Miracle-dz/UniFAR-FTUs huggingface dataset:
Method of adaptive pixel averaging for impulse noise reduction in digital images

In this paper a new nonlinear method for salt and pepper and random-valued impulse noise reduction in digital images is proposed. The authors achieved high effectiveness combined with a low computational complexity. The proposed solution is based on iterative process of noisy pixels detection using noise profiles and replacement of these pixels using the average values of neighboring noise free pixels. Noise profiles are trained using artificially noised test images. Experiments were performed on standard test images in order to compare proposed approach with other popular techniques. A quality measurement was the peak signal-to-noise ratio (PSNR). In many instances the proposed method was better than other existing techniques of impulse noise reduction.
Stereo vision based obstacle avoidance strategy for quadcopter UAV

This paper presents a visual control method of a small UAV in which binocular stereo vision and ultrasonic system are fused to implement obstacle avoidance strategy. This combination of sensors allows us to get a complete characterization of the environment and describe obstacle avoidance. It means that stereo vision system can detect obstacles around UAV and calculate a new path by RRT-algorithm. The ultrasonic sensor is introduced to compensate for the situations where something may lead to the failure of the obstacle detection. The method presented in this paper can improve the automatic level of the flying control system. Experiments on obstacle avoidance demonstrate the effectiveness of the proposed method and the performance of the UAV platform.
Fast access for ZigBee-enabled IoT devices using raspberry Pi

With the development of Internet of Things (IoT) technology, the application of IoT is more and more importantly widespread. Meanwhile, how to achieve fast access for various IoT devices, especially for a large-scale of IoT devices, also has been a significant challenge in the development of IoT systems. To address this issue, a fast access protocol for ZigBee-enabled IoT devices is proposed in this paper, in which, the ZigBee module embedded in a registering IoT device could automatically obtain a Personal Area Network (PAN) address and instantly access the target ZigBee wireless network through the signaling interactions among the involved protocol entities. After that, the operation and control of the accessed IoT devices are conducted based on the ZigBee communications. To evaluate the effectiveness and efficiency of this protocol, a prototype system is developed. The experiment and analysis results also have been shown to demonstrate the performance by comparing with the existing access methods.
3D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Data

We present a convolutional network that is equivariant to rigid body motions. The model uses scalar-, vector-, and tensor fields over 3D Euclidean space to represent data, and equivariant convolutions to map between such representations. These SE(3)-equivariant convolutions utilize kernels which are parameterized as a linear combination of a complete steerable kernel basis, which is derived analytically in this paper. We prove that equivariant convolutions are the most general equivariant linear maps between fields over R^3. Our experimental results confirm the effectiveness of 3D Steerable CNNs for the problem of amino acid propensity prediction and protein structure classification, both of which have inherent SE(3) symmetry.
Chinese speech recognition and task analysis of aldebaran Nao robot

It is one of the important goals for human-computer interaction to make the robot understand the Chinese instruction. The paper presents a scheme for Chinese instruction processing. After the user's speech instruction is obtained, the first thing to do is to conduct a voice preprocessing and Voice Activity Detection (VAD), and then the speech recognition by means of Baidu Cloud Speech Recognition technology. For recognized sentences, Chinese segmentation is carried out by using the Chinese segmentation algorithm combining dictionary and statistics. Then the professional keyword database is utilized to extract the key words, with the aim of attaining the verbs to perform the task and the nouns related to the places and objects in the task, so that the robot can receive the task semantic analysis. The method proves to achieve high accuracy and meet the task analysis requirement of NAO robot by experiments.
Research on spacial trajectory optimization for spray painting robot oriented to 3D entities

In this paper, the spatial trajectory optimization method of spray painting robot for 3D entity objects is proposed. A simple mathematic model of paint deposition rate is established by experimental method, and the 3D entity is patched by the surface modeling method based on FPAG. After planning the spray path on each patch, the improved GA algorithm and ACO algorithm are used to solve the TTOI problem. The feasibility of each algorithm is verified by simulation experiments. Finally, the spray painting experiments are carried out on the self-developed off-line programming experiment platform of spray painting robot and the results of two algorithms are compared. The experimental results show that the trajectory optimization of spray painting robot for 3D entity proposed can completely meet the requirements of the uniformity of paint thickness.
Applications of Data Mining Techniques for Vehicular Ad hoc Networks

Due to the recent advances in vehicular ad hoc networks (VANETs), smart applications have been incorporating the data generated from these networks to provide quality of life services. In this paper, we have proposed taxonomy of data mining techniques that have been applied in this domain in addition to a classification of these techniques. Our contribution is to highlight the research methodologies in the literature and allow for comparing among them using different characteristics. The proposed taxonomy covers elementary data mining techniques such as: preprocessing, outlier detection, clustering, and classification of data. In addition, it covers centralized, distributed, offline, and online techniques from the literature.
Use of machine learning in the forecast of clinical consequences of cancer diseases

This paper is about existing problems of concerning precise forecasting of clinical consequences of a certain type of disease which are analyzed. It is shown that existing systems of machine learning are differently effective and have automated training, assessment and interpretation of deep learning models. The choice of software SurvivalNet (SN) that allows users to train and interpret deep survival models is substantiated.
Radar micro-Doppler of wind turbines: Low-frequency polarimetric extension of simplified analytical model

A simplified polarimetrie model for radar signal scattering on wind turbines (WT) is proposed, which can be used for interpretation of observation in time and Doppler domains. This model uses representation of real WT as slowly rotating linear wire structures. The earlier proposed model has been extended to more general cases of WT with arbitrary orientation and full polarimetric observations in mono- and bi-static cases, which can be used for analysis of low-frequency signals scattering. It gives a possibility to estimate the influence of scattered on WT electromagnetic waves not only on radars but also on communication links.
Intersession Instability in fNIRS-Based Emotion Recognition

Emotion recognition based on neural signals is a promising technique for the detection of patients’ emotions for enhancing healthcare. However, emotion-related neural signals, such as from functional near infrared spectroscopy (fNIRS), can be affected by various psychophysiological and environmental factors. There is a paucity of literature regarding data instability and classification instability in fNIRS-based emotion recognition systems, phenomenon which may lead to user dissatisfaction and abandonment. We collected data in an fNIRS-based 2-class emotion recognition test-retest experiment (3 week interval) with visual stimuli emotion induction to examine data instability and its impact on classification accuracy. We found a 22.2% average deterioration of emotion classification accuracy between the two sessions, suggesting that classification instability is a serious problem. We found that the changes in the distributions of the selected neural signal features, as evaluated by Kullback–Leibler (KL) divergence, were a likely cause of the accuracy decline. We analyzed the data instability and our results showed that instability of spatial activation patterns and instability of the hemodynamic response in the most activated region are correlated with accuracy decline. Finally, we propose a method for mitigating classification instability in fNIRS-based emotion recognition based on feature selection for stable features, the first such method to our knowledge. This new feature selection criterion considers not only the separability of features (evaluated by Fisher Score) but also their stability over time (evaluated by KL divergence between feature distributions at different time points). Testing showed that this method led to an approximately 5% improvement in cross-session generalization accuracy.
Millimetre band detectors based on GaN/AlGaN HEMT

We report on experimental studies of resonant and non-resonant detection on GaN/AlGaN high electron mobility transistors in the millimeter-wave band at room temperate. The implementation of detectors strongly correlated with designed planar antennas, that ensure THz radiation coupling. In the present work, we study bow-tie and the dual-dipole detectors, for which responsivity of non-resonant detection shows maximums at 170 GHz (1380 V/W) and 175 GHz (630 V/W), respectively. We observe wide bandwidth ∼ 40 GHz around 183 GHz and demonstrate a response in the range 780 GHz–805 GHz with the maximum at 796 GHz (1250 V/W) for bow-tie detector.
A game theoretic load-aware network selection

This paper presents a load-aware network selection model to help users to determine whether or not to connect to a macro cell (MC) or a WiFi access point (AP) in a vertically integrated cellular WiFi system. The problem is formulated as a game theoretic model in which users selfishly maximize their throughput. Unlike most existing work, we do not assume that users have complete information about the other users' dynamics, which is more realistic in a communication network with distributed users where conveying such information would create enormous overhead. To do so, we provide a simple procedure that introduces a full hierarchy among the users reflecting in an accurate way their channel quality and then allowing them to sequentially choose their preferred network. Interestingly, the result for each user in the payoff is not far from his optimum.
Speeding up differential power analysis using integrated power traces

Side-channel attacks, including differential power analysis (DPA), are still an emerging topic. To make a deep research about DPA, one needs to be able to perform it as fast as possible. There are many possible ways to decrease the time of the attack. In this paper, we propose a way to decrease the duration of the correlation computations of this kind of attack by decreasing the number of samples per a power trace using an integration based aggregation method. We comprehensively describe this idea and present the results of an experimental evaluation focusing on the time efficiency of this approach.
Recognition of switch layouts in the task of rolling stock positional navigation

The paper suggests an approach to using the switch layouts to correct the readings of the locomotive navigation system equipped with a vision system. The coordinates of the intersection centers can be obtained from the information of digital road maps, and the current distance to them is calculated from the information of the vision system and the adopted mathematical model of the convergence/divergence of the railways of the turnout switch. The developed algorithm quality is checked on a large amount of full-scale data.
Dynamic Pricing with Finitely Many Unknown Valuations

Motivated by posted price auctions where buyers are grouped in an unknown number of latent types characterized by their private values for the good on sale, we investigate revenue maximization in stochastic dynamic pricing when the distribution of buyers' private values is supported on an unknown set of points in [0,1] of unknown cardinality $K$. This setting can be viewed as an instance of a stochastic $K$-armed bandit problem where the location of the arms (the $K$ unknown valuations) must be learned as well. In the distribution-free case, we prove that our setting is just as hard as $K$-armed stochastic bandits: no algorithm can achieve a regret significantly better than $\sqrt{KT}$, (where T is the time horizon); we present an efficient algorithm matching this lower bound up to logarithmic factors. In the distribution-dependent case, we show that for all $K>2$ our setting is strictly harder than $K$-armed stochastic bandits by proving that it is impossible to obtain regret bounds that grow logarithmically in time or slower. On the other hand, when a lower bound $\gamma>0$ on the smallest drop in the demand curve is known, we prove an upper bound on the regret of order $(1/\Delta+(\log \log T)/\gamma^2)(K\log T)$. This is a significant improvement on previously known regret bounds for discontinuous demand curves, that are at best of order $(K^{12}/\gamma^8)\sqrt{T}$. When $K=2$ in the distribution-dependent case, the hardness of our setting reduces to that of a stochastic $2$-armed bandit: we prove that an upper bound of order $(\log T)/\Delta$ (up to $\log\log$ factors) on the regret can be achieved with no information on the demand curve. Finally, we show a $O(\sqrt{T})$ upper bound on the regret for the setting in which the buyers' decisions are nonstochastic, and the regret is measured with respect to the best between two fixed valuations one of which is known to the seller.
Mammography Assessment using Multi-Scale Deep Classifiers

Applying deep learning methods to mammography assessment has remained a challenging topic. Dense noise with sparse expressions, mega-pixel raw data resolution, lack of diverse examples have all been factors affecting performance. The lack of pixel-level ground truths have especially limited segmentation methods in pushing beyond approximately bounding regions. We propose a classification approach grounded in high performance tissue assessment as an alternative to all-in-one localization and assessment models that is also capable of pinpointing the causal pixels. First, the objective of the mammography assessment task is formalized in the context of local tissue classifiers. Then, the accuracy of a convolutional neural net is evaluated on classifying patches of tissue with suspicious findings at varying scales, where highest obtained AUC is above $0.9$. The local evaluations of one such expert tissue classifier is used to augment the results of a heatmap regression model and additionally recover the exact causal regions at high resolution as a saliency image suitable for clinical settings.
Outfit Generation and Style Extraction via Bidirectional LSTM and Autoencoder

When creating an outfit, style is a criterion in selecting each fashion item. This means that style can be regarded as a feature of the overall outfit. However, in various previous studies on outfit generation, there have been few methods focusing on global information obtained from an outfit. To address this deficiency, we have incorporated an unsupervised style extraction module into a model to learn outfits. Using the style information of an outfit as a whole, the proposed model succeeded in generating outfits more flexibly without requiring additional information. Moreover, the style information extracted by the proposed model is easy to interpret. The proposed model was evaluated on two human-generated outfit datasets. In a fashion item prediction task (missing prediction task), the proposed model outperformed a baseline method. In a style extraction task, the proposed model extracted some easily distinguishable styles. In an outfit generation task, the proposed model generated an outfit while controlling its styles. This capability allows us to generate fashionable outfits according to various preferences.
A pipeline time-to-digital converter with a programmable time amplifier

This paper presents a pipeline time-to-digital converter (TDC) using a programmable time amplifier (TA). The TA adds time intervals that contain quantization errors in different stages of the first conversion step to achieve time amplification. Therefore, the TA has advantages of programmability, good linearity and wide input range. The TDC is designed in a 65nm CMOS technology. A time resolution up to 5ps at 230MHz is achieved. The total power consumption is 3mW under a 1V supply.
Vagal Tank Theory: The Three Rs of Cardiac Vagal Control Functioning – Resting, Reactivity, and Recovery

The aim of this paper is to set the stage for the vagal tank theory, showcasing a functional resource account for self-regulation. The vagal tank theory, building on neurophysiological, cognitive and social psychology approaches, will introduce a physiological indicator for self-regulation that has mainly been ignored from cognitive and social psychology, cardiac vagal control (also referred to as cardiac vagal activity). Cardiac vagal control reflects the contribution of the vagus nerve, the main nerve of the parasympathetic nervous system, to cardiac regulation. We propose cardiac vagal control to be an indicator of how efficiently self-regulatory resources are mobilized and used. Three systematic levels of cardiac vagal control analysis are suggested: resting, reactivity, and recovery. Based on this physiological indicator we derive the metaphor of the vagal tank, which can get depleted and replenished. Overall, the vagal tank theory will enable to integrate previous findings from different disciplines and to stimulate new research questions, predictions, and designs regarding self-regulation.
The Phenomenology of Habits: Integrating First-Person and Neuropsychological Studies of Memory

There is an ongoing debate how one can integrate the subjective (first-person) dimension of experiences more thoroughly into neuropsychological research. In cognitive experimental memory research, for instance, cognitive psychology begins by separating the act of recollection from the context where recollections occur, so as to make memory research suitable for study in the experimental conditions of the laboratory. It is the claim of this article that the challenge for memory research consists not merely in the (possible) loss of meaning entailed by transforming embedded recollected experiences into operationalized cognitive functions. Rather, from the outset, the first-person experiential basis of the entire research procedure is often insufficiently elaborated and hence risks neglecting or misrepresenting significant dimensions of the phenomena it studies. I demonstrate this with regard to habits understood as procedural memories. Research based on the paradigm of embodied cognition and phenomenology has shown that procedural memory-based skills and habits are not necessarily confined to sub-personal (unconscious) processing mechanisms. This paradigm states that some cognitive processes involve not only the brain but also the pre-reflectively experienced lived-body. The key idea is that we have experiential access to bodily processes that are not yet conceptualized or reflexively mediated. In the final part of my paper, I delineate how such experiences can be integrated into the neuropsychological study of habits via the method of ‘front-loaded phenomenology. ’
Selection of the sex‐linked inhibitor of apoptosis in mountain pine beetle (Dendroctonus ponderosae) driven by enhanced expression during early overwintering

Abstract The mountain pine beetle (Dendroctonus ponderosae) is an insect native to western North America; however, its geographical range has recently expanded north in BC and east into Alberta. To understand the population structure in the areas of expansion, 16 gene‐linked microsatellites were screened and compared to neutral microsatellites using outlier analyses of F st and F ct values. One sex‐linked gene, inhibitor of apoptosis (IAP), showed a strong signature of positive selection for neo‐X alleles and was analyzed for evidence of adaptive variation. Alleles of IAP were sequenced, and differences between the neo‐X and neo‐Y alleles were consistent with neutral evolution suggesting that the neo‐Y allele may not be under functional constraints. Neo‐Y alleles were amplified from gDNA, but not effectively from cDNA, suggesting that there was little IAP expression from neo‐Y alleles. There were no differences in overall IAP expression between males and females with the common northern neo‐X allele suggesting that the neo‐X allele in males compensates for the reduced expression of neo‐Y alleles. However, males lacking the most common northern neo‐X allele thought to be selected for in northern populations had reduced overall IAP expression in early October—at a time when beetles are preparing for overwintering. This suggests that the most common allele may have more rapid upregulation. The reduced function of neo‐Y alleles of IAP suggested by both sequence differences and lower levels of expression may foster a highly selective environment for neo‐X alleles such as the common northern allele with more efficient upregulation.
Non-coding RNA-linked epigenetic regulation in cardiac hypertrophy

Cardiac hypertrophy is an adaptive enlargement of myocardium in response to pressure overload caused various pathological insults, which is accompanied by alteration of a complex cascade of signaling pathways. During the hypertrophy process, many changes occur at cellular level including gene reprogramming by turning off chromatin regulators. Studies from the past decade have demonstrated that the abnormal epigenetic modifications, such as DNA methylation, histone modification, and oxidative modification of nucleic acid, could lead to changes in chromosome structure and cardiac dysfunction. Increasing evidence indicates that non-coding RNAs (ncRNAs) have functional significance in modulating the gene expression during those pathological events in the heart. Emerging evidences have highlighted that ncRNAs might serve as a signal for changing the state of chromatin, however, the knowledge about the ncRNA-linked epigenetic regulatory mechanisms in cardiac pathologies is still largely unexplored. In this review, we summarize the current information on association between ncRNAs and epigenetic modifications in cardiac hypertrophy, and we have discussed their crosstalk. In addition, this review provides insights into their therapeutic and diagnostic potential for treating hypertrophic heart disease.
Effect of distance metrics on positioning accuracy

RF fingerprinting based positioning systems have come to the forefront among indoor positioning systems as they provide both high precision and can use existing infrastructure like Wi-Fi access points effectively. In this study, an architecture that constructs the RF signal map of the environment in real-time is proposed. Therefore, with the positioning request, both the fingerprint of the target terminal and RF signal map of the environment are obtained in an online manner. At the stage of comparing the RF fingerprint of the target terminal with RF signal map of the environment, effects of different distance metrics (Manhattan, Euclidean, Chebyshev, Canberra, and Sorensen) and location estimation methods on the positioning accuracy were inspected. It was shown through experiments that the Chebyshev is the best distance metric among others. Moreover, it was seen that location estimation methods have no major impact on positioning accuracy.