Miracle-dz/UniFAR-FTUs huggingface dataset:
KNNENS: A k-Nearest Neighbor Ensemble-Based Method for Incremental Learning Under Data Stream With Emerging New Classes

In this brief, we investigate the problem of incremental learning under data stream with emerging new classes (SENC). In the literature, existing approaches encounter the following problems: 1) yielding high false positive for the new class; i) having long prediction time; and 3) having access to true labels for all instances, which is unrealistic and unacceptable in real-life streaming tasks. Therefore, we propose the $k$ -Nearest Neighbor ENSemble-based method (KNNENS) to handle these problems. The KNNENS is effective to detect the new class and maintains high classification performance for known classes. It is also efficient in terms of run time and does not require true labels of new class instances for model update, which is desired in real-life streaming classification tasks. Experimental results show that the KNNENS achieves the best performance on four benchmark datasets and three real-world data streams in terms of accuracy and F1-measure and has a relatively fast run time compared to four reference methods. Codes are available at https://github.com/Ntriver/KNNENS.
Semi-Parametric and Parametric Estimations and Tests of Adoption: An Application to Bovine Somatotropin Technology

Although the parametric model is widely used for analyzing binary choice behavior, that approach is sensitive to model misspecification. We use the rBST adoption behavior of New York State dairy farms as an application to illustrate the impact of this misspecification. In our application the conventional Probit model over predicts the adoption rate, and over estimates the impact of rBST on milk production per cow; almost twice the impact compared to the semi-parametric estimation.
CUNI Basque-to-English Submission in IWSLT18

We present our submission to the IWSLT18 Low Resource task focused on the translation from Basque-to-English. Our submission is based on the current state-of-the-art self-attentive neural network architecture, Transformer. We further improve this strong baseline by exploiting available monolingual data using the back-translation technique. We also present further improvements gained by a transfer learning, a technique that trains a model using a high-resource language pair (Czech-English) and then fine-tunes the model using the target low-resource language pair (Basque-English).
Word Rewarding for Adequate Neural Machine Translation

To improve the translation adequacy in neural machine translation (NMT), we propose a rewarding model with target word prediction using bilingual dictionaries inspired by the success of decoder constraints in statistical machine translation. In particular, the model first predicts a set of target words promising for translation; then boosts the probabilities of the predicted words to give them better chances to be output. Our rewarding model minimally interacts with the decoder so that it can be easily applied to the decoder of an existing NMT system. Extensive evaluation under both resource-rich and resource-poor settings shows that (1) BLEU score improves more than 10 points with oracle prediction, (2) BLEU score improves about 1.0 point with target word prediction using bilingual dictionaries created either manually or automatically, (3) hyper-parameters of our model are relatively easy to optimize, and (4) undergeneration problem can be alleviated in exchange for increasing over-generated words.
A Machine Translation Approach for Modernizing Historical Documents Using Backtranslation

Human language evolves with the passage of time. This makes historical documents to be hard to comprehend by contemporary people and, thus, limits their accessibility to scholars specialized in the time period in which a certain document was written. Modernization aims at breaking this language barrier and increase the accessibility of historical documents to a broader audience. To do so, it generates a new version of a historical document, written in the modern version of the document’s original language. In this work, we propose several machine translation approaches for modernizing historical documents. We tested these approaches in different scenarios, obtaining very encouraging results.
Analyzing Knowledge Distillation in Neural Machine Translation

Knowledge distillation has recently been successfully applied to neural machine translation. It allows for building shrunk networks while the resulting systems retain most of the quality of the original model. Despite the fact that many authors report on the benefits of knowledge distillation, few have discussed the actual reasons why it works, especially in the context of neural MT. In this paper, we conduct several experiments aimed at understanding why and how distillation impacts accuracy on an English-German translation task. We show that translation complexity is actually reduced when building a distilled/synthesised bi-text when compared to the reference bi-text. We further remove noisy data from synthesised translations and merge filtered synthesised data together with original reference, thus achieving additional gains in terms of accuracy.
Impact of the Use of Proton Pump Inhibitors on Pembrolizumab Effectiveness for Advanced Urothelial Carcinoma

Background/Aim: This study aimed to clarify the impact of proton pump inhibitors (PPIs) on oncological outcomes in patients who received pembrolizumab for advanced urothelial carcinoma (UC). Patients and Methods: Forty advanced UC patients treated with pembrolizumab were retrospectively reviewed and divided into two groups (PPI: 15 patients; without PPI: 25 patients). Tumor response and survival were compared between these groups. The factors associated with survival were also investigated. Results: The objective response rate was significantly lower in the group with PPIs compared with the group without PPIs. Both progression-free survival (PFS) and overall survival (OS) were significantly shorter in the group with PPIs than in the group without PPIs. The use of PPIs was a significant predictor of poor PFS and OS in multivariate analysis. Conclusion: The use of PPIs was negatively associated with tumor response and survival in patients with advanced UC treated with pembrolizumab.
Beyond a suggestive morphology: were Wardaman stone points exclusively spear armatures?

Abstract Stone points were introduced across northern Australia during the mid-Holocene. The reasons behind this novel technological development are unclear but, given their morphology, an obvious interpretation is that they were used as spear tips. However, others have theorised that points, along with backed artefacts elsewhere, may have been multifunctional tools. This paper tests the hypothesis that lithic points were primarily spear tips by applying a macrofracture analysis, supported by experimentation, in order to identify diagnostic impact fractures on a large sample of points from Wardaman Country in the Northern Territory. The analysis suggests that points were rarely used as spear tips overall, though they were more commonly employed as projectiles when first introduced to Wardaman Country and when transported away from large residential sites to regions where mobile hunting equipment might be expected.
Physical Layer Cooperation - Theory and Practice

Abstract Information theory has long pointed to the promise of physical layer cooperation inboosting the spectral efﬁciency of wireless networks. Yet, the optimum relaying strategyto achieve the network capacity has till date remained elusive. Recently however, arelaying strategy termed Quantize-Map-and-Forward (QMF) was proved to achievethe capacity of arbitrary wireless networks within a bounded additive gap. This thesiscontributes to the design, analysis and implementation of QMF relaying by optimizing itsperformance for small relay networks, proposing low-complexity iteratively decodablecodes, and carrying out over-the-air experiments using software-radio testbeds to assessreal-world potential and competitiveness.The original QMF scheme has each relay performing the same operation, agnosticto the network topology and the channel state information (CSI); this facilitates theanalysis for arbitrary networks, yet comes at a performance penalty for small networksand medium SNR regimes. In this thesis, we demonstrate the beneﬁts one can gainfor QMF if we optimize its performance by leveraging topological and channel stateinformation. We show that for the N-relay diamond network, by taking into accounttopological information, we can exponentially reduce the QMF additive approximationgap from Q(N) bits/s/Hz to Q(logN) bits/s/Hz, while for the one-relay and two-relaynetworks, use of topological information and CSI can help to gain as much as 6 dB.Moreover, we explore what beneﬁts we can realize if we jointly optimize QMF andhalf-duplex scheduling, as well as if we employ hybrid schemes that combine QMF andDecode-and-Forward (DF) relay operations.To take QMF from being a purely information-theoretic idea to an implementable strategy,we derive a structure employing Low-Density-Parity-Check (LDPC) ensembles for therelay node operations and message-passing algorithms for decoding. We demonstratethrough extensive simulation results over the full-duplex diamond network, that ourdesigns offer a robust performance over fading channels and achieves the full diversityorder of our network at moderate SNRs.Next, we explore the potential real-world impact of QMF and present the design andexperimental evaluation of a wireless system that exploits relaying in the context ofWiFi. We deploy three main competing strategies that have been proposed for relaying,Amplify-and-Forward (AF), DF and QMF, on the WarpLab software radio platform. Wepresent experimental results—to the best of our knowledge, the ﬁrst ones–that compareQMF, AF and DF in a realistic indoor setting. We ﬁnd that QMF is a competitive schemev
Water Resources Management at the Mexican Borders

Abstract An analysis of the main water-related issues at the Mexican borders is presented, including the border with the United States, Belize, and Guatemala. International Treaties are described and the hydrological conditions at the regions covered by such treaties are analyzed, including droughts and water quality as well as other issues pertaining to water management, such as population, climate, and hydraulic infrastructure. Conjunctive water management between Mexico and the United States has a long and exemplary history. The border is more than 3,000 km long and is one of the busiest bi-national borders in the world, with a population increase index well above that of any of the two countries. This relationship started formally about a century ago with the signature of a Treaty in 1906 and has continued until the present time. As most relationships among neighboring countries, this one has not always been exempt from controversy and differences in opinions, but these divergences have always been solved with good will and taking both countries' interests into consideration. Problems related both with water quantity and quality have been present in the Mexico-USA relationship as well as, more recently, issues dealing with ecology conservation. Due to the greater activity at the border between the United States and Mexico, this region is more deeply described, but hydrologic problems and characteristics of the borders with Guatemala and Belize are also addressed.
Effect of oligosaccharides on the antioxidant, lipid and inflammatory profiles of rats with streptozotocin-induced diabetes mellitus

Abstract Prebiotics, gut microbiota-fermentable substances, delay the development of type I diabetes. In the present study, we investigated the effect of two prebiotics (galacto-oligosaccharides and xylo-oligosaccharides) on the antioxidant protection, lipid profile, and inflammatory activity of rats with streptozotocin-induced diabetes. The following markers were studied – malondialdehyde, 8-hydroxy-2′-deoxyguanosine, ferric reducing ability of plasma (FRAP), triacylglycerols, total cholesterol (TC), high-density lipoproteins, C-reactive protein (CRP), and interleukin-6. Diabetes was induced in male Wistar experimental rats by streptozotocin injection, while the non-diabetic controls were injected with saline. Afterward the oligosaccharides were administered orally to the experimental animals. The blood collected following the decapitation was analyzed by ELISA. A modified protocol was used only for measuring the FRAP values. The galacto-oligosaccharides and xylo-oligosaccharides lowered the malondialdehyde levels in the diabetic rats (p < 0.05). The galacto-oligosaccharides decreased the serum levels of 8-hydroxy-2′-deoxyguanosine (p = 0.01), while the xylo-oligosaccharides increased the FRAP (p < 0.05) in the experimental animals. None of the oligosaccharides affected triacylglycerol and interleukin-6 concentrations, but the galacto-oligosaccharides decreased the TC and CRP levels in the diabetic animals. Both oligosaccharides exert a beneficial effect on the antioxidant protection of the diabetic rats, but have a minor effect on their lipid and inflammatory profiles.
Electronic noses based on metal oxide nanowires: A review

Abstract Metal oxides are ideal for the fabrication of gas sensors: they are sensitive to many gases while allowing the device to be simple, tiny, and inexpensive. Nonetheless, their lack of selectivity remains a limitation. In order to achieve good selectivity in applications with many possible interfering gases, the sensors are inserted into an electronic nose that combines the signals from nonselective sensors and analyzes them with multivariate statistical algorithms in order to obtain selectivity. This review analyzes the scientific articles published in the last decade regarding electronic noses based on metal oxide nanowires. After a general introduction, Section 2 discusses the issues related to poor intrinsic selectivity. Section 3 briefly reviews the main algorithms that have hitherto been used and the results they can provide. Section 4 classifies the recent literature into fundamental research, agrifood, health, security. In Section 5, the literature is analyzed regarding the metal oxides, the surface decoration nanoparticles, the features that differentiate the sensors in a given array, the application for which the device was developed, the algorithm used, and the type of information obtained. Section 6 concludes by discussing the present state and points out the requirements for their use in real-world applications.
Who Dunnit: The Party Mystery Game for Analyzing Network Structure and Information Flow

Abstract The activity facilitates students’ understanding of network measures, including different types of node centrality, shortest paths, cliques, and communities, and their implications for information flow in groups or organizations. The goal of the game is for students (a minimum of 10 and maximum of 28 participants in a network; a larger class can be divided into two or more networks) to solve a company mystery by exchanging information clues with other students based on an imposed communication network configuration. The activity can be debriefed by discussing the game outcomes, analyzing the network structure (using a software to input data and calculate key network measures), and evaluating the practicality of the game. Examples of network configuration, data sets, and a script which uses the igraph package in R are included.
Quantitative Reliability Evaluation of Silicon Carbide-Based Inverters for Multiphase Electric Drives for Electric Vehicles

Abstract Reliability of power converter in the electric drivetrain of a vehicle can be a criterion for the comparison of various converter topologies, cooling system designs and control strategies. Therefore, reliability prediction is important for the design and control of vehicles. This paper presents an approach for quantitative evaluation of the reliability of converters for multiphase motor drives for electric vehicles (EVs) after considering the driving cycle. This paper provides a good background of reliability quantification so that it is easy to extend the presented approach to other applications. The models of subsystems have been selected to have excellent computational efficiency with good accuracy which is necessary for simulating long driving cycles. A simple vehicle model is used to obtain the traction motor torque demand for various points of the driving cycle. A multiphase interior permanent magnet synchronous motor has been used for traction motor. The operating voltage and currents of the motor are found using maximum torque per ampere (MTPA) control of IPMSM. The analytical loss calculation has been used to find the losses of switching devices of the converter. A thermal model of silicon carbide (SiC) MOSFET has been used to calculate junction temperature from the losses. The model developed here gives the failure rate and mean time between failures (MTBF) of switching devices of the inverter, which can be used to determine the failure rate. The model has been used to find the transition probabilities of a Markov model which can be used to quantify the reliability of converters of multiphase electric drives.
Tracking Changes in Fish Diversity in the South-Eastern Adriatic Sea (Albania) Based on Local Ecological Knowledge

Abstract Evidence from the last two centuries has identified changes in the biodiversity of the Mediterranean basin triggered by the arrival of new species. Compared to the central and northern areas, the southern part of the Adriatic Sea, specifically the Albanian coast, has been less studied. The aim of this study is to evaluate the prevalence of new/non-native fish species on the north Albanian coast in order to provide the first data on their presence. Data collection was carried out from March to August 2019 using LEK (Local Ecological Knowledge) protocol in five different locations along the northern part of the Albanian coast. The research followed a predefined methodology previously successfully applied in different countries of the Mediterranean basin by using a semi-structured questionnaire. During this study, 9 new and non-native species were recognized by the fishers, while only 5 of them have shown an evident abundance increase based on their respective catches in recent years. Results suggest that some of the analyzed species, such as blue crab Callinectes sapidus and bluefish Pomatomus saltatrix, represent important members of the fish fauna in north Albania coastal waters (including the lagoons). Future studies regarding the assessment of their impact and management strategies are highly recommended.
A Comprehensive Overview of the Impacting Factors on a Lithium-Ion-Battery’s Overall Efficiency

Abstract This comprehensive overview of the impacting factors on lithium-ion-battery’s (LIB) overall efficiency presents the most relevant influencing factors on a battery’s performance. Dissected into their respective short-term and long-term influences, the working principles behind the efficiency influencing factors are presented. With a strong focus on battery characterisation, charge-profiles and battery management systems (BMSs), the authors present results of their own practical research with a detailed literary analysis, allowing a broad coverage of the complex topic. Finally, the authors present a principle model that indicates the interactions between the different involved components of the battery.
Mean-field formulation for mean-variance asset-liability management with cash flow under an uncertain exit time

Abstract The asset-liability management problem with cash flow under an uncertain exit time has been investigated in this article, which is based on the fundamental framework of the mean-variance model in the multi-period version. The liability and random cash flow will affect asset optimization, while the investor may be forced to withdraw from investments with a random probability at each period in our model. The closed-form expressions for the mean-variance optimal portfolio selection and its corresponding efficient frontier are obtained by employing the mean-field formulation and dynamic programming approach. Moreover, some numerical examples are provided to illustrate the validity and accuracy of the theoretical results.
The Design of a Powered Ankle Prosthesis With Electrohydrostatic Actuation

This paper presents the design and modelling of a new powered ankle prosthesis which combines electrohydrostatic actuation with a controllable passive damper. The new powered ankle prosthesis can switch quickly between passive mode and powered assistance mode, and is intended to just give assistance at certain points within the gait cycles, such as during toe push-off. The design concept and a prototype built to demonstrate the concept are presented. A simulation model was developed to help analyse the performance characteristics. The structure and parameterisation of the simulation model are described. A comparison between simulation results and experiment results is undertaken in order to validate the model and assist in the optimisation of the design. Some results from an initial trial with amputees are included in the paper. According to subjective feedback from the amputees, the new powered ankle prosthesis provides sufficient force at push-off to assist with walking. Future investigations will be focusing on the compactness, weight reduction and control of the powered ankle prosthesis. © 2015 ASME
A Data-driven Model for Mobile Game New Version Update Evaluation

Game analytics has been used in game development and game research. However, less work focus on the game publishing side, especially on the new version update evaluation. This paper shows how game analytics can be used to guide game version updates. We innovatively view mobile game publishing as maintaining a fish tank and use our Fish Tank Model (FTM) to evaluate how game version updates improve players’ activation and game revenue. First, we define some key metrics for evaluating mobile game performance based on FTM. Second, we introduce a real game project to develop and apply FTM to the new version update. Third, based on analyzing the changes before and after the game version update, we provide suggestions on how to improve the new version. Finally, we summarize how to use our data-driven model to guide the mobile game new version update evaluation and continue to improve the game content.
The Potential Impact Of Covid-19 Upon Counter Productive Work Behavior Of Academic Staff: Moderating Role Of Emotional Intelligence

COVID-19, as a pandemic, has put the world into a predicament and it has affected almost every walk of life. The purpose of this study was to assess the moderating effect of Emotional intelligence (EI) on the relationship between Personality traits (positive and negative effect), Job stressor, on Counterproductive work behavior ensue due to pandemic. An online survey was conducted from (N=301) academic staff from seven tertiary education sectors from southern region of Sindh (Pakistan). Quantitative Survey method were analyzed using the SPSS AMOS to test the moderation between variables during COVID-19. The results showed that Job stressor and Personality traits with negative effect have a strong positive relationship with CWB whereas EI and Personality traits with positive effect negatively impact upon the CWB of academia staff of Sindh, besides the Emotional Intelligence plays as a significant moderating role. The research will be helpful for States academic policy makers and universities to formulate more inclusive strategies to control such behavior among the employees and will help in formulation of strategies.