Miracle-dz/UniFAR-FTUs huggingface dataset:
An Experimental Approach for Determination of Locators Reaction Forces in Milling Fixtures

The contact stiffness between workpiece and fixture locating system is one of the decisive factors for maintenance of the stability of workpiece during the machining process. In order to estimate the contact stiffness, it is needed to determine the locator reaction forces. These forces are created by the clamping forces, cutting forces, workpiece weight and friction effects of the contact between the workpiece and fixture locating system. Some analytical approaches have already been presented for calculating the location reaction forces. However, there are six equations for a (3-2-1) locating system, but 18 unknown parameters. Therefore, an optimization solution is proposed in the literature to obtain the reaction forces which involves several simplifying assumptions which result in considerable errors in the solution. In this study, in addition to presenting the mathematical model of the total system, an experimental approach has been proposed in order to determine the locator reaction forces. This can provide a suitable means for evaluating the optimization solutions and analytical models for determining the locator reaction forces and contact stiffness and diminishing the errors.
AN EFFICIENT WAY TO DETECT FAULTY NODE IN WSN

: Detection of fault in wireless sensor network is tedious task, because of the large scale of sensor nodes in the network. Thus to identify the fault node, to collect information from individual nodes need to be process. Because of the large volume of the sensor nodes irrelevant data or inaccurate of data passed to the base center, median, mean mode approach are used . Neighbor coordination approach is used in this paper. This paper presents a novel approach for detecting sensors which produce faulty data in adistributed way as well as identifying the type of datafaults using trust concepts to gain a high degree ofconfidence. We validate our method with simulations results.
IMPROVE THE LIFETIME OF WSN THROUGH MODIFIED TEEN PROTOCOL: A REVIEW

: WSNs are used in environmental monitoring, security, medical applications, etc. The sensor nodes are usually randomly deployed in a specific region. These sensor nodes collect their data and send it to the Base Station (BS) via some routing protocol. These nodes cannot be recharged from time to time to keep them alive. They must follow a protocol which must ensure the efficient use of their power, so that those nodes may serve as long as possible without any external assistance. A routing technique plays a key role in their energy consumption. Many of the routing protocols use clustering as their routing technique. So clustering plays a very important role in prolonging the stability period and network life time. The Cluster Heads (CHs) collect the data from all the nodes in their cluster, aggregate it and then finally send it to the BS. These sensor nodes must follow a certain routing protocol to send their data efficiently to the BS. The main objective of all routing protocols is to minimize the energy consumption so that the network lifetime and particularly the stability period of the network may be enhanced. By network lifetime we mean the time duration from the start of the network till the death of the last node, whereas, stability period means the time duration from the start of the network till the death of the first node. This has object to develop an energy efficient increased lifetime threshold sensitive clustering algorithm by dynamic selection of cluster heads using multi-hops and multi-path, that leads to load balancing on different-different clusters. This results in the enhancement of cluster heads or normal nodes network lifetime and comparison of performance of the proposed protocol with TEEN. In this work we propose an energy efficient multipath routing algorithm in WSN. This protocol is designed to improve the latency, resiliency and efficiency through discovering multiple paths from the source to the
ESTIMATION OF ORTHOGONAL FREQUENCY DIVISION MULTIPLEXING

: Although tremendous progress has been created on the past years on channel estimation in of dm systems still it's thought-about as area of concern in wireless communication. a singular channel estimation technique with virtual sub carriers is projected throughout this work specifically a low-complexity but near-optimal DFT-based channel skilled with leak nulling is projected for OFDM systems victimization virtual subcarriers. The flow of the projected approach is initially starting with time-domain (TD) index set estimation considering the leak result then followed by low-complexity TD postprocessing to suppress the leak. The projected channel skilled approach outperforms this channel estimators in terms of efficiency and performance. Finally, the performance and quality of the projected algorithm analysed by simulation results.
OPTICAL COHERENCE TOMOGRAPHY IMAGE DENOISING USING SSR AND NON-LOCAL MEANS

: We demonstrate the usefulness of utilizing a segmentation step for improving the performance of sparsity based image reconstruction algorithms. In specific, we will focus on retinal optical coherence tomography (OCT) reconstruction and propose a novel segmentation based reconstruction framework with sparse representation, termed segmentation based sparse reconstruction (SSR). The SSR method uses automatically segmented retinal layer information to construct layer-specific structural dictionaries. In addition, the SSRmethod efficiently exploits patch similarities within each segmented layer to enhance the reconstruction performance. Our experimental results on clinical-grade retinal OCT images demonstrate the effectiveness and efficiency of the proposed SSR method for both denoising and interpolation of OCT images.
CLUSTERING BASED ON DBSCAN

: DBSCAN is a density based clustering algorithm extracting the arbitrary shapes of the normal lanes from AIS data. This paper presents a parallel DBSCAN algorithm on top of Hadoop the experiment conducted in the paper shows that the proposed method can work well with maritime data although the performance is not satisfying. A discussion about the method's limitation and potential issues is shown at the end of the paper.
PERFORMANCE EVALUATION OF E-FARHD ALGORITHM WITH LEVENSTEIN DISTANCE ON SOYBEAN DATASET

: Association Rule Mining (ARM) with fuzzy logic concept make possible the easy progression of drawing out of latent repeated or recurrent patterns based on their individual frequencies in the outline of association rules from several transactional and relational datasets including items to imply the most topical trends in the specified dataset. These extract recurring patterns or fuzzy association rules make use of either for physical data analysis or also inclined to force further mining tasks like categorization or classification commonly known as associative classification and collecting or clustering generally ARM-driven clustering, like document clustering is employed which helps out province area professionals to mechanize decision-making
Business Cycle Accounting for the Chinese Economy

We evaluate sources of business cycle fluctuations in China after 1978 with business cycle accounting method developed by Chari, Kehoe, and McGrattan (2007). We find that efficiency wedge, which represents institutional change and technology advance, was the main source of economic fluctuations in 1978 2006. The amplitude of it fluctuation declined after 1992, which resulted in moderation of business cycle fluctuations. We also find that distortions manifest themselves as taxes on investment, which represents frictions in the capital market, became another economic fluctuation source after 1992, which is different from results of business cycle accounting on US and Japan data. Our results also show that government consumption and net exports played minor roles in generating business cycles. Our results point out several promising directions for future research on China’s business cycle.
IRIS AUTHENTICATION SYSTEM USING HYBRID TECHNIQUE –SURF

: Iris recognition system is one of the reliable and accurate biometric system. In IRIS recognition the Vital step around which the whole process revolves is localization of IRIS as well as pupil boundary. At present scenario there is need to develop an efficient technique for removal of noise in processing as well as novel feature extractor for accuracy point of view.
UGV-UAV Object Geolocation in Unstructured Environments

A robotic system of multiple unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs) has the potential for advancing autonomous object geolocation performance. Much research has focused on algorithmic improvements on individual components, such as navigation, motion planning, and perception. In this paper, we present a UGV-UAV object detection and geolocation system, which performs perception, navigation, and planning autonomously in real scale in unstructured environment. We designed novel sensor pods equipped with multispectral (visible, near-infrared, thermal), high resolution (181.6 Mega Pixels), stereo (near-infrared pair), wide field of view (192 degree HFOV) array. We developed a novel on-board software-hardware architecture to process the high volume sensor data in real-time, and we built a custom AI subsystem composed of detection, tracking, navigation, and planning for autonomous objects geolocation in real-time. This research is the first real scale demonstration of such high speed data processing capability. Our novel modular sensor pod can boost relevant computer vision and machine learning research. Our novel hardware-software architecture is a solid foundation for system-level and component-level research. Our system is validated through data-driven offline tests as well as a series of field tests in unstructured environments. We present quantitative results as well as discussions on key robotic system level challenges which manifest when we build and test the system. This system is the first step toward a UGV-UAV cooperative reconnaissance system in the future.
3D extinction mapping of the Milky Way using Convolutional Neural Networks: Presentation of the method and demonstration in the Carina Arm region

Context. Several methods have been proposed to build 3D extinction maps of the Milky Way (MW), most often based on Bayesian approaches. Although some studies employed machine learning (ML) methods in part of their procedure, or to specific targets, no 3D extinction map of a large volume of the MW solely based on a Neural Network method has been reported so far. Aims. We aim to apply deep learning as a solution to build 3D extinction maps of the MW. Methods. We built a convolutional neural network (CNN) using the CIANNA framework, and trained it with synthetic 2MASS data. We used the Besan\c{c}on Galaxy model to generate mock star catalogs, and 1D Gaussian random fields to simulate the extinction profiles. From these data we computed color-magnitude diagrams (CMDs) to train the network, using the corresponding extinction profiles as targets. A forward pass with observed 2MASS CMDs provided extinction profile estimates for a grid of lines of sight. Results. We trained our network with data simulating lines of sight in the area of the Carina spiral arm tangent and obtained a 3D extinction map for a large sector in this region ($l = 257 - 303$ deg, $|b| \le 5$ deg), with distance and angular resolutions of $100$ pc and $30$ arcmin, respectively, and reaching up to $\sim 10$ kpc. Although each sightline is computed independently in the forward phase, the so-called fingers-of-God artifacts are weaker than in many other 3D extinction maps. We found that our CNN was efficient in taking advantage of redundancy across lines of sight, enabling us to train it with only 9 sightlines simultaneously to build the whole map. Conclusions. We found deep learning to be a reliable approach to produce 3D extinction maps from large surveys. With this methodology, we expect to easily combine heterogeneous surveys without cross-matching, and therefore to exploit several surveys in a complementary fashion.
ExtraPhrase: Efficient Data Augmentation for Abstractive Summarization

Neural models trained with large amount of parallel data have achieved impressive performance in abstractive summarization tasks. However, large-scale parallel corpora are expensive and challenging to construct. In this work, we introduce a low-cost and effective strategy, ExtraPhrase, to augment training data for abstractive summarization tasks. ExtraPhrase constructs pseudo training data in two steps: extractive summarization and paraphrasing. We extract major parts of an input text in the extractive summarization step and obtain its diverse expressions with the paraphrasing step. Through experiments, we show that ExtraPhrase improves the performance of abstractive summarization tasks by more than 0.50 points in ROUGE scores compared to the setting without data augmentation. ExtraPhrase also outperforms existing methods such as back-translation and self-training. We also show that ExtraPhrase is significantly effective when the amount of genuine training data is remarkably small, i.e., a low-resource setting. Moreover, ExtraPhrase is more cost-efficient than the existing approaches
Monitoring the Economy in Real Time: Trends and Gaps in Real Activity and Prices

We propose two specifications of a real-time mixed-frequency semi-structural time series model for evaluating the output potential, output gap, Phillips curve, and Okun's law for the US. The baseline model uses minimal theory-based multivariate identification restrictions to inform trend-cycle decomposition, while the alternative model adds the CBO's output gap measure as an observed variable. The latter model results in a smoother output potential and lower cyclical correlation between inflation and real variables but performs worse in forecasting beyond the short term. This methodology allows for the assessment and real-time monitoring of official trend and gap estimates.
Emerging Concepts in Nuclear Structure Based on the Shell Model

Some emerging concepts of nuclear structure are overviewed. (i) Background: the many-body quantum structure of atomic nucleus, a complex system comprising protons and neutrons (called nucleons collectively), has been studied largely based on the idea of the quantum liquid (à la Landau), where nucleons are quasiparticles moving in a (mean) potential well, with weak “residual” interactions between nucleons. The potential is rigid in general, although it can be anisotropic. While this view was a good starting point, it is time to look into kaleidoscopic aspects of the nuclear structure brought in by underlying dynamics and nuclear forces. (ii) Methods: exotic features as well as classical issues are investigated from fresh viewpoints based on the shell model and nucleon–nucleon interactions. The 70-year progress of the shell–model approach, including effective nucleon–nucleon interactions, enables us to do this. (iii) Results: we go beyond the picture of the solid potential well by activating the monopole interactions of the nuclear forces. This produces notable consequences in key features such as the shell/magic structure, the shape deformation, the dripline, etc. These consequences are understood with emerging concepts such as shell evolution (including type-II), T-plot, self-organization (for collective bands), triaxial-shape dominance, new dripline mechanism, etc. The resulting predictions and analyses agree with experiment. (iv) Conclusion: atomic nuclei are surprisingly richer objects than initially thought.
HardBoost: Boosting Zero-Shot Learning with Hard Classes

This work is a systematical analysis on the so-called hard class problem in zero-shot learning (ZSL), that is, some unseen classes disproportionally affect the ZSL performances than others, as well as how to remedy the problem by detecting and exploiting hard classes. At first, we report our empirical finding that the hard class problem is a ubiquitous phenomenon and persists regardless of used specific methods in ZSL. Then, we find that high semantic affinity among unseen classes is a plausible underlying cause of hardness and design two metrics to detect hard classes. Finally, two frameworks are proposed to remedy the problem by detecting and exploiting hard classes, one under inductive setting, the other under transductive setting. The proposed frameworks could accommodate most existing ZSL methods to further significantly boost their performances with little efforts. Extensive experiments on three popular benchmarks demonstrate the benefits by identifying and exploiting the hard classes in ZSL.
Molecular Noise in Synaptic Communication

In synaptic molecular communication (MC), the activation of postsynaptic receptors by neurotransmitter (NTs) is governed by a stochastic reaction-diffusion process. This randomness of synaptic MC contributes to the randomness of the electrochemical downstream signal in the postsynaptic cell, called postsynaptic membrane potential (PSP). Since the randomness of the PSP is relevant for neural computation and learning, characterizing the statistics of the PSP is critical. However, the statistical characterization of the synaptic reaction-diffusion process is difficult because the reversible bi-molecular reaction of NTs with receptors renders the system nonlinear. Consequently, there is currently no model available which characterizes the impact of the statistics of postsynaptic receptor activation on the PSP. In this work, we propose a novel statistical model for the synaptic reaction-diffusion process in terms of the chemical master equation (CME). We further propose a novel numerical method which allows to compute the CME efficiently and we use this method to characterize the statistics of the PSP. Finally, we present results from stochastic particle-based computer simulations which validate the proposed models. We show that the biophysical parameters governing synaptic transmission shape the autocovariance of the receptor activation and, ultimately, the statistics of the PSP. Our results suggest that the processing of the synaptic signal by the postsynaptic cell effectively mitigates synaptic noise while the statistical characteristics of the synaptic signal are preserved. The results presented in this paper contribute to a better understanding of the impact of the randomness of synaptic signal transmission on neuronal information processing.
Multilingual Open Text 1.0: Public Domain News in 44 Languages

We present a new multilingual corpus containing text in 44 languages, many of which have limited existing text resources for natural language processing. The ﬁrst release of the corpus contains over 2.7 million news articles and an additional 1 million short snippets (photo captions, video descriptions, etc.) published between 2001–2021 and collected from Voice of America’s news websites. We describe our process for collecting, ﬁltering, and processing the data. The source material is in the public domain, our collection is licensed using a creative commons license (CC BY 4.0), and all software used to create the corpus is released under the MIT License. The corpus will be regularly updated as additional documents are published.
Impact of Stop Sets on Stopping Active Learning for Text Classification

Active learning is an increasingly important branch of machine learning and a powerful technique for natural language processing. The main advantage of active learning is its potential to reduce the amount of labeled data needed to learn high-performing models. A vital aspect of an effective active learning algorithm is the determination of when to stop obtaining additional labeled data. Several leading state-of-the-art stopping methods use a stop set to help make this decision. However, there has been relatively less attention given to the choice of stop set than to the stopping algorithms that are applied on the stop set. Different choices of stop sets can lead to significant differences in stopping method performance. We investigate the impact of different stop set choices on different stopping methods. This paper shows the choice of the stop set can have a significant impact on the performance of stopping methods and the impact is different for stability-based methods from that on confidence-based methods. Furthermore, the unbiased representative stop sets suggested by original authors of methods work better than the systematically biased stop sets used in recently published work, and stopping methods based on stabilizing predictions have stronger performance than confidence-based stopping methods when unbiased representative stop sets are used. We provide the largest quantity of experimental results on the impact of stop sets to date. The findings are important for helping to illuminate the impact of this important aspect of stopping methods that has been under-considered in recently published work and that can have a large practical impact on the performance of stopping methods for important semantic computing applications such as technology assisted review and text classification more broadly.
Ranked Enumeration of Join Queries with Projections

Join query evaluation with ordering is a fundamental data processing task in relational database management systems. SQL and custom graph query languages such as Cypher offer this functionality by allowing users to specify the order via the ORDER BY clause. In many scenarios, the users also want to see the first k results quickly (expressed by the LIMIT clause), but the value of k is not predetermined as user queries are arriving in an online fashion. Recent work has made considerable progress in identifying optimal algorithms for ranked enumeration of join queries that do not contain any projections. In this paper, we initiate the study of the problem of enumerating results in ranked order for queries with projections. Our main result shows that for any acyclic query, it is possible to obtain a near-linear (in the size of the database) delay algorithm after only a linear time preprocessing step for two important ranking functions: sum and lexicographic ordering. For a practical subset of acyclic queries known as star queries, we show an even stronger result that allows a user to obtain a smooth tradeoff between faster answering time guarantees using more preprocessing time. Our results are also extensible to queries containing cycles and unions. We also perform a comprehensive experimental evaluation to demonstrate that our algorithms, which are simple to implement, improve up to three orders of magnitude in the running time over state-of-the-art algorithms implemented within open-source RDBMS and specialized graph databases.