Miracle-dz/UniFAR-FTUs huggingface dataset:
The Psychology of Uncertainty and Three-Valued Truth Tables

Psychological research on people's understanding of natural language connectives has traditionally used truth table tasks, in which participants evaluate the truth or falsity of a compound sentence given the truth or falsity of its components in the framework of propositional logic. One perplexing result concerned the indicative conditional if A then C which was often evaluated as true when A and C are true, false when A is true and C is false but irrelevant“ (devoid of value) when A is false (whatever the value of C). This was called the “psychological defective table of the conditional. ” Here we show that far from being anomalous the “defective” table pattern reveals a coherent semantics for the basic connectives of natural language in a trivalent framework. This was done by establishing participants' truth tables for negation, conjunction, disjunction, conditional, and biconditional, when they were presented with statements that could be certainly true, certainly false, or neither. We review systems of three-valued tables from logic, linguistics, foundations of quantum mechanics, philosophical logic, and artificial intelligence, to see whether one of these systems adequately describes people's interpretations of natural language connectives. We find that de Finetti's (1936/1995) three-valued system is the best approximation to participants' truth tables.
A Comparison Study Between Soft Computing and Statistical Regression Techniques for Software Effort Estimation

In this paper, we conduct a comparison between soft computing and statistical regression techniques in terms of a software development estimation regression problem. Our study includes both support vector regression (SVR) and artificial neural network (ANN) as soft computing methodologies on one side, and stepwise multiple linear regression and log linear regression as statistical regression methods on the other side. The experiments are conducted using NASA93 dataset from the well-known PROMISE software repository. Multiple dataset preprocessing steps are performed in order to guarantee confident results including outliers study. We rely on the holdout technique associated with 25 random repetitions with confidence interval calculation within 95% statistical confidence level. Pred(30) evaluation criteria, from literature, is employed to compare between different models. Also, examining different feature combinations, in the case of SVR, shows significant impact on the model precision.
Extracellular histones are the ligands for the uptake of exosomes and hydroxyapatite‐nanoparticles by tumor cells via syndecan‐4

The mechanisms by which exosomes (nano‐vesicular messengers of cells) are taken up by recipient cells are poorly understood. We hypothesized that histones associated with these nanoparticles are the ligands which facilitate their interaction with cell surface syndecan‐4 (SDC4) to mediate their uptake. We show that the incubation with fetuin‐A (exosome‐associated proteins) and histones mediates the uptake of exosomes that are normally not endocytosed. Similarly, hydroxyapatite‐nanoparticles incubated with fetuin‐A and histones (FNH) are internalized by tumor cells, while nanoparticles incubated with fetuin‐A alone (FN) are not. The uptake of exosomes and FNH, both of which move to the perinuclear region of the cell, is attenuated in SDC4‐knockdown cells. Data show that FNH can compete with exosomes for uptake and that both use SDC4 as uptake receptors.
The International Trauma Questionnaire: development of a self‐report measure of ICD‐11 PTSD and complex PTSD

The purpose of this study was to finalize the development of the International Trauma Questionnaire (ITQ), a self‐report diagnostic measure of post‐traumatic stress disorder (PTSD) and complex PTSD (CPTSD), as defined in the 11th version of the International Classification of Diseases (ICD‐11).
MeDReaders: a database for transcription factors that bind to methylated DNA

Abstract Understanding the molecular principles governing interactions between transcription factors (TFs) and DNA targets is one of the main subjects for transcriptional regulation. Recently, emerging evidence demonstrated that some TFs could bind to DNA motifs containing highly methylated CpGs both in vitro and in vivo. Identification of such TFs and elucidation of their physiological roles now become an important stepping-stone toward understanding the mechanisms underlying the methylation-mediated biological processes, which have crucial implications for human disease and disease development. Hence, we constructed a database, named as MeDReaders, to collect information about methylated DNA binding activities. A total of 731 TFs, which could bind to methylated DNA sequences, were manually curated in human and mouse studies reported in the literature. In silico approaches were applied to predict methylated and unmethylated motifs of 292 TFs by integrating whole genome bisulfite sequencing (WGBS) and ChIP-Seq datasets in six human cell lines and one mouse cell line extracted from ENCODE and GEO database. MeDReaders database will provide a comprehensive resource for further studies and aid related experiment designs. The database implemented unified access for users to most TFs involved in such methylation-associated binding actives. The website is available at http://medreader.org/.
White-Matter Structural Connectivity in Relation to Humor Styles: An Exploratory Study

To investigate the potential relationship between white matter (WM) microstructure and humor styles, diffusion tensor images of brain WM and humor style tendencies were obtained from thirty healthy adults. Using connectivity efficiency measures from graph theoretical analysis and controlling for the influence of gender, age, educational level, and the big five personality traits, we preliminarily examined the prediction of humor styles from brain network efficiency. The results showed that the local efficiency within particular brain networks positively predicted a self-enhancing humor style and negatively predicted an aggressive humor style. The node efficiency of the left superior temporal gyrus distinguished the benevolent or hostile way that individuals coped with interpersonal embarrassment. These findings from this exploratory study support the hypothesis that WM structure influences humor styles, and provide the initial evidence and implications regarding the relationship between biological mechanisms and mental health for future research.
Design and Evaluation of a Rate-Based Congestion Control Mechanism in CoAP for IoT Applications

CoAP is an application protocol that provides standardised RESTful services for IoT devices. Since COAP messages are encapsulated into UDP datagrams, COAP specification provides: i) optional reliability mechanisms through retransmissions, and ii) simple congestion control mechanisms based on retransmission timeouts. Recent studies have demonstrated that these congestion control schemes may significantly underperform when operating with bursty traffic. To address these limitations, in this paper we propose COAP-R, an alternative solution for regulating the sending rate of CoAP sources, which adopts a rate-based approach for traffic control. Key features of COPA-R are: i) to leverage the tree-based routing structure of IoT networks to estimate the maximum throughput that can be obtained on the bottleneck link of every upward route, and ii) to perform in a distributed manner a max-min fair allocation of available network capacity on the basis of estimated bottleneck bandwidths. The proposed approach is evaluated by means of simulations considering a scenario in which traffic is generated in bursts, for instance as consequence of events detected by sensors. Our simulations demonstrate that the proposed approach ensures a fair allocation of network resources, and leads to a 40% decrease of the data collection delays when compared to COAP.
Effects of Visual Realism and Moving Detail on Cybersickness

In this study we compare two conditions of visual detail - modern graphics and detail reduction through cel-shading - in experiencing Cybersickness during virtual movement along a preprogrammed path within a scene depicting a real-world outdoor museum viewed with Oculus CV1. The Cybersickness experience was quantified with the Fast-Motion-Sickness (FMS) scale and the Simulator Sickness Questionnaire (SSQ). We found weak evidence for realistic graphics being more sickness-inducing. Also, FMS scores peaked whenever a participant was entering a building.
Isolation and characterization of antagonistic bacteria with the potential for biocontrol of soil-borne wheat diseases

Bacillus amyloliquefaciens subsp. plantarum XH-9 is a plant-beneficial rhizobacterium that shows good antagonistic potential against phytopathogens by releasing diffusible and volatile antibiotics, and secreting hydrolytic enzymes. Furthermore, the XH-9 strain possesses important plant growth-promoting characteristics, including nitrogen fixation (7.92 ± 1.05 mg/g), phosphate solubilization (58.67 ± 4.20 μg/L), potassium solubilization (10.07 ± 1.26 μg/mL), and the presence of siderophores (4.92 ± 0.46 μg/mL), indole-3-acetic acid (IAA) (7.76 ± 0.51 μg/mL) and 1-aminocyclopropane-1-carboxylic acid deaminase (ACC-deaminase) (4.67 ± 1.21 nmol/[mg•h]). Moreover, the XH-9 strain showed good capacities for wheat, corn, and chili root colonization, which are critical prerequisites for controlling soil-borne diseases as a bio-control agent. Real-time quantitative polymerase chain reaction experiments showed that the amount of Fusarium oxysporum DNA associated with the XH-9 strain after treatment significantly decreased compared with control group. Accordingly, wheat plants inoculated with the XH-9 strain showed significant increases in the plant shoot heights (14.20%), root lengths (32.25%), dry biomass levels (11.93%), and fresh biomass levels (16.28%) relative to the un-inoculated plants. The results obtained in this study suggest that the XH-9 strain has potential as plant-growth promoter and biocontrol agent when applied in local arable land to prevent damage caused by F. oxysporum and other phytopathogens. Importance Plant diseases, particularly soilborne pathogens, play a significant role in the destruction of agricultural resources. Although these diseases can be controlled to some extent with crop and fungicides, while these measures increase the cost of production, promote resistance, and lead to environmental contamination, so they are being phased out. Plant growth-promoting rhizobacteria are an alternative to chemical pesticides that can play a key role in crop production by means of siderophore and indole-3-acetic acid production, antagonism to soilborne root pathogens, phosphate and potassium solubilization, and nitrogen fixation. These rhizobacteria can also promote a beneficial change in the microorganism community by significantly reducing its pathogenic fungi component. Their use is fully in accord with the principles of sustainability.
From chaining blocks to breaking even: A study on the profitability of bitcoin mining from 2012 to 2016

Bitcoin is a widely-spread payment instrument, but it is doubtful whether the proof-of-work (PoW) nature of the system is financially sustainable on the long term. To assess sustainability, we focus on the bitcoin miners as they play an important role in the proof-of-work consensus mechanism of bitcoin to create trust in the currency. Miners offer their services against a reward while recurring expenses. Our results show that bitcoin mining has become less profitable over time to the extent that profits seem to converge to zero. This is what economic theory predicts for a competitive market that has a single homogenous good. We analyze the actors involved in the bitcoin system as well as the value flows between these actors using the e3value methodology. The value flows are quantified using publicly available data about the bitcoin network. However, two important value flows for the miners, namely hardware investments and expenses for electricity power, are not available from public sources. Therefore, we contribute an approach to estimate the installed base of bitcoin hardware equipment over time. Using this estimate, we can calculate the expenses miner should have. At the end of our analysis period, the marginal profit of mining a bitcoin becomes negative, i.e., to a loss for the miners. This loss is caused by the consensus mechanism of the bitcoin protocol, which requires a substantial investment in hardware and significant recurring daily expenses for energy. Therefore, a sustainable crypto currency needs higher payments for miners or more energy efficient algorithms to achieve consensus in a network about the truth of the distributed ledger.
A 3D Atrous Convolutional Long Short-Term Memory Network for Background Subtraction

Background subtraction, or foreground detection, is a challenging problem in video processing. This problem is mainly concerned with a binary classification task, which designates each pixel in a video sequence as belonging to either the background or foreground scene. Traditional approaches for tackling this problem lack the power of capturing deep information in videos from a dynamic environment encountered in real-world applications, thus often achieving low accuracy and unsatisfactory performance. In this paper, we introduce a new 3-D atrous convolutional neural network, used as a deep visual feature extractor, and stack convolutional long short-term memory (ConvLSTM) networks on top of the feature extractor to capture long-term dependences in video data. This novel architecture is named a 3-D atrous ConvLSTM network. The new network can capture not only deep spatial information but also long-term temporal information in the video data. We train the proposed 3-D atrous ConvLSTM network with focal loss to tackle the class imbalance problem commonly seen in background subtraction. Experimental results on a wide range of videos demonstrate the effectiveness of our approach and its superiority over existing methods.
Secure two-user AF relaying networks using cooperative jamming

In this paper, we investigate secure two-user AF relaying networks based on a destination assisted cooperative jamming (DACJ). We consider two sources, one destination, one eavesdropper and multiple amplify-and-forwards (AF) relays. In the first time slot, each source broadcasts the information signal to the relays. In order to protect the source message from being eavesdropped, the destination simultaneously transmits a jamming signal to each relay. The selected relay amplifies and forwards the combined signal to the destination and eavesdropper. We introduce reactive relay selection and power allocation to decrease the power consumption. Closed-form expressions for lower bound of ergodic secrecy capacity (ESC) are provided. Numerical results are presented to verify the exactness of the proposed mathematical analysis.
Selective Cytotoxicity of Dihydroorotate Dehydrogenase Inhibitors to Human Cancer Cells Under Hypoxia and Nutrient-Deprived Conditions

Human dihydroorotate dehydrogenase (HsDHODH) is a key enzyme of pyrimidine de novo biosynthesis pathway. It is located on the mitochondrial inner membrane and contributes to the respiratory chain by shuttling electrons to the ubiquinone pool. We have discovered ascofuranone (1), a natural compound produced by Acremonium sclerotigenum, and its derivatives are a potent class of HsDHODH inhibitors. We conducted a structure–activity relationship study and have identified functional groups of 1 that are essential for the inhibition of HsDHODH enzymatic activity. Furthermore, the binding mode of 1 and its derivatives to HsDHODH was demonstrated by co-crystallographic analysis and we show that these inhibitors bind at the ubiquinone binding site. In addition, the cytotoxicities of 1 and its potent derivatives 7, 8, and 9 were studied using human cultured cancer cells. Interestingly, they showed selective and strong cytotoxicity to cancer cells cultured under microenvironment (hypoxia and nutrient-deprived) conditions. The selectivity ratio of 8 under this microenvironment show the most potent inhibition which was over 1000-fold higher compared to that under normal culture condition. Our studies suggest that under microenvironment conditions, cancer cells heavily depend on the pyrimidine de novo biosynthesis pathway. We also provide the first evidence that 1 and its derivatives are potential lead candidates for drug development which target the HsDHODH of cancer cells living under a tumor microenvironment.
Improved UAV Detection with the MIMO Radar MIRA-CLE Ka using Range-Velocity Processing and TDMA Correction Algorithms

We present the improvements of UAV detection by using range-velocity processing instead of range-Doppler processing to perform longer coherent processing intervals (CPI). This increases the signal-to-noise ratio (SNR) and the ability to improve the classification by adding a micro-Doppler analysis even if the target moves along several range cells over the recording time. Furthermore an approach is presented for the correction of the time division multiplexing access (TDMA), which is used for the synthesis of the virtual MIMO (Multiple-Input Multiple-Output) array, resulting in a misplaced target localization or grating lobes.
Joint Feature and Similarity Deep Learning for Vehicle Re-identification

In this paper, a joint feature and similarity deep learning (JFSDL) method for vehicle re-identification is proposed. The proposed JFSDL method applies a siamese deep network to extract deep learning features for an input vehicle image pair simultaneously. The siamese deep network is learned under the joint identification and verification supervision. The joint identification and verification supervision is realized by linearly combining two softmax functions and one hybrid similarity learning function. Moreover, based on the hybrid similarity learning function, the similarity score between the input vehicle image pair is also obtained by simultaneously projecting the element-wise absolute difference and multiplication of the corresponding deep learning feature pair with a group of learned weight coefficients. Extensive experiments show that the proposed JFSDL method is superior to multiple state-of-the-art vehicle re-identification methods on both the VehicleID and VeRi data sets.
Global Adaptive Stabilization and Tracking Control for High-Order Stochastic Nonlinear Systems With Time-Varying Delays

This paper discusses adaptive control problems of high-order stochastic nonlinear time-delay systems. Compared with the existing results, the considered system involves uncertain parameters, intricate stochastic drift terms, and unknown time-varying delays. Based on a modified method of adding a power integrator and Lyapunov–Krasoviskii functional technique, new controllers are successfully constructed to achieve the objectives of adaptive stabilization and adaptive tracking, respectively. Examples are given to show the effectiveness of the proposed control strategies.
Object Size Perception in Immersive Virtual Reality: Avatar Realism Affects the Way We Perceive

How does the representation of an embodied avatar influence the way in which a human perceives the scale of a virtual environment? It has been shown that the scale of the external environment is perceived relative to the size of one's body. However, the influence of avatar realism on the perceived scale has not been investigated, despite the fact that it is common to embody avatars of various representations, from iconic to realistic. This study examined how avatar realism would affect perceived graspable object sizes as the size of the avatar hand changes. In the experiment, we manipulated the realism (high, medium, and low) and size (veridical and enlarged) of the avatar hand, and measured the perceived size of a cube. The results showed that the size of the cube was perceived to be smaller when the avatar hand was enlarged for all degrees of realism of the hand. However, the enlargement of the avatar hand had a greater influence on the perceived cube size for the highly realistic avatar than for the medium-level and low-level realism conditions. This study shed new light on the importance of the avatar representation in a three-dimensional user interface field, in how it can affect the manner in which we perceive the scale of a virtual environment.
Chest X-Ray Image Segmentation Using Encoder-Decoder Convolutional Network

This paper presents a deep learning method of segmenting lungs in chest X-Ray image using Encoder-Decoder Convolutional Network on the JSRT (Japanese Society of Radiological Technology) lung nodule dataset. The result of the segmentation has proven efficient enough to be applicable in real world medical environments to bring ease in determining the area occupied by the lungs and some other medical diagnosis.
Active Learning of DC Circuits: Spreading the Use of The VISIR Remote Lab In Argentina

The VISIR+ Project was launched in 2015 with the objective of spreading the use of the VISIR remote laboratory in Latin America, promoting an enquiry-based approach to teaching and learning on electric and electronic circuits. Within the scope of this project, several didactical implementations were achieved both in Argentina and in Brazil. This article describes the latest implementation of VISIR in a pre-university technical school in Argentina (associated partner of the VISIR+ team) in three Physics courses with students having different background knowledge and interests. A set of activities comprising simulations, remote and hands-on experiments were designed in order to engage students in the learning process. The implementation was assessed in terms of the learning objectives and outcomes. Results showed that VISIR is an effective tool to promote active learning. Moreover, this experience seems highly valuable to encourage the dissemination of VISIR in Argentina.
Full and Half-Wavelength Ultrasonic Percussive Drills

Ultrasonic percussive drills are a leading technology for small rock drilling applications where power and weight-on-bit are at a premium. The concept uses ultrasonic vibrations to excite an oscillatory motion in a free-mass, which then delivers impulsive blows to a drilling-bit. This is a relatively complex dynamic problem involving the transducer, the free-mass, the drilling-bit and, to a certain extent, the rock surface itself. This paper examines the performance of a full-wavelength transducer compared to a half-wavelength system, which may be more attractive due to mass and dimensional drivers. To compare the two approaches, 3-D finite-element models of the ultrasonic percussive stacks using full and half-wavelength ultrasonic transducers are created to assess delivered impulse at similar power settings. In addition, impact-induced stress levels are evaluated to optimize the design of drill tools at a range of internal spring rates before, finally, experimental drilling is conducted. The results suggest that full-wavelength systems will yield much more effective impulse but, interestingly, their actual drilling performance was only marginally better than half-wavelength equivalents.