Miracle-dz/UniFAR-FTUs huggingface dataset:
Knowledge and information transfer in the Peruvian agricultural sector: A social networking analysis approach

In order to break up with old paradigms that diminish the positive impact of innovation in the agricultural sector, it is needed that knowledge transfer becomes an innovator process. In this line, an inclusive approach for knowledge transference process consists of mapping all actors involved in the agricultural sector. The aim of this research is to develop a model from a social network perspective, in order to understand the innovation process through visualization of knowledge and information flows from traditional positions, the small-scale agricultural holdings and the relationship interaction with institutions into the Peruvian Agricultural Innovation System (SNIA by its Spanish initials). Data for this study were collected from the Peruvian Agricultural National Survey (ENA by its Spanish initials) of year 2017. We explore co-attendees interaction, through a two-model affiliation network of farmers who seek agricultural information into the SNIA.
Integration of Fuzzy Model Theory and FCA for Big Data Mining

In this paper, we explore two different approaches to Big Data Mining: the Fuzzy Model Theory and the Formal Concept Analysis. We carry out the integration of these two approaches for solving the problem of constructing semantic models of domains. In the present paper, we focus on the third and fourth levels of sematic models, which formalizes via case models and fuzzy models of domains. We represent the basic notions of the FCA on the fuzzy model language and describe which formula extensions formal contexts allow us to find a new knowledge about the given domain.
Visual-Guided Robot Arm Using Multi-Task Faster R-CNN

The limitation of current visual recognition methods is a big obstacle for the application of automated robot arm systems into industrial projects, which require high precision and speed. In this work, we present a Faster RCNN based multi-task network, a deep neural network model, that is able to simultaneously perform three tasks including object detection, category classification and object angle estimation. Afterward, the outputs of all three tasks are utilized to decide a picking point and a rotated gripper angle for the pick-and-place robot arm system. The test results show that our network achieves a mean average precision of 86.6% at IoU (Intersection over Union) of 0.7, and a mean accuracy of 83.5% for the final prediction including object localization and angle estimation. In addition, the proposed multi-task network takes approximately 0.072 seconds to process an image, which is acceptable for pick-and-place robot arms.
Exponentially Weighted Kernel Recursive Least P-Power Algorithm

This paper presents a novel exponentially weighted kernel recursive least p-power (EW-KRLP) algorithm. The proposed algorithm is derived exploiting the cost function of recursive weighted least p-power error instead of widely used second-order statistical measure of error, achieving the good tracking ability of non-stationarity and the superior robustness in the presence of impulsive noise. Simulations demonstrate the EW-KRLP algorithm has better convergence performance than the existing kernel adaptive filtering approaches in identifying the non-stationary nonlinear system under the assumption of non-Gaussian impulsive noise modeled by the symmetric α-stable distribution.
Compound Palette Mode for Screen Content Coding

The Joint Video Exploration Team (JVET) has been developing an emerging standard Versatile Video Coding (VVC), which includes screen contents as one of its requirements. Intra block copy (IBC) and palette coding are the two powerful coding tools for screen content coding. In this paper, a compound palette mode is proposed to exploit the advantages of both IBC and palette coding, which allows samples to be reconstructed by either IBC predictions or palette entries. The proposed method is evaluated with VVC reference software VTM4 on typical sequences containing "text and graphics with motion". Experimental results report significant coding gain that the proposed scheme can achieve 7.80% and 1.03% BD-rate savings under AI conditions on average when compared with VTM4 and the existing palette scheme.
Semantic Segmentation of Sheep Organs by Convolutional Neural Networks

The recognition of individual organs by machine vision is an important step towards the processing of lamb meat and the extraction of edible offal. This paper presents the first semantic segmentation method to localise and label sheep organs in images of the extracted organ package. The challenges around the semantic segmentation of lamb organs include the lack of available data sets, the variable orientation, shape and appearance of organs, and the wet and specular surface. To address these challenges, we created a new dataset for sheep organ segmentation consisting of images and pixel-wise labels for seven types of organs and tissues. To produce baseline results on the data set, a convolutional neural network is trained to partition images into segments corresponding to individual organs.
Preliminary Tests of the Miniaturization of a Novel Concept of Angular Sensors

This work was done in the framework of the MIRACLE project devoted to the development of a flexible, articulated robotic endoscope to perform laser osteotomies (bone cutting). In order to control this robot with the required accuracy of 0.2 mm at the tip position, a tracking system needs to feed back the pose of the tip and the shape of the endoscope. One of the possible designs of such a robotic endoscope is based on an articulated device that can be tracked by measuring the bending angle at each joint. Recently, we presented (Iafolla et al., 2018) a novel angular sensor called ASTRAS (Angular Sensor for TRAcking Systems), which measures a one degree of freedom rotation by processing a shadow image cast through a shadow mask onto an image sensor. One ASTRAS will be placed at each joint of the articulated robotic endoscope in order to measure their bending angles. The planned diameter of the MIRACLE endoscope is 10 mm and it will contain several components such as the optical fiber for the laser, irrigation and sucking systems, and robot control mechanisms. However, the latest version of ASTRAS covered a volume of 40x40x55 mm3. For this reason, the most demanding requirement for the design of the final ASTRAS sensor is its miniaturization to fit the sensor inside the joint of the endoscope. Consequently, in this contribution, we investigated the possibility of miniaturizing ASTRAS and assess its limitations due to some fundamental aspects. In particular, we built a prototype based on a NanEye camera by AMS (1x1x0.5 mm3) and a micro-milled shadow mask with 0.08 mm wide slits. The tests of this prototype showed encouraging results and will be discussed. In particular, at such small dimensions, there are no unexpected optical effects (e.g. optical diffraction due to the mask) worsening the measurement. Indeed, the precision of 5 arcsec and the linearity error of 23 arcsec RMS are comparable to those of the previous bigger prototype. Further considerations and concepts related to the miniaturization of ASTRAS are also discussed in this paper.
A 7.6-nW 1-kS/s 10-Bit SAR ADC for Biomedical Applications

This paper presents a 10-bit successive approximation register analogue-to-digital converter with energy-efficient switching scheme for biomedical applications. The energy-efficient switching scheme achieves an average digital-to-analog converter switching energy of 63.56 $CV_{ref}^{2}$, achieving an reduction of 95.34% compared with the conventional capacitor switching scheme for CDACs. With the switching scheme, the ADC can lower the dependency on the accuracy of $V_{cm}$ and complexity of digital control logic and DAC driver circuit. Moreover, dynamic circuits are used to reduce power consumption of comparator and SAR digital logic. The prototype is designed and fabricated in a 180 nm CMOS with a core size of 500 µm x 300 µm (0.15 mm2). It consumes 7.6 nW at 1 kS/s sampling rate and 1.8-V supply with an achieved signal-to-noise-and distortion ratio of 45.90 dB and a resulting figure of merit of 51.7 fJ/conv. -step.
Adaptive Load Frequency Control of Power Systems Using Electro-Search Optimization Supported by the Balloon Effect

The goal of this paper is to introduce an adaptive load frequency control (LFC) technique for power systems. The concept of the proposed adaptive technique is built on the on-line tuning of the gain of an integral controller using Electro-Search optimization (ESO) supported by a modification called the ‘balloon effect’. The main target is to regulate the frequency of isolated and interconnected power systems. The balloon effect is designed to obtain the input/output signals of the power system plant at any moment; then, these signals are utilized to calculate the value of the open loop transfer function of the plant at the same time. This will emphatically affect the specified objective function of the ESO approach and increase its sensitivity to and effect on system difficulties such as system step load changes, parameter uncertainties and the effect of the penetrations resulting from renewable sources. Delayed time resulting from the communication process between the area and control center is considered in the dynamic model of interconnected MGs, and nonlinearities, such as governor dead bands (GDBs) and generation rate constraints (GRCs), are included in the simulation model. The simulation results demonstrate the effectiveness and success of the proposed controller tuned by the ESO approach with a balloon effect and provide better performance of frequency regulation than a fixed controller, as well as other methods such as standard Jaya and ESO methods.
An Improved Simultaneously Magnetic Actuation and Localization Method based on Magnetic Sensor Array

Magnetically actuated wireless capsule robot has been a promising medical apparatus for minimally invasive examination and operation in the gastrointestinal tract. Position and orientation information of the robot are essential for an effective and safe feedback control. However, simultaneous actuation and localization is still a challenge. In our previous work, we proposed a multi-magnet based method. To further improve the tracking accuracy, in this paper we propose a method which utilizes the prior known pose information of the external actuation magnet. Moreover, the method also can simplify the calculation. A magnetic sensor array is used to sample the magnetic field from the external magnet and internal magnet. By subtracting the magnetic field of the external magnet according to the prior pose information, pose of the internal magnet then can be estimated by using nonlinear optimization algorithm. Experimental results verified the proposed method.
Lightweight Certificate-Based Public/Private Auditing Scheme Based on Bilinear Pairing for Cloud Storage

Ensuring the integrity of outsourced data is one of several functional requirements in cloud storage services, although designing an effective and secure public auditing scheme remains an ongoing research challenge. For example, a number of existing schemes are designed based on symmetric bilinear pairing, which are computationally expensive. While certificate-based auditing schemes can simplify certificate management and mitigate key escrow concern, such schemes are not popular in the literature. Therefore, we propose a lightweight certificate-based public/private auditing scheme based on asymmetric bilinear pairing for cloud storage. Specifically, we describe a new method of designing lightweight auditing schemes. Additionally, our proposed scheme is proven to be secure in the random oracle model. We then implement our scheme by using type D curve of the pairing-based library. The findings demonstrate that our auditing scheme significantly reduces client’s computation cost in the tag-generation (TagGen) phase, particularly in comparison to several other competing schemes. For example, when the block size is 10 KB, our on-line computation cost in the TagGen phase takes only 0.45 seconds, while it requires at least 62.83 seconds in other schemes. Furthermore, the findings show that when the number of blocks is fixed, the on-line computation cost in our TagGen phase is constant despite varying file sizes.
The Influence of Emotions on Time Perception in a Cognitive System for Social Robotics

In this paper, we discuss some evidences provided by neuroscience and psychology studies on human time perception, in terms of its representation and its psychological distortion due to emotional state variations. We propose, then, a novel model inspired by these recent findings to be applied in social robotics control architectures, with a specific reference to an existing and already tested bio-inspired cognitive architecture called SEAI (Social Emotional Artificial Intelligence). An hypothesis on how to represent emotional state influence on time perception in SEAI will be presented, discussing the consequent potential of the system with this integrated feature.
Your instruction may be crisp, but not clear to me!

The number of robots deployed in our daily surroundings is ever-increasing. Even in the industrial setup, the use of coworker robots is increasing rapidly. These cohabitant robots perform various tasks as instructed by co-located human beings. Thus, a natural interaction mechanism plays a big role in the usability and acceptability of the robot, especially by a non-expert user. The recent development in natural language processing (NLP) has paved the way for chatbots to generate an automatic response for users’ query. A robot can be equipped with such a dialogue system. However, the goal of human-robot interaction is not focused on generating a response to queries, but it often involves performing some tasks in the physical world. Thus, a system is required that can detect user intended task from the natural instruction along with the set of pre- and post-conditions. In this work, we develop a dialogue engine for a robot that can classify and map a task instruction to the robot’s capability. If there is some ambiguity in the instructions or some required information is missing, which is often the case in natural conversation, it asks an appropriate question(s) to resolve it. The goal is to generate minimal and pin-pointed queries for the user to resolve an ambiguity. We evaluate our system for a telepresence scenario where a remote user instructs the robot for various tasks. Our study based on 12 individuals shows that the proposed dialogue strategy can help a novice user to effectively interact with a robot, leading to satisfactory user experience.
Mining Mobile Intelligence for Wireless Systems: A Deep Neural Network Approach

Wireless big data contain valuable information on users' behaviors and preferences, which can drive the design and optimization for wireless systems. The fundamental issue is how to mine mobile intelligence and further incorporate them into wireless systems. To this end, this article discusses two challenges on big data based wireless system design and optimization, and proposes a unified framework to tackle them with the help of Deep Neural Networks (DNNs) and online learning techniques. In particular, we propose a DNN architecture by incorporating an embedding layer to project different types of raw data to a latent space and utilize a regression or classification function to predict the mobile access pattern. It outperforms the best traditional machine learning algorithm (76% vs. 63%) significantly. Moreover, combining the proposed DNN architecture with online learning techniques, we show two cases on how to apply the mobile intelligence for wireless video applications, including video adaption and video pre-fetching. In the former case, we utilize the proposed DNN method to predict the dynamics of user count within the coverage of base stations, and adaptively adjust the bitrate for video streaming to improve the video watching experience. In the latter one, we utilize the proposed method to predict the user trajectory, i.e., the associated base stations, and conduct content prefetching to reduce the access latency. Evaluating the performance with a real wireless dataset, we show that the perceived video QoE and cache hit ratio are greatly improved (0.7db and 25% respectively).
Convolutional Neural Network for Pineapple Ripeness Classification Machine

This paper proposes an applying to use a neural network for a pineapple sorting machine based on the skin color to classification the level of the pineapple’s ripeness by dividing it into unripe pineapples, partial ripe pineapple, and fully ripe pineapples. The using Convolutional Neural Network (CNN) to help classify made the machines used for classification without many labor worker. The result after prediction from confusion metrics is 79% accuracy for unripe pineapples, 82% for partial ripe pineapples and 100% for fully ripe pineapples after training has test accuracy 98.38% and 90.77% when evaluating with the test set.
Detecting Input Sanitization Errors in Scala

Scala programming language combines object-oriented and functional programming in one concise, high-level language, and the language supports static types that help to avoid bugs in complex programs. This paper proposes a dynamic taint analyzer called ScalaTaint for Scala applications. The analyzer traces the propagation of malicious inputs from untrusted sources to sensitive sink methods in programs that can be exploited by adversaries. In this work, we evaluated the accuracy of ScalaTaint with a security benchmark suite including 7 projects in Scala. As a result, our analyzer could report 49 vulnerabilities within 753,372 lines of code. Moreover, the result of our performance measurement on ScalaBench shows 67% runtime overhead that demonstrates the usefulness and efficiently of our technique in comparison with similar tools.
LACS: A High-Computational-Efficiency Accelerator for CNNs

Convolutional neural networks (CNNs) have become continually deeper. With the increasing depth of CNNs, the invalid calculations caused by padding-zero operations, filling-zero operations and stride length (stride length>1) represent an increasing proportion of all calculations. To adapt to different CNNs and to eliminate the influences of padding-zero operations, filling-zero operations and stride length on the computational efficiency of the accelerator, we draw upon the computation pattern of CPUs to design an efficient and versatile CNN accelerator, LACS (Loading-Addressing-Computing-Storing). We reduce the amount of data movements between registers and the on-chip buffer from O( $\mathrm {k}\times \mathrm {k}$ ) to O(k) by a bypass buffer mechanism. Finally, we deploy LACS on a field-programmable gate array (FPGA) chip and analyze the factors that affect the computational efficiency of LACS. We also run popular CNNs on LACS. The results show that LACS achieves an extremely high computational efficiency, 98.51% when executing AlexNet and 99.66% when executing VGG-16, significantly exceeding state-of-the-art accelerators.
DAEGEN: A Modular Compiler for Exploring Decoupled Spatial Accelerators

Specialized hardware accelerators, particularly those that are programmable and flexible to target multiple problems in their domain, have proven to provide orders of magnitude speedup and energy efficiency. However, their design requires extensive manual effort, due to the need for hardware-software codesign to balance the degree and forms of specialization to the domains or program behaviors of interest. This article provides the first steps towards one approach for automating much of these processes. The insight behind our work is to recognize that decoupled spatial architectures both define a rich design space with many tradeoffs for different kinds of applications, and also can be composed out of a simple set of well-defined primitives. Therefore, we propose a modular accelerator design framework, DAEGEN, a.k. a. Decoupled Access Excution Accelerator Generator. This article defines an initial compiler and architecture primitives, and we discuss key challenges.
Muscular Activity Monitoring and Surface Electromyography (sEMG) with Graphene Textiles

In this study, we report, for the first time, wearable graphene textile electrodes for monitoring of muscular activity and surface electromyography (sEMG) applications. The feasibility of graphene textiles in wearable muscular monitoring was successfully demonstrated by the acquisition of sEMG signals with wearable graphene textiles, and their performance was benchmarked against commercial, wet Ag/AgCl electrodes. Comparisons were performed in terms of signal-to-noise ratio (SNR), cross correlation and sensitivity to power-line interference. Despite their larger susceptibility to power line interference, graphene textile electrodes displayed excellent similarity with Ag/AgCl electrodes in terms of signal-to-noise ratio (SNR) and signal morphology; with correlation values reaching up to 97 % for sEMG signals acquired from the biceps brachii muscle.
A Cache Replacement Policy with Considering Fluctuation Patterns of Total Priority Value

In recent computer systems, a high-performance memory system is required for improving the computing performance. To improve the performance of the memory system, the capacity and the associativity of cache memories tend to be enlarged in all the memory hierarchy. Therefore, a high-performance cache replacement policy is required, which can be implemented with a small overhead and can adapt the behavior of various applications. Under such a situation, the adaptive demotion policy with considering global fluctuations of priority values (ADP-G) is proposed. ADP-G is based on the adaptive demotion policy (ADP) which can adapt the behavior of various applications by changing its insertion, promotion, demotion and selection (IPDS) policies. ADP-G selects the appropriate IPDS policies from two alternatives, one is suitable for a temporal locality and the other has a scan/thrashing tolerant. For this selection, ADP-G uses the global fluctuations of priority values of all the blocks in the cache. Although ADP-G is a high-performance cache replacement policy as compared with conventional ones, it causes a problem when the performance of the scan/thrashing tolerant IPDS policies is higher than that of the temporal locality IPDS policies. To solve this problem, this paper proposes a cache replacement policy with considering fluctuation patterns of total priority value. At first, the fluctuation patterns of total priority value are analyzed. Next, a novel scheme which can select the appropriate IPDS policies is proposed based on these analyses. The proposed scheme selects the appropriate IPDS policies based on the fluctuation patterns of total priority value. Experimental results show the proposed cache replacement policy achieves a reduction of cache misses compared to LRU policy and ADP-G when the L3 cache size is 4MB. In the geometric mean of all the benchmarks, the proposed cache replacement policy achieves a 6.0% and 11% MPKI reduction compared to ADP-G and LRU policy, respectively.
Autonomous Chess Playing Robot

Chess is an ancient strategy board game that is played on an 8x8 board. Although digital games have become attractive today, chess still retains its popularity in the onscreen version of the game. There has also been considerable development in the chess game engines to play against a human counterpart. The objective of this work is to integrate these chess engines with an actual board game experience and create an autonomous chess player. The system is designed around the use of an open source chess engine and a computer numeric control (CNC) controlled magnetic moving mechanism for moving around the chess pieces. The moves from the human counterpart are taken through an overhead computer vision system. The robot makes the game much more interactive and builds a link between the human and computer system.