or monitor the treatment responses.
Smooth Reference Line Generation for a Race Track with Gates based on Defined Borders

As racing sports pushed the technological limits of vehicles in the past, automated racing has the potential to directly evaluate new technologies from research in a competitive environment. The required speed of implementation and the fair evaluation criteria enable very fast progress in selecting potential strategies, architectures and algorithms. By this, automated racing strongly contributes to science as well as to future every day implementations of driving automation. In this publication, the approaches used by the team Autonomous-Racing-Graz at the ROBORACE final races in season alpha are revealed. The contribution describes how to generate a smooth reference line in case of non-smooth borders and additional precision gates on a race track. The generation of an initial smooth reference line is key to assure convergence in later optimization. The described data processing is based on track geometry defined by borders only. The specific challenge addressed in the approach was the incorporation of tight, short gates and the low computational effort of the algorithms, enabling fast tuning and adaptation during racing events onsite.
The effect of biogenic irrigation intensity and solute exchange on diagenetic reaction rates in marine sediments

The activities of infaunal macrobenthos strongly influence the pathways, rates, and extent of organic matter remineralization and associated reactions in marine sediments. Solute transport during irrigation is a particularly important process that stimulates microbial activity and net remineralization, both within and adjacent to the bioturbated zone. Part of the stimulation proximal to the bioturbated zone is due to redox oscillation and oxidant supply during transport, but part of both the near and far-field effects are a result of other factors. Experiments designed to simulate different degrees of diffusive exchange, and thus infaunal abundances or activity, demonstrate a regular and strong dependence of anaerobic remineralization on diffusive transport. For example, net production of NH 4 + , HPO 4 = , I - , and Mn ++ increases as the effective distance between burrows becomes ≤2 cm (burrow abundance ≥800 m -2 ) in otherwise identical anoxic sediment. Corresponding changes in sedimentary bacterial numbers, exoenzyme activity, per cell growth rate (RNA), and solid phase properties (N, C/N, P) indicate that the increases in net rates are due in part to an absolute increase in total production. Transport-reaction models and experimental results demonstrate that relative decreases in the uptake of solutes into biomass, abiogenic precipitation reactions, and increased removal of inhibiting metabolites all occur simultaneously, enhancing both total and net remineralization. The phenonomenological first-order rate constant for organic matter decomposition is therefore a function not only of the reductant and oxidant pool properties, but also the environmental transport regime. Solid phase reaction products can differ substantially as a function of diffusive openness. For example, both organic P and the organic Plinorganic P ratio increase in more diffusively-open (irrigated) compared to diffusively-closed, anoxic sediment. The sensitivity of solute concentrations, microbial activity and diagenetic reaction balances to diffusive transport regime, indicates that macrofauna can functionally manipulate these properties through relatively small changes in burrow spacing patterns and individual burrow geometries.
Shared Perception for Connected and Automated Vehicles

Connected and automated vehicles (CAVs) have the potential to improve the safety of automated driving by utilizing increased awareness about their surroundings in real time vehicle control. In this paper we propose a framework for a shared perception system suitable for CAVs and explain the algorithms used in the system. Finally, we experimentally demonstrate the benefit of our shared perception system for automated vehicles in uncertain environments.
Possibilities of resonant near-field microwave sounding in the diagnosis of burn wounds

The possibilities of the method of resonant near-field microwave sounding in relation to the diagnosis of burn wounds are considered. Resonant sensors have been developed and the impedance model of measurement systems has been developed. It is experimentally established that the real part of the dielectric permittivity of a burn wound is less than in the area of the intact site. The dependence of the electrodynamic parameters of wound tissues on the depth of penetration of the burn was also established, which can be used for rapid analysis of the degree of thermal damage.
Properties of Multiple-Valued Partition Functions

We focus on a set of r-valued n-variable functions that are defined by a partition P on the set of rn input vectors. Specifically, each block of P specifies input vectors, all of which map to the same function value. For example, a symmetric function is defined by a partition where input vectors in the same block are permutations of each other. Given the partition P and the set S of functions associated with P, we analyze the set S' of functions that are a maximal distance from S. Such functions hold promise for use in crypto-systems.In this paper, we characterize functions in S'. From this, we compute the distance to their corresponding partition functions. We show that, when r and n increase without bound, this distance approaches the maximum possible, rn. Bent functions achieve only half the maximum possible distance when n is large. We show that functions a maximal distance from partition functions tend to have a uniform distribution across the r possible function values. Such functions tend to be immune to statistics-based attacks. Finally, we show that, if the set S' of functions is maximally distant from a set S of partition functions, then the converse is true; that is, S is maximally distant from S'.
Nonovershooting Regulation of an Under-Sensed and Under-Actuated Linear Inverted Pendulum

This paper studies a nonovershooting regulation problem of an under-sensed and under-actuated system using two-degree-of-freedom (2DOF) control. An under-sensed and under-actuated linear inverted pendulum, which only has a single position sensor unlike the standard set-up, is investigated. For this system, first we present a regulation task that comprises three requirements, namely a nonovershooting step response, the largest robust stability margin and a 2DOF controller of the minimum order. Then we study the issue of controller design and develop a fourth-order 2DOF controller via the Youla-parameterization and the loop-shaping approaches. Finally, the proposed 2DOF controller is demonstrated to be effective.
Differential Dynamics of the Levels of Low Molecular Weight DNA Fragments in the Plasma of Patients With Ischemic and Hemorrhagic Strokes

Introduction: To evaluate Low-Molecular-weight (LMW) DNA as a possible prognostic biomarker in acute ischemic and hemorrhagic stroke. Methods: LMW DNA samples were isolated from plasma and cerebrospinal fluid by phenol deproteinization, analyzed by gradient polyacrylamide electrophoresis and quantified by spectrophotometry. Results: Two common types of stroke, i.e. ischemic and hemorrhagic, differ by the temporal dynamics of cell-free DNA (cfDNA) accumulation. In hemorrhagic stroke, an initial increase in LMW DNA levels, most likely reflects an extent of the tissue damage, while in ischemic patients, the LMW DNA levels increase in parallel with the damage caused by hypoxia and subsequent compensatory reperfusion. Conclusion: These time-course data specify optimal assessment windows with maximum differentiating power for stroke outcomes: 24–48 hours post-event for ischemic stroke, and as close as possible to the moment of hospital admission for hemorrhagic stroke. These data also indicate the role of apoptosis in the formation of ischemic focus.
CONTROLLING THE CHAOTIC MOTION OF FLOW IN A THERMAL CONVECTION LOOP WITH SLIDING MODE CONTROL

This study deals with controlling the chaotic motion of flow in a thermal convection loop by using a sliding mode control signal. Time series, two-dimensional phase plots and three-dimensional phase plane of the chaotic flow in the thermal convection are presented graphically from its differential equations. Based on the sliding mode control theory, a Lyapunov function is assigned for ensuring the global asymptotic stability of the error system. Routh–Hurwitz criterions are also used for determining the signs of sliding mode parameters. Simulation results are demonstrated to verify the correctness of the theoretical results. They also show that the chaotic motion of flow in a thermal convection loops effectively controlled owing to a sliding mode controller.
Gravitational Wave Data Analysis: Computing Challenges in the 3G Era

Cyber infrastructure will be a critical consideration in the development of next generation gravitational-wave detectors. The demand for data analysis computing in the 3G era will be driven by the high number of detections as well as the expanded search parameter space for compact astrophysical objects and the subsequent parameter estimation follow-up required to extract the nature of the sources. Additionally, there will be an increased need to develop appropriate and scalable computing cyberinfrastructure, including data access and transfer protocols, and storage and management of software tools, that have sustainable development, support, and management processes. This report identifies the major challenges and opportunities facing 3G gravitational-wave observatories and presents recommendations for addressing them. This report is the fourth in a six part series of reports by the GWIC 3G Subcommittee: i) Expanding the Reach of Gravitational Wave Observatories to the Edge of the Universe, ii) The Next Generation Global Gravitational Wave Observatory: The Science Book, iii) 3G R&D: R&D for the Next Generation of Ground-based Gravitational Wave Detectors, iv) Gravitational Wave Data Analysis: Computing Challenges in the 3G Era (this report), v) Future Ground-based Gravitational-wave Observatories: Synergies with Other Scientific Communities, and vi) An Exploration of Possible Governance Models for the Future Global Gravitational-Wave Observatory Network.