Miracle-dz/UniFAR-FTUs huggingface dataset:
Self-organizing CPGs in the control loop of a biomorphic fish robot

The rhythmic motor activity of animals is based on the work of interconnected populations of neurons, the so-called central pattern generators (CPGs). Recently, the use of artificial CPGs as controllers of robotic devices, in particular in fish robots, has been proposed. In the current paper, we present a software package that allows simulating CPG and demonstrating their work using a virtual robot. Using the developed tools, we have studied the possibility of CPG self-organization based on the formation of closed neural circuits in a network with Spike-timing-dependent plasticity (STDP). Modeling activity in modular neural networks with STDP revealed an increase in unidirectional intermodular connections leading to circulating dynamics. In the case of bidirectional intermodular connections, network exhibits a variety of dynamic patterns of neural activity. The use of a self-organizing CPG in the control loop of a virtual fish robot showed the gradual formation of a locomotor pattern as the neural network is structurally and functionally reorganized.
A Comparison of Machine and Deep Learning Models for Detection and Classification of Android Malware Traffic

With the increasing popularity of mobile-app services, malicious software is increasing as well. Accordingly, the interest of the scientific community in Machine and Deep Learning solutions for detecting and classifying malware traffic is growing. In this work, we provide a fair assessment of the performance of a number of data-driven strategies to detect and classify Android malware traffic. Three models are taken into account (Decision Tree, Random Forest, and 1-D Convolutional Neural Network) considering both flat (i.e. non-hierarchical) and hierarchical approaches. The experimental analysis performed using a state-of-art dataset (CIC-AAGM2017) reports that Random Forest exhibits the best performance in a flat setup, while moving to a hierarchical approach could cause significant variation in precision and recall. Such results push for further investigating advanced hierarchical setups and learning schemes.
Evaluation of the Cost-Effective Indoor Wireless Positioning System Using RSSI Method

This paper deals with using low-cost radio modules for indoor localization purposes. A comparison of the various positioning techniques is introduced in the beginning parts. In addition, the trilateration method’s mathematical background is briefly introduced. Bluetooth Low Energy has been chosen for real environment tests and deployment of the proposed methods. The system consists of newly developed and manufactured hardware units. The paper then describes the microprocessor and post-processing algorithms used. The designed system was installed in the laboratory room for verification and measurement purposes. Finally, the platform was evaluated regarding the final accuracy in the real installation.
Fast and Energy-Efficient Watermarking of HEVC-Compressed Video Bitstreams

Most research results in video watermarking refer to the embedding of messages into uncompressed video, which needs full decoding and then full encoding of the content. In some applications, such a method is too complex, and watermarking of compressed bitstreams is required. Watermarking of compressed bitstreams is more difficult as the number of bits is very limited, and the compressed video is very sensitive to all modifications that may yield visible artifacts in the decoded watermarked video. In the paper, an original watermarking method is described for HEVC-compressed video. The main idea of the technique is to embed a message into nonzero residual transform coefficients in such a way that potential drift is limited. The watermark detection is based on reference, unwatermarked video. The experimental verification demonstrates the strong robustness of the embedded watermarks.
Polygonal Shapes Reconstruction from Acoustic Echoes Using a Mobile Sensor and Beamforming

Mapping a structure, such as a metal plate in a ship hull, using acoustic echoes typically requires making assumptions on its shape (e.g. rectangular). This work introduces a more general approach based on beamforming to recover the geometry of arbitrary polygonal-shaped bounded areas from acoustic reflections. Our method only requires a single omni-directional emitter-receiver acoustic device mounted on a mobile platform. We apply beamforming to the acoustic measurements in the geometrical boundary space. We subsequently retrieve the edges from the beamforming results via the minimization of a regularized cost criterion, using a simulated annealing optimizer. We also design a boundary rejection criterion so that their exact number can be recovered based only on a specified upper bound. We assess our method on different geometries in a simulation environment and a real-world setting using ultrasonic guided waves measurements. The results demonstrate that it is efficient for achieving the targeted objectives.
Exploring On-Device Learning Using Few Shots for Audio Classification

Few shot learning (FSL) improves the generalization of neural network classifiers to unseen classes and tasks using small annotated samples of data. Recently, there have been attempts to apply few shot learning in the audio domain for various applications. However, the focus has been mainly on accuracy. Here, we take a holistic view and investigate various system aspects such as latency, storage and memory requirements of few shot learning methods in addition to improving the accuracy with very deep learning models for the tasks of audio classification. To this end, we not only compare the performance of different few shot learning methods but also, for the first time, design an end-to-end framework for smartphones and wearables which can run such methods completely on-device. Our results indicate the need to collect large datasets with more classes as we show much higher gains can be obtained with very deep learning models on big datasets. Surprisingly, metric-based methods such as ProtoTypical Networks can be realized practically on-device and quantization helps further (50%) in reducing the resource requirements, while having no impact on accuracy for the audio classification tasks.
Vector Symbolic Architectures as a Computing Framework for Emerging Hardware

This article reviews recent progress in the development of the computing framework vector symbolic architectures (VSA) (also known as hyperdimensional computing). This framework is well suited for implementation in stochastic, emerging hardware, and it naturally expresses the types of cognitive operations required for artificial intelligence (AI). We demonstrate in this article that the field-like algebraic structure of VSA offers simple but powerful operations on high-dimensional vectors that can support all data structures and manipulations relevant to modern computing. In addition, we illustrate the distinguishing feature of VSA, “computing in superposition,” which sets it apart from conventional computing. It also opens the door to efficient solutions to the difficult combinatorial search problems inherent in AI applications. We sketch ways of demonstrating that VSA are computationally universal. We see them acting as a framework for computing with distributed representations that can play a role of an abstraction layer for emerging computing hardware. This article serves as a reference for computer architects by illustrating the philosophy behind VSA, techniques of distributed computing with them, and their relevance to emerging computing hardware, such as neuromorphic computing.
Selection, Confounding, and Attrition Biases in Randomized Controlled Trials of Rehabilitation Interventions

Abstract A thorough knowledge of biases in intervention studies and how they influence study results is essential for the practice of evidence-based medicine. The objective of this review was to provide a basic knowledge and understanding of the concept of biases and associated influence of these biases on treatment effects, focusing on the area of rehabilitation research. This article provides a description of selection biases, confounding, and attrition biases. In addition, useful recommendations are provided to identify, avoid, or control these biases when designing and conducting rehabilitation trials. The literature selected for this review was obtained mainly by compiling the information from several reviews looking at biases in rehabilitation. In addition, separate searches by biases and looking at reference lists of selected studies as well as using Scopus forward citation for relevant references were used. If not addressed appropriately, biases related to intervention research are a threat to internal validity and consequently to external validity. By addressing these biases, ensuring appropriate randomization, allocation concealment, appropriate retention techniques to avoid dropouts, appropriate study design and statistical analysis, among others, will generate more accurate treatment effects. Based on their impact on clinical results, a proper understanding of these concepts is central for researchers, rehabilitation clinicians, and other stakeholders working on this field.
Expanded Excel-Based Simulator Tool for Resource-Allocation Optimization for Distributed Network Protocols

A simple Excel-based simulation tool described in earlier papers was developed to simulate the operation and performance of distributed network systems having time-varying data traffic patterns. Such networks use protocols to increase or decrease resources allocated. These resource-allocation protocols can have settable parameters such as numerical thresholds. Improper choices of these parameters can cause undesirable results such as allocations oscillating between higher and lower values. This paper describes modifications to the simulator to analyze the degree to which oscillations occur, to generate and store multiple sets of message-generation data allowing faster simulations, and to present additional message-delivery performance results. Also, the user interface for the simulator has been expanded to clearly show all simulation results and to show them side by side for two protocols that are being compared. The new simulator features and new user interface are presented.
Synchronization events in a spiking neural network

Synchronization in neural populations is one of the mechanisms of brain functioning in a normal state. However, excessive synchronization, including synchronization in large neural populations, is undesirable and, according to experimental studies, is observed in a variety of pathologies, including epilepsy. The importance and prevalence of this phenomenon attracts much attention of many researchers. Methods of mathematical modeling and the theory of dynamical systems contribute to a deeper understanding of this effect. In this paper, we propose a model of a recurrent spiking neural network with a limited excitability resource. According to the results, the model demonstrates synchronization events corresponding to the epileptic brain activity.
Sorted Channel Based User Search for Initial Access in 5G and Beyond Cellular Networks

Millimeter waves (mmWaves) are considered as promising candidates for high bandwidth and high data rate transmission in 5G and beyond cellular networks. However, such high frequencies require directional transmission due to their inherent path loss and sensitivity to blockage. With directional transmission, it is difficult to locate a Mobile User (MU) in a cell which leads to mis-detections and delayed Initial Access (IA). In this paper, we propose a novel IA scheme that leverages the Channel State Information (CSI) to search sectors. Our scheme searches the sectors in the descending order of channel quality and, thus, named the Sorted Search Scheme (SSS). We argue that our CSI-based approach reduces the latency of IA in 5G and beyond networks and show that SSS has a low Mean Detection Latency (MDL) while maintaining the Mis-detection Probability (MDP) of the benchmark Exhaustive Search Scheme (ESS). Further, the analytical expressions of MDP and MDL are derived for all fading scenarios. Numerical simulation show that our SSS reduces the MDL by 16%, thereby making it useful for low-latency applications. It is also shown that even partial or imperfect CSI can reduce the MDL.
A Nonminimal Coupling Model and its Short-Range Solar System Impact

The objective of this work is to present the effects of a nonminimally coupled model of gravity on a Solar System short range regime. For this reason, this study is only valid when the cosmological contribution is considered irrelevant. The action functional of the model involves two functions $f^1(R)$ and $f^2(R)$ of the Ricci scalar curvature $R$, where the last one multiplies the matter Lagrangian. Using a Taylor expansion around $R=0$ for both functions $f^1(R)$ and $f^2(R)$, it was found that the metric around a spherical object is a perturbation of the weak-field Schwarzschild metric. The $tt$ component of the metric, a Newtonian plus a Yukawa perturbation term, is constrained using the available observational results. First it is shown that this effect is null when the characteristic mass scales of each function $f^1(R)$ and $f^2(R)$ are identical. Besides, the conclusion is that the nonminimal coupling only affects the Yukawa contribution strength and not its range and that the Starobinsky model for inflation is not experimentally constrained. Moreover, the geodetic precession effect, obtained also from the radial perturbation of the metric, reveals to be of no relevance for the constraints.
A Novel Fake News Detection Approach Using Machine Learning

The largest number of smartphone users choose to check statistics through social networks over the Internet. Social media websites post facts and provide certifications. This question may relate to the credibility of knowledge and articles distributed on social networks such as WhatsApp, Facebook pages, Twitter, and various microblogs and social networking sites.It is very dangerous for society to simply accept rumors and false information as truth. An hour’s need is to prevent gossip, especially within developing countries such as India, and to prevent interest in relevant and credible news articles.This article will show you the versions and how to detect fake news accordingly. We used machine learning and natural language processing to gather news and tried to use Vector’s support engine to determine if the news was real or fake. The proposed options is compared with the existing model. The proposed version works correctly and determines accuracy with up to 93.6% accuracy.
Gland segmentation in gastric histology images: detection of intestinal metaplasia

Gastric cancer is one of the most frequent causes of cancer-related deaths worldwide. Gastric intestinal metaplasia (IM) of the mucosa of the stomach has been found to increase the risk of gastric cancer and is considered as one of the precancerous lesions. Therefore, early detection of IM may have a valuable role in histopathological risk assessment regarding the possibility of progression to cancer. Accurate segmentation and analysis of gastric glands from the histological images plays an important role in the diagnostic confirmation of IM. Thus, in this paper, we propose a framework for segmentation of gastric glands and detection of IM. More specifically, we propose the GAGL-Net for the segmentation of glands. Then, based on two features of the extracted glands we classify the tissues into normal and IM cases. The results showed that the proposed gland segmentation approach achieves an F1 score equal to 0.914. Furthermore, the proposed methodology shows great potential for the IM detection achieving an accuracy score equal to 96.6%. To evaluate the efficiency of the proposed methodology we used a publicly available dataset and we created the GAGL dataset consisting of 59 Whole Slide Images (WSI) including both IM and normal cases.
Detection Mechanism of Money Laundering based on Random Walk and Skip-Grim Model

Money laundering means that criminals use the services provided by banks to transfer a large amount of illegal funds to untraceable destination accounts. Most of the related works are rule-based and machine learning based anti-money laundering systems. However, the anti-money laundering systems based on machine learning are affected by the data scale of money laundering transactions. The rule-based anti-money laundering systems require a lot of manual work and cannot adapt to the changing money laundering behavior. Therefore, this paper designs a money laundering detection mechanism based on random walk and skip-grim model. This detection mechanism preferentially constructs an account transfer graph. Then, on this basis, it generates random transfer trajectories for each account using the random walk algorithm. Thereafter, the transfer characteristics of each user are automatically analyzed from the random transfer trajectories utilizing the skip-grim model. Finally, it compares the extracted transfer characteristics of different users and combines the cosine similarity to identify illegal money laundering users. Last, we use python programming language and cbank dataset to evaluate the performance of the proposed scheme, and we compare our work with the related works, flowscope and Martin jullum. The extensive simulation results validate that, the proposed scheme has better identification effect, correctly identifying more than 80% of the money laundering accounts, and the misjudgment rate is less than 20 %.
CATALOGUE OF REFACTORINGS FOR ASPECTJ

aspect GeneralPolicy { protected interface Participant {} public Data Participant. _sensitiveData; //. } aspect ConcretePolicy extends GeneralPolicy { //code using Participant. _sensitiveData } We can add in the superaspect the following ‘declare warning’: abstract aspect GeneralPolicy { protected interface Participant {} public Data Participant. _sensitiveData; declare warning: (set(public Data Participant+._sensitiveData) || get(public Data Participant+._sensitiveData)) && ! within(GeneralPolicy+): "field _sensitiveData is aspect protected. Not visible here. "; //. }aspect GeneralPolicy { protected interface Participant {} public Data Participant. _sensitiveData; declare warning: (set(public Data Participant+._sensitiveData) || get(public Data Participant+._sensitiveData)) && ! within(GeneralPolicy+): "field _sensitiveData is aspect protected. Not visible here. "; //. } After all warnings are gone, we change the ‘declare warning’ to ‘declare error’. Example: protecting an inter-type method Suppose the same abstract aspect as in the previous example also includes method processSensitiveData(), which we also would like to protect: abstract aspect GeneralPolicy { protected interface Participant {} public Data Participant. _sensitiveData; public void processSensitiveData() { //code using Participant. _sensitiveData } //. }aspect GeneralPolicy { protected interface Participant {} public Data Participant. _sensitiveData; public void processSensitiveData() { //code using Participant. _sensitiveData } //. } We create the following ‘declare warning’: abstract aspect GeneralPolicy { protected interface Participant {} public Data Participant. _sensitiveData; public void processSensitiveData() { //code using caspule. _sensitiveData } declare warning: call(void processSensitiveData()) && ! within(GeneralPolicy+): "method processSensitiveData is aspect protected. Not visible here. "; //. }aspect GeneralPolicy { protected interface Participant {} public Data Participant. _sensitiveData; public void processSensitiveData() { //code using caspule. _sensitiveData } declare warning: call(void processSensitiveData()) && ! within(GeneralPolicy+): "method processSensitiveData is aspect protected. Not visible here. "; //. } Likewise, the ‘declare warning’ should be changed to ‘declare error’ when all the warnings are gone.
Study of Different Gain Medium in Open-cavity Multi-wavelength Brillouin-Raman Fiber Laser

Earlier reported works used 7 km dispersion compensating fiber (DCF) as gain medium in open-cavity multi-wavelength Brillouin-Raman fiber laser. In this paper, we investigate the laser performance using five different fiber spools. Our initial results reported here show shorter DCF may offer optimum number of channels, side-mode suppression ratios and flatness.
Breast Imaging by Cascaded CNN from Joint Microwave and Ultrasonic Data

In the context of early breast tumor characterization, combining electromagnetic (EM) and ultrasound (US) modalities is of interest, since both non-ionizing and low-cost, and harboring complementary resolution features. Here, a new Convolutional Neural Network (CNN) structure is proposed, denoted as Structurally-Aware Complex Cascaded Neural Network (SACC-CNN). It consists of two parts, the Structurally-Aware Reconstruction Net (SARNet) and the Structurally-Aware Classification Net (SACNet). SACNet outputs the tissue type map which is then fed to the SARNet, which reconstructs the EM and US parameters. These two parts can be seen as two independent modules. A physics-guided loss function is implemented in the SARNet network to enhance structural similarity. Main features of the approaches, illustrated by simulation, are described.
Entangling the motion of two optically trapped objects via time-modulated driving fields

We study entanglement of the motional degrees of freedom of two tethered and optically trapped microdisks inside a single cavity. By properly choosing the position of the trapped objects in the optical cavity and driving proper modes of the cavity, it is possible to equip the system with linear and quadratic optomechanical couplings. We show that a parametric coupling between the fundamental vibrational modes of two tethered microdisks can be generated via a time-modulated input laser. For a proper choice of the modulation frequency, this mechanism can drive the motion of the microdisks into an inseparable state in the long time limit via a two-mode squeezing process. We numerically confirm the performance of our scheme for current technology and briefly discuss an experimental setup that can be used for detecting this entanglement by employing the quadratic coupling. We also comment on the perspectives for generating such entanglement between the oscillations of optically levitated nanospheres.
Alzheimer’s Detection And Categorization using a Deep-Learning Approach

Alzheimer's disease (AZM) is a deadly neurology syndrome caused by memory loss. It is the most common kind of dementia. It is a syndrome that begins with modest memory loss and gradually deteriorates your thinking abilities, occasions, and ability to execute day-to-day routine tasks. It affects the areas of the brain that govern cognition, memory, and language. It has the potential to have a significant influence on a person's ability to carry out everyday responsibility. Because no drug or cure has yet been discovered, it is critical to forecast it at an early stage. The prediction of Alzheimer's disease is done manually by evaluating MRI scans; nevertheless, the risks of human mistake are quite high with this method. The purpose of this research is to improve prediction accuracy in order to detect AZM at an early stage and prevent the patient from progressing to a severe state of the disease. In this research, a Convolutional Neural Network (CNN) with Softmax classifier (i.e., CNN+Softmax) has been used to identify and categories the AZM MRI into four distinct classes, namely mild demented (MD), moderate demented (MOD), non-demented (ND), very mild demented (VMD). The proposed CNN+Softmax model is then compared by replacing the dense layer of CNN to Support Vector Machine (SVM) classifier, i.e., CNN+SVM and the work is calculated on the various grids which includes accuracy, precision, recall and f1-score. On test data, the presented approach surmounted the CNN+SVM model with a classification accuracy of 98.59 percent.