Miracle-dz/UniFAR-FTUs huggingface dataset:
New Field Server Addition Method for Low-Price Rice Cultivation Management System

We are developing a rice cultivation management systems using a field server to reduce the workload of farmers. The feature of our field server is a reasonable price. In order to achieve the reasonable price, the accuracy of time synchronization is sacrificed. At present, it is possible to manage environmental data for crops and fields using sensors. However, there are some points to be improved in the current field server. The first is transmission scheduling in advance is had to set up. The second is that field servers cannot be added during the operation. This paper describes a method to notify all nodes of additional field server information and scheduling to solve the above problem. Simultaneous transmission type flooding method is described in detail. We will also report the experimental results. This protocol is robust for the rice cultivation management systems because the field server is stable. Therefore, it meets farmers' expectation to utilize a reasonable field server.
Traffic-Aware Rule-Cache Assignment in SDN: Security Implications

Cache-based flow-rule management in SDN aims to address the issue of limited ternary content addressable memory (TCAM) in switches by combining software switches (cache) with TCAM hardware. This preserves the fast packet-processing capabilities of hardware, while providing a large flow-space for rules pertaining to QoS management and security. However, to improve the reliability of such software caches, and meet requirements such as scalability and fault-tolerance, they should be placed in a distributed manner in the network. The dynamic assignment of these software caches with the hardware switches is challenging; static or improper assignment may lead to increased load imbalance, which affects overall network performance, and may make the network more vulnerable to attacks such as denial-of-service. Therefore, in this paper, we consider the traffic-aware rule cache assignment problem with an aim to lower the overall delay and network overhead. We model the problem using a many-to-many matching framework, and show that the proposed algorithm arrives at a pairwise stable outcome. Extensive simulation results show that the proposed scheme reduces the average delay by 10% and 35%, and the total network overhead by 19% and 39% compared to minimumdistance based and random based cache assignment schemes, respectively.
Exact representation of piecewise affine functions via neural networks

Piecewise affine (PWA) control laws often arise in automatic control. A popular example is explicit model predictive control (MPC). On the other hand, it is well known that artificial neural neutworks (ANNs) with rectifier or maxout activations lead to PWA input-output relations. Against this background, it is natural to investigate whether ANNs can be parametrized in such a way that they exactly reproduce the input-output relation of a given PWA function, e.g., a given explicit MPC law. We briefly summarize a known but numerically demanding approach based on maxout networks and provide a novel and efficient method using rectifier networks for the special case of one-dimensional inputs.
Recent Developments of the Internet of Things in Agriculture: A Survey

A rise in the population has immensely increased the pressure on the agriculture sector. With the advent of technology, this decade is witnessing a shift from conventional approaches to the most advanced ones. The Internet of Things (IoT) has transformed both the quality and quantity of the agriculture sector. Hybridization of species along with the real-time monitoring of the farms paved a way for resource optimization. Scientists, research institutions, academicians, and most nations across the globe are moving towards the practice and execution of collaborative projects to explore the horizon of this field for serving mankind. The tech industry is racing to provide more optimal solutions. Inclusion of IoT, along with cloud computing, big data analytics, and wireless sensor networks can provide sufficient scope to predict, process, and analyze the situations and improve the activities in the real-time scenario. The concept of heterogeneity and interoperability of the devices by providing flexible, scalable, and durable methods, models are also opening new domains in this field. Therefore, this paper contributes towards the recent IoT technologies in the agriculture sector, along with the development of hardware and software systems. The public and private sector projects and startup’s started all over the globe to provide smart and sustainable solutions in precision agriculture are also discussed. The current scenario, applications, research potential, limitations, and future aspects are briefly discussed. Based on the concepts of IoT a precision farming framework is also proposed in this article.
x64Unpack: Hybrid Emulation Unpacker for 64-bit Windows Environments and Detailed Analysis Results on VMProtect 3.4

In spite of recent remarkable advances in binary code analysis, malware developers are still using complex anti-reversing techniques to make analysis difficult. To protect malware, they use packers, which are (commercial) tools that contain various anti-reverse engineering techniques such as code encryption, anti-debugging, and code virtualization. In this paper, we present x64Unpack: a hybrid emulation scheme that makes it easier to analyze packed executable files and automatically unpacks them in 64-bit Windows environments. The most distinguishable feature of x64Unpack compared to other dynamic analysis tools is that x64Unpack and the target program share virtual memory to support both instruction emulation and direct execution. Emulation runs slow but provides detailed information, whereas direct execution of the code chunk runs very fast and can handle complex cases regarding to operating systems or hardware devices. With x64Unpack, we can monitor major API (Application Programming Interface) function calls or conduct fine-grained analysis at the instruction-level. Furthermore, x64Unpack can detect anti-debugging code chunks, dump memory, and unpack the packed files. To verify the effectiveness of x64Unpack, experiments were conducted on the obfuscation tools: UPX 3.95, MPRESS 2.19, Themida 2.4. 6, and VMProtect 3.4. Especially, VMProtect and Themida are considered as some of the most complex commercial packers in 64-bit Windows environments. Experimental results show that x64Unpack correctly emulates the packed executable files and successfully produces the unpacked version. Based on this, we provide the detailed analysis results on the obfuscated executable file that was generated by VMProtect 3.4.
Efficient ResNet Model to Predict Protein-Protein Interactions With GPU Computing

Protein-protein interactions (PPI) play an important role in the cell activities of organisms. The deep research about PPI can help humans understand the mechanism of life activities and apply protein functions better. Nowadays, PPI prediction algorithms based on amino acid sequences using the recurrent neural network (RNN) can overcome the disadvantages of traditional biological experimental methods and achieve high accuracy. However, these algorithms are usually time-consuming and cannot take full advantage of graphics processing units (GPU) with efficient computation performance to accelerate PPI prediction, because the RNN model considers the time series of sequences. In this paper, we propose an efficient algorithm based on the residual network (ResNet) model to predict PPI (ResPPI). Our algorithm uses the embedding method to represent amino acid sequences, combining the advantages of powerful feature extraction capabilities of the ResNet with deep layers and GPU performance. The experimental results show that the ResPPI algorithm can ensure high accuracy and reduce training time greatly. Based on the ordinary GPU device, compared with the state-of-the-art LSTM model, the speed of the ResPPI algorithm is five times faster than that of the LSTM, whereas the ResPPI algorithm can achieve similar accuracy to the LSTM. Besides, in the case of unbalanced datasets, the ResPPI algorithm can perform better.
Robot Agreeableness and User Engagement in Verbal Human-Robot Interaction

It is a well-established fact in the field of Human-Robot Interaction that the personality a social robot is endowed with (i.e., the one it expresses by its gestures and words) plays a key role towards the engagement experienced by any person interacting with it. In this article, we address the problem of designing the conversational style of a social robot to convey different degrees of Agreeableness, which is one of the Big Five personality traits, and investigate the relation between the person’s engagement and the robot’s degree of agreeableness.We propose to use subjective perception of time elapsed as an indicator for engagement and design two robot behaviours, one making the robot always agree with the person’s opinion on topics of discussion (the compliant behaviour) and one making the robot always disagree (the contrasting behaviour). In an experiment involving 14 participants, we assess whether the robot adopting the contrasting behaviour is perceived as more engaging than the one adopting the compliant behaviour, and use participants’ previous interactions with vocal assistants as a reference for estimating the naturalness, enjoyability and politeness of the conversation with the robot. Results suggest that designing social robots that are able to disagree with the person they’re interacting with might be key to make them more engaging and entertaining.
A Novel Generative Topic Embedding Model by Introducing Network Communities

Topic models have many important applications in fields such as Natural Language Processing. Topic embedding modelling aims at introducing word and topic embeddings into topic models to describe correlations between topics. Existing topic embedding methods use documents alone, which suffer from the topical fuzziness problem brought by the introduction of embeddings of semantic fuzzy words, e.g. polysemous words or some misleading academic terms. Links often exist between documents which form document networks. The use of links may alleviate this semantic fuzziness, but they are sparse and noisy which may meanwhile mislead topics. In this paper, we utilize community structure to solve these problems. It can not only alleviate the topical fuzziness of topic embeddings since communities are often believed to be topic related, but also can overcome the drawbacks brought by the sparsity and noise of networks (because community is a high-order network information). We give a new generative topic embedding model which incorporates documents (with topics) and network (with communities) together, and uses probability transition to describe the relationship between topics and communities to make it robust when topics and communities do not match. An efficient variational inference algorithm is then proposed to learn the model. We validate the superiority of our new approach on two tasks, document classifications and visualization of topic embeddings, respectively.
Controllability Robustness Against Cascading Failure for Complex Logistic Network Based on Dynamic Cascading Failure Model

Inspired by the empirical dynamic characteristics of the load of real logistics network, we propose a dynamic cascading failure model against cascading failure, which is more suitable for complex logistics network by adding dynamic factors based on the nonlinear load-capacity model under initial residual capacity load-redistribution strategy. The simulation is executed on the complex logistics network model and the results show that the controllability robustness and economy after cascading failure based on the dynamic cascading failure model is feasible and effective. It can effectively reduce the logistics cost and enhance controllability robustness against cascading failure by adjusting the network cost <inline-formula> <tex-math notation="LaTeX">$e$ </tex-math></inline-formula> and capacity parameter <inline-formula> <tex-math notation="LaTeX">$\gamma $ </tex-math></inline-formula>, so as to balance the controllability robustness and economy for the complex logistics network.
Lecture mécanographique des mythes

TURNER, V. 1962 " Three symbols of passage in Ndembu circumcision ritual ", in : M. GLUCKMAN, ed. Essays in the ritual of social relations. Ann Arbor, Mich. , Michigan University Press. 1964a " The interpretation of symbols in Ndembu ritual ", in : M. GLUCKMAN, ed. Closed systems and open minds. Edinburgh, Oliver and Boyd. 1964b " Witchcraft and sorcery : Taxonomy versus dynamics ", Africa 34 : 314-324.
A rule-based assistive control algorithm for safe navigation for a powered wheelchair*

This paper addresses the problem of safe navigation in an environment with randomly placed static obstacles. We convert a commercial powered wheelchair into a semi-autonomous vehicle with limited sight (environment awareness), by instrumenting it using off-the-shelf ultrasonic sensors and associated electronic boards (Arduino). In the continuity of our previous work where we had used stochastic dynamic programming to formulate optimization problems which led to relatively large size look-up tables that can be used as supervisory control, here we propose to extract rules using those results (that data). The advantage of this approach is a low-computational cost for future online implementation, and the drawback is a suboptimal policy. The feasibility is assessed by running simulations in a fairly realistic environment (Unity3D).
Blockchain-Based Solution to Prevent Plastic Pipes Fraud*

Use of counterfeit plastic pipes has caused considerable financial damage to the states and companies. This article proposes a blockchain-based supply chain management system for its market. It can make pipes’ use and life cycles transparent, thus guarantee the invariability of pipes mass produced and used. The article provides a high-level technical description, containing ERP-blockchain connection and application for project-specific labels printing, mobile applications for manufactures and pipes installers.
ℋ2 Control for Differential-Algebraic Systems

For a differential-algebraic system we construct an optimal ${\mathcal{H}_2}$ stabilizing controller and give a formula for the minimal ${\mathcal{H}_2}$ norm in terms of realizations and solutions to appropriate Riccati equations. Additionally, via the Youla parametrization, we construct a suboptimal controller that is guaranteed to be proper and which ensures an ${\mathcal{H}_2}$ norm that is arbitrarily close to the optimal one.
Improving Musical Tag Annotation with Stacking and Convolutional Neural Networks

Personalized music systems usually rely on manual song annotations (tags) as a mechanism for querying and navigating large music collections. However, the manual annotation is a hard task given the large amount of music available nowadays. Automatic song annotation based on content analysis is a potential solution to this problem and has recently been gaining attention. In this work, we propose to extend the Stacking prediction framework to use Convolutional Neural Networks (CNNs) in order to improve music tag annotation task. In general, the Stacked prediction consists of a technique in which the output of the first stage of learning is used as input in the second stage. In our work we have tried two proposals of extension. The first one consists of using the weights learned by the CNN in the first stage of training as input in the second stage. In the second proposal, we use the autoencoder technique with the weights learned by CNN in the first stage to generate images 50% smaller than the ones used as input of the first stage. The weights and the images obtained in the first stage are used as input in the second stage. We evaluated our proposals with five different CNN models in three datasets well-known in the literature (FMA, MillionSong, and MagnaTagATune), obtaining interesting results.
Vehicle Control with Prediction Model Based Monte-Carlo Tree Search

In this paper, we propose a model-based Monte-Carlo Tree Search (model-based MCTS) algorithm for the vehicle planning and control problem. While driving vehicles, we need to predict the future states of other vehicles to avoid collisions. However, because the movements of the vehicles are determined by the intentions of the human drivers, we need to use a prediction model which captures the intention of the human behavior. In our model-based MCTS algorithm, we introduce a neural-network-based prediction model which predicts the behaviors of the human drivers. Unlike conventional MCTS algorithms, our method estimates the rewards and Q-values based on intention-considering future states, not from the pre-defined deterministic models or self-play methods. For the evaluation, we use environments where the other vehicles follow the trajectories of pre-collected driver datasets. Our method shows novel results in the collision avoidance and success rate of the driving, compared to other reinforcement learning and imitation learning algorithms.
Novel Test Methods for 5G Network Performance Field Trial

In this paper, several novel test methods are proposed for 5G mobile network field trials. As it is known, field trial plays an important role in wireless network commercialization process. However, several new technologies have been considered in 5G communication networks, such as massive MIMO. Therefore, conventional filed trial methods used in 4G mobile networks cannot fulfill the new test requirements for 5G mobile networks. Four new field trial methods have been discussed in our work. Moreover, real measurement data has been collected to demonstrate the proposed methods from 5G large scale filed trial supported by China Mobile Communications Group Co., Ltd.
Six-Axis Force/Torque Fingertip Sensor for an Anthropomorphic Robot Hand

To manipulate objects using a robot hand, it is important to measure the information of the various forces on the fingertips. In this letter, a six-axis force/torque (F/T) fingertip sensor for a robot hand is introduced. The sensor was developed to provide the ability to measure six-axis F/T while remaining feasible for robot fingertip integration because of its miniaturization, light weight, and low cost (thanks to its simple manufacturing process). In particular, a novel highly sensitive shear force measurement method is proposed that uses the eccentricity of two cylinders. The designed six-axis F/T sensor was also fabricated. We demonstrate that the developed sensor can be easily installed into the human-sized fingertip of a robot, and we performed an accuracy evaluation using several experiments.
Efficient Blind-Spot Neural Network Architecture for Image Denoising

Image denoising is an essential tool in computational photography. Standard denoising techniques, which use deep neural networks at their core, require pairs of clean and noisy images for its training. If we do not possess the clean samples, we can use blind-spot neural network architectures, which estimate the pixel value based on the neighbouring pixels only. These networks thus allow training on noisy images directly, as they by-design avoid trivial solutions. Nowadays, the blind-spot is mostly achieved using shifted convolutions or serialization. We propose a novel fully convolutional network architecture that uses dilations to achieve the blind-spot property. Our network improves the performance over the prior work and achieves state-of-the-art results on established datasets.
An Efficient Spatial Channel Covariance Estimation via Joint Angle-Delay Power Profile in Hybrid Massive MIMO Systems

In this paper, an efficient construction method for channel covariance matrices (CCMs) together with joint angle-delay power profile (JADPP) and sparsity map estimation is proposed for single-carrier (SC) mm-wave wideband massive multiple-input multiple-output (MIMO) channels when hybrid beamforming architecture is utilized. We consider slow-time beam acquisition mode for training stage of time division duplex (TDD) based systems where pre-structured hybrid beams are formed to scan intended angular sectors. The joint angle-delay sparsity map together with power intensities of each user channels is obtained by using a novel constant false alarm rate (CFAR) thresholding algorithm inspired from adaptive radar detection theory. The proposed thresholding algorithm employs a spatio-temporal adaptive matched filter (AMF) type estimator, taking the strong interference due to simultaneously active multipath components (MPCs) of different user channels into account, in order to estimate JADPP of each user. After applying the proposed thresholding algorithm on the estimated power profile, the angle-delay sparsity map of the massive MIMO channel is constructed, based on which the CCMs are formed with significantly reduced amount of training snapshots. The proposed techniques attain the channel estimation accuracy of minimum mean square error (MMSE) filter with true knowledge of CCMs. At the same time, they allow non-orthogonal pilot sequences among different users while reducing the training overhead (which is basically constant with the number of active users in the system) considerably.
A Stiffness-controlled Robotic Palm based on a Granular Jamming Mechanism

This paper presents a new type of a robotic palm based on a granular jamming mechanism to improve grasping performance. The granular jamming principle is adopted to alter the shape and stiffness of the robotic palm by controlling a transition between a solid-state and a fluid-state of a granular material used. The robotic palm incorporates a specifically designed granular chamber that is optimized for dealing with large volume change. The control system is also developed for the proposed granular jamming mechanism to be electrically operated without any pneumatic components. In addition, the stiffness of the palm can be precisely regulated by the feedback control of the negative pressure applied to the granular chamber. We evaluate the shape-adaptability of the robotic palm for various objects. As a result, the robotic palm could accommodate the various shapes of the testing objects by conformably altering its shape during contact. Moreover, the stiffness-controllability is also investigated for the three different sizes of granular materials. The stiffness increases up to 30 fold under fully jammed state for the small size of the grain. Finally, we evaluate the grasping performance of the robotic palm with a commercially available robot hand. 1.7 times higher grasping force was attained with the conformably deformed and stiffened surface, compared to the flat skin of the rigid palm. Therefore, the stiffness-controlled robotic palm can improve grasping performance with the enhanced shape-adaptability and stiffness-controllability.