Miracle-dz/UniFAR-FTUs huggingface dataset:
Toward Dexterous Manipulation With Augmented Adaptive Synergies: The Pisa/IIT SoftHand 2

In recent years, a clear trend toward simplification emerged in the development of robotic hands. The use of soft robotic approaches has been a useful tool in this prospective, enabling complexity reduction by embodying part of grasping intelligence in the hand mechanical structure. Several hand prototypes designed according to such principles have accomplished good results in terms of grasping simplicity, robustness, and reliability. Among them, the Pisa/IIT SoftHand demonstrated the feasibility of a large variety of grasping tasks, by means of only one actuator and an opportunely designed tendon-driven differential mechanism. However, the use of a single degree of actuation prevents the execution of more complex tasks, like fine preshaping of fingers and in-hand manipulation. While possible in theory, simply doubling the Pisa/IIT SoftHand actuation system has several disadvantages, e.g., in terms of space and mechanical complexity. To overcome these limitations, we propose a novel design framework for tendon-driven mechanisms, in which the main idea is to turn transmission friction from a disturbance into a design tool. In this way, the degrees of actuation (DoAs) can be doubled with little additional complexity. By leveraging on this idea, we design a novel robotic hand, the Pisa/IIT SoftHand 2. We present here its design, modeling, control, and experimental validation. The hand demonstrates that by opportunely combining only two DoAs with hand softness, a large variety of grasping and manipulation tasks can be performed, only relying on the intelligence embodied in the mechanism. Examples include rotating objects with different shapes, opening a jar, and pouring coffee from a glass.
Optimal Search for Marine Target Using Multiple Unmanned Aerial Vehicles

This paper focuses on planning the optimal paths of multiple unmanned aerial vehicles (UAVs) for searching a marine target. First, the marine target search mission is modeled in detail by formulating the target probability map, the UAV point-mass model, the sensor detection model, etc. Then, the consensus theory with state predictor is adopted to fuse the updated target probability maps, which UAVs preserve independently. With the simultaneous consideration of the local searching reward and the future searching reward, the motions or paths of UAVs are then optimized in real time by the distributed model predictive control (DMPC). Finally, the simulation results are given to verify the high searching efficiency of our proposed method.
Toward a New Methodology for an Efficient Test of Reconfigurable Hardware Systems

This paper deals with the test of a reconfigurable hardware system (RHS). The latter is a hardware device that allows to change the hardware resources at runtime in order to modify the system functions and therefore to dynamically adapt the system to its environment. The increasing functional complexity of embedded systems and the transition to the RHS make the hardware testing a challenging task, especially under the confine of providing a high quality with a low cost. Considering the fact that the hardware test represents a key cost factor in a production process, an optimal test strategy can be advantageous in the competitive industrial market. Accordingly, this paper introduces a new methodology for an efficient hardware test of RHS. For an RHS, the number of stuck-at faults can be very large, which leads to a significant slowdown in the testing process. Because of the redundancy of faults between the different circuits composing an RHS, the proposed methodology aims at minimizing the number of faults using the inter-circuits relationships and consequently at providing an optimal fault set that can be effectively used for testing. Efficient techniques for test generation and test set validation are proposed to provide the test patterns for faults reduced by inter-circuits fault collapsing. The application of the generated test patterns is typically sufficient to provide an overall fault coverage. The proposed methodology is implemented in a new visual environment named TnTest. An experimental study confirms and validates the expected findings. Note to Practitioners—This paper addresses possible challenges for future generations of adaptive embedded systems. It proposes an original methodology for an efficient reconfigurable hardware system (RHS) hardware test. The main objective is to significantly reduce time and cost needed for the testing process. For an RHS, the number of stuck-at faults can be very large, which can cause a major slowdown in the hardware test. Based on the inter-circuits relations existing between the different circuits composing an RHS, the proposed methodology decreases considerably not only the number of the faults but also the test patterns needed for testing. The application of the generated test patterns is typically sufficient to provide an overall fault coverage. The proposed methodology is implemented in a new visual software environment named TnTest, which is capable of providing the smallest fault set as well as the efficient test set that can be effectively used for testing. This environment can be applied to test any embedded device that can be deployed in any new application based on flexible technologies. It can also be useful in manufacturing industries for a required improvement of the production process in relation to time and cost.
Effect of ZnS size on the dielectric and energy storage properties of ZnS/polymer composites

Dielectric polymer composites capable of withstanding a high electric field are highly desired in electronic devices and electric power systems, to ensure reliability and provide high energy density. In this work, ZnS particles with three sizes (100 nm, 500 nm and 3um) were prepared by using polyvinyl pyrrolidone (PVP) and cetyltrimethyl ammonium bromide (CATB) as capping agent. The ZnS filled PVDF films were obtained by a solution mixing and coating process. The dielectric properties and energy storage performance were investigated in detail. The films filled with 100nm-ZnS showed much higher breakdown strength, than the 500nm-ZnS and 3um-ZnS filled ones. When the loading of ZnS (100 nm) is 3 wt %, the film achieved enhanced energy storage properties (Eb~496.6 kV/mm, energy density~6.47 J/cm3).
The response of boreal peatland community composition and NDVI to hydrologic change, warming, and elevated carbon dioxide

Widespread changes in arctic and boreal Normalized Difference Vegetation Index (NDVI) values captured by satellite platforms indicate that northern ecosystems are experiencing rapid ecological change in response to climate warming. Increasing temperatures and altered hydrology are driving shifts in ecosystem biophysical properties that, observed by satellites, manifest as long‐term changes in regional NDVI. In an effort to examine the underlying ecological drivers of these changes, we used field‐scale remote sensing of NDVI to track peatland vegetation in experiments that manipulated hydrology, temperature, and carbon dioxide (CO2) levels. In addition to NDVI, we measured percent cover by species and leaf area index (LAI). We monitored two peatland types broadly representative of the boreal region. One site was a rich fen located near Fairbanks, Alaska, at the Alaska Peatland Experiment (APEX), and the second site was a nutrient‐poor bog located in Northern Minnesota within the Spruce and Peatland Responses Under Changing Environments (SPRUCE) experiment. We found that NDVI decreased with long‐term reductions in soil moisture at the APEX site, coincident with a decrease in photosynthetic leaf area and the relative abundance of sedges. We observed increasing NDVI with elevated temperature at the SPRUCE site, associated with an increase in the relative abundance of shrubs and a decrease in forb cover. Warming treatments at the SPRUCE site also led to increases in the LAI of the shrub layer. We found no strong effects of elevated CO2 on community composition. Our findings support recent studies suggesting that changes in NDVI observed from satellite platforms may be the result of changes in community composition and ecosystem structure in response to climate warming.
Facial Expression Recognition Method Based on Sparse Batch Normalization CNN

In order to overcome the problem that the convolution neural network facial expression recognition model training is prone to gradient disappear or gradient explode and the fully connected layers with highly complex connection leads to the over-fitting. A sparse batch normalization convolution neural network model is proposed. In the early convolution process, the larger convolution kernels are used continuously to integrate the facial expression features. Batch normalization is applied to the middle layer of the model to adjust the data size and distribution so that the model can be trained quickly. At the fully connected layers, a low retention ratio Dropout connection forms reduce model calculations and over-fitting risk. The performance of the model is validated on the JAFFE dataset and the CK + dataset, respectively, and the recognition accuracy is over 95%. The model has better recognition performance than the common deep learning model and traditional methods.
A Survey of Recommendation Systems in Twitter

Social data mining is a major research area in this new era of technology. Various popular social networking websites such as Facebook, YouTube, Twitter provide a platform to the people for exchanging information and maintain a connection with the friends, relatives, and other active users. Twitter presents a platform to the active users for expressing their views and opinions on a trending topic by posting 280-character tweets. This feature of Twitter makes it different from other social networking sites. This popular microblogging site has approximately 328 million users and the number of tweets that are generated every day is approximately 500 million. Hence, the amount of information that users receive daily on their timeline is quite large. Recommender Systems have been introduced to solve this major problem of information overload. These systems help users to find useful and interesting information. Information filtering is a major step to provide important and useful tweets to the active users. They may miss out the important information due to overwhelming tweets on their timeline. The paper presents the different approaches and techniques that recommender systems have implemented to recommend the important tweets as well as followees to the users based on their behavior and other important features.
Summary of Cloud Computing Technology in Smart Grid

Smart Grid Network (SGN) is one of the innovative trends towards efficient and intelligent use of the conventional and unconventional resources of energy with respect to electric power generation, transmission and distribution. Future smart grids are expected to have reliable, efficient, secured, and cost-effective power management with the implementation of distributed architecture. To focus on these requirements, in this paper we provide a comprehensive research on different cloud computing applications for the smart grid architecture, in three different areas-energy management, cloud monitoring and security. In these areas, the utility of cloud computing applications is discussed, while giving directions on future opportunities for the development of the smart grid.
Challenges in Automation of Test Cases for Mobile Payment Apps

Software Engineering is a field of new challenges every day. With every passing day, new technologies emerge. There was an era of web Applications, but the time has changed and most of the web Applications are available as Mobile Applications as well. The Mobile Applications are either android based or iOS based. To deliver error free, secure and reliable Application, it is necessary to test the Applications properly. Software testing is a phase of software development life cycle, where we test an Application in all aspects. Nowadays different type of tools are available for testing an Application automatically but still we have too many challenges for applying test cases on a given Application. In this paper the authors will discuss the challenges of automation of test cases for a Mobile based payment Application.
A Multi-Model Estimation of Distribution Algorithm for Agent Routing Problem in Multi-Point Dynamic Task

The agent routing problem in multi-point dynamic task (ARP-MPDT) is a multi-task routing problem of a mobile agent. In this problem, there are multiple tasks to be carried out in different locations. As time goes on, the state of each task will change nonlinearly. The agent must go to the task points in tum to perform the tasks, and the execution time of each task is related to the state of the task point when the agent arrives at the point. ARP-MPDT is a typical NP-hard optimization problem. In this paper, we establish the nonlinear ARP-MPDT model. A multi-model estimation of distribution algorithm (EDA) employing node histogram models (NHM) and edge histogram models (EHM) in probability modeling is used to solve the ARP-MPDT. The selection ratio of NHM and EHM probability models is adjusted adaptively. Finally, performance of the algorithm for solving the ARP-MPDT problem is verified by the computational experiments.
Regulation by biotic stress of tannins biosynthesis in Quercus ilex: Crosstalk between defoliation and Phytophthora cinnamomi infection.

Sustainability of the Mediterranean forest is threatened by oak decline, a disease of holm oak and other Quercus species that is initiated by infection with the oomycete Phytophthora cinnamomi. Focusing on the role of tannins in the chemical defense of plants, this work investigated whether tannins content in Quercus ilex is regulated by biotic stress. Screening of published genomes allowed the identification of Quercus sequences encoding enzymes for early steps of the biosynthesis of phenolic compounds, like hydrolysable tannins and condensed tannins (CT) among others, plus genes involved in the late steps of CT biosynthesis. Four days after treatment of Q. ilex seedlings by mechanical defoliation, P. cinnamomi infection and both stressors simultaneously, mRNA concentrations for tannins biosynthesis enzymes were measured in leaves. Among the transcript amount for shikimate dehydrogenase (SDH, EC 1.1.1.25), anthocyanidin reductase (EC 1.3.1.77), anthocyanidin synthase (EC 1.14.11.19) and leucoanthocyanidine reductase (EC 1.17.1.3), defoliation induced gene expression for SDH2 isoenzyme. About 4 days after infection of roots by P. cinnamomi, this up-regulation was canceled and SDH enzyme activity decreased. Furthermore, during this late stage of biotrophic interaction the pathogen switched off the correlation engaged by defoliation between the expression of SDH1 and SDH2 encoding genes and chemical defenses corresponding to total tannins, which were down-regulated. Thus, tannins biosynthesis in seedlings of Q. ilex is induced after mechanical defoliation whereas infection by the pathogen interferes with this regulation, potentially increasing the susceptibility of plants to herbivory and aggravating the impact of biotic stress.
Fault Estimation for Nonlinear Repetitive Systems: An Iterative Learning Approach

In this paper, the fault estimation (FE) problem for a class of nonlinear repetitive systems with bounded initial state errors is investigated by using an accelerated iterative learning (IL) approach. In each iteration, only the boundedness condition is known, which can reduce the conservativeness of IL methods when applied to industrial production. The traditional IL algorithm is modified by adding a weight coefficient to accelerate the learning speed and weaken or even eliminate the negative influence of initial state errors on the convergence of fault estimation errors. A sufficient condition is established for guaranteeing accurate fault estimation under the proposed IL-based scheme as the iteration numbers increase. Finally, illustrative examples are presented to verify the effectiveness of the proposed modified fault estimation scheme.
The Effect of Visual Attention on Peripheral Stereopsis

We used a discrimination task to investigate the effect of stereo vision on eccentricity. The eccentricities were 15 arc deg, 30 arc deg, 35 arc deg, 42 arc deg and 50 arc deg. Participants distributed attention to locations indicated by a predictive cue. Attention areas were defined by a capital H. The participants must attention the direction that the cue indicated and reported the type of the targets-concave or convex. The results showed that the perception of stereopsis declined with the increasing eccentricities. Within the central 30 arc deg of the visual field, the stereopsis was clear. But beyond 30 arc deg of the visual field, specially beyond 42 arc deg, it was very hard to distinguish crossed disparities and uncrossed disparities.
Lattice Structure Design and Optimization With Additive Manufacturing Constraints

Lattice structures with different desired physical properties are promising for a broad spectrum of applications. The availability of additive manufacturing (AM) technology has relaxed the fabricating limitation of lattice structures. However, manufacturing constraints still exist for AM-fabricated lattice structures, which have a significant influence on the printing quality and mechanical properties of lattice struts. In this paper, a design and optimization strategy is proposed for lattice structures with the consideration of manufacturability to ensure desired printing quality. The concept of manufacturable element is used to link the design and manufacturing process. A meta-model is constructed by experiments and the artificial neural network to obtain the manufacturing constraints. Sizes of struts are optimized by a bidirectional evolutionary structural optimization-based algorithm with these manufacturing constraints. An arm of quadcopter is redesigned and optimized to validate the proposed method. Its result shows that optimized heterogeneous lattice structures can improve the stiffness of the model compared to the homogeneous lattice structure and the original design. Both the Von-Mises stress and the maximum displacement are reduced without increasing the weight of designed part. And by considering the manufacturability constraints, the optimized design has been successfully fabricated by the selected additive manufacturing process. Note to Practitioners—Lattice structures might fail to be fabricated by the additive manufacturing technique if the designed model exceeds the processability of the machine. Our approach has the capability of considering the manufacturing constraints in the design and optimization process. We conducted experiments to investigate the manufacturability and proposed a method that can give the domain of the design variables for a selected manufacturing process. And we also designed an algorithm that can optimize the lattice structure inside the domain of design variables. It ensures that the lattice model can be successfully fabricated by the selected process and the performance is dramatically increased compared to the original design. Engineers can use our approach to optimize the lattice structure automatically without knowing the knowledge of optimization and manufacturability.
A Hierarchical Model with Pseudoinverse Learning Algorithm Optimazation for Pulsar Candidate Selection

Pulsars search has always been one of the most concerned problem in the field of astronomy. Nowadays, with the development of astronomical instruments and observation technology, the amount of data is getting bigger and bigger. Radio pulsar surveys have generated and will generate vast amounts of data. To handle big data, developing new technologies and frameworks to efficiently and accurately analyze these data become increasing urgent. The number of positive and negative samples in pulsar candidate data set is very unbalanced, if we only use these a few positive samples to train a deep neural network (DNN), the trained DNN is prone because of the problem of overfitting and will affect the generalization ability. Motivated by the mixtures of experts network architecture, we proposed a hierarchical model for pulsar candidate selection which assembles a set of trained base classifiers. Moreover, training a neural network always takes a lot of time because of using gradient descent (GD) based algorithm. In this work, we utilize the pseudoinverse learning algorithm instead of GD based algorithm to train proposed model. With the designed network architecture and adopted training algorithm, our model has the advantages not only with high steady-state precision but also good generalization performance.
Managing Customers Online Recovery – An Insight for E-Retailers Using Conjoint Analysis

It is essential for the retailers to avert any sort of complications that the customers’ might face from the product/service they receive from the retailers. In case any kind of complication arise, they should be fixed instantaneously. The customer tends to become distrustful about online buying and more specifically to the e-retailer in case they are not assured that their problem will be fixed speedily, accurately and without any hassle. Redress is referred as the retort or answer to the customer’s objection or grievances that trade provides to them in case any such problem arises during the transaction. It is very important for online retailers to be ready with strategies and processes to counteract the problems and to re-establish and fortify buyer trust. Here in this paper study of the redress/recovery aspects of online customers has been done through the E-RecS-QUAL model. Recovery attributes followed by e-retailers are analysed and those that are important and preferred by the customers are identified using the conjoint analysis. The aim is also to develop a broader conceptual model of e- recovery ascertaining significant levels for every attribute and also finding the relative prominence of the attribute resulting in an overall grading/preference This would help the e-retailers to focus more on crucial attributes and may also help in curbing the extra cost and resources borne by them and at the same time pleasing the customer to a greater extent.
Reliability Analysis of System-in-Package Module Based on Physics of Failure

With the rapid development of SiP (System in Package) technology, reliability evaluation cannot be achieved by a single theory and traditional methods. In this paper, the reliability analysis technology based on PoF (Physics of Failure) is proposed. The performance degradation modeling, experiment design and reliability estimation based on Monte Carlo method are integrated to address the problems existing in the reliability work of the high-reliability and long-life product of SiP modules. Combining with an applicative example of reliability analysis case for a SiP module of plastic package, systematic analysis was conducted, including historical failure information research, typical failure mode, highly accelerated stress tests’ design, fault excitation, and failure mechanism analysis, in order to make further improvement on reliability analysis technology of SiP modules in electronic industry.
Energy-Aware Container Consolidation Based on PSO in Cloud Data Centers

In the last few years, the container-based Cloud computing paradigm has gradually emerged as a flexible approach for energy efficient resource utilization. Cloud providers aim to optimize resource utilization and energy consumption while performing container consolidation, which involves virtual machine (VM) selection and VM placement. Most of the related works focus either on VM selection or VM placement, neglecting the interplay of the two optimization problems. This paper proposes a Two-stage Multi-type Particle Swarm Optimization approach, named TMPSO, to energy-aware container consolidation in Cloud data centers. Our experimental evaluation based on benchmark datasets demonstrates that our proposed algorithm shows further energy saving comparing with some existing approaches.