Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning Deep Visuomotor Policies for Dexterous Hand Manipulation

Multi-fingered dexterous hands are versatile and capable of acquiring a diverse set of skills such as grasping, in-hand manipulation, and tool use. To fully utilize their versatility in real-world scenarios, we require algorithms and policies that can control them using on-board sensing capabilities, without relying on external tracking or motion capture systems. Cameras and tactile sensors are the most widely used on-board sensors that do not require instrumentation of the world. In this work, we demonstrate an imitation learning based approach to train deep visuomotor policies for a variety of manipulation tasks with a simulated five fingered dexterous hand. These policies directly control the hand using high dimensional visual observations of the world and propreoceptive observations from the robot, and can be trained efficiently with a few hundred expert demonstration trajectories. We also find that using touch sensing information enables faster learning and better asymptotic performance for tasks with high degree of occlusions. Video demonstration of our results are available at: https://sites.google. com/view/hand-vil/
Vangi 1 INJURY RISK-BASED CRITERIA FOR THE APPLICATION OF ADAPTIVE LOGIC TO ADAS SYSTEMS

Performance improvement of advanced driver assistance systems (ADAS) yields two major benefits: an increasingly fast progress towards autonomous driving and a simultaneous advance in vehicle safety. The high safety level provided by ADAS result primarily from the possibility to avoid possible impacts in correspondence of critical road scenarios. Nevertheless, specific obstacles (e.g., stationary vehicles, buildings) can interpose between the opponent vehicles and the working field of the sensors, weakening their functions: in these particular conditions, the impact can be inevitable (inevitable collision state – ICS). The systems currently available on the market are not capable to properly handle an ICS, because its occurrence is not conceived. In the present work intervention criteria for ADAS are introduced which are based on the vehicle occupants’ injury risk (IR), particularly useful in case of ICS. In a critical road scenario, the ADAS must first avoid the impact with maximum margin (maximum clearance between vehicles) and, in case of ICS, minimize impact severity and IR. Referring to a system capable of intervening on braking and steering, the ADAS must monitor the surrounding and act on the degrees of freedom adapting to the possible evolution of the scenario, following an adaptive logic. The sequence of optimal interventions based on such adaptive logic tends toward the best possible outcome. The context (model-in-the-loop) of the adaptive intervention employing the proposed criteria is first introduced, proposing a solution for testing its actual functioning (software-in-the-loop) with a view to its physical implementation (hardware-in-the-loop). The major criticality of the approach consists in the impact phase reconstruction, because IR is also a function of post-impact parameters (e.g., the velocity change ΔV experienced by the vehicle in the crash). To highlight the potential benefits offered by an adaptive ADAS and to monitor its behavior, a software has been developed based on the software-in-the-loop solution introduced. The best intervention selection is based on a database filled with results of simulations: the outcomes associated to each braking and steering intervention are summarized in the database, for many critical scenarios; the ADAS retrieves information from the database and, through IR-based criteria, selects the most favorable action. Testing the logic functioning in correspondence of three critical road scenarios in which two vehicles are involved, at each instant it is observed that the developed intervention logic aims at creating eccentrical impact configurations, associated to low ΔV; the low values of resulting impact severity demonstrate how the intervention criteria based on IR represent an important tool for the development of increasingly performing ADAS devices.
Unifying the framework of Multi-Layer Network and Visual Analytics

The notion of multi-layer networks introduces a general framework and common vocabulary for existing ideas in complex network theory. In doing so, it is possible to understand and compare these dierent ideas in a new and more fruitful manner. However, to make this operationalizable to the visualization and visual analytics community, we need more clarity. For example: What is a layer? What are the semantics of interlayer edges, and specifically, identity links between layers? Can dierent multilayered networks be expressed or implemented in the same way? And vice versa, can one multilayered network be expressed or implemented in dierent ways?
Is World Heritage inscription a sustainable tourism option ? Evidence from China

Since the 1990s, China has been enthusiastic about UNESCO World Heritage application, seeing a substantial rise of the associated tourism profits. It is rather questionable, however, if world heritage inscription is a sustainable option for heritage tourism. This study investigates a specific case of UNESCO World Heritage site in China -Fujian Tulou -from the perspective of stakeholder collaboration. Mixed research methods, including on-site observation, in-depth interview and questionnaire survey were used to facilitate the investigation. The findings indicate that various conflicts of interest exist among stakeholder groups involved in Tolou heritage tourism development. It is found that certain institutional barriers in current China may pose challenges to effective stakeholder collaboration which is considered crucial to sustainable heritage tourism development.
WITH CONVOLUTIONAL SEQUENCE-TO-SEQUENCE MODELS

Automatic Music Transcription (AMT) is a fundamental problem in Music Information Retrieval (MIR). The challenge is to translate an audio sequence to a symbolic representation of music. Recently, convolutional neural networks (CNNs) have been successfully applied to the task by translating frames of audio [44, 46]. However, those models can by their nature not model temporal relations and long time dependencies. Furthermore, it is extremely labor intense to get annotations for supervised learning in this setting. We propose a model that overcomes all these problems. The convolutional sequence to sequence (Cseq2seq) model applies a CNN to learn a low dimensional representation of audio frames and a sequential model to translate these learned features to a symbolic representation directly. Our approach has three advantages over other methods: (i) extracting audio frame representations and learning the sequential model is jointly trained end-to-end, (ii) the recurrent model can capture temporal features in musical pieces in order to improve transcription, and (iii) our model learns from entire sequences as opposed to temporally accurately annotated onsets and offsets for each note thus making it possible to train on large already existing corpora of music. For the purpose of testing our method we created our own dataset of 17K monophonic songs and respective MusicXML files. Initial experiments proof the validity of our approach.
BHA-160 : Constructional Design of Hash Function based on NP-hard Problem

Secure hash function is used to protect the integrity of the message transferred on the unsecured network. Changes on the bits of the sender’s message are recognized by the message digest produced by the hash function. Hash function is mainly concerned with data integrity, where the data receiver needs to verify whether the message has been altered by eavesdropping by checking the hash value appended with the message. To achieve this purpose, we have to use a secure hash function that is able to calculate the hash value of any message. In this paper, we introduce an alternative hash function based on NP-hard problem. The chosen NP-hard problem is known as Braid Conjugacy problem. This problem has proved to be secure against cryptanalysis attacks. Keywords—Hash function; integrity message; cryptanalysis; attack; NP-hard problem
The disappearance of words in connected speech

The category of the word is well established in meta-language pursuits, especially in linguistics. It is the basis for the development, over centuries, of the methods of lexicography, which have produced various types of lexica, including pronunciation dictionaries. The latter take the concept of the canonical phonetic representations of word citation forms in a language as their point of departure. At least some of them also list phonetic variants, which may occur in different utterance contexts (phonetic environments, levels of style). The pronunciation dictionaries presently available for various languages, differ enormously in the extent of taking the phonetic variability of words into account. At one end of the scale we find the DUDEN lexicon for German [1], which does not provide any contextual variants at all; at the other end are the English reference works by Jones/ Gimson/Roach [9] and Wells [37], which give detailed information on phonetic variation of words in utterances. WDA (Wörterbuch der deutschen Aussprache) [39] is located in between, but closer to DUDEN.
Knowledge Management Based on Fuzzy Clustering Algorithm with Picard Iteration

Knowledge management of concepts was essential in educational environment. The popular fuzzy c-means algorithm (FCM) converges to a local minimum of the objective function. Fuzzy clustering algorithms are based on Euclidean distance function, which can only be used to detect spherical structural clusters. A Fuzzy C-Means algorithm based on Mahalanobis distance (FCM-M) was proposed to improve those limitations of GG and GK algorithms, but it is not stable enough when some of its covariance matrices are not equal. Hence, different initializations may lead to different results. The important issue is how to avoid getting a bad local minimum value to improve the cluster accuracy. The particle swarm optimization (PSO) is a popular and robust strategy for optimization problems. But the main difficulty in applying PSO to real-world applications is that PSO usually need a large number of fitness evaluations before a satisfying result can be obtained. A new improved Fuzzy Clustering Algorithm with Picard Iteration is proposed. In this paper, the improved new algorithm, Use the best performance of clustering algorithm in data analysis and interpretation. Each cluster of data can easily describe features of knowledge structures. Manage the knowledge structures of Concepts to construct the model of features in the pattern recognition completely. Keywords—Fuzzy Clustering Algorithm, Picard Iteration, Mahalanobis Distance.
Surface Reconstruction From Normals: A Robust DGP-Based Discontinuity Preservation Approach

In 3D surface reconstruction from normals, discontinuity preservation is an important but challenging task. However, existing studies fail to address the discontinuous normal maps by enforcing the surface integrability in the continuous domain. This paper introduces a robust approach to preserve the surface discontinuity in the discrete geometry way. Firstly, we design two representative normal incompatibility features and propose an efficient discontinuity detection scheme to determine the splitting pattern for a discrete mesh. Secondly, we model the discontinuity preservation problem as a light-weight energy optimization framework by jointly considering the discontinuity detection and the overall reconstruction error. Lastly, we further shrink the feasible solution space to reduce the complexity based on the prior knowledge. Experiments show that the proposed method achieves the best performance on an extensive 3D dataset compared with the state-of-the-arts in terms of mean angular error and computational complexity.
Assessing the robustness of meta-analytic results in information systems: publication bias and outliers

Abstract Meta-analytic studies serve to generate cumulative knowledge and guide evidence-based practice. However, publication bias and outliers threaten the accuracy and robustness of meta-analytic results. Unfortunately, most meta-analytic studies in information systems (IS) research do not assess the presence of these phenomena. Furthermore, some methods commonly used for the detection of publication bias are now recognised as inappropriate. We conduct a comprehensive assessment of four previously published meta-analytic studies in IS. We use multiple methods to assess the effects of publication bias and outliers on the meta-analytic results. Our findings indicate that publication bias and/or outliers have affected the results of three of the four meta-analytic studies. Some methods indicate that select meta-analytic means were misestimated by potentially more than 100%. Our analyses offer methodological exemplars that can be followed to assess the potential adverse effects of publication bias and outliers on meta-analytic results, including their combined effects. We make additional contributions to scientific knowledge by evaluating the performance of different publication bias assessment methods used across scientific disciplines. In brief, we highlight the importance of a rigorous assessment of publication bias and outliers on meta-analytic results to improve the trustworthiness of our cumulative knowledge and evidence-based practice.
Comparison of Virtual Oscillator and Droop Control Preprint

Virtual oscillator control (VOC) and droop control are distinct methods to ensure synchronization and power sharing of parallel inverters in islanded systems. VOC is a control strategy where the dynamics of a nonlinear oscillator are used to derive control states to modulate the switch terminals of an inverter. Since VOC is a time-domain controller that reacts to instantaneous measurements with no additional filters or computations, it provides a rapid response during transients and stabilizes volatile dynamics. In contrast, droop control regulates the inverter voltage in response to the measured average real and reactive power output. Given that real and reactive power are phasor quantities that are not well-defined in real time, droop controllers typically use multiplicative operations in conjunction with low-pass filters on the current and voltage measurements to calculate such quantities. Since these filters must suppress low frequency ac harmonics, they typically have low cutoff frequencies that ultimately impede droop controller bandwidth. Although VOC and droop control can be engineered to produce similar steady-state characteristics, their dynamic performance can differ markedly. This paper presents an analytical framework to characterize and compare the dynamic response of VOC and droop control. The analysis is experimentally validated with three 120 V inverters rated at 1 kW, demonstrating that for the same design specifications VOC is roughly 8 times faster and presents almost no overshoot after a transient.
DETERMINATION OF CHROMATIC CHARACTERISTICS OF THE HYDROALCOHOLIC EXTRACTS OBTAINED FROM THE FRUITS OF SOME CHERRY AND SOUR CHERRY VARIETIES R ă

Data on chromatic parameters allow objective assessment of fruit color at maturity of consumption, by analyzing hydroalcoholic extracts obtained and monitoring color development during their storage. This paper aims to determine the chromatic parameters (L, a, b, C, H°) and anthocyanin content of ethanolic extracts obtained from fruits of four varieties of sour cherry (Prunus cerasus L.) and six varieties of cherry (Prunus avium L .) grown in the experimental field of Research Development Station for Fruit growing, Iasi, using CIE Lab-76 method and data digitization program Vincolor modified. Bitter cherry variety Amar de Maxut, presented the most important content in anthocyanins (443.72±1.54 mg/100g) and total phenolic compounds (794.62±0.08 mg GAE/100g), this variety having the most intensely colored fruits of the analyzed varieties. The color of each extract was simulated electronically using the program Digital Colour Atlas 3.0 Demoversion, based on values obtained from spectral analysis (λ 380-780 nm).
Extraction Networks Scale Inference Networks * Co nf id en ce Recursive Refinement Networks

We present a novel method that estimates confidence map of an initial disparity by making full use of tri-modal input, including matching cost, disparity, and color image through deep networks. The proposed network, termed as Locally Adaptive Fusion Networks (LAF-Net), learns locally-varying attention and scale maps to fuse the trimodal confidence features. The attention inference networks encode the importance of tri-modal confidence features and then concatenate them using the attention maps in an adaptive and dynamic fashion. This enables us to make an optimal fusion of the heterogeneous features, compared to a simple concatenation technique that is commonly used in conventional approaches. In addition, to encode the confidence features with locally-varying receptive fields, the scale inference networks learn the scale map and warp the fused confidence features through convolutional spatial transformer networks. Finally, the confidence map is progressively estimated in the recursive refinement networks to enforce a spatial context and local consistency. Experimental results show that this model outperforms the state-ofthe-art methods on various benchmarks.
Enhancement of Phytochemical Analysis and Drying Efficiency of Annual Herbaceous harvested in Tunisia Flora: Used as Efficiency Remedy in Ayurveda Therapy

The aim of the present study was to detect, quantify and identify the phenolic compounds in Tribulus terrstris (used as remedy in infertility disorder by Ayurveda therapy) extract hydrolyzedby HCL, using High-Performance Liquid Chromatography (HPLC) to detect and quantify non-volatiles compounds. Gas-Chromatography Coupled to Mass Spectrometry (GC-MS) was performed to detect and quantify volatile-compounds. A total of sixteen, eighteen[1], and twenty[2] compounds from Tribulus extract, respectively in seeds, leaves and roots were identified as belonging to various structural classes, mainly phenolic compounds. Analysis by HPLC finger print allowed the identification of procatechic-acid in leaves part detected with high peakarea (8.2 mg / g MS).
Automatic Logo Detection and Removal

The latest trend in advertisement is to display video logos in a context devoid of ads such as movies or television shows where the product is featured in the story line of the show. Such embedded marketing strategies are considered distractions and are often the cause of decreasing viewing pleasure for audiences. This paper presents a method for automatically detecting and removing logos from digital images given the logo of interest. During the logo detection phase, the initial logo boundary is localized using Speed Up Robust Features (SURF) technique and the logo is completely removed after further refinement by active contour based on a Mumford-Shah segmentation method. In the image filling part, we use exemplarbased inpainting to fill in the removed logo location in a visually plausible way. The combination of these three methods allows robust detection and removal of the target region without manual selection.
Internet Credit Risk Scoring Based on Simulated Annealing and Genetic Algorithm

Credit risk which can be reduced by credit scoring is the focus of financial risk control. In the construction of Internet credit scoring model, we encounter the problem that variables have high dimension. To solve this problem, feature selection is necessary. Simulated annealing and genetic algorithm can be used to do feature selection. This article gives an empirical analysis of individual credit valuation using data from a microfinance internet platform. Experimental result shows both of the logistic model based on simulated annealing and logistic model based on genetic algorithm have better prediction ability and model interpretability comparing to traditional full variable logistic regression. Also, the logistic model based on simulated annealing is slightly better than logistic model based on genetic algorithm Keywords-credit scoring; benchmark experiment; feature selection; simulated annealing; genetic algorithm
FINANCIAL FORECASTING USING DECISION TREE (REPTree & C4.5) AND NEURAL NETWORKS (K*) FOR HANDLING THE MISSING VALUES

Missing values are a widespread problem in data analysis. The purpose of this paper is to design a model to handle the missing values in predicting financial health of companies. Forecasting business failure is an important and challenge task for both academic researchers and business practitioners. In this study, we compare the classification of accuracy in decision tree methods (REP tree, C4.5) and with ANN method (K*) to handle the missing values.
Microsatellite markers – a tool for molecular characterization of cattle genetic resources

Viryanski, D. (2019). Microsatellite markers – a tool for molecular characterization of cattle genetic resources. Bulgarian Journal of Agricultural Science, 25(1), 158–165 Molecular markers are essential tool for determining the specifi c genetic makeup on individual and population level. They are valuable approach for the genetic improvement of farm animals. In cattle breeding their application is useful for improvement of breeding programs for desired traits, better productivity and high quality products. These markers provide more accurate genetic information and better knowledge of the animal genetic resources. This review gives a brief summary on the application of one of more advanced DNA-based molecular markers in cattle breeding – the microsatellites.
Learning to Generate Textures on 3D Meshes

Recent years have seen a great deal of work in photoreal-istic neural image synthesis from 2D image datasets. However, there are only a few works that exploit 3D shape information to aid in image synthesis. To this end, we leverage data from 2D image datasets as well as 3D model corpora to generate textured 3D models. In particular, we propose a framework for texturing meshes from multiview images. Our framework ﬁrst uses 2.5D information rendered using the 3D models along with user inputs as an intermediate view dependent representation. These intermediate representations are then used to generate realistic textures for particular views in an unpaired manner. Finally, we use a differentiable renderer to combine the generated multiview texture into a single textured mesh. We demonstrate results of multiview texture synthesis for example meshes.
Supplementary Material for “ On zero-shot recognition of generic objects ”

Figure 1 of this supplementary material reproduces Figure 1 of the original paper to help the following discussion. This figure illustrates the configuration of visual classes of the standard test splits within the Wordnet hierarchy. It should be noted that the 2-hops test split is a super-set of the 1-hop split: it contains both classes annotated in green and blue. Similarly, the all test split is a super-set of the 2-hops test split: it contains all blue, green and black classes. In the generalized ZSL setting, training classes (red) are also included in the test set.