Miracle-dz/UniFAR-FTUs huggingface dataset:
Best of both worlds: human-machine collaboration for object annotation (preliminary version)

Despite the recent boom in large-scale object detection, the long-standing goal of localizing every object in the image remains elusive. The current best object detectors can accurately detect at most a few object instances per image. However, manually labeling objects is quite expensive. This paper brings together the latest advancements in automatic object detection and in crowd engineering into a principled framework for accurately and efficiently localizing the objects in an image. Our proposed image annotation system seamlessly integrates multiple computer vision models with multiple sources of human input in a Markov Decision Process. The system is light-weight and easily extensible, performs self-assessment, and is able to incorporate novel objects instead of relying on a limited training set. It will be made publicly available.
Keyboard acoustic emanations revisited

We examine the problem of keyboard acoustic emanations. We present a novel attack taking as input a 10-minute sound recording of a user typing English text using a keyboard and recovering up to 96% of typed characters. There is no need for training recordings labeled with the corresponding clear text. A recognizer bootstrapped from a 10-minute sound recording can even recognize random text such as passwords: In our experiments, 90% of 5-character random passwords using only letters can be generated in fewer than 20 attempts by an adversary; 80% of 10-character passwords can be generated in fewer than 75 attempts by an adversary. In the attack, we use the statistical constraints of the underlying content, English language, to reconstruct text from sound recordings without knowing the corresponding clear text. The attack incorporates a combination of standard machine learning and speech recognition techniques, including cepstrum features, Hidden Markov Models, linear classification, and feedback-based incremental learning.
PRODUCTION ENGINEERING ARCHIVES

Article history Received 12.03. 2018 Accepted 31.05. 2018 Available online 16.07. 2018 Abstract Driving a road vehicle is a very complex task in terms of controlling it, substituting a human driver with a computer is a real challenge also from the technical side. An important step in vehicle controlling is when the vehicle plans its own trajectory. The input of the trajectory planning are the purpose of the passengers and the environment of the vehicle. The trajectory planning process has several parts, for instance, the geometry of the path-curve or the speed during the way. Furthermore, a traffic situation can also determine many other parameters in the planning process. This paper presents a basic approach for trajectory design. To reach the aim a map will be given as a binary 2204 x 1294 size matrix where the roads will be defined by ones, the obstacles will be defined by zeros. The aim is to make an algorithm which can find the shortest and a suitable way for vehicles between the start and the target point. The vehicle speed will be slow enough to ignore the dynamical properties of the vehicle. The research is one of the first steps to realize automated parking features in a self-drive car.
NICT’s participation to WAT 2019: Multilingualism and Multi-step Fine-Tuning for Low Resource NMT

In this paper we describe our submissions to WAT 2019 for the following tasks: English–Tamil translation and Russian–Japanese translation. Our team,“NICT-5”, focused on multilingual domain adaptation and back-translation for Russian–Japanese translation and on simple fine-tuning for English–Tamil translation . We noted that multi-stage fine tuning is essential in leveraging the power of multilingualism for an extremely low-resource language like Russian–Japanese. Furthermore, we can improve the performance of such a low-resource language pair by exploiting a small but in-domain monolingual corpus via back-translation. We managed to obtain second rank in both tasks for all translation directions.
Sick of your Job? Health E ects of Non-Optimal Employment Situations

In an empirical study based on data from the German Socio-Economic Panel, the e ect of job quality on individual health is analyzed. Identifying di erent levels of job quality, including job satisfaction and security, educational and working hours mismatch, it can be shown that there are severe e ects on the individual health situation. This can be shown for objective and subjective health measures while it does not a ect health-damaging behavior like smoking or being overweight. Applying di erent panel models and an instrumental variable approach, negative e ects of adverse job characteristics on individual health are identi ed. JEL classi cations : I14; J24; J28
Large-scale Exact Diagonalization of spin models with non-Abelian symmetries for the study of Many-Body Localization

In this thesis, we developed a massively parallel exact-diagonalization application to study effects of the non-Abelian symmetries namely the SU(2) symmetries on Many-body localization (MBL) in disordered 1D Heisenberg spin chains using the well known parallel libraries PETSc and SLEPc. Using the shift-invert spectral transformation technique, we study the eigenstates in the middle of the spectrum. We are able to look at the level statistics of the spin chain of sizes up to L = 26. Also, the code can calculate the Renyi entropy of the highly-excited eigenstates.
AN EFFICIENT TECHNIQUE TO CANCER CLASSIFICATION USING FAST IMPROVED BACTERIAL FORAGING OPTIMIZATION

Microarray data play an essential role in the development of efficient cancer diagnoses and classification System with Gene Expression Data. These expression data are usually unnecessary and noisy, and only a subset of them present discrete profiles for different classes of samples. Thus, selecting high discriminative genes from gene appearance data has become increasingly in the field of bioinformatics. In my work, a multi-objective biogeography based optimization technique is proposed to select the small subset of informative relevant to the classification. To choose multiple highly revealing gene subsets for cancer classification and diagnosis, a Fast improved Bacterial Foraging (FIBF) optimization is proposed to eliminate inappropriate, unnecessary, or noisy genes in changed granules at different stages and selects very informative genes with potentially different biological functions in balance. To show the efficiency of the expectations approach, we calculate the appearance of this technique with the Sensitivity, specificity and accuracy. Using gene microarray datasets dataset is leukaemia (including both binary and multi-class classification problems), we reveal experimentally that our proposed scheme can recognize significant empirical success and is biologically related for cancer diagnosis and drug. Keywords— Microarray data; leukaemia; Bacterial Foraging; swarm optimization; transductive; Sensitivity, Specificity and accuracy; Naïve bayes; Random forest, Decision stump.
LiSTOMS: A Light-Weighted Self-Tuning Ontology Mapping System

We argue that it is more practical to address the ontology mapping self-tuning problem in a whole system context instead of in a single matcher context. In this paper we introduce RMOMS, a Reference Model for Ontology Mapping Systems, consisting of six parts, the Preprocessor, the Dispatcher, the Matcher(s), the Aggregator, the Pruner, and the User Interface, with which to disassemble the self-tuning problem into more feasible units. We propose Maximum Weight Bipartite Graph Matching method for self-tuning matchers and Stable Match method for self-tuning aggregator, and test them in LiSTOMS, a light-weighted prototype sample of RMOMS. With comparison with some notable systems, LiSTOMS shows leading recall rate and competing precision rate.
Adaptive Off-Policy Policy Gradient Methods

Reinforcement learning (RL) is learning how to map states to actions so as to maximize a numerical reward. The mapping from states to actions can be seen as a sequence of decisions making an agent has to make when interacting with the environment. A general and flexible model for the environment in reinforcement learning is a Markov Decision Processes (MDPs). When applying RL to a real-world problem modeled as a MDP, perhaps the most important task is control, where we seek an optimal policy so that the agent obtains maximal expected cumulative reward when following this policy. Policy gradient methods are one of many RL algorithms used for control. They’re conceptually simple yet proved to be powerful in RL problems such as robotics and games. Conventional policy gradient methods learn a policy (for the control task) while interacting with the environment using the same policy. An alternative way is to use off-policy policy gradient methods where we separate the target policy that is being learned for the control task from the behavior policy that is deployed to generate data. Importance sampling is a common technique that enables learning from data generated by following a different behavior policy from the target policy. However, off-policy methods usually have higher variance but it is not always the case. In certain problems, such as the presence of significant rare event, off-policy methods tend to have better performance. An essential but natural question for off-policy policy gradient methods is which behavior policy one should use. Using a better behavior policy is a potential way to effectively reduce the policy gradient variance and thus improve learning. We proposed a general framework for searching and using an optimal behavior policy for off-policy policy gradient methods. We then give a simplified, more data-efficient and practical algorithm which is an instantiation of the general framework. Finally we will introduce three potential objectives to use on how to updating the behavior policy, and talk abou the pros and cons of each objective. Our work also shows some of the difficulties of optimizing two policies at the same time.
Assessment of respiratory drive with esophageal diaphragmatic electromyography in patients with acute respiratory distress syndrome treated with prone position ventilation.

Background Prone position ventilation (PPV) is an important strategy for patients with severe acute respiratory distress syndrome (ARDS). This prospective study investigated the use of electromyography of the diaphragm (EMGdi) for monitoring respiratory drive in patients with moderate to severe ARDS during long-term PPV. Methods An integrated nostril-gastric feeding tube containing an esophageal electrode and balloon was placed in 14 patients with severe ARDS prior to PPV. EMGdi and trans-pulmonary pressure (∆PL) data were collected before PPV (baseline), every 2 h during PPV, and 2 h after the restoration of supine position ventilation (post-2 h SPV). Results In ARDS patients, the static compliance of the chest wall was significantly decreased after PPV. EMGdi levels were slightly lower in the early, middle, and late stages of PPV compared with baseline. Patients who received neuromuscular blocker experienced a greater drop in EMGdi from baseline than those who did not. Conclusions For ARDS patients, EMGdi was slightly decreased after prolonged PPV. This is contrary to the change in diaphragm electromyography during normal body position changes. Monitoring EMGdi regularly during PPV in ARDS patients is feasible and can be used as a reference for lung protective ventilation strategies.
Personalised cardiovascular disease risk information as a motivator of behaviour change in individuals at high cardiovascular disease risk

Introduction: Cardiovascular disease (CVD) risk assessment is becoming increasingly common in routine clinical practice. Consequently many individuals are likely to be identified as being at increased CVD risk and risk reducing strategies implemented with a view to preventing future CVD. There are many steps along the pathway from CVD risk assessment to the prevention of CVD events. First, CVD risk needs to be accurately estimated using an appropriate CVD risk calculator. Secondly CVD risk information needs to be effectively communicated to the individual identified as being at increased risk. Thirdly, the risk information communicated needs to be capable of motivating behaviour change and finally behaviour change needs to result in a reduction in CVD risk. The evidence base for many of these steps has yet to be fully established. Aims: The overall aims of this work were first to adapt the United Kingdom Prospective Diabetes Study (UKPDS) Risk Engine to better display risk and achievable risk. Secondly to investigate lay perceptions of risk and to develop two interventions designed to reduce CVD risk. The two interventions were a personalised 10-year CVD risk estimate and a brief lifestyle advice intervention. Finally, the capacity of these interventions to increase physical activity and improve CVD risk factors in adults at increased CVD risk was tested. Methods: Three focus groups were held to investigate lay perceptions of risk and to inform the design of the UKPDS Risk Engine interface and a brief lifestyle advice intervention designed to motivate risk reducing behaviours. The two interventions were then tested in a 2x2 factorial randomised controlled trial. Results: The focus group results demonstrated that public interest and understanding of risk was high. In addition participants expressed clear views regarding how risk information and lifestyle advice should be presented. 194 participants at increased 10-year CVD risk (≥ 20%) were recruited from 4 Oxfordshire general practices. Neither a personalised 10-year CVD risk estimate nor a brief lifestyle advice intervention was capable of increasing physical activity or reducing estimated 10-year CVD risk in this group. Conclusions: Whilst public interest in CVD risk appeared to be high this study was unable to demonstrate that a 10-year personalised CVD risk estimate or a brief lifestyle advice intervention was able to increase physical activity in adults at increased CVD risk.
1 Antibody screening by microarray technology – 2 direct identification of selective high-affinity clones 3

The primary screening of hybridoma cells is a time-critical and laborious step during the 10 development of monoclonal antibodies. Often critical errors occur in this phase, which support the 11 notion that the generation of monoclonal antibodies with hybridoma technology is difficult to 12 control and hence a risky venture. We think that it is crucial to improve the screening process to 13 eliminate most of the immanent deficits of the conventional approach. With this new microarray14 based procedure, several advances could be achieved: Selectivity for excellent binders, high 15 throughput, reproducible signals, avoidance of misleading avidity (multivalency) effects, and 16 simultaneous performance of competition experiments. The latter can directly be used to select 17 clones of desired cross-reactivity properties. In this paper, a model system with two excellent clones 18 against carbamazepine, two weak clones and blank supernatant has been designed to examine the 19 effectiveness of the new system. The excellent clones could be detected largely independent of the 20 IgG concentration, which is unknown during the clone screening, since the determination and 21 subsequent adjustment of the antibody concentration is not possible in most cases. Furthermore, in 22 this approach, the enrichment, isolation and purification of IgG for characterization is not necessary. 23 Raw cell culture supernatant can be used directly, even when fetal calf serum (FCS) or other complex 24 media had been used. In addition, an improved method for the oriented antibody-immobilization 25 on epoxy-silanized slides is presented. Based on the results of this model system, we conclude that 26 this approach should be preferable to most other protocols leading to many of false positives, 27 causing expensive and lengthy confirmation steps to weed out the poor clones. 28
Session Number: Parallel Session 4A: Global and National Flows of People and Jobs Time: TUESDAY, AUGUST 26, AFTERNOON Paper Prepared for the30th General Conference of The International Association for Research in Income and Wealth

Argentina constitutes an interesting case to be ana lyzed given that during the nineties it reached high growth rates and a more stable macroec on mic environment but also witnessed significant rises in unemployment, inequa lity and poverty. Moreover, notwithstanding the stabilization of the economy an d the reduction of inflation, labour and income instability grew during the decade. Open unemployment reached unprecedented high levels while the incidence of pr eca ious employment also grew. Both phenomena usually led to higher occupational i stability, as short-term jobs are typical among those non-registered wage earners. Oc cupational turnover would also have been stimulated by modifications introduced to lab ur regulations as the new types of fixed-term -lower costcontracts and the trial period. This document analyses the characteristics of labou r mobility in Greater Buenos Aires (Argentina) from 1991 to 2002. The main purpose is to study the flows from employment and unemployment identifying those group s of people with the larger occupational turnover and the factors associated to lab ur instability. In particular, the paper investigates the influence of tenure and pers onal and occupational attributes, as well as macro variables –in particular, the effect of business cycle–, on the employment and unemployment duration. The analysis is based on censored quantile regression for duration data. This paper is the first attempt to u se this econometric technique for labour dynamic in Argentina.
Machine fault identification using Acoustic Imaging and Deep Convolutional Neural Networks

Prognostic Health Management (PHM) is an appealing approach to increase the manufacturing throughput in semiconductor industries. Acoustic imaging is a promising non-intrusive sensing technique for machine condition monitoring. In this research, we investigate whether acoustic imaging in combination with deep CNNs can identify the root-causes of performance degradation in the motion stages of ASM Pacific Technology wirebonder machines.
Home, school, and community’s effects on Indonesian secondary students’ self-identity changes

This study examines the effects of home, school, and community factors on Indonesian secondary students’ self-identity changes in relation to their use of English. The subjects of this research were 1707 Indonesian high school students from four big cities in Indonesia. The instrument was a questionnaire on the influence of English exposure at home, school, and community and six categories of self-identity changes: self-confidence, subtractive, additive, productive, split, and zero changes. The analysis showed that there were three noticeable findings, they were (1) the issue of self-identity change on Indonesian students was not evident, (2) the greatest influence on the six self-identity indicators came from community factors altogether although slightly, and (3) the biggest influence of all was home factors on the subjects’ self-confidence. A conclusion is then made with a recommendation.
EFFECT OF INTEGRATED INORGANIC AND ORGANIC NITROGEN FERTILIZER ON QUANTITY AND QUALITY OF POTATOES PLANT GROWN ON NEW RECLAIMED SANDY SOIL.

The integrated use of organic and inorganic fertilizers on the yield of potatoes was evaluated in a field experiment at newly reclaimed sandy soil at El-Kattaba Region El-Monofia Governorate-Egypt, during two successive years (growing seasons of winter 2011/2012 and 2012/2013). The organic sources used were farmyard manure, poultry manure and compost. These were integrated in different proportions with mineral nitrogenous fertilizer as ammonium nitrate to supply 120 kg Nfed -1 N. All the organic fertilizers were applied at planting time and mineral fertilizer (ammonium nitrate) was applied as divided into 4 doses, the 1 st dose was added after planting by 15 days and the other doses applied every 15 days. The results showed that integrated use in different proportion increased the plant height, number of stems, total chlorophyll, nitrogen concentration in potato tubers and protein content, but mineral nitrogen fertilizer individually gave higher values compared with the integrated organic and mineral nitrogen fertilizers. However, the maximum and minimum values of potato yield and nitrate content in potato tuber were obtained by application of poultry manure combined with mineral nitrogen fertilizer and organic nitrogen individually, respectively.Organic matter OF content in soil was increased in all treatments compared with sole mineral nitrogen fertilizer and contrary, the values of bulk density BD decreased generally. Therefore,the combined use of organic manure with inorganic fertilizer application was recognized as the most suitable way for ensuring high quantity and quality crop yield and reducing the harmful effects of using nitrogenous chemical fertilizers, but also for sustaining soil fertility status.
Benn, Y and Webb, TL and Chang, BPI and Harkin, B (2016)What is the psychological impact of self-weighing? A meta-analysis. Health Psychology

Many people self-weigh and many interventions addressing weightrelated problems such as obesity promote self-weighing. However, while self-weighing has been associated with weight loss, there is mixed evidence regarding the psychological impact of this behaviour. The present review aimed to quantify the relationship between self-weighing and: (i) affect (e.g., anxiety, depression); (ii) psychological functioning (e. g., self-esteem); (iii) body-related attitudes and (iv) disordered eating. A computerized search of scientific databases in September 2014 and subsequent ancestry and citation searches identified 29 independent tests of the relationship between self-weighing on psychological outcomes. Meta-analysis was used to quantify the size of the association across the tests. Results indicated that there was no association between self-weighing and affect, body-related attitudes or disordered eating. There was, however, a small-sized negative association between selfweighing and psychological functioning. The age of participants, obesity status, the extent of weight loss, duration of self-weighing and study design (RCT versus correlational) were found to influence at least some of the psychological outcomes of self-weighing. The findings suggest that, for the most part, self-weighing is not associated with adverse psychological outcomes. However, in some cases the association between self-weighing and psychological outcomes may be more negative than in others. ARTICLE HISTORY Received 12 August 2015 Revised 4 January 2016 Accepted 4 January 2016
What Do Reviewers Want? Reflections on Editing the Journal for the Past Year

The peer review process can be challenging. In this essay, the journal’s editor and editorial assistant present a summary of reviewers’ comments to authors from the past year. In presenting themes across 79 reviews, this essay arms authors with knowledge about reviewers’ expectations for manuscripts submitted to the journal. A secondary aim of this essay is to encourage reviewers to continue providing supportive and helpful feedback. As the journal heads into its third year of publishing, we are well on our way to creating the fi rst home for high-quality risk and crisis communication research from around the globe.