Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptive In-network Collaborative Caching for Enhanced Ensemble Deep Learning at Edge

To enhance the quality and speed of data processing and protect the privacy and security of the data, edge computing has been extensively applied to support data-intensive intelligent processing services at edge. Among these data-intensive services, ensemble learning-based services can, in natural, leverage the distributed computation and storage resources at edge devices to achieve efficient data collection, processing, and analysis. Collaborative caching has been applied in edge computing to support services close to the data source, in order to take the limited resources at edge devices to support high-performance ensemble learning solutions. To achieve this goal, we propose an adaptive in-network collaborative caching scheme for ensemble learning at edge. First, an efficient data representation structure is proposed to record cached data among different nodes. In addition, we design a collaboration scheme to facilitate edge nodes to cache valuable data for local ensemble learning, by scheduling local caching according to a summarization of data representations from different edge nodes. Our extensive simulations demonstrate the high performance of the proposed collaborative caching scheme, which significantly reduces the learning latency and the transmission overhead.
Personal trainers’ health advice in the fitness gym space from a gender perspective

ABSTRACT Purpose: This study aimed to describe and problematize the advice on exercise and diet that personal trainers (PTs) provide to their clients from a gender perspective.Method: The present study had an explorative design, and the interviews were analysed using an interpretative qualitative approach. Seven focus group discussions were conducted with 19 PTs (aged 23–47 years). Results: The findings indicated that the PTs had a gender-neutral health advice approach to both women and men, guiding them towards a relaxed attitude to exercise and diet, prioritizing and rationalizing their exercise and diet and eating a natural diet. PTs also had a gendered health advice approach as regards women who showed unhealthy exercise and eating behaviours; advising them to eat more and exercise less, to focus on performance rather than appearance and to avoid heavy weightlifting. Some PTs acted evasively and did not give advice to men.Conclusions: Both approaches include advice that reflect health as control and health as release. From a gender perspective, PTs health advice both challenged and reproduced the stereotypical male norm in the fitness gym space. However, PTs gendered health advice may create different opportunities for men and women to promote their health and well-being in the fitness gym.
MULTIOBJECTIVE OPTIMIZATION AS ALTERNATIVE SEARCH OF SUSTAINABLE COMPOSITE STRUCTURES

More attention is being paid these days to sustainable design. The recent increases in structural material prices is further evidence that global consumption will raise both awareness and need to design for the efficient and rational use of materials. From a structural engineering point of view the biggest impact can be made by focusing on optimizing the structural systems to reduce the material requirements of a project supported by sustainability concepts. The integration of the materials selection process into optimization must be seen as tool to reach the sustainability of composite structures being the scope of the proposed multiobjective design approach.
Char2Subword: Extending the Subword Embedding Space from Pre-trained Models Using Robust Character Compositionality

Byte-pair encoding (BPE) is a ubiquitous algorithm in the subword tokenization process of language models. BPE provides multiple benefits, such as handling the out-of-vocabulary problem and reducing vocabulary sparsity. However, this process is defined from the pre-training data statistics, making the tokenization on different domains susceptible to infrequent spelling sequences (e.g., misspellings as in social media or character-level adversarial attacks). On the other hand, pure character-level models, though robust to misspellings, often lead to unreasonably large sequence lengths and make it harder for the model to learn meaningful contiguous characters. To alleviate these challenges, we propose a character-based subword transformer module (char2subword) that learns the subword embedding table in pre-trained models like BERT. Our char2subword module builds representations from characters out of the subword vocabulary, and it can be used as a drop-in replacement of the subword embedding table. The module is robust to character-level alterations such as misspellings, word inflection, casing, and punctuation. We integrate it further with BERT through pre-training while keeping BERT transformer parameters fixed. We show our method's effectiveness by outperforming a vanilla multilingual BERT on the linguistic code-switching evaluation (LinCE) benchmark.
New hybrid control of a 2 DoF Robot Arm

Robot arms have been using in different systems, which the control of designed in desired trajectory is the main task. Also, it is anticipated that while in operation the developed 2DoF robot arm will be constantly encountered with noises such as friction forces. A new integral sliding mode control (NISMC) is therefore being introduced to suppress noise due to its robustness. Then, New hybrid control system (NHISMC) is proposed, which constantly calculates an error value and applies a correction value to the system. This will enhance trajectory and minimize tracking error. In comparison with two other controllers, such as traditional sliding mode control (SMC) and NISMC, experimental results confirmed the efficacy of the proposed control method.
Unsupervised Dense Shape Correspondence using Heat Kernels

In this work, we propose an unsupervised method for learning dense correspondences between shapes using a recent deep functional map framework. Instead of depending on ground-truth correspondences or the computationally expensive geodesic distances, we use heat kernels. These can be computed quickly during training as the supervisor signal. Moreover, we propose a curriculum learning strategy using different heat diffusion times which provide different levels of difficulty during optimization without any sampling mechanism or hard example mining. We present the results of our method on different benchmarks which have various challenges like partiality, topological noise and different connectivity.
CoWaBoo: A Descriptive Protocol of Learning Driven Applications

— Algorithms, data, services seem to create a net of semantic stability for users to consume information but come with concrete disadvantages regarding the way we understand, discuss and teach them. Social bookmarking applications are no exception to this, as they follow a similarly opaque way to organize and publish data. This article will examine the possibility to shift from predetermined results to open and descriptive protocols and applications that revisits fundamental web user activities such as search, classification, group formation and valorization of participation. This approach combines both a data handling protocol (CoWaBoo) and an application (collective observatories) that serve as wider concept and practice of application use and development. Our initial results from 2015 and 2016 university group course works, contributes to shifting our attention from how things end up, to how things become , an important competence in the field of ICT training and education.
Identification of orientation of galaxies in the Galaxy Zoo dataset using spectral clustering.

This work identifies the orientation of galaxies in the Galaxy zoo data set. The images are first identified by the number of principal components required to represent 99 percent of the variance of the image. K means clustering is used to separate the galaxies on the basis of their central brightness along with outlier separation. Spectral clustering is then used to separate circularly symmetric galaxies and the remaining galaxies are identified according to their orientation as flat , left and right on the basis of the alignment of the main axis. It is also seen that spectral clustering fails to make this classification when the galaxy images are noisy and works only when applied on a smaller subset of the total number of images in the Galaxy zoo data set. This method also fails in the presence of multiple galaxies in the image, considering them as an individual entity.
Conditional beta and uncertainty factor in the cryptocurrency pricing model

This research is to assess cryptocurrencies with the conditional beta, compared with prior studies based on unconditional beta or fixed beta. It is a new approach to building a pricing model for cryptocurrencies. Therefore, we expect that the use of conditional beta will increase the explanatory ability of factors in previous pricing models. Besides, this research is also a pioneer in placing the uncertainty factor in the cryptocurrency pricing model. Earlier studies on cryptocurrency pricing have ignored this factor. However, it is a significant factor in the valuation of cryptocurrencies because uncertainty leads to investor sentiment and affects prices.
The Discovery of Tunable Universality Class in Superconducting $\beta$-W Thin Films.

The interplay between quenched disorder and critical behavior in quantum phase transitions is conceptually fascinating and of fundamental importance for understanding phase transitions. However, it is still unclear whether or not the quenched disorder influences the universality class of quantum phase transitions. More crucially, the absence of superconducting-metal transitions under in-plane magnetic fields in 2D superconductors imposes constraints on the universality of quantum criticality. Here, we discover the tunable universality class of superconductor-metal transition by changing the disorder strength in $\beta$-W films with varying thickness. The finite-size scaling uncovers the switch of universality class: quantum Griffiths singularity to multiple quantum criticality at a critical thickness of $t_{c \perp 1}\sim 8 nm$ and then from multiple quantum criticality to single criticality at $t_{c\perp 2}\sim 16 nm$. Moreover, the superconducting-metal transition is observed for the first time under in-plane magnetic fields and the universality class is changed at $t_{c \parallel }\sim 8 nm$. The discovery of tunable universality class under both out-of-plane and in-plane magnetic fields provides broad information for the disorder effect on superconducting-metal transitions and quantum criticality.
Interpreting convolutional networks trained on textual data

There have been many advances in the artificial intelligence field due to the emergence of deep learning. In almost all sub-fields, artificial neural networks have reached or exceeded human-level performance. However, most of the models are not interpretable. As a result, it is hard to trust their decisions, especially in life and death scenarios. In recent years, there has been a movement toward creating explainable artificial intelligence, but most work to date has concentrated on image processing models, as it is easier for humans to perceive visual patterns. There has been little work in other fields like natural language processing. In this paper, we train a convolutional model on textual data and analyze the global logic of the model by studying its filter values. In the end, we find the most important words in our corpus to our models logic and remove the rest (95%). New models trained on just the 5% most important words can achieve the same performance as the original model while reducing training time by more than half. Approaches such as this will help us to understand NLP models, explain their decisions according to their word choices, and improve them by finding blind spots and biases.
Review on Room Layout Estimation from a Single Image

Goal of room layout estimation is to predict the spatial layout of a room from a monocular image. It aims at finding the wall-floor, wall-wall, wall-ceiling boundaries from a single room image. The room layout can provide a better understanding of the 3D scenes. The room layout can be integrated into mixed reality games to supply a far better immersiveness experience, or utilized in other related augmented reality applications such room redecoration. Various studies have been introduced for room layout estimation. This paper review several works which deal with room layout estimation. Keywords—Room layout estimation, scene understanding, deep
Restrained Generative Adversarial Network against Overfitting in Numeric Data Augmentation

In recent studies, Generative Adversarial Network (GAN) is one of the popular schemes to augment the image dataset. However, in our study we find the generator G in the GAN fails to generate numerical data in lower-dimensional spaces, and we address overfitting in the generation. By analyzing the Directed Graphical Model (DGM), we propose a theoretical restraint, independence on the loss function, to suppress the overfitting. Practically, as the Statically Restrained GAN (SRGAN) and Dynamically Restrained GAN (DRGAN), two frameworks are proposed to employ the theoretical restraint to the network structure. In the static structure, we predefined a pair of particular network topologies of G and D as the restraint, and quantify such restraint by the interpretable metric Similarity of the Restraint (SR). While for DRGAN we design an adjustable dropout module for the restraint function. In the widely carried out 20 group experiments, on four public numerical class imbalance datasets and five classifiers, the static and dynamic methods together produce the best augmentation results of 19 from 20; and both two methods simultaneously generate 14 of 20 groups of the top-2 best, proving the effectiveness and feasibility of the theoretical restraints.
Variance-Reduced Off-Policy TDC Learning: Non-Asymptotic Convergence Analysis

Variance reduction techniques have been successfully applied to temporal-difference (TD) learning and help to improve the sample complexity in policy evaluation. However, the existing work applied variance reduction to either the less popular one time-scale TD algorithm or the two time-scale GTD algorithm but with a finite number of i.i. d. \ samples, and both algorithms apply to only the on-policy setting. In this work, we develop a variance reduction scheme for the two time-scale TDC algorithm in the off-policy setting and analyze its non-asymptotic convergence rate over both i.i. d. \ and Markovian samples. In the i.i. d. \ setting, our algorithm achieves a sample complexity $O(\epsilon^{-\frac{3}{5}} \log{\epsilon}^{-1})$ that is lower than the state-of-the-art result $O(\epsilon^{-1} \log {\epsilon}^{-1})$. In the Markovian setting, our algorithm achieves the state-of-the-art sample complexity $O(\epsilon^{-1} \log {\epsilon}^{-1})$ that is near-optimal. Experiments demonstrate that the proposed variance-reduced TDC achieves a smaller asymptotic convergence error than both the conventional TDC and the variance-reduced TD.
Low-Rank Matrix Recovery with Scaled Subgradient Methods: Fast and Robust Convergence Without the Condition Number

Many problems in data science can be treated as estimating a low-rank matrix from highly incomplete, sometimes even corrupted, observations. One popular approach is to resort to matrix factorization, where the low-rank matrix factors are optimized via first-order methods over a smooth loss function, such as the residual sum of squares. While tremendous progresses have been made in recent years, the natural smooth formulation suffers from two sources of ill-conditioning, where the iteration complexity of gradient descent scales poorly both with the dimension as well as the condition number of the low-rank matrix. Moreover, the smooth formulation is not robust to corruptions in the measurements. In this paper, we propose scaled subgradient methods to minimize a family of nonsmooth and nonconvex formulations – in particular, the residual sum of absolute errors – which is guaranteed to converge at a fast rate that is almost dimension-free and independent of the condition number, even in the presence of corruptions. We illustrate the effectiveness of our approach when the observation operator satisfies certain mixed-norm restricted isometry properties, and derive state-of-the-art performance guarantees for a variety of problems such as robust low-rank matrix sensing and quadratic sampling.
CoCo: Controllable Counterfactuals for Evaluating Dialogue State Trackers

Dialogue state trackers have made significant progress on benchmark datasets, but their generalization capability to novel and realistic scenarios beyond the held-out conversations is less understood. We propose controllable counterfactuals (CoCo) to bridge this gap and evaluate dialogue state tracking (DST) models on novel scenarios, i.e., would the system successfully tackle the request if the user responded differently but still consistently with the dialogue flow? CoCo leverages turn-level belief states as counterfactual conditionals to produce novel conversation scenarios in two steps: (i) counterfactual goal generation at turn-level by dropping and adding slots followed by replacing slot values, (ii) counterfactual conversation generation that is conditioned on (i) and consistent with the dialogue flow. Evaluating state-of-the-art DST models on MultiWOZ dataset with CoCo-generated counterfactuals results in a significant performance drop of up to 30.8% (from 49.4% to 18.6%) in absolute joint goal accuracy. In comparison, widely used techniques like paraphrasing only affect the accuracy by at most 2%. Human evaluations show that CoCo-generated conversations perfectly reflect the underlying user goal with more than 95% accuracy and are as human-like as the original conversations, further strengthening its reliability and promise to be adopted as part of the robustness evaluation of DST models.
Context-aware Decoder for Neural Machine Translation using a Target-side Document-Level Language Model

Although many end-to-end context-aware neural machine translation models have been proposed to incorporate inter-sentential contexts in translation, these models can be trained only in domains where parallel documents with sentential alignments exist. We therefore present a simple method to perform context-aware decoding with any pre-trained sentence-level translation model by using a document-level language model. Our context-aware decoder is built upon sentence-level parallel data and target-side document-level monolingual data. From a theoretical viewpoint, our core contribution is the novel representation of contextual information using point-wise mutual information between context and the current sentence. We demonstrate the effectiveness of our method on English to Russian translation, by evaluating with BLEU and contrastive tests for context-aware translation.
An extreme particle accelerator in the Galactic plane: HESS J1826−130

The unidentified very-high-energy (VHE;E> 0.1 TeV)γ-ray source, HESS J1826−130, was discovered with the High Energy Stereoscopic System (HESS) in the Galactic plane. The analysis of 215 h of HESS data has revealed a steadyγ-ray flux from HESS J1826−130, which appears extended with a half-width of 0.21° ± 0.02stat° ± 0.05sys°. The source spectrum is best fit with either a power-law function with a spectral index Γ = 1.78 ± 0.10stat± 0.20sysand an exponential cut-off at 15.2−3.2+5.5TeV, or a broken power-law with Γ1= 1.96 ± 0.06stat± 0.20sys, Γ2= 3.59 ± 0.69stat± 0.20sysfor energies below and aboveEbr= 11.2 ± 2.7 TeV, respectively. The VHE flux from HESS J1826−130 is contaminated by the extended emission of the bright, nearby pulsar wind nebula, HESS J1825−137, particularly at the low end of the energy spectrum. Leptonic scenarios for the origin of HESS J1826−130 VHE emission related to PSR J1826−1256 are confronted by our spectral and morphological analysis. In a hadronic framework, taking into account the properties of dense gas regions surrounding HESS J1826−130, the source spectrum would imply an astrophysical object capable of accelerating the parent particle population up to ≳200 TeV. Our results are also discussed in a multiwavelength context, accounting for both the presence of nearby supernova remnants, molecular clouds, and counterparts detected in radio, X-rays, and TeV energies.
Synthetic Data Generators - Sequential and Private

We study the sample complexity of private synthetic data generation over an unbounded sized class of statistical queries, and show that any class that is privately proper PAC learnable admits a private synthetic data generator (perhaps non-efﬁcient). Previous work on synthetic data generators focused on the case that the query class D is ﬁnite and obtained sample complexity bounds that scale logarithmically with the size |D| . Here we construct a private synthetic data generator whose sample complexity is independent of the domain size
Comparative analysis of sequential community detection algorithms based on internal and external quality measure

Abstract Community detection is a dominant research field in social media mining. Numerous algorithms have been developed to detect community structure in network. Each of them focuses on different characteristic of data and generates different communities for same dataset. So cluster validation or comparative analysis of these algorithms is required to compare the efficiency of these algorithms. There are two known ways for cluster validation: Internal Quality Measure (IQM) and External Quality Measure (EQM). IQM evaluates the cluster without reference to external information while EQM requires already known clusters i.e. ground truth for comparison. Most of the researchers have compared Community Detection algorithms on the basis of either IQM or EQM but not both. In this paper, we have tried to compare nine existing sequential community detection algorithms on the basis of both IQM and EQM and we observed that overall performance of Louvain, Fastgreedy and Walktrap algorithm is more efficient than other algorithms. From this paper, researchers working in this area can get information about major sequential community detection algorithms and their efficiency on small real world datasets. They can incorporate the findings to develop more efficient algorithms.
Comparative study of stirred and fluidized tank reactor for hydroxyl-kojic acid derivatives synthesis and their biological activities

Abstract Background Study on the synthesis of kojic acid derivatives (KADs) in solvent-free system using scalable reactors and their biological activities is still lacking. Methods In this study, two types of KADs, were synthesized using saturated-fatty acid [lauric acid (LA)] and unsaturated-fatty acid [oleic acid (OA)] in stirred tank reactor (STR) and fluidized tank reactor (FTR). The yield and biological activities of the synthesized KADs were evaluated and compared. Results The highest yield of KADs (42.95%) was obtained in the synthesis using OA, with molar ratio of 1:1, enzyme loading of 5% (w/v), temperature of 70°C, using immobilized lipase N435 in STR. However, FTR may provide biocatalyst protection and reusability with reduced loss of KADs yield up to three cycles. In antioxidant assay, the hydroxyl-unsaturated-fatty acid of kojic acid (HUFA-KA) showed better activity as compared to hydroxyl-saturated-fatty acid of kojic acid (HSFA-KA) at concentrations ranging from 125 to 2000 μg/mL. In contrast, HSFA-KA showed better cytotoxicity effect against G361 melanoma cell as compared to HUFA-KA. Conclusion The yield of KADs obtained in STR was higher than that obtained in FTR. HUFA-KA could be used as potential lipophilic antioxidant while HSFA-KA has the potential to be used to treat melanoma skin disorder.