Miracle-dz/UniFAR-FTUs huggingface dataset:
Encouraging Neural Machine Translation to Satisfy Terminology Constraints.

Encouraging Neural Machine Translation to Satisfy Terminology Constraints. We present a new approach to encourage neural machine translation to satisfy lexical constraints. Our method acts at the training step and thereby avoiding the introduction of any extra computational overhead at inference step. The proposed method combines three main ingredients. The first one consists in augmenting the training data to specify the constraints. Intuitively, this encourages the model to learn a copy behavior when it encounters constraint terms. Compared to previous work, we use a simplified augmentation strategy without source factors. The second ingredient is constraint token masking, which makes it even easier for the model to learn the copy behavior and generalize better. The third one, is a modification of the standard cross entropy loss to bias the model towards assigning high probabilities to constraint words. Empirical results show that our method improves upon related baselines in terms of both BLEU score and the percentage of generated constraint terms.
Adjusting serum urate level by affecting membrane transporters involved in the disposition of urate

: This thesis investigates roles of urate-related transporters (e.g. URAT1/ SLC22A12 , URATv1/ SLC2A9 , and BCRP/ ABCG2 ) in adjusting serum urate (SUA) level under physiological and disease conditions. In Chapter 2, functional cooperation of sodium-dependent monocarboxylate transporters (SMCTs) and urate transporter 1 (URAT1) was demonstrated indicating that SMCTs indirectly regulates urate reabsorption mediated by URAT1 function; therefore SMCT may serve as a potential target for the alteration of renal urate handling. In Chapter 3, interaction of whisky congeners with urate transporters was studied as a new rationale for their SUA-lowering effect. Since URAT1-mediated urate uptake was significantly inhibited by whisky congeners, it is conceivable that SUA-lowering effect of drinking whisky may be caused by blocking URAT1 by congeners abundantly contained in whisky. In Chapter 4, association of increased uremic toxins with extra-renal elimination of urate mediated by urate efflux transporter, breast cancer resistance protein (BCRP). In patients with chronic kidney disease, although renal function is greatly reduced, SUA level is relatively maintained. Indoxyl sulfate, as a model uremic toxin, induced significantly transport activity of BCRP in Caco-2 cells, such compensatory increase of extra-renal urate excretion may contribute to balance SUA. In conclusion, SUA can be adjusted directly or indirectly by urate transporters, providing their physiological significance in urate homeostasis.
Deep reinforcement learning trading strategy based on lstm-a2c model

Stocks are gaining more and more attention as a form of investment. A good stock trading strategy can help investors gain considerable returns in the rapidly changing stock market. In this paper, we use a deep reinforcement learning model combining LSTM deep neural network and A2C reinforcement learning algorithm, construct time-series data, extract the time-series features of the data using LSTM, and adopt reinforcement learning algorithm to let the agent learn by trial and error in the trading environment, and finally get an end-to-end quantitative trading model adapted to the market. The results show that the model returns higher than 30% on both Dow Jones 30 stocks and 30 stocks data of A-shares. the LSTMA2C model works better on 10 days of time-series data than using only 1 day of data, and returns up to 91% on 30 stocks of A-shares in 2020 with a Sharpe ratio of 2.18.
Nudging Towards Health? Examining the Merits of Nutrition Labels and Personalization in a Recipe Recommender System

Food recommender systems show personalized recipes to users based on content liked previously. Despite their potential, often recommended (popular) recipes in previous studies have turned out to be unhealthy, negatively contributing to prevalent obesity problems worldwide. Changing how foods are presented through digital nudges might help, but these are usually examined in non-personalized contexts, such as a brick-and-mortar supermarket. This study seeks to support healthy food choices in a personalized interface by adding front-of-package nutrition labels to recipes in a food recommender system. After performing an offline evaluation, we conducted an online study (N = 600) with six different recommender interfaces, based on a 2 (non-personalized vs. personalized recipe advice) x 3 (No Label, Multiple Traffic Light, Nutri-Score) between-subjects design. We found that recipe choices made in the non-personalized scenario were healthier, while the use of nutrition labels (our digital nudge) reduced choice difficulty when the content was personalized.
Walking with PACE - Personalized and Automated Coaching Engine

We design and implement a personalized and automated physical activity coaching engine, PACE, which uses the Fogg’s behavioral model (FBM) to engage users in mini-conversation based coaching sessions. It is a chat-based nudge assistant that can boost (encourage) and sense (ask) the motivation, ability and propensity of users to walk and help them in achieving their step count targets, similar to a human coach. We demonstrate the feasibility, effectiveness and acceptability of PACE by directly comparing to human coaches in a Wizard-of-Oz deployment study with 33 participants over 21 days. We tracked coach-participant conversations, step counts and qualitative survey feedback. Our findings indicate that the PACE framework strongly emulated human coaching with no significant differences in the overall number of active days, step count and engagement patterns. The qualitative user feedback suggests that PACE cultivated a coach-like experience, offering barrier resolution via motivational and educational support. We use traditional human-computer interaction approaches, to interrogate the conversational data and report positive PACE-participant interaction patterns with respect to addressal, disclosure, collaborative target settings, and reflexivity. As a post-hoc analysis, we annotated the conversation logs from the human coaching arm and trained machine learning (ML) models on these data sets to predict the next boost (AUC 0.73 ± 0.02) and sense (AUC 0.83 ± 0.01) action. In future, such ML-based models could be made increasingly personalized and adaptive based on user behaviors.
The META4RS Proposal: Museum Emotion and Tracking Analysis For Recommender Systems

In this paper, we present the rationale and the ideas behind META4RS, a museum itinerary recommender system. The system leverages deep learning techniques to acquire data about the visitor’s position while ensuring her anonymity. Moreover, the visitor’s appraisal of the artwork she observes is inferred implicitly based on the emotional reactions she expresses while watching a given artwork. We are not aware of any such recommender system proposed in the research literature. However, this system should ensure several advantages: (i) it is non-intrusive since it makes use of simple badges and off-the-shelf cameras while ensuring the anonymity of the visitor; (ii) it is independent of the type of museum; (iii) it offers personalized itineraries to visitors based on their implicitly inferred interests and preferences. Specifically, we illustrate the background and describe the architecture of the proposed system, discussing the steps required for its implementation. We also provide details of what has already been done and what remains to be done, outlining the open problems.
Including Social Expectations for Trustworthy Proactive Human-Robot Dialogue

Trust forms an important factor in human-robot interaction and is highly influencing the success or failure of a mixed team of humans and machines. Similarly, to human-human teamwork, communication and proactivity are one of the keys to task success and efficiency. However, the level of proactive robot behaviour needs to be adapted to a dynamically changing social environment. Otherwise, it may be perceived as counterproductive and the robot’s assistance may not be accepted. For this reason, this work investigates the design of a socially-adaptive proactive dialogue strategy and its effects on humans’ trust and acceptance towards the robot. The strategy is implemented in a human-like household assistance robot that helps in the execution of domestic tasks, such as tidying up or fetch-and-carry tasks. For evaluation of the strategy, users interact with the robot while watching interactive videos of the robots in six different task scenarios. Here, the adaptive proactive behaviour of the robot is compared to four different levels of static proactivity: None, Notification, Suggestion, and Intervention. The results show that proactive robot behaviour that adapts to the social expectations of a user has a significant effect on the perceived trust in the system. Here, it is shown that a robot expressing socially-adaptive proactivity is perceived as more competent and reliable than a non-adaptive robot. Based on these results, important implications for the design of future robotic assistants at home are described.
ARIDF: Automatic Representative Image Dataset Finder for Image Based Localization

Information system department, University of Haifa, Israel, ishimshoni@is.haifa. ac.il With the growth of the commercial interest in indoor Location-based Services (ILBS), a lot of effort was put into the development of indoor positioning systems. One way to track users in indoor environment is using image-based localization. The user captures an image in front of a desirable place and the system locates him. Such systems use image matching algorithms, and usually they try to match the current image that was captured by the user with all the images that exist in the dataset. The big challenge is the dataset preparation; it has to be representative and minimal as much as it can be to reduce the number of comparisons. Previous works used special equipment to map or scan the environments, and others used human operators who have expertise in image matching algorithms. In this work, we present ARIDF, an automated method that finds a minimal and representative dataset for image-based localization. The human operators should not have any previous knowledge and experience about image matching algorithms.
GMAP 2022: Workshop on Group Modeling, Adaptation and Personalization

Group modeling adaptation and personalization is an area explored in parallel by two different research communities. On the one hand, the user modeling community focuses on the preferences aggregation problem: how to combine preferences of individuals in a group so as to personalize, adapt, and explain content for this group to consume or experience? On the other hand, the computer-supported collaboration community focuses on the group formation problem: how to construct a group that will work together efficiently to solve a particular task? This area becomes increasingly significant as work becomes more flexible, online, and distributed. The connecting tissue between both communities is the urgent need to design algorithms, whether for recommending group content or group formations, that steer away from top-down algorithmic decision-making, which has proven to stifle user agency and create power inequalities between users and algorithms. The aim of the workshop is, for the first time, to bring together the two communities working on the two sides of Group Recommendations, with an overall goal to rethink group recommendation and shift paradigms from the current algorithm-centric to a user- and group-centric focus.
ESTIMACIÓN DE LA VOLATILIDAD DE LOS FONDOS DE INVERSIÓN ABIERTOS EN BOLIVIA

En el presente documento se desarrollan conceptos y aplicaciones relacionadas con modelos de econometria financiera, el objetivo principal fue la determinacion del nivel de volatilidad de los rendimientos reportados por los Fondos de Inversion Abiertos en Bolivia. Mediante la aplicacion de los modelos GARCH, MMEP y de Volatilidad Estocastica, se pudo determinar la volatilidad condicional e incondicional. Por otro lado, mediante el uso de los modelos GJR y EGARCH se evaluo la presencia de asimetria en la volatilidad de los rendimientos, finalmente se realizo la estimacion del modelo GARCH-M para establecer el nivel de rentabilidad ajustada al riesgo. Los resultados obtenidos mediante el analisis de 28 series de tiempo en un periodo de 5 anos, permitieron establecer una linea base para medir el nivel de riesgo existente en la rentabilidad alcanzada por los Fondos de Inversion Abiertos.
TLOV REBROADCASTING METHOD FOR VANET

— Vehicular ad hoc network is surging in popularity in which vehicle constitute the mobility nodes in network. Profits of using improved VANET technology are reduce traffic congestion, provide general information services and provide safety critical applications such as enhanced lane and distance control and emergency break warning etc. Safety alert application is one of the most important applications for vehicular network communication. It disseminates emergency messages to all vehicles in a crucial problem in traffic scenarios as case of an accident. The dissemination of safety messages may prevent secondary accidents. In this paper, we first introduced a method named as TLOV rebroadcasting method for VANET. Then analyze it & compare it with other known traditional solutions for broadcast storm problem. This method is designed to make effective alarm message dissemination in ad hoc network of vehicles in a highway or a road. This is a method which is simple and have better performance compared to other broadcasting method . Using TLOV its try to find out vehicle that is most suitable to rebroadcast alert message. When TLOV applying, we also try to introduce priority concepts in which we give priority to big vehicle during selection of last vehicle.
Joint Feature Selection and Classification for Positive Unlabelled Multi–Label Data Using Weighted Penalized Empirical Risk Minimization

Abstract We consider the positive-unlabelled multi-label scenario in which multiple target variables are not observed directly. Instead, we observe surrogate variables indicating whether or not the target variables are labelled. The presence of a label means that the corresponding variable is positive. The absence of the label means that the variable can be either positive or negative. We analyze embedded feature selection methods based on two weighted penalized empirical risk minimization frameworks. In the first approach, we introduce weights of observations. The idea is to assign larger weights to observations for which there is a consistency between the values of the true target variable and the corresponding surrogate variable. In the second approach, we consider a weighted empirical risk function which corresponds to the risk function for the true unobserved target variables. The weights in both the methods depend on the unknown propensity score functions, whose estimation is a challenging problem. We propose to use very simple bounds for the propensity score, which leads to relatively simple forms of weights. In the experiments we analyze the predictive power of the methods considered for different labelling schemes.
Human impacts on mammals in and around a protected area before, during, and after COVID‐19 lockdowns

The dual mandate for many protected areas (PAs) to simultaneously promote recreation and conserve biodiversity may be hampered by negative effects of recreation on wildlife. However, reports of these effects are not consistent, presenting a knowledge gap that hinders evidence‐based decision‐making. We used camera traps to monitor human activity and terrestrial mammals in Golden Ears Provincial Park and the adjacent University of British Columbia Malcolm Knapp Research Forest near Vancouver, Canada, with the objective of discerning relative effects of various forms of recreation on cougars (Puma concolor), black bears (Ursus americanus), black‐tailed deer (Odocoileus hemionus), snowshoe hares (Lepus americanus), coyotes (Canis latrans), and bobcats (Lynx rufus). Additionally, public closures of the study area associated with the COVD‐19 pandemic offered an unprecedented period of human‐exclusion through which to explore these effects. Using Bayesian generalized mixed‐effects models, we detected negative effects of hikers (mean posterior estimate = −0.58, 95% credible interval [CI] −1.09 to −0.12) on weekly bobcat habitat use and negative effects of motorized vehicles (estimate = −0.28, 95% CI −0.61 to −0.05) on weekly black bear habitat use. We also found increased cougar detection rates in the PA during the COVID‐19 closure (estimate = 0.007, 95% CI 0.005 to 0.009), but decreased cougar detection rates (estimate = −0.006, 95% CI −0.009 to −0.003) and increased black‐tailed deer detection rates (estimate = 0.014, 95% CI 0.002 to 0.026) upon reopening of the PA. Our results emphasize that effects of human activity on wildlife habitat use and movement may be species‐ and/or activity‐dependent, and that camera traps can be an invaluable tool for monitoring both wildlife and human activity, collecting data even when public access is barred. Further, we encourage PA managers seeking to promote both biodiversity conservation and recreation to explicitly assess trade‐offs between these two goals in their PAs.
An improved EEGNet for single-trial EEG classification in rapid serial visual presentation task

As a new type of brain–computer interface (BCI), the rapid serial visual presentation (RSVP) paradigm has attracted significant attention. The mechanism of RSVP is detecting the P300 component corresponding to the target image to realize fast and correct recognition. This paper proposed an improved EEGNet model to achieve good performance in offline and online data. Specifically, the data were filtered by xDAWN to enhance the signal-to-noise ratio of the electroencephalogram (EEG) signals. The focal loss function was used instead of the cross-entropy loss function to solve the classification problems of unbalanced samples. Additionally, the subject-specific data were fed to the improved EEGNet model to obtain a subject-specific model. We applied the proposed model at the BCI Controlled Robot Contest in World Robot Contest 2021 and won the second place. The average recall rate of the four participants reached 51.56% in triple classification. In the offline data benchmark dataset (64 subjects-RSVP tasks), the average recall rates of groups A and B reached 76.07% and 78.11%, respectively. We provided an alternative method to identify targets based on the RSVP paradigm.
Seeing Risks or Solutions: Psychological Distance and Ecological Worldview Moderated the Effect of Disgust Images on Attention to Environmental Messages

Disgust imageries often shun readers away from attending to important environmental messages. Based on the Limited Capacity Model and Construal Level Theory, an eye-tracking experiment was conducted to test the influence of psychological distance and disgust images on attention to environmental messages. Results indicated that psychological distance moderated disgust images’ influence on attention to environmental communication messages. Disgust images increased attention to environmental messages only under the far distance condition. Additionally, disgust images increased attention to risk information but not efficacy information under the far distance condition, whereas non-disgust images increased attention to efficacy information under the close distance condition. All effects reported above were more salient among participants holding a more pro-environment worldview. Theoretical advances include the use of far psychological distance to reduce defensive reactions and the differential impact of disgust images on risk and efficacy information at the close or far psychological distance. Applied implications highlight the potentials of disgusting/non-disgusting images in emphasizing risk or efficacy information and reducing message avoidance.
Xiong Fu Powder Regulates the Intestinal Microenvironment to Protect Bones Against Destruction in Collagen-Induced Arthritis Rat Models

Background Changes in the intestinal microenvironment affected bone destruction in rheumatoid arthritis (RA), and spleen deficiency (SD) was closely related to the intestinal microenvironment. In this study, we aimed to explore the aggravation of SD on collagen-induced arthritis (CIA) and the bone protection of compound Xiong Fu powder (XFP) on CIA with SD (SD-CIA) based on the intestinal microenvironment. Method An SD-CIA rat model was established using Rheum officinale Baill. decoction combined with CIA and then treated with XFP. The aggravating action of SD on CIA rats and the efficacy of XFP were evaluated using AI scores, H&E staining of the joint, and level of serum anti–collagen type II antibody (Col II Ab). Bone destruction was assessed by micro-CT and TRACP staining. In addition, flow cytometry, qRT-PCR, and ELISA were used to evaluate gut mucosal immunity. Moreover, metagenomic sequencing was used to determine the distribution and function of the gut microbiota. Results Compared with that in CIA rats, bone destruction in SD-CIA rats was aggravated, as manifested by increased AI scores, more severe joint pathological changes and radiological damage, and increased number of osteoclasts (OCs) in the ankle joint. Meanwhile, the proportion of Tregs/Th17 cells was biased toward Th17 cells in Peyer’s patches. Furthermore, the gene levels of TNF-α, IL-1β, IL-6, and IL-17 were increased. In contrast, the expression of IL-10 and sIgA was decreased in the jejunum and ileum. XFP treatment improved bone damage and intestinal mucosal immune disorders compared with the SD-CIA group. In addition, the distribution and function of the gut microbiota were altered in the SD-CIA group. After XFP treatment, the community and function of the gut microbiota were regulated, manifested as increased abundance of several Lactobacillus species, such as L. acidophilus, which regulates the intestinal Tregs/Th17 cells and quorum sensing pathways, followed by promoting probiotic adhesion to the intestines. Conclusion SD can aggravate bone destruction in CIA rats. Compound XFP may attenuate bone destruction in SD-CIA rats by regulating the intestinal microenvironment. One of the mechanisms is the cross-talk between sIgA secretion regulated by intestinal mucosal Tregs and Th17 cells and adhesion of Lactobacillus mediated by quorum sensing.
The Influence of COVID-19 Pandemic on Management Earnings Forecasts

As the biggest black swan event of 2020, the COVID-19 pandemic has significantly weakened the ability of corporate stakeholders to monitor companies on site. In this context, exploring whether the on-site supervision restrictions triggered by the COVID-19 pandemic affect management earnings forecast disclosure is crucial to protect investors' interests and promote the stable development of the capital market. Based on quarterly data of Chinese A-share listed companies' earnings forecasts, this paper finds that: First, when the company's registry region is more severely affected by the COVID-19 pandemic, the company has less willingness to disclose its management earnings forecast. And those released forecasts tend to have lower qualities. Second, a higher level of media monitoring and a better legal environment can mitigate the negative impacts of the COVID-19 pandemic on both the willingness and the quality of management earnings forecast disclosure. Furthermore, mediating effect analysis shows that, the reduced on-site monitoring activities that were originally implemented by independent directors, institutional investors, and analysts during the epidemic period greatly limited stakeholders' monitoring efficiency, and thus cause significant influence on the disclosure of management earnings forecasts.