Miracle-dz/UniFAR-FTUs huggingface dataset:
Adversarial Robustness via Runtime Masking and Cleansing

Deep neural networks are shown to be vulnerable to adversarial attacks. This motivates robust learning techniques, such as the adversarial training, whose goal is to learn a network that is robust against adversarial attacks. However, the sample complexity of robust learning can be signiﬁcantly larger than that of “standard” learning. In this paper, we propose improving the adversarial robustness of a network by leveraging the potentially large test data seen at runtime. We devise a new defense method, called runtime masking and cleansing (RMC), that adapts the network at run-time before making a prediction to dynamically mask network gradients and cleanse the model of the non-robust features inevitably learned during the training process due to the size limit of the training set. We conduct experiments on real-world datasets and the results demonstrate the effectiveness of RMC empirically.
Detecting Out-of-Distribution Examples with Gram Matrices

When presented with Out-of-Distribution (OOD) examples, deep neural networks yield conﬁdent, incorrect predictions; detecting OOD examples is challenging, and the potential risks are high. In this paper, we propose to detect OOD examples by identifying inconsistencies between activity patterns and predicted class. We ﬁnd that characterizing activity patterns by Gram matrices and identifying anomalies in Gram matrix values can yield high OOD detection rates. We identify anomalies in the Gram matrices by simply comparing each value with its respective range observed over the training data. Unlike many approaches, this can be used with any pre-trained softmax classiﬁer and neither requires access to OOD data for ﬁne-tuning hyperparameters, nor does it require OOD access for inferring parameters. We empirically demonstrate applicability across a variety of architectures and vision datasets and, for the important and surprisingly hard task of detecting far out-of-distribution examples, it generally performs better than or equal to state-of-the-art OOD detection methods (including those that do assume access to OOD examples).
A Comprehensive Study on Machine Learning Approaches with Big Data

: Big Data has altered the adjustments in the period of information stockpiling and its examination. Big Data Analytics is used to understand the information productivity that builds the extent of expectation and discoveries of hidden patterns. The age of information ought to be successfully figured out how to enhance the computational efficiencies of the frameworks. Machine Learning (ML) has turned into a remarkable computational tool for read, examine, giving bits of knowledge and choices. Information expectations, presumptions and choices are commonly founded on information variety that is tending to as a testing issue particularly with expanding levels and information complexity. In customary machine learning ideas, the information size and its structure have ended up being incapable. Information qualities, for example, volume and veracity challenge the idea of machine learning. In this work, the difficulties of machine learning are featured with Big Data, and all together interrogate its measurements velocity, veracity, and volume. Furthermore, this paper likewise centers around developing machine learning (EML) systems alongside different answers for the experts helping towards the better arrangement with fitting use-case demonstrating. The EML approaches are considered to feature the sagacious qualities of machine learning by methods for its execution with Big Data.
A Laterally Averaged Nonhydrostatic Ocean Model

A laterally averaged nonhydrostatic model for stratified flow in dynamically narrow domains is presented. Averaging laterally yields the computational efficiency of a two-dimensional model, while retaining some effects associated with variable domain width, such as flow acceleration through contracting channels. The model may be run in both hydrostatic and nonhydrostatic modes, and in the latter case it converges rapidly if the flow is approximately hydrostatic. The model’s strengths and weaknesses are illustrated with a series of test cases of increasing complexity. Side-by-side comparisons with laboratory observations show the ability of the model to simulate the structures of nonhydrostatic flows, including shear instabilities and overturning internal waves, with discrepancies becoming apparent mainly for transition to three-dimensional turbulence. Similar results are demonstrated in an application to the stratified sill flow in Knight Inlet, British Columbia. The model reproduces nonhydrostatic features thought to be dynamically important to this system, including the generation of largeamplitude lee waves and shear instabilities.
Intellectual capital of the European Union 2008

In 2004 the report Intellectual capital of the European Union was published (Andriessen and Stam, 2004). This report provided insight in the value of the intellectual capital of the 15 countries of the European Union, in relationship to the goals set by the European Council in March 2000. Since this report, the EU grew from 15 to 27 countries and the Lisbon goals were reformulated in 2005. The aim of this paper is to repeat the measurement of the intellectual capital (IC) of the enlarged European Union (EU) in relationship to the new Lisbon goals. In order to become the most competitive and dynamic knowledge-based economy, the EU decided to focus on “delivering stronger, lasting growth and creating more and better jobs” (CEC, 2005d, p.7). In this paper we translate this overall goal in 38 indicators. As the data was not available for all the new member states, we decided to limit our paper to the so-called EU-19. Based on our measurements we conclude that the EU-19 is still behind Japan and far behind the USA, however the EU is catching up as both Japan and the USA have considerably lower growth figures than the EU-19. From an IC perspective, the EU is geographically divided. The Nordic countries are still the best performing countries. The southern European countries and the new member states stay behind. However, as the new member states invest more in their IC, it might be expected that their positions will improve in the future. The aim of our paper was to measure the progress of the Lisbon Agenda for growth and jobs. Based on our measurements we conclude that the EU-19 is successful in terms of creating more and better jobs, but not successful in terms of delivering stronger, lasting growth.
ALGEBRA STRUCTURE SENSE IN A WEB ENVIRONMENT: DESIGN AND TESTING OF THE EXPRESSION MACHINE

The article reports on outcomes from a study that aims to investigate the role of affordances, level-up and feedback in the web environment Expression Machine in developing the algebra structure sense of tertiary education students. Algebraic substitution is the main procedure involved in the way the software works. Its design and testing methodology are based on the Human - Computer Interaction aspect of Activity Theory. From this approach, the study redefines the notion of algebra structure sense formulated in previous works. Results from the experimental sessions show that some features of the environment favor the development of sutudents structure sense, specifically when they deal with substitution and factorization tasks. At the same time, it was possible to identify aspects to be improved, for instance, adding categories of tasks with increasing structural complexity and less visually salient, which may require a greater cognitive demand from students.
AN ARTIFICIAL NEURAL NETWORK METHOD TO FORECAST ELECTRIC LOAD Acad

The paper presents a system using an artificial neural network (ANN) to efficiently learn the shape of a non stationary sequence. The system has the potential to learn, classify and predict any stationary or non stationary signals, in particular the chronological load curves of power systems, at various time scales. A large variety of classification and forecasting applications to non stationary sequences can be efficiently solved by using this adaptive retraining technique combined with the two step system architecture.
Natural Language Names Recognition Method − Fault Tolerant to the Most Common Mistypings

A heuristic method is proposed in the paper − for a fault-tolerant key-access to database records of natural (and/or artificial) language texts using an extra-dictionary of keys. The “approximate-string-matching” problem known from the practice of information retrieval systems can be resolved by the method, namely the problem for obtaining the proper key being a character string of substantial length that the most common typist errors should to be expected (i.e. character substitution, omission, insertion, neighbouring reversal, etc.). A new organization of dictionary search has been proposed through a direct construction of hypotheses for words and texts under recognition, based on a preliminary knowledge about statistics of the most probable input errors. The “similarity of strings” relation is build following the principle of “clustering without metrics” through a heuristic model of probability kind over the essential sets of strings only, in this way improving the effectiveness of the method in comparison with known methods based on metrics (most frequently versions of the "Damerau-Lewenstein" metric). Finally, a comparative analysis of results is briefly reported for two experiment systems: (i) a geographic information system (about 3500 objects for search) and (ii) an English-Bulgarian computer dictionary (of about 22000 words and phrases). Key-words: Natural language text processing, Approximate string matching, Most probable typist errors correction, Database access, Recognition/classification improvement by post-processing using a dictionary.
Photonic Crystal Coupled to N-V Center in Diamond

In this work we aim to exploit one of the most studied defect color centers in diamond , the negatively charged nitrogen vacancy (NV−) color center, a three level system which emits a single photon at a wavelength of 637nm providing a possible deterministic single photon emitter very useful for quantum computing applications. Moreover the possibility of placing aNV−in a photonic crystal cavity will enhance the coupling between photons andNV−center. This could also allow us to address the ground state of the NV−center, whose spin, could be used as qubit. It is also remarkable to notice that for quantum computing purposes it is very useful to increase the light collection from the NV−centers, and in order to do that we performed a study of another structure, the solid immersion lens, which consists of an hemisphere whose center is at the position of an emitter, in this case the NV−center, increasing the collection of the light from it. In order to create these structures we used a method called focused ion beam which allowed us to etch directly into the diamond many different kinds of structures. In order to allow an interaction between these structures and the NV−centers we need to have a method to locate the NV−center precisely under the etched structures. We developed a new technique (Marseglia et al. (2011)) where we show how to mark a single NV−center and how to etch a desired structure over it on demand. This technique gave very good results allowing us to etch a solid immersion lens onto a NV−previously located and characterized, increasing the light collection from the NV−of a factor of 8×.
Convex Calibrated Surrogates for the Multi-Label F-Measure

The F-measure is a widely used performance measure for multi-label classification, where multiple labels can be active in an instance simultaneously (e.g. in image tagging, multiple tags can be active in any image). In particular, the F-measure explicitly balances recall (fraction of active labels predicted to be active) and precision (fraction of labels predicted to be active that are actually so), both of which are important in evaluating the overall performance of a multi-label classifier. As with most discrete prediction problems, however, directly optimizing the F-measure is computationally hard. In this paper, we explore the question of designing convex surrogate losses that are calibrated for the F-measure - specifically, that have the property that minimizing the surrogate loss yields (in the limit of sufficient data) a Bayes optimal multi-label classifier for the F-measure. We show that the F-measure for an s-label problem, when viewed as a 2 s × 2 s loss matrix, has rank at most s 2 + 1, and apply a result of Ramaswamy et al. (2014) to design a family of convex calibrated surrogates for the F-measure. The resulting surrogate risk minimization algorithms can be viewed as decomposing the multi-label F-measure learning problem into s 2 + 1 binary class probability estimation problems. We also provide a quantitative regret transfer bound for our surrogates, which allows any regret guarantees for the binary problems to be transferred to regret guarantees for the overall F-measure problem, and discuss a connection with the algorithm of Dembczynski et al. (2013). Our experiments confirm our theoretical findings.
Production of peptides and amino acids from microbial biomass in food and feed industries: biotechnological aspects

Introduction. Microbial biomass is a popular source of food ingredients and feed additives. Its high use has made it focus of many relevant studies. Yeast and fungal biomasses proved to be useful substrates that improve the quality and biological value of functional products. They differ in the content and composition of proteins and polysaccharides. The present research dealt with the enzymatic decomposition of proteins found in a novel fungal and yeast biomass. The research objective was to describe the peptide and amino acid composition of their enzymatic hydrolysates. Study objects and methods. The research featured a new fungal and yeast biomass mix. Aspergillus oryzae is a mycelial fungus and a popular industrial producer of hydrolytic enzymes in food industry. As for the yeast, it was the Saсcharomyces cerevisiae strain, which is often used in baking. Results and discussion. The total content of identified amino acids in the fungal and yeast biomass was 306.0 mg/g, which was 1.5 times higher than in the fungal biomass alone. The biomass mix demonstrated a higher biological value of proteins than the yeast biomass. A set of experiments made it possible to compile a scheme for the biocatalytic destruction of polymers in the fungal and yeast biomass under the effect of fungal intracellular and endogenous enzymes. The article also contains a thorough description of the obtained enzymatic hydrolysates with various fractional compositions of peptides and free amino acids. Peptides with the molecular weight in the range of up to 29.0 kDa decreased by 2.1 times after 5 h of hydrolysis and by 10.7 times after 18 h. The designed conditions doubled the release of amino acids and increased the content of low-molecular-weight peptides up to 75.3%. Conclusion. The research provided a new algorithm for the biocatalytic conversion of microbial biomass. Regulating the conditions of enzymatic hydrolysis made it possible to obtain enzymatic hydrolysates with a desired degree of protein degradation. They could serve as peptides and amino acids in functional food and feed products.
A Two-Parameter Power Garima Distribution with Properties and Applications

Shanker [1] has discussed some of its statistical properties including shapes for varying values of parameter, moments, skewness, kurtosis, hazard rate function, mean residual life function, stochastic ordering, mean deviations, order statistics, Bonferroni and Lorenz curves, entropy measure and stressstrength reliability. The condition under which Garima distribution is over-dispersed, equi-dispersed, and under-dispersed has been also discussed along other one parameter lifetime distributions. Maximum likelihood estimation and method of moments have been discussed for estimating its parameter. An application of Garima distribution for modeling life time data from behavioral science has been also discussed in Shanker [1]. Shanker [2] has also introduced discrete Poisson-Garima distribution and discussed its statistical properties, estimation of parameter and applications. Shanker & Shukla [3,4] have studied size-biased Poisson-Garima distribution and Zero-truncated Poisson-Garima distribution.
Evolutionary Topology Search for Tensor Network Decomposition

tent variable model (Anandkumar et al., 2014), data restora-Tensor network (TN) decomposition is a promising framework to represent extremely high-dimensional problems with few parameters. However, it is challenging to search the (near-)optimal topological structures for TN decomposition, since the number of candidate solutions exponentially grows with increasing the order of a tensor. In this paper, we claim that the issue can be practically tackled by evolutionary algorithms in an affordable manner. We encode the complex topological structures into binary strings, and develop a simple genetic meta-algorithm to search the optimal topology on Hamming space. The experimental results by both synthetic and real-world data demonstrate that our method can effectively discover the ground-truth topology or even better structures with a small number of generations, and signiﬁcantly boost the representational power of TN decomposition compared with well-known tensor-train (TT) or tensor-ring (TR) models. Our code is available at https://github.com/ minogame/icml2020-TNGA .
3 Conditional Adversarial Domain Adaptation

Adversarial learning has been embedded into deep networks to learn disentangled and transferable representations for domain adaptation. Existing adversarial domain adaptation methods may struggle to align different domains of multimodal distributions that are native in classification problems. In this paper, we present conditional adversarial domain adaptation, a principled framework that conditions the adversarial adaptation models on discriminative information conveyed in the classifier predictions. Conditional domain adversarial networks (CDANs) are designed with two novel conditioning strategies: multilinear conditioning that captures the cross-covariance between feature representations and classifier predictions to improve the discriminability, and entropy conditioning that controls the uncertainty of classifier predictions to guarantee the transferability. Experiments testify that the proposed approach exceeds the state-of-the-art results on five benchmark datasets.
Feasibility of 3-Dimensional Visual Guides for Preparing Pediatric Zirconia Crowns: An In Vitro Study

This study evaluated the feasibility of a tooth preparation guide for prefabricated zirconia crowns (PZCs). Three-dimensional surface data for PZCs of the left maxillary primary first molar and left mandibular primary second molar were obtained using a model scanner. The tooth preparation data were digitally designed to harmonize with the adjacent teeth on the mixed dentition model and visualized using a color-coded map, which presents the required amount of tooth reduction. Twenty participants were recruited for preparing teeth with and without using the tooth preparation guide. The following three parameters were evaluated: tooth preparation time, harmony score, and amount of tooth reduction. The preparation time when using the guide was significantly reduced (p < 0.05), and a significant difference was observed in the harmony scores for the maxillary primary first molar preparation. Furthermore, the amount of tooth reduction was significantly different for both maxillary and mandibular primary molars (p < 0.05) in terms of the occlusal distal surface and buccal line angle in the maxillary primary first molars, and the smooth surfaces, proximal surfaces, and mesial line angles in the mandibular primary second molars. Thus, the results suggest that a tooth preparation guide could facilitate better tooth preparation for PZCs.
WHAT DETERMINES UPPER SECONDARY SCHOOL PARTICIPATION? - INTERGENERATIONAL EFFECTS OF EDUCATION OUTCOMES IN ALBANIA

Educational attainment is considered an intergenerational transmission mechanism. The link between schooling of children and their parents could be due to unobserved inherited characteristics, and/or, through the additional household income associated with higher levels of education or parental support. We empirically observe the choice of young Albanians 15 to 18 years old whether or not to enrol in school through a Probit model using cross section data from LSMS 2002. A number of policy issues are addressed. First, we can identify which teenagers are more likely to enter post-compulsory schooling. Second, we can examine to what extent parents' background affect decisions of their children to enter upper secondary education, and what is the role of the schooling system in reducing or magnifying such relationships. Drawing on these findings, our analysis concludes with the development of policy recommendations to target the movement towards higher participation in upper secondary school.
Unpacking Knowledge Sharing in Universities through Critical Lens

Knowledge is commonly regarded as the most strategic asset in any organization. Higher education institutions (HEIs) are generally considered as knowledge intensive organizations engaged in producing, promoting and sharing knowledge. Managing knowledge flows in knowledge sharing (KS) processes is vital for universities to respond to the challenges of the dynamic internal and external environments in achieving their goals. Hence, a critical review of literature in knowledge sharing is presented to provide alternative and critical lens of deeply understanding the needs and complexities of promoting and innovating knowledge sharing practices and to consider for future studies. Keywords—academics; knowledge, knowledge sharing; university
15 Generalized Low-Rank Optimization for Ultra-dense Fog-RANs

Expectations for new wireless networks have become higher since mobile data has grown exponentially and more diverse user services have emerged. Intensive deployment of network infrastructure can alleviate this pressure from the network architecture level and make it still competitive. For the improvement of total energy efﬁciency and network capacity, it is vital to deploy lots of radio access nodes that are equipped with computing and storage ability, and this is also beneﬁcial for low-latency services when providing access for many moving equipments. The beneﬁts of network densiﬁcation can be exploited using the emerging fog radio access network (Fog-RAN) architecture through placing the computation and storage resources in the network edge. However, such great promises come with arduous scientiﬁc problems. It is a must to make some innovative work for the design of such complicated networks considering kinds of types resources. In this chapter, we will develop a generalized low-rank optimization model for performance enhancements in ultra-dense Fog-RANs, supported by various motivating design objectives, including mobile edge caching, wireless distributed computing