Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning data augmentation policies using augmented random search

Previous attempts for data augmentation are designed manually, and the augmentation policies are dataset-specific. Recently, an automatic data augmentation approach, named AutoAugment, is proposed using reinforcement learning. AutoAugment searches for the augmentation polices in the discrete search space, which may lead to a sub-optimal solution. In this paper, we employ the Augmented Random Search method (ARS) to improve the performance of AutoAugment. Our key contribution is to change the discrete search space to continuous space, which will improve the searching performance and maintain the diversities between sub-policies. With the proposed method, state-of-the-art accuracies are achieved on CIFAR-10, CIFAR-100, and ImageNet (without additional data). Our code is available at this https URL.
A New Vision of Language

A metaphor that has dominated linguistics for the entire duration of its existence as a discipline views sentences as edifices consisting of Lego-like building blocks. It is assumed that each sentence is constructed (and, on the receiving end, parsed) ab novo, starting (ending) with atomic constituents, to logical semantic specifications, in a recursive process governed by a few precise algebraic rules. The assumptions underlying the Lego metaphor, as it is expressed in generative grammar theories, are: (1) perfect regularity of what Saussure called langue, (2) infinite potential recursivity of syntactic structures, (3) unlimited human capacity for linguistic creativity, (4) the impossibility of acquiring structural knowledge from examples, and (5) the impossibility of such knowledge being stored in a memory-intensive form (ensembles of exemplars). Although these assumptions still reign in Lego-land (Lasnik, 2002; Hauser et al., 2002; Jackendoff, 2002), cognitive psychologists contend that the empirical evidence for the psychological reality of generative grammar is patchy at best, while the mathematical apparatus it postulates is overly complicated (viz. “Mathematics versus Psychology”; Tomasello, 1998, p.ix). The drive for psychological realism should not, however, preclude a theory of language from being mathematically rigorous. Indeed, the theoretical framework envisaged by Langacker (1987), which calls for a reexamination of the classical generative stance and sees grammar as a potentially large collection of units of varying complexity, or constructions (Croft, 2001), does allow for computationally sophisticated modeling of acquisition and processing (Solan et al., 2003). The new computational model gives up the logicism of generative grammar in favor of information-theoretic learning of distributed construction patterns. The structure and the meaning of a sentence (which can be thought of as the proverbial elephant groped by the blind men) are thus represented by the chorus of responses of construction detectors, which can be further processed by methods that are being worked out for another cognitive domain with similar computational needs: vision (Edelman and Intrator, 2003). Recent empirical findings indicate that (1) langue, and not merely parole, is imperfect (Chipere, 2001), just as the other faculties of the mind are, (2) people can handle only very shallow true recursion or center embedding (MacDonald and Christiansen, 2002), and (3) language is more formulaic than creative (Wray, 2002). The newly emerging computational work shows also that (4) linguistic knowledge can be learned from scratch, and (5) reliance on constructions (parameterized phrases, or patterns of usage) rather than “rules” need not overload lexicon-like memory. This suggests that language is, after all, continuous with the rest of cognition, and that the new metaphor — representing structure by a fuzzy Chorus of Phrases — may in the long run help revert the grounds of the crumbling Lego castle of generative linguistics happily back to nature.
Reconfigurable 26GHz Liquid Metal Antenna Capable of Low Loss Continuous Beam Steering

For this first time this paper presents a concept for a high gain millimeter wave beam steerable antenna that supports continuous steering over a 360° scan range and promises to yield extremely low power losses. The concept does not require phase shifters which suffer from high insertion losses at millimeter wave frequencies or mechanical movement, in the conventional sense, which has the disadvantages of poor reliability and slow response times due to high inertia.
Red Blood Cell Classification: Deep Learning Architecture Versus Support Vector Machine

In medical field, the classification of red blood cells (RBCs) are used as an indicator to classify the type of abnormality presence in RBCs. The problem of classifying abnormal cells manually such as achantocyte, sickle cell, elliptocyte, tear drop and normal healthy cell under the microscope tend to give inaccurate result and errors. This paper proposed a method to classify abnormalities based on deformed shaped RBCs image by using SVM and Deep learning in comparison on the RBCs cell Classification. Classifying normal cells of RBCs indicate a healthy patient and Classifying achanthocyte, sickle cell, elliptocyte, teardrop cells indicate presence of disease. And is very important in medical field to detect and classify disease in early stage because it saves and protects human lives. The patients waiting time for blood test is longer because the time taken to generate the result of the blood test is more due to high demand and less equipment. This lead to comparison of the two classifiers in order to predict the one that will best perform on RBCs in order to achieved maximum accuracy for the classification. This study suggested that SVM classifier outperformed deep learning classifier because the SVM can classify the cells in all condition either small or large dataset while deep learning performs mainly on large dataset only.
Sentiment Detection of Comment Titles in Booking.com Using Probabilistic Latent Semantic Analysis

In a competitive and dynamic environment, many hotels compete to provide the best quality for customers. Hotel quality affects the brand image of a hotel marked from whether or not customers are satisfied. Booking.com website is chosen as the ideal data source as being able to utilize User Generated Content (UGC). UGC, serves as a data source ensuring the authenticity of data for customer reviews in Ponorogo, Indonesia. This experiment uses English text classification, to determine customer satisfaction and dissatisfaction based on the text they write on the title of customer testimony. The classification method used is PLSA and python programming language is used for preprocessing data. The data sampling is performed by data crawling using WebHarvy. The test results show that PLSA slightly outperforms previous research methods, namely LSA.
Probing on Classification Algorithms and Features of Brain Signals Suitable for Cancelable Biometric Authentication

The brain signal gives a uniqueness and an identity to a person. The capturing process of brain signals are called Electroencephalography. EEG measures vary according to the state of mind, the activity to which he or she is running, and his state of mind. This paper includes a study on feature extraction parameters and the classification algorithms for user authentication using the EEG. Here we calculated the classification scores for three classifiers named weighted KNN, fine Gaussian SVM and LDA. According to the results calculated, weighted KNN has better accuracy and precision value as compared to that of Fine Gaussian SVM and LDA. The accuracy and precision values of Weighted KNN is 91.7% and 97.09% respectively. Weighted KNN has less error rate than that of Fine Gaussian SVM and LDA. LDA and SVM together can achieve higher accuracy and higher precision with lesser error rate. So Weighted KNN can be used as a better classifier than Fine Gaussian SVM and LDA.
Complex Network Node Centrality Measurement Based on Multiple Attributes

Finding key nodes in complex networks plays an important role in improving the robustness of the network. In view of the limitations of traditional methods, a method of node centrality measurement based on multiple attributes (NCMMA) is proposed in this paper. Firstly, the centrality measurement of complex network nodes is formulated, the method of evaluating the accuracy of node centrality measurement is given. Secondly, the local characteristic indicator, global characteristic indicator and emergence characteristic indicator of complex network are defined, then, NCMMA is designed to synthesize these indicators. Finally, nodes ranking experiments and propagation experiments are performed on ARPA-NET(Advanced Research Project Agency NET), scale-free network and small world network, to compare NCMMA and the traditional methods. The results show that the proposed NCMMA is feasible and the key nodes obtained by the NCMMA have higher influence than key nodes obtained by traditional methods in the propagation experiments based on Susceptible-Infected(SI) epidemic model.
Economic Benefit and Improve of Stability in the Power System through the Ultra-High Speed Fault Clearing in Transmission Lines

Ultra-high speed fault clearing in transmission lines is a topic that has been developing for many years. The main objective for this work is to show improve of stability of the power system and show the economic benefits through an economic dispatch that is achieved through the increase of the maximum power transfer capacity in the transmission lines, that is, it increases the capacity limit for stability in transmission lines through ultra-high speed fault clearing. For the simulation was used an electrical system as an example implemented in the software DIgSILENT and MATLAB with different events and fault clearing times from 2 cycles to 6 cycles that will compare ultra-high speed fault clearing and conventional fault clearing. These benefits allows improving the economic dispatch.
Uplink Multiple Base Stations Diversity for UNB based IoT networks

Ultra-Narrow-Band has been a promising candidate for low-power long-distance transmissions in the context of IoT. Its radio access is random at time and frequency, and can degrade the network performance. We propose to apply signal combining and interference cancellation technologies across multiple base stations in UNB networks, in order to take advantage of their spatial diversity. We evaluate and compare the performance enhancement of each technology, compared to single BS case. These technologies exploiting multi-BS diversity are proved to be significantly beneficial in improving UNB networks’ scalability. We can gain until 28 times better performance with one iteration global SIC. We highlight that these results provide us a choice among the technologies according to the improvement needs and the implementation complexity.
Reduced carbon use efficiency and increased microbial turnover with soil warming

Global soil carbon (C) stocks are expected to decline with warming, and changes in microbial processes are key to this projection. However, warming responses of critical microbial parameters such as carbon use efficiency (CUE) and biomass turnover (rB) are not well understood. Here, we determine these parameters using a probabilistic inversion approach that integrates a microbial‐enzyme model with 22 years of carbon cycling measurements at Harvard Forest. We find that increasing temperature reduces CUE but increases rB, and that two decades of soil warming increases the temperature sensitivities of CUE and rB. These temperature sensitivities, which are derived from decades‐long field observations, contrast with values obtained from short‐term laboratory experiments. We also show that long‐term soil C flux and pool changes in response to warming are more dependent on the temperature sensitivity of CUE than that of rB. Using the inversion‐derived parameters, we project that chronic soil warming at Harvard Forest over six decades will result in soil C gain of <1.0% on average (1st and 3rd quartiles: 3.0% loss and 10.5% gain) in the surface mineral horizon. Our results demonstrate that estimates of temperature sensitivity of microbial CUE and rB can be obtained and evaluated rigorously by integrating multidecadal datasets. This approach can potentially be applied in broader spatiotemporal scales to improve long‐term projections of soil C feedbacks to climate warming.
Rapid TiO2 Nanotubes Formation in Aged Electrolyte and Their Application as Photocatalysts for Cr(VI) Reduction Under Visible Light

Heterogeneous photocatalysis based on semiconductor oxide is a promising technology for treating polluted industrial waste water. Cr(VI) is an example of toxic and mobile water pollutant originated mainly from industrial processes like steel industry and electroplating. The removal of Cr(VI) can be done by photoreduction on a surface of photocatalysts like TiO2 nanotubes (TNTs). Here we describe a fast-rate formation of highly ordered TNTs via two steps anodization processes in an aged electrolyte containing fluoride-ethylene glycol. First step anodization was done for template formation as well as for saturating the electrolyte with TiF62– and second anodization was done to grow TNTs. The template was formed by removing initial anodic layer through sonication. Anodization voltage was kept constant at 60 V. After an hour of anodization, 22.70 μm long TNTs were formed with the rate of ∼350 nm/min. The as-anodized TNTs were then annealed in N2 gas at different temperatures. The annealed TNTs were then used as photocatalyst for the photoreduction of Cr(VI) under sunlight in a parabolic trough reflector. The TNTs annealed at 450 °C in nitrogen show the best photoreduction activity with ∼80% Cr(VI) removal after 5 h exposure to sunlight. This is due to the presence of anatase TiO2 as well as a possibility of nitrogen insertion, which may have narrowed the band gap of the oxide.
Once in the Door: Gender, Tryouts, and the Initial Salaries of Managers

Although women pursue managerial credentials at nearly the same rate as men, gender disparities in wages exist because of the shortfall in wages women sustain relative to men at the onset of their careers. This article develops a tryout approach to test for the presence of demand-side contributions to initial wage inequality while also developing and testing theory on why it may be lessened through internships. Using detailed data on graduates from an elite management program from 2009–2010, our analyses reveal that internships are associated with the gap in men’s and women’s initial salaries. For men, there is no difference in salary offers from employers where an internship occurs versus one where an internship does not occur. However, women receive higher salaries from employers where an internship first takes place. This paper was accepted by Olav Sorenson, organizations.
Nonlinear analysis of EEG complexity in episode and remission phase of recurrent depression

Biomarkers of major depressive disorder (MDD), its phases and forms have long been sought. Objectives were to examine whether the complexity of EEG activity, measured by Higuchi's fractal dimension (HFD) and sample entropy (SampEn), differs between healthy subjects, patients in remission, and in episode phase of the recurrent depression and whether the changes are differentially distributed between hemispheres and cortical regions.
The Design and Analysis of a Two-Stage PV Converter with Quasi-Z Source Inverter

The single-phase photovoltaic (PV) systems are being widely researched due to several environmental and financial advantages. The PV converters are implemented either in single stage of two-stage topologies according to intermediate dc bus requirements. In the two-stage topologies, the recent researches are based on transformerless structures to eliminate bulky and lossy transformer isolation. Therefore, several methods including stray current grounding have been proposed in the literature to obtain isolation without the use of transformer. This paper proposes a two-stage PV converter with quasi Z-source (qZS) inverter in the second stage. The first stage of PV converter is comprised by a conventional boost converter that the maximum power point tracking (MPPT) controller is improved with integral regulator addition to incremental conductance algorithm. The MPPT efficiency of improved system is measured as 98.8% regarding to the designed controller. In addition to its efficiency, MPPT controller stabilized the intermediate dc bus voltage against the various irradiation values applied to PV plant. The qZS network is designed to ensure continuous conduction mode (CCM) operation considering shoot-through and non-shoot-through states. The switching control of H-bridge inverter is realized by the designed grid tracking controller that samples grid voltage as a reference input and decomposes to detect phase, frequency, and amplitude values. The power factor of inverter output is measured at 0.988 and total harmonic distortion (THD) ratios of current and voltage waveforms are at 1.71 %.
Assessment of Gait Parameters in Virtual Environment

Human gait is the result of complex interplay of the nervous, musculoskeletal and cardiorespiratory systems. Unimpaired cognition and executive control are needed for safe walking. Balance and gait problems may be precursors of falls, which are the leading cause of elderly injuries. This article concerns using the Corridor application, run on the multi-sensory virtual reality system (CAREN, Computer Assisted Rehabilitation ENvironment) to evaluate chosen gait parameters of elderly participants. The presented application influences the conditions of postural stability by skewing the image of space in different directions, especially side to side. This provoking fall prevention strategy can be described by set of changed gait parameters. Differences are observed in the hip and ankle joints, spatio-temporal parameters and the results that differed during the gait with dual-task. To better evaluate the gait asymmetry the arm swing asymmetry and step length symmetry indices were used.
Assessing the SNR Influence in the Estimation of the Mean Frequency of Lower Limbs sEMG Signals

The mean frequency of the power spectrum (MNF) is commonly used to describe the frequency content of an electromyographic (EMG) signal. The objective of this study is to determine the minimum/desirable signal to noise ratio (SNR) value to achieve a reliable measurement of the MNF in superficial EMG (sEMG) signals of lower limbs during gait. To this end, measurements of MNF and SNR were taken in nine muscles of 21 subjects, and recorded signals were contaminated with different noise levels. The minimum threshold of a desirable SNR was determined using the K-means algorithm. A lower bound of 5.51 dB was determined as the SNR value for sEMG acquisition, while 12.28 dB is the desirable SNR value for recording sEMG signals. The methodology presented throughout this paper helps in the determination of the minimum SNR value necessary to validate the sEMG acquisition process that can be used, for example, as a control signal for identifying motion intention in the development of control systems devoted for a lower limb exoskeleton.
Fault Prediction for Satellite Communication Equipment Based on Deep Neural Network

Aiming at the problem of fault prediction for satellite communication system, a prediction model based on deep learning is proposed in this paper. Firstly, the equipment parameters are summed up, and then two kinds of states covering normal and abnormal situations are determined. After feature learning, self-encoding network is used to obtain new features which can characterize the deep feature of the data. Then the tagged data extracted from monitoring equipment are applied to train the prediction classifier which is the combination of deep belief network and softmax classifier. The deep belief network is composed of limited Boltzmann machine as well as BP network. BP network is used for parameters adjustment. Finally, the effects of fault prediction including the performance of model and average prediction accuracy are tested through simulation.
An In-Network Aggregation Scheme for Erasure Coding Storage Systems in Data Centers

In the erasure coding storage system, when a storage node fails, it is necessary to extract data blocks from other remaining storage nodes to a new node and repair. This would lead to the incast problem at the new node. Up to now, the incast problem during the repair process has been solved relying on the path planning and resource allocation. Although these solutions improve the performance at some extent, they are still very resource-consuming. In this work, we show that the incast problem can be resolved economically via the in-network aggregation. Specifically, we assume that switches in data centers have certain data processing capabilities and can aggregate data flows. Taking the fat-tree data center as an example, we propose a set of in-network repair methods for erasure coding storage systems. The incast problem caused by repairing data blocks is solved naturally during the data transmission. Compared with prior methods, our approach not only avoids the overhead of extra path computing, but also significantly reduces the link cost of repairing data blocks while ensuring similar or faster repair speed.
Techno-Economic Feasibility Study on an Off-Grid Renewable Energy Microgrid for an Isolated Greenhouse in Romania

In the last decade small scale hybrid renewable energy microgrids have been considered a suitable option for energy access in isolated areas where grid extension is economically unfeasible. A techno-economic feasibility study on an off-grid renewable energy microgrid for a greenhouse in Oradea, Romania, is carried out in this paper in order to meet the electricity demand in a reliable, sustainable and cost-effective manner. The microgrid consists of a geothermal energy generator, a biomass generator, a photovoltaic generator and battery storage to supply a vegetable greenhouse. The microgrid works islanded. The capacity of each generator is optimized with regard to the needs of the load and the minimum net present cost of the microgrid. Finally, sensitivity analysis is performed to determine the dependency of the energy costs due to the variable price of the biomass resource, the increase in the load demand and the energy management dispatch strategy.
Simultaneous Visual Analysis of Multiple Software Hierarchies

We propose a tree visualization technique for comparison of structures and attributes across multiple hierarchies. Many software systems are structured hierarchically by design. For example, developers subdivide source code into libraries, modules, and functions. This design propagates to software configuration and business processes, rendering software hierarchies even more important. Often these structural elements are attributed with reference counts, code quality metrics, and the like. Throughout the entire software life cycle, these hierarchies are reviewed, integrated, debugged, and changed many times by different people so that the identity of a structural element and its attributes is not clearly traceable. We argue that pairwise comparison of similar trees is a tedious task due to the lack of overview, especially when applied to a large number of hierarchies. Therefore, we strive to visualize multiple similar trees as a whole by merging them into one supertree. To merge structures and combine attributes from different trees, we leverage the Jaccard similarity and solve a matching problem while keeping track of the origin of a structure element and its attributes. Our visualization approach allows users to inspect these supertrees using node-link diagrams and indented tree plots. The nodes in these plots depict aggregated attributes and, using word-sized line plots, detailed data. We demonstrate the usefulness of our method by exploring the evolution of software repositories and debugging data processing pipelines using provenance data.