Miracle-dz/UniFAR-FTUs huggingface dataset:
Social Complexity and Brain Evolution: Comparative Analysis of Modularity and Integration in Ant Brain Organization

The behavioral demands of living in social groups have been linked to the evolution of brain size and structure, but how social organization shapes investment and connectivity within and among functionally specialized brain regions remains unclear. To understand the influence of sociality on brain evolution in ants, a premier clade of eusocial insects, we statistically analyzed patterns of brain region size covariation as a proxy for brain region connectivity. We investigated brain structure covariance in young and old workers of two formicine ants, the Australasian weaver ant Oecophylla smaragdina, a pinnacle of social complexity in insects, and its socially basic sister clade Formica subsericea. As previously identified in other ant species, we predicted that our analysis would recognize in both species an olfaction-related brain module underpinning social information processing in the brain, and a second neuroanatomical cluster involved in nonolfactory sensorimotor processes, thus reflecting conservation of compartmental connectivity. Furthermore, we hypothesized that covariance patterns would reflect divergence in social organization and life histories either within this species pair or compared to other ant species. Contrary to our predictions, our covariance analyses revealed a weakly defined visual, rather than olfactory, sensory processing cluster in both species. This pattern may be linked to the reliance on vision for worker behavioral performance outside of the nest and the correlated expansion of the optic lobes to meet navigational demands in both species. Additionally, we found that colony size and social organization, key measures of social complexity, were only weakly correlated with brain modularity in these formicine ants. Worker age also contributed to variance in brain organization, though in different ways in each species. These findings suggest that brain organization may be shaped by the divergent life histories of the two study species. We compare our findings with patterns of brain organization of other eusocial insects.
Altered Physical Performance Following Advanced Special Operations Tactical Training

Abstract Winters, JD, Heebner, NR, Johnson, AK, Poploski, KM, Royer, SD, Nagai, T, Randall, CA, Abt, JP, and Lephart, SM. Altered physical performance following advanced special operations tactical training. J Strength Cond Res 35(7): 1809–1816, 2021—The purpose of this study was to determine how the unique challenges of specific military tactical training phases influence overall physical performance characteristics. Broad jump, 5-10-5, 300-yd shuttle, percent body fat (%BF), anaerobic power (AP) and anaerobic capacity (AC), maximal oxygen uptake (V̇o2max), isokinetic knee extension/flexion strength, shoulder internal/external rotation strength, and trunk extension/flexion strength were collected on 73 United States Marine Corps Forces Special Operations Command (MARSOC) students (age: 27.4 ± 3.8 years, height: 178.7 ± 6.6 cm, and body mass: 85.8 ± 9.4 kg) at the beginning of (P1), in between (P2), and at the completion of 2 distinct tactical training phases (P3). Linear mixed models were used to analyze within-subject performance changes over the 3 time points, and post hoc Bonferroni pairwise comparisons analyzed performance changes between each testing time point. There were significant changes in broad jump (p < 0.0001), 5-10-5 agility time (p < 0.001), %BF (p = 0.011), AP (p < 0.0001), V̇o2max (p = 0.001), and both right and left shoulder internal rotation strength (p = 0.004 and p = 0.015, respectively) between P1 and P2. There were also significant changes in 300-yd shuttle run time (p = 0.001), AP (p < 0.0001), AC (p < 0.0001), left knee extension strength (p = 0.006), trunk flexion strength (p < 0.0001), and left shoulder external rotation strength (0.027) between P2 and P3. Identifying the effect that specific tactical training phases may have on physical performance will allow for the development of effective phase-specific evidence-based human performance programs, reducing performance deficits and thereby reducing the risk of injury.
Probabilistic Estimation of Honeypot Detection in Internet of Things Environment

With the emergence of the Internet of Things (IoT) and the increasing number of resource-constrained interconnected smart devices, there is a noticeable increase in the number of cyber security crimes. In the face of the possible attacks on IoT networks such as network intrusion, denial of service, spoofing and so on, there is a need to develop efficient methods to locate vulnerabilities and mitigate attacks in IoT networks. Without loss of generality, we consider only intrusion-related threats to IoT. A honeypot is a system used to understand the potential dynamic threats and act as a proactive measure to detect any intrusion into the network. It is used as a trap for intruders to control unauthorized access to the network by analyzing malicious traffic. However, a sophisticated attacker can detect the presence of a honeypot and abort the intrusion mission. Therefore it is essential for honeypots to be undetectable. In this paper, we study and analyze possible techniques for SSH and telnet honeypot detection. Moreover, we propose a new methodology for probabilistic estimation of honeypot detection and an automated software implemented this methodology.
A Model for Automatic Recognition of Vertical Texts in Natural Scene Images

Text recognition plays an important role in recognizing texts presented in the images as they provide important information. Scene text recognition has been an active research topic with rapid growth of development to improve the performance of text recognition with better reliability and accuracy. However, scene text recognition is challenging due to images containing inconsistent lighting, low resolution and blurriness. In addition, scene texts are usually taken from outdoor signboards, signage and road signs, which contain various orientation and fancy font styles to attract attention. Various researchers have proposed methods for recognizing different orientations of scene texts, such as horizontal texts, curved texts and rotated texts. However, to data there is a lack of research in recognizing vertical texts in natural scene images. In this research, a model for effective automatic recognition of vertical texts in natural scene images has been proposed, consisting of two major processes which are text localization and segmentation and text recognition. This proposed model recognizes three different types of vertical scene texts, which are top-to-bottom vertical texts, bottom-to-top vertical texts and horizontal-stacked vertical texts.
Implementation of Smart Parking System in Jelgava City in Latvia

Development of smart city concept is challenging for every city. Smart city approach consists of several components and one of them is smart traffic. Parking is one of the smart traffic components. Due to the rapid growth of vehicles on the streets, finding an available parking space is becoming a big obstacle in modern life. For cities with large number of residents or places where sports or artistic events take place, search for a free parking lot is a major problem and can be a frustrating experience. Parking management system affects and improves people's daily life experience and is an important component of a modern traffic system. Smart parking system allows drivers to obtain real-time parking data, by using Internet of things technologies to gather and process information from parking lots. Thus, intelligent, innovative and efficient ways for parking are needed to coordinate the parking demands efficiently. Authors of this paper share the approach, which is implemented in Jelgava city in Latvia, where two smart parking systems are developed. One system is based on inductive sensors, which can recognize vehicle on each parking lot. Second approach is based on vehicle identification on video stream, which is used to register cars on parking entrance and exit, counting total number of free parking lots, but without indicating which particular lots are free or occupied.
Optimization Based on Taxi Carpooling Preferences and Pricing

The development of network technology and the popularization of smart phones make it possible for passengers to travel, through the taxi software platform to transfer their position and price. Taxi drivers display their prices, service levels and locations through the platform. According to the preference of both parties, a multi-objective co-pricing optimization algorithm based on preference is proposed, and the application of pricing optimization method is illustrated through an example. This model comprehensively balances the preferences of all parties, further promotes the co-operation of taxis, and reduces the waste of social resources. It eases the pressure of traffic congestion.
3-D Quantitative Tracking of Phagosomes using Quantitative Phase Microscopy

We present a method for quantitative tracking of phagosomes without labeling. Current imaging methods, used for phagosome research, have limitations in live-cell quantitative analysis. We reconstruct three-dimensional (3-D) refractive index (RI) tomograms from two-dimensional holograms with changing illumination angle. RI information enables to obtain intracellular phagosomes quantitatively. Time-lapse imaging provides 3-D information's of phagosom movement. Collectively, we propose that quantitative phase imaging techniques can be a useful tool for phagocytosis research.
Investigating Brute Force Attack Patterns in IoT Network

Internet of Things (IoT) devices may transfer data to the gateway/application server through File Transfer Protocol (FTP) transaction. Unfortunately, in terms of security, the FTP server at a gateway or data sink very often is improperly set up. At the same time, password matching/theft holding is among the popular attacks as the intruders attack the IoT network. Thus, this paper attempts to provide an insight of this type of attack with the main aim of coming up with attack patterns that may help the IoT system administrator to analyze any similar attacks. This paper investigates brute force attack (BFA) on the FTP server of the IoT network by using a time-sensitive statistical relationship approach and visualizing the attack patterns that identify its configurations. The investigation focuses on attacks launched from the internal network, due to the assumption that the IoT network has already installed a firewall. An insider/internal attack launched from an internal network endangers more the entire IoT security system. The experiments use the IoT network testbed that mimic the internal attack scenario with three major goals: (i) to provide a topological description on how an insider attack occurs; (ii) to achieve attack pattern extraction from raw sniffed data; and (iii) to establish attack pattern identification as a parameter to visualize real-time attacks. Experimental results validate the investigation.
Determining Recommendations for Preventing Elderly People from Cardiovascular Disease Complication Using Objective Distance

With increasing numbers of elderly people of the world's populations, Cardiovascular Disease (CVD) has become a critical health concern. It represents the main cause of death worldwide. Accordingly, preventing aged people from CVD has been extremely considered nowadays. The aim of this study is to determine proper recommendations to the elderly who have high risks of CVD complication for preventing themselves. Therefore, this paper applies the concept of an objective distance which is a measurement used as the indicator for recommendations. It defines the distance between the current status of the elderly people and objective level of each attribute for deriving the proper recommendations. Significantly, this distance provides the effective direction for their personal lifestyle adjustment. An experiment is performed in the context of hypertension, which is one of the main risk factors of CVDs. Data is gathered from 60 elderly people who are at the high risk of CVD in Chiang Rai, Thailand. A result of this experiment presents that the proposed objective distance can be efficiently employed for providing the appropriate recommendations to each elderly person. Also, they successfully modified their lifestyle behaviors for protecting themselves from CVDs, accounted for 90 percent accuracy.
The Influence of the Gut Microbiome on Host Metabolism Through the Regulation of Gut Hormone Release

The microbial community of the gut conveys significant benefits to host physiology. A clear relationship has now been established between gut bacteria and host metabolism in which microbial-mediated gut hormone release plays an important role. Within the gut lumen, bacteria produce a number of metabolites and contain structural components that act as signaling molecules to a number of cell types within the mucosa. Enteroendocrine cells within the mucosal lining of the gut synthesize and secrete a number of hormones including CCK, PYY, GLP-1, GIP, and 5-HT, which have regulatory roles in key metabolic processes such as insulin sensitivity, glucose tolerance, fat storage, and appetite. Release of these hormones can be influenced by the presence of bacteria and their metabolites within the gut and as such, microbial-mediated gut hormone release is an important component of microbial regulation of host metabolism. Dietary or pharmacological interventions which alter the gut microbiome therefore pose as potential therapeutics for the treatment of human metabolic disorders. This review aims to describe the complex interaction between intestinal microbiota and their metabolites and gut enteroendocrine cells, and highlight how the gut microbiome can influence host metabolism through the regulation of gut hormone release.
An Investigation of Cyberinfrastructure Adoption in University Libraries

This study aims to understand factors that affect university libraries’ adoption of cyberinfrastructure for big data sharing and reuse. A cyberinfrastructure adoption model which contains 10 factors has been developed based on the technology-organization-environment (TOE) framework and the literature regarding tradeoffs of applying cyberinfrastructure. This paper describes the proposed cyberinfrastructure adoption model and explains the survey instruments. The next steps of the study are also presented.
DF-CSPG: A Potential Game Approach for Device-Free Localization Exploiting Joint Sparsity

Device-free localization (DFL) plays an increasingly dominant role in some security and military applications, aiming at locating targets without carrying any extra devices. Most research utilize received signal strength, causing poor accuracy due to insufficient information. In this letter, we propose DF-CSPG, a new DFL algorithm using compressive sensing (CS) theory and potential game (PG) approach to exploit channel station information (CSI). It is the first time that the PG approach is applied in DFL. At first, the CS-based localization model is built by exploiting CSI and then investigated from a game-theoretic perspective in which the subcarriers are assumed as players. Then, we prove that the proposed DFL game is an exact PG. Based on these, the best response theory is utilized to obtain the equilibriums. Finally, simulation results show that DF-CSPG can achieve more accurate and robust performance.
Performance Evaluation of Artificial Megneto-Dielectric Metamaterial with Microstrip Patch Antenna for Wireless System

This paper we presents the performance evaluation of artificial magneto dielectric substrate with microstrip patch antenna for wireless system. From the result, at the resonance frequency 2.45 GHz have the radiation efficiency and the real gain are 88.87% and 6.89 dBi, respectively. The microstrip patch antenna on finite artificial magneto dielectric substrate by used metamaterial structure. The unit cell consist of multi square loop pinned in the dielectric material. The feature of the antenna can be rectify with controlling permeability ($\boldsymbol{\mu_{eff}}$) and permittivity ($\boldsymbol{\varepsilon_{eff}}$).
Development of Titanium-Based Transition-Edge Single-Photon Detector

Superconducting transition-edge sensors (TESs), working as single-photon detectors with high quantum efficiency, low dark count rate, and photon number resolving capability, have been successfully used in quantum information experiments as well as astronomical transient object observations. Aiming to achieve high count rate, we are developing titanium-based superconducting TES single-photon detectors with a critical temperature of ∼300 mK. Ti-based TESs with active area from 10 × 10 μm to 40 × 40 μm are first fabricated on silicon substrate coated with SiOx. The fabricated TES devices show a thermal conductance scaling with the active area and an effective time constant of ∼4 μs from pulsed laser diode illumination. In order to get high quantum efficiency, we further design and fabricate a 1550-nm optical cavity based on dielectric mirror and antireflection layer, and study its absorption with an IR spectrometer. Ti TES devices on the dielectric mirror are then fabricated and the superconducting transition is measured.
Robot Learning by Demonstration Interaction System Based on Multiple Information

On the basis of demonstration experience gained through demonstration learning, this paper proposes an interactive robot learning system, which uses wearable sensors that can detect surface electromyography signals (sEMG)and inertial information. Gesture recognition and trajectory calculation are the main process in our system. Robot grasping and other hand actions, can be marked and controlled through human gesture recognition. A comparison of 4 groups of feature extraction project and multiple kernel relevance vector machine (MKRVM)based on multiple kernel expansion via kernel alignment was used to get better recognition performance. After hand trajectory estimation, the operator's trajectory data can be encoding by Gaussian mixture model (GMM), then it can generalize them to deal with new situations by Gaussian mixture regression (GMR). To ensure the success of the crawling operation, the reinforcement learning algorithm is used to correct the grab attitude and position. This paper proposes a kind of Q-learning algorithm based on statistical coding parameters in demonstration learning named GM_QL. By the introduction of the demonstration programming experience, as a priori knowledge of the crawling operation, the convergence speed and learning efficiency of the reinforcement learning process are obviously superior to the traditional Q learning algorithm.
Analysis of User Influence Types in Online Social Networks: An Example of VKontakte

This paper considers conceptually different methods to define the influence levels of agents in social networks using the actional model developed earlier, namely, the general and initial actions-based ones. We study the general and initial actions-based influence levels of users that are selected by a given attribute in the VKontakte online social network.
Equity in Spatial Access to Bicycling Infrastructure in Mid-Sized Canadian Cities

The impacts of active transportation planning on equity are often overlooked, potentially leading to disparities in who receives benefits of infrastructure investment. This study examined income inequalities in spatial access to bicycling infrastructure in three mid-sized Canadian cities: Victoria and Kelowna (British Columbia), and Halifax (Nova Scotia), using non-spatial and spatial methods. We compiled municipal bicycling infrastructure data and calculated access to bicycling infrastructure (m/km2) for dissemination areas (DAs) within each city. We analyzed trends in access across median household income quintiles, and characterized spatial patterns using a local measure of spatial autocorrelation. DAs in Kelowna (n = 168) had the greatest access to infrastructure (median infrastructure = 2,915 m/km2), followed by Victoria (n = 386 DAs; median = 2,157 m/km2), and Halifax (n = 312 DAs; median = 0 m/km2). Lower income areas in Victoria and Kelowna had greater access to infrastructure compared with higher income areas. The majority of DAs in Halifax had no infrastructure (59%), consistent across income quintiles. Spatial pattern analysis identified clusters of low income areas with poor access in each city, which may be targets for strategic, equitable investment. Although in many cities bicycling infrastructure planning is not driven by equity considerations, there is increasing political pressure to ensure equitable access to safe bicycling. Measuring and mapping trends in access to transportation resources from an equity perspective are requisite steps in the pathway toward healthy, sustainable cities for all.
Sensing and Data Analysis for Assessing Human Balance Ability

This paper presents the development of a novel balance sensor capable of assessing human balance ability. This sensor can effectively detect the tread force distribution information under human feet with high resolution and frame rate while they are trying to balance themselves. The principle and performance of the sensor are discussed in the paper. We have used this sensor to capture tread force distribution variation videos from 6 categories of different human balancing actions. A novel 3D convolutional neural network is developed to classify these 6 actions. The high accuracy of video classifications have been achieved using 3D CNN. In addition, our preliminary experimental results demonstrates that applying deep learning method, this sensor is able to recognize different human motion patterns. Ongoing work is to exploit its potential use in neural disease diagnosis.
WDXI: The Dataset of X-Ray Image for Weld Defects

We establish the dataset of X-ray image for weld defects called WDXI. WDXI consists of 13,766 X-ray images, including seven significant types of weld defects. The images of metal plates, metal fittings and other welded parts are included in the data set. Qualified nondestructive testing(NDT)inspectors annotates every image. After that the image is audited by a higher level inspector. The inspector gives the final result based on the image and image test report. According to the report, we have sorted out the defect type, defect size, quality level of each weld, and other detailed information during the NDT process. We remove the letters and numbers which was placed manually when irradiated and extract the weld area from the image. The window width and window level method is used to enhance the image quality of weld area and highlights details of defects. On this data set, we trained a 5-layer Convolution neural network(CNN)to identify the type of defects. The rate of top-l accuracy reached 46.6%. Although the result is not very high, it can provide a useful example of how to use this data set. And this result also shows that there is still an extensive research space for this task in the future. The WDXI dataset will be publicly available in the future for academic research purposes which may promote the development of industrial radiographic NDT. WDXI will be the foundation for the study of weld defects classification and localization. It will provide data support for the majority of researchers to test their models.