Miracle-dz/UniFAR-FTUs huggingface dataset:
Redundancy Reduction of Musculoskeletal Model for Robots with Group Sparse Neural Network

Creating robots with similar behavior and appearance to humans has been a long-term dream for roboticists. However, the highly redundant characteristic of the muscle system brings tremendous difficulty for the control and manufacture of the musculoskeletal robot. In this paper, we propose a heuristic pruning-based recurrent neural network method with group sparse regularization to reduce redundancy of muscles in musculoskeletal model, which can maintain the motion accuracy of musculoskeletal robots without activating the redundant muscles. Experiments on an upper extremity musculoskeletal model validate the effectiveness of the proposed method.
Limit Cycle of a Single-Neuron System and Its Circuitry Design

In this paper, we investigate the limit cycle of a single-neuron system and its circuit design. By transforming the system into Lienard-type and using Poincaré-Bendixson theorem as well as the symmetry of this systems, we obtain the existence conditions of limit cycle of the system. Then, by comparing the integral value of the differential of positive definite function along two assumed limit cycles, we prove that the system cannot produce two coexisting limit cycles, which means that the system has at most one limit cycle. In addition, we give the numerical simulation, and realize the circuit design of the single-neuron system by using Multisim. The waveform diagram and phase diagram of the numerical simulation and circuit simulation are obtained respectively. By comparing the results of numerical and circuit simulation, the effectiveness of our mathematical analysis and the feasibility of circuit design are better illustrated.
Design of Rate-Compatible Low-Density Parity-Check Codes with Constant Check Matrix

In this paper, we propose a “bit duplication” design method for rate-compatible low-density parity-check codes (LDPC). The main idea of the method is to change the structure of codewords during its transmission over the communication system. This method can be effectively applied to create a set of codes, allowing to obtain lower-rate codes from the mother codes. To evaluate the proposed method, the dependence of the bit error probability on the signal-to-noise ratio (SNR) was simulated in Matlab. The result shows that the proposed method is superior to the conventional method of puncturing information bits.
Cross-modality Lossless Compression of PET-CT Images

The huge amount of data resulting from the acquisition of medical images with multiple modalities can be overwhelming for storage and sharing through communication systems. Thus, efficient compression algorithms must be introduced to reduce the burden of storage and communication resources required by such amount of data. However, since in the medical context all details are important, the adoption of lossless image compression algorithms is paramount. This paper proposes a novel lossless compression scheme tailored to jointly compress the modality of computerized tomography (CT) and that of positron emission tomography (PET). Different approaches are adopted, namely image-to-image translation techniques and redundancies between both images are also explored. To perform the image-to-image translation approach, we resort to lossless compression of the original CT data and apply a cross-modality image translation generative adversarial network to obtain an estimation of the corresponding PET. Then, the residue that results from the differences between the original PET and its estimation is also compressed. Thus, instead of compressing two independent image modalities, i.e., both images of the original PET-CT pair, in the proposed approach only the CT is independently encoded along with the PET residue. The performed experiments using a publicly available PET-CT pair dataset show that the proposed scheme attains up to 8.9 % compression gains for the PET data, in comparison with the naive approach, and up to 3.5 % gains for the PET-CT pair.
Trains Detection Using State of Polarization Changes Measurement and Convolutional Neural Networks

Fiber optic infrastructure security is of growing interest. The current distributed sensor systems are robust and expensive solutions, and their practical applications are uncommon. Research into simple and cost-effective solutions based on changes in the state of polarization is crucial. This paper expands the use of a vibration sensor based on the sensing of rapid changes in the state of polarization (SOP) of light in a standard single-mode optical fiber by using a convolutional neural network to detect trains running along the optical fiber infrastructure. It is a simple system that determines ongoing events near the optical fiber route by simply determining the signal boundaries that define the idle state. By using a neural network, it is possible to eliminate the distortion caused by the temperature changes and, for example, to improve detection in the the zones where the vibrations are not strong enough for a simple threshold resolution.
Predicting Mutation Status and Recurrence Free Survival in Non-Small Cell Lung Cancer: A Hierarchical ct Radiomics – Deep Learning Approach

Non-Small Cell Lung Cancer (NSCLC) is the world’s leading cause of cancer deaths. A significant portion of these patients develop recurrence despite curative resection. Prognostic modeling of recurrence free survival in NSCLC has been attempted using computed tomography (CT) imaging features. Radiomic features have also been used to identify mutation subtypes in various cancers, however, the implications of such features on eventual patient outcome are unclear. Studies have shown that genetic mutation subtypes in lung cancers (KRAS and EGFR) have imaging correlates that can be detected using radiomic features from CT scans. In this study, we provide a degree of interpretability to quantitative imaging features predictive of mutation status by demonstrating their association with recurrence free survival using a hierarchical CT radiomics – deep learning pipeline.
Time Of Arrival Delineation In Echo Traces For Reflection Ultrasound Tomography

Ultrasound Computed Tomography (USCT) is an imaging method to map acoustic properties in soft tissues, e.g., for the diagnosis of breast cancer. A group of USCT methods rely on a passive reflector behind the imaged tissue, and they function by delineating such reflector in echo traces, e.g., to infer time-of-flight measurements for reconstructing local speed-of-sound maps. In this work, we study various echo features and delineation methods to robustly identify reflector profiles in echos. We compared and evaluated the methods on a multi-static data set of a realistic breast phantom. Based on our results, a RANSAC based outlier removal followed by an active contours based delineation using a new “edge” feature we propose that detects the first arrival times of echo performs robustly even in complex media; in particular 2.1 times superior to alternative approaches at locations where diffraction effects are prominent.
Effect of different levels of boron and zinc on flowering, fruiting and growth parameter of winter season guava (Psidium guajava L.) cv. L-49.

of the subtropical and tropical regions in the world. Guava (Psidium guajava L.), the apple of the tropics, is one of the most popular fruits grown in tropical, sub-tropical and some parts of arid regions of India. The fruit belongs to the family Myrtaceae, which has 140 genera and 3000 species widely distributed throughout the tropical and subtropical regions of the world. In India, it has been introduced in early seventeenth century and gradually became a crop of commercial significance all over the country. Guava fruit are climacteric (Akamine and Goo, 1979; Brown and Wills, 1983), with a relatively short shelf life due to their rapid rate of ripening (Pantastico et al., 1975). It is a good source of ascorbic acid, pectin, sugars and certain minerals. Its skin and flesh colors vary from variety to variety depending on the amount and type of pigments. The fruit softens very rapidly during ripening (Wilson, 1980). Guava has earned the popularity as “Poor man’s apple” available in plenty to every person at very low price during the season. It is no inferior to apple for its nutritive value. It is pleasantly sweet and refreshingly acidic in flavor and emits sweet aroma. It is wholly edible along with the skin. The fruits Members of the Research Forum
Application of the User’s Digital Footprint in the Adaptive Authentication Problem

This paper describes the application of adaptive user authentication based on a calculated digital footprint. The digital footprint is considered as an integrated indicator that the user is unique in its set of characteristics and behavior on the network. The main classes of digital footprint sources are marked, with the greatest attention paid to browser fingerprints. Using a mathematically supported method based on the comparison of the digital footprint, an experiment is conducted to determine the authenticity of the user in the three cases under consideration. The experiment showed that the method works correctly and successfully and can give better results by optimizing its parameters: the weights of criteria, the weights of browser fingerprints and the threshold value of user’s uniqueness.
Detecting and Controlling the Occurrence of Data Congestion in a High-density VANETs Environment

Vehicular Ad-hoc NETworks (VANETs) is a specialized kind of Mobile Ad-hoc Network (MANETs) class as it works with the safety of vehicles that are moving on the roads. There are several difficulties, and data congestion is a leading hurdle. This paper starts with generating an appropriate route that will be employed to identify data congestion in a high-density VANETs environment. Next, a suitable approach is introduced for controlling and managing that detected data congestion. Subsequently, the network's performance after introducing our proposed approach is analyzed by considering three distinct parameters, including Packet Loss Ratio (PLR), Delay, and Throughput. These metrics are applied to investigate the performance of four different algorithms: CABS, UO-Tabu, MO-Tabu, and VANETomo, along with our proposed algorithm. The comparison outcomes declared that our recommended algorithm provides more reliable results than the other algorithms. Our suggested algorithm provides the immense value for the Throughput and the least value for the PLR and Average Delay. This paper is concluded by projecting to select additional novel metrics and strategies to detect and manage the data congestion occurring in VANETs.
Iterative Balancing Algorithm for Multistage DC Amplifiers

The article describes a simple implementation of the algorithm for calculating the offset voltage compensation at the output of digitally controlled programmable DC amplifiers that form a single multistage measuring path. The article presents the calculation of the bias voltage based on the analytical model of the amplifying circuit, a balancing mechanism based on an external control system is proposed. The main result of the algorithm is to find the optimal values of the control codes of the electronic potentiometers for each stage, which ensure that the voltage at the amplifier output is close to zero at a known measured value of the bias voltage at the input or output of the first amplifier stage. The advantage of the algorithm is the simplicity of implementation, the absence of current overloads of electronic potentiometers during the balancing of the measuring path as a whole, a wide range of variation of the parameters of the amplifying stages.
The Impact of Firm Informationization on Audit Fee: Based on the Analysis of OLS Regression Model

Nowadays, the development and application of new technology have a positive impact on the enterprise's operating efficiency and internal control management. In order to explore how firm informationization affects audit fee, we use OLS regression model to examine the relationship between the informationization and audit fee. At the same time, we introduce internal control disclosure and its five elements to explore their moderating effects on the relationship between informationization and audit fee. This article selects the data of Chinese listed companies from 2011 to 2014. Through empirical analysis, we find that informationization of firm has a significantly positive effect on audit fee. Furthermore, internal control disclosure has a significant negative moderating effect on the relationship between informationization and audit fee, but the five elements of internal control disclosure have different moderating effects on the relationship between informationization and audit fee. This study extends literatures on informationization and provides practical suggestions for informationization of companies.
Lnista-Net: Learning Nonlocal Regularized Iterative Shrinkage-Thresholding Network For Noninvasive Cardiac Transmembrane Potential Imaging

ECG inverse problem is a very classic ill-posed problem. Myocardial transmembrane potential (TMP) provide a wealth of physiological information and is often selected as the reconstructed cardiac source signal. Researcher have solved these problems by constructing convex optimization models with constraints or building end-to-end neural network. Based on the nonlocal self-similarity of TMP distribution, we propose a novel method to reconstruct the dynamic distribution of TMP. Combining the physical interpretation and convergence theory provided by the optimization method with the advantages of deep learning to automatically extract features from big data, we propose a learning nonlocal regularized Iterative Shrinkage-Thresholding Network (LNISTANET) for noninvasive cardiac TMP imaging. Experiments verify the superiority of this method over existing methods from the spatial and temporal distribution of TMP sequences.
Evaluating The Effects of Aim Lab Training on Filipino Valorant Players' Shooting Accuracy

Being an MMOFPS gamer is quickly becoming a lucrative career option as professional FPS e-sports player, FPS game content creator, and FPS game streamer. However, to be able to have this as a career, an aspiring player should improve their in-game skills where shooting accuracy is one of the main ingredients for success. The study focused on Valorant, the fastest rising FPS e-sport of 2020, and Aim Lab, the best known FPS training software, with 6 Filipino nationals as participants. For 1 week, the participants were subjected to train on Aim Lab's recommended training set every day before playing Valorant. The results of the study concluded that doing the 6 recommended training on Aim Lab, for a total of 6 minutes every day, before playing Valorant for 1 week led to a significant improvement in the average damage output generated per round. The study also concluded that Aim Lab, as an FPS training software, is deemed acceptable by the participants as per System Usability Scale standards.
Summary of: Adaptive Metamorphic Testing with Contextual Bandits

Metamorphic Testing (MT) is a software testing paradigm that aims at using user-specified properties of a program under test to either check its expected outputs or to generate new test cases [1] , [2] . More precisely, MT tackles the so-called oracle problem which occurs whenever predicting the expected outputs of a system is just too difficult or even impossible. A typical example where MT has been successfully deployed is for testing machine learning models. For instance, in supervised machine learning, we train models for classification problems, but testing these models is hard as only stochastic behaviors of these models can be specified [3] . Indeed, we initially train these models with existing labelled datasets and then we exploit them to classify new data samples. Testing these models means only to reserve some portion of the labelled datasets to control that the correct classification is given for these reserved datasets. However, nothing is really available to test these models on unlabelled data samples.
Fabricación de liposomas para el encapsulamiento de drogas de uso médico o veterinario

The methodology for the preparation of nanometric size liposomes has been developed. The liposomes were prepared using mixtures of Soy phosphatidilcholine and cholesterol. After a thin layer of the lipid mixture was obtained, this was hydrated with phosphate buffer under ultrasound. The nanometric sizes were obtained after the initial liposome solution was extruded through a membrane with 100 nm pore size. As a result, the final solution contained liposomes with sizes in the order of ∼ 50 nm. The size of the liposomes in the final solution was obtained using dynamic light scattering.
Ammonia Volatilization From Different Fertilizer Sources And Effects Of Temperature And Soil ph1

Improved understanding of nitrogen sources, environmental factors, and nitrification effects on NH 3 volatilization is needed for optimal management of nitrogen in crop production systems. In the laboratory, a sponge-trapping and KCl-extraction method was modified for measuring NH 3 volatilization from different N sources as affected by temperature and soil pH. The kinetics of NH 3 volatilization from four N sources surface applied to an Alfisol (a Riviera fine sand, pH 7.9) followed an initial rapid reaction, and then a slow reaction, which was adequately described by a Langmuir kinetic model. The potential maximum NH 3 volatilization (q m ) under the experimental conditions, as predicted by the Langmuir equation, decreased in the order: NH 4 HCO 3 (23.2% of applied NH 4 -N) > (NH 4 ) 2 SO 4 (21.7%) > CO(NH 2 ) 2 (21.4%) > NH 4 NO 3 (17.6%). With an increase in NH 4 -N application rate, NH 3 volatilization increased significantly for (NH 4 ) 2 SO 4 , CO(NH 2 ) 2 , and NH 4 HCO 3 but decreased for NH 4 NO 3 . Ammonia volatilization was minimal at the initial soil pH of 3.5 and increased rapidly with increasing pH up to 8.5. The potential maximum NH 3 volatilization increased by 2- and 3-fold, respectively, with an increase in the incubation temperature from 5 to 25 °C, and from 25 to 45 °C, respectively. The greatly enhanced NH 3 volatilization at 45 °C, compared with that at 25 °C, was related to the inhibition of nitrification at the high temperature, which increased the availability of NH 4 for NH 3 volatilization over a prolonged period of time.
On the Fatigue Reduction of WTs via Coordinating Inertial Response and Pitch Angle Regulation

As one of the dominant renewable power generations, wind turbines (WTs) are expected to provide inertial response to contribute to power balance maintenance in modern power system. In practice, active power of variable-speed WTs can be regulated through expending inertial response signal and pitch angle control (PAC). For existing inertial control schemes, active power of WTs can be effectively smoothed when operating within the rated zone of rotor speed, while they will actually result in more considerable fatigue on the pitch servo system when rotor speed reaches the upper speed limit for high wind speed or maintaining relative low power reference inertially. To address this issue, a practical control scheme coordinating inertial response and pitch angle regulation is proposed in this paper. By differentially managing inertial response according to the rotor speed operation state and setting up threshold-limited returning rule for PAC, proposed scheme can effectively reduce pitch manipulation while sustaining desired inertial contribution. Simulation results show that this control scheme is highly effective and is easy to implement for application.
Design and Development of Multi-Pattern Matching Rules for Detecting Cryptocurrency Mining in Packet Inspection

In recent years, cryptocurrency built by blockchain technology is getting more and more popular in both information technology and financial industries. Because of providing resources to support hash computing (known as mining) , participants (known as miners) will get cryptocurrency rewards. Therefore, it becomes the reason that attracts many people to join the mining process. Since exchanging cryptocurrency reward to legal money is possible, it interests people with bad intention to use improper means to obtain computing resources for mining, such as using malware to manipulate hacked equipment to install miner software or enabling drive-by download attack to acquire computing resource from web client. This research studies Monero, a cryptocurrency often selected by malwares, trying to investigate its communication progress between miner and mining pool, and extracts packets to get signatures for developing detection rules with corresponding patterns. The experiment results show that applied rules are able to detect suspicious networking activities related to Monero mining. By using implemented rules in this paper, it aims to help network administrators investigate whether the equipment have been infected by Monero mining malware.
LoRa Connectivity Analysis for Urban Coverage in Real Mobile Environments

For the last few years, LoRa technology has been affirming itself as one of the most prominent and widely adopted low power wide area technologies. Very compatible with Internet of Things (IoT) applications and urban environments, this technology is capable of long range communications although with small bandwidths, and consequently, low data rates. Nevertheless, the quality of LoRa communications, like many other wireless technologies, are highly dependent on the transmission's context. In this work, we evaluate the performance of LoRa communications in a mobile sensing scenario, and study its capability to give wireless coverage to a small-size city. Experimental results show that velocity has a consistent and increasing impact on the packet delivery ratio, and that the coverage capability is much more dependent on the terrain distribution and Line-of-Sight (LoS), than on the distance between the mobile node and the gateway.
Edge2Guard: Botnet Attacks Detecting Offline Models for Resource-Constrained IoT Devices

In today's IoT smart environments, dozens of MCU-based connected device types exist such as HVAC controllers, smart meters, smoke detectors, etc. The security conditions for these essential IoT devices remain unsatisfactory since: (i) many of them are built with cost as the driving design tenet, resulting in poor configurations and open design; (ii) their memory and computational resource constraints make it highly challenging to implement practical attack protection mechanisms; and (iii) currently, manufacturers use simplified light protocol versions to save memory for extra features (to boost sales). When such issues and vulnerabilities are exploited, devices can be compromised and converted into bots whereby severe DDoS attacks can be launched by a botmaster. Such tiny devices are safe only when connected to networks with defense mechanisms installed in their networking devices like routers and switches, which might not be present everywhere, e.g. on public/free Wi-Fi networks. To safeguard tiny IoT devices from cyberattacks, we provide resource-friendly standalone attack detection models termed Edge2Guard (E2G) that enable MCU-based IoT devices to instantly detect IoT attacks without depending on networks or any external protection mechanisms. During evaluation, our top-performing E2G models detected and classified ten types of Mirai and Bashlite malware with close to 100% detection rates.