Miracle-dz/UniFAR-FTUs huggingface dataset:
Experimental investigation of the combination of absorptive and diffusing treatments in classrooms

The present study investigates the more successful configurations of sound-absorbing and diffusing treatments for achieving good acoustics in classrooms. The measurements were carried out in a 1/10 scale model classroom systematically adding diffusers to one or more of four surfaces of the room. 13 different configurations of diffusers with absorptive treatments were investigated. Adding diffusers on the ceiling were more effective to increasing the early arriving reflection energy (G50) than adding absorptive materials on the entire ceiling. The late arriving reflection energy (Glate) was decreased with increasing amounts of diffusing treatments of upper front or rear wall and this resulted in achieving higher early-to-late ratios (C50). Adding diffusers on the upper front wall (AC100DUFW26) achieved more uniform acoustical conditions over the receiver positions than adding diffusers on the upper rear wall (AC100DURW26). Adding diffusers on the ceiling and absorptive materials on the lower front wall (AC75DC25ALFW26) achieved better acoustical conditions than adding the absorptive materials on the entire ceiling and lower front wall (AC100ALFW26).
On the convergence of finite element methods

In this note we study the convergence of monotone P1 finite element methods on unstructured meshes for fully nonlinear Hamilton-Jacobi-Bellman equations arising from stochastic optimal control problems with possibly degenerate, isotropic diffusions. Using elliptic projection operators we treat discretisations which violate the consistency conditions of the framework by Barles and Souganidis. We obtain strong uniform convergence of the numerical solutions and, under non-degeneracy assumptions, strong L2 convergence of the gradients.
Pose Estimation of Similar Shape Objects using Convolutional Neural Network trained by Synthetic data

The objective of this paper is accurate 6D pose estimation from 2.5D point clouds for object classes with a high shape variation, such as vegetables and fruit. General pose estimation methods usually focus on calculating rigid transformations between known models and the target scene, and do not explicitly consider shape variations. We employ deep convolutional neural networks (CNN), which show robust and state of the art performance for the 2D image domain. In contrast, normally the performance of pose estimation from point clouds is weak, because it is hard to prepare large enough annotated training data. To overcome this issue, we propose an autonomous generation process of synthetic 2.5D point clouds covering different shape variations of the objects. The synthetic data is used to train the deep CNN model in order to estimate the object poses. We propose a novel loss function to guide the estimator to have larger feature distances for different poses, and to directly estimate the correct object pose. We performed an evaluation using real objects, where the training was conducted with artificial CAD models downloaded from a public web resource. The results indicate that our approach is suitable for real world robotic applications.
WEALTH AND INCOME DISTRIBUTION : A REVIEW TOWARDS NEW TRENDS

The increasing inequality observed in recent years between the income of 1% of the population with respect to the rest, a new interest arose in the study of the distribution of wealth and income, questioning the effectiveness of traditional economic models even provoking economists to consider other approaches to study this problem. In present paper a revision of some of these approaches is made, from the statistical part, going over Pareto ́s work, and the later works of Gibrat, Piketty, among others, and how complexity sciences have contributed through the econophysics and Agents-Based Models, from a Bottom-Up and Top-Down perspective, until the emergence of complex networks. Getting with it a contribution to find a more robust view of the problem.
Master Thesis in Statistics and Machine Learning Defining and predicting fast-selling clothing options

This thesis aims to find a definition of fast-selling clothing options and to find a way to predict them using only a few weeks of sale data as input. The data used for this project contain daily sales and intake quantity for seasonal options, with sale start 2016-2018, provided by the department store chain Åhléns. A definition is found to describe fast-selling clothing options as those having sold a certain percentage of their intake after a fixed number of days. An alternative definition based on cluster affiliation is proven less effective. Two predictive models are tested, the first one being a probabilistic classifier and the second one being a k-nearest neighbor classifier, using the Euclidean distance. The probabilistic model is divided into three steps: transformation, clustering, and classification. The time series are transformed with B-splines to reduce dimensionality, where each time series is represented by a vector with its length and B-spline coefficients. As a tool to improve the quality of the predictions, the B-spline vectors are clustered with a Gaussian mixture model where every cluster is assigned one of the two labels fast-selling or ordinary, thus dividing the clusters into disjoint sets: one containing fast-selling clusters and the other containing ordinary clusters. Lastly, the time series to be predicted are assumed to be Laplace distributed around a B-spline and using the probability distributions provided by the clustering, the posterior probability for each class is used to classify the new observations. In the transformation step, the number of knots for the B-splines are evaluated with cross-validation and the Gaussian mixture models, from the clustering step, are evaluated with the Bayesian information criterion, BIC. The predictive performance of both classifiers is evaluated with accuracy, precision, and recall. The probabilistic model outperforms the k-nearest neighbor model with considerably higher values of accuracy, precision, and recall. The performance of each model is improved by using more data to make the predictions, most prominently with the probabilistic model.
Assessing an Information Systems Master's Curriculum Program: Revisiting the ACM's MSIS 2006 Model Curriculum

The field of Information systems continues to change dynamically with the painful impact for reacting to those changes felt by both undergraduate and masters’ level programs. The purpose of this paper is to report the results of a comparative selfstudy of one MSIS program as a measure to assess its competitiveness among a set of other comparable, competitive and aspirant masters’ programs. The focus of the study is determine the viability of one specific master’s curriculum used currently to prepare students for professional careers in information systems in order to meet the marketplace challenges created by the ever-evolving information systems business needs. The fundamental methodology used in this study is based on that which was employed in a previous study conducted to assess the ‘fit’ of 86 MSIS programs with the MSIS 2000 Model Curriculum (Vijayaraman, et.al.) Findings will be presented that reflect shared learning objectives, curriculum content, currency, and relevancy necessary to assess whether changes to the current curriculum are necessary to establish a more competitive position among the three categories of comparable, competitive and aspirant university MSIS programs. One benefit that resulted from this initiative is the recognition that there has been relatively little research directed at assessing the overall direction of current MSIS programs and the need to revisit the need for a new MSIS model curriculum. The last endorsed MSIS model curriculum was published in 2006 (Gorgone, et.al. 2006), almost 10 years ago. It is the hope of presenting the results of this study that a discussion can begin to address the challenge of maintaining a viable MSIS curriculum that meets the current and future demands of the business community.
Feature Extraction Build Classifier sEMG & IMU Raw Signals Recognized Gesture Classifier Update Classifier First Time Use True False sEMG & IMU Raw Signals Data Segmentation Feature Extraction Online Testing Calibration Data Segmentation

While most wearable gesture recognition approaches focus on the forearm or fingers, the wrist may be a more suitable location for practical use. We present the design and validation of a real-time gesture recognition wristband based on surface electromyography (sEMG) and inertial measurement unit (IMU) sensing fusion, which can recognize 8 air gestures and 4 surface gestures with 2 distinct force levels. Ten healthy subjects performed an initial gesture recognition experiment, followed by a second experiment 1 hour later and a third experiment 1 day later. Classification accuracies for the initial experiment were 92.6% and 88.8% for air and surface gestures, respectively, and there were no changes in accuracy results during testing 1 hour and 1 day later (p>0.05). These results demonstrate the feasibility of wrist-based gesture recognition paving the way for potential future integration in to a smart watch or other wristworn wearable for intuitive human computer interaction.
LHCb Run 2 trigger performance

During the first long shutdown of the LHC (2013-2014, LS1), the LHCb detector remained essen- tially unchanged, while the trigger system has been completely revised. Upgrades to the LHCb computing infrastructure have allowed for high quality decay information to be calculated by the software trigger, making separate offline event reconstruction unnecessary. Reaching the ultimate precision of the LHCb experiment in real time as the data arrive has the power to transform the experimental approach to processing large quantities of data.
Runtime Evolution for Online Gaming: A Case Study using JBoss and Drools

In this paper we describe a rule based approach to online game development. Our goal is to ease the evolution of an online game by allowing far reaching change in gaming scenarios after game deployment has occurred and during game play. This is achieved by making use of a rules based engine (Drools) within the JBoss platform. We use a simple gaming scenario to demonstrate how far reaching change is possible without the difficulty of altering program code when rules are separated out from other application level logic.
The housing and neighbourhood impact of Britain ’ s changing ethnic mix

Housing pressures in British cities partly result from the net migration of people to Britain over the past ten years. Immigration has also changed the nature of minority ethnic communities, while longestablished minority ethnic communities are changing with successive generations. What does recent research tell us about the nature of these changes, and their impact on policy and practice in housing and neighbourhood renewal? This paper:
Relationship Between Bone Morphology and Bone Quality in Female Femurs : Implication for Additive Risk of Alternative

Calcium (Ca) storage in bone has a relationship with eggshell production. Forced molting by feeding restriction for older hens improves eggshell quality but leads to a decline in the bone quality. Dietary minerals improve the eggshell and bone quality; however, the effect on bone quality post-molting has not been clarified. This study evaluated the effects of dietary Ca and minerals on the eggshell and bone quality during both preand post-molting periods. The bone quality was evaluated by measurement of bone density and Fourier transform infrared spectroscopy (FT-IR) analysis. The eggshell quality was evaluated by morphological observation of the mammillary cores by scanning electron microscope and FT-IR analysis. The high Ca concentration feed group showed a low bone density post-molting and a high carbonate/phosphate ratio pre-molting. In high mineral concentration feed group, the eggshell strength, the thickness, and the proportion of large mammillary core areas were significantly higher (p < 0.05) than control group post-molting. These results suggest that the eggshell strength increases as the proportion of mammillary core areas increases. Furthermore, the high carbonate/phosphate ratio promoted a decrease in bone density. Therefore, the concentration of dietary mineral is strongly related with the maintenance of eggshells and bone quality post-molting. Keyword Hens · Fourier transform infrared spectroscopy (FT-IR) · Scanning electron microscope (SEM) · Carbonate/phosphate ratio (C/P ratio) · Bone density · Mammillary cores · Calcium (Ca) N. Ishikawa · C. Nishii · K. -e. Yamauchi · Y. Matsumoto (*) Faculty of Agriculture, Kagawa University, Miki, Japan e-mail: myoshiki@ag.kagawa-u.ac. jp H. Mishima Department of Dental Engineering, Tsurumi University School of Dental Medicine, Yokohama, Japan
Effect of Autocomplete Function on Processing Web IDE for Novice Programmers

Web IDE is suitable for novice learners because it can reduce the troublesome of constructing programming environment. Since most of the Web IDEs utilize text-based programming, a certain level of typing skills is required for learners. For that reason, the performance of learning programming may be affected by the typing skill of the learner. In order to mitigate the influence of the typing skill, we introduced automatic code completion to the Web IDE for Processing language. We investigated how the automatic completion function was used for novices through an actual course. Since the snippet of “if” and “for” statements already contained parenthesis and curly brackets, the auto-complete function significantly reduce the typing of these special characters. From a questionnaire survey, a strong positive correlation was found between the use frequency of the function and the necessity that learner feels.
Using Conversational Agents To Support Learning By Teaching

Conversational agents are becoming increasingly popular for supporting and facilitating learning. Conventional pedagogical agents are designed to play the role of human teachers by giving instructions to the students. In this paper, we investigate the use of conversational agents to support the ’learningby-teaching’ paradigm where the agent receives instructions from students. In particular, we introduce Curiosity Notebook: an educational application that harvests conversational interventions to facilitate students’ learning. Recognizing such interventions can not only help in engaging students within learning interactions, but also provide a deeper insight into the intricacies involved in designing conversational agents for educational purposes.
Control Theory Based Hysteresis Switch for Congestion Control in Data Centers

n this report, we empirically and analytically study TCP packet loss cycles which greatly affect the performance of TCP applications. Specifically, the short cycles of TCP in data center environments degrade the Flow Completion Time (FCT) of most time-sensitive (e.g., partition-aggregate) TCP applications. In particular, for small flows, short loss cycles may lead to losses only recoverable by Retransmission-Timeout (RTO) which expands the FCT by 2-4 orders of magnitude. We find that short loss cycles are merely the symptom of a pathology of TCP’s AIMD algorithm design targeting moderate-bandwidth high-delay Internet and is shown to be inadequate for the high-bandwidth low-delay data centers. Hence, the common sense calls for an alternative methodology that can effectively expand the TCP loss cycle long enough for short flows to finish the transfer within one or fewer RTTs. To this end, in this report, we propose a switch-based control which synthesizes Hysteresis Switching Congestion Control (HSCC). HSCC switches between TCP and a slow CBR in order to expand the TCP loss cycle within data centers. The proposed scheme is studied analytically then it is evaluated via means of small and large scale NS2 simulation and real experimentation in a real testbed cluster. The results show considerable improvements in the FCT distribution and the missed deadlines. HSCC
FROM LARGE ENSEMBLES WITH PERTURBED PHYSICS

State-of-the-art climate models are characterized by substantial deficiencies and their projections for the 21st century differ considerably depending on their specific initial conditions and physical parameterizations. Perturbed physics ensembles represent a promising tool to delimit the range of model uncertainty in a probabilistic sense. Yet large ensembles with complex climate models are still constrained by available computer resources. Here, an extended energy balance model with a stochastic weather term, feedback, and natural and anthropogenic forcings is used to investigate the potential of large perturbed physics ensembles. Up to 100,000 simulations for the 20th and 21st centuries are realized, assembling appropriate random numbers for 9 different model parameters. Bayesian model averaging is applied to filter the simulations in the face of observed temperatures and measurement error. The resulting ensemble mean has learned noticeably from the data and is very close to the observed time series of global-mean temperature. It differs markedly from the CMIP3 and CMIP5 multimodel ensemble means, especially in terms of more realistic decadal temperature variations due to the learning effect by means of the Bayesian approach. The sensitivity of the model and the Bayesian filter is assessed with respect to the model design and the estimated observational error, respectively.
Associative Embedding for Team Discrimination

Assigning team labels to players in a sport game is not a trivial task when no prior is known about the visual appearance of each team. Our work builds on a Convolutional Neural Network (CNN) to learn a descriptor, namely a pixel-wise embedding vector, that is similar for pixels depicting players from the same team, and dissimilar when pixels correspond to distinct teams. The advantage of this idea is that no per-game learning is needed, allowing efficient team discrimination as soon as the game starts. In principle, the approach follows the associative embedding framework to differentiate instances of objects. Our work is however different in that it derives the embeddings from a lightweight segmentation network and, more fundamentally, because it considers the assignment of the same embedding to unconnected pixels, as required by pixels of distinct players from the same team. Excellent results, both in terms of team labelling accuracy and generalization to new games/arenas, have been achieved on panoramic views of a large variety of basketball games involving players interactions and occlusions. This makes our method a good candidate to integrate team separation in many CNN-based sport analytics pipelines.
Operations and Investment of Energy Storage in a Tree Network

Energy storage has become an indispensable part of electricity networks, and the investment in energy storage is expected to grow rapidly. We consider the problem of energy storage siting (where to invest storage facilities) and sizing (how much capacity to invest), with the objective of minimizing the total investment and generation costs, in a tree network model that captures the critical features of electricity systems, including convex costs, stochastic demand, storage efficiency, and line losses. We derive the structures of the optimal storage operating policy, which provide insights on how storage efficiency and line losses affect storage operations. On storage siting and sizing, our theory recommends that storage should be first placed at the leaf nodes with positive minimum demand, and further storage investment should consider relative benefits of leaf versus central storage. Through numerical analysis, we find that determining the total amount of storage investment (sizing) is typically more important than allocating storage capacity across the central and leaf nodes (siting).
SEED: Searching Encrypted Email Dependably. A design specification for secured webmail

Webmail services are a convenient, internet-based access point for email management. A webmail user must trust the service provider to honor the user’s individual privacy while accomodating their email contents. Webmail users are increasingly conscious of the risk to their privacy as many webmail services have fallen victim to cyberattacks where unwanted observers have exploited server vulnerabilities to steal user private data. The relationship of trust between webmail provider and webmail user has been further called into question with the reveal of NSA snooping of user email, often with the tacit approval of the webmail provider. We augment a modern webmail service with end-to-end encryption of user email data. Our system, SEED, is designed to respect the original functionality of the webmail service. Most notably, we enable search of encrypted message bodies using the webmail service’s built-in search engine. With an ancillary web browser extension called SEED add-on, the user is able to manage email in the webmail client while decrypting sensitive email information in a separate local process. The browser extension manages the user’s encryption keys and decrypts email ciphertext automatically such that the user remains ignorant of the underlying cryptographic implementation as they browse their email. Built upon Gmail, SEED stores a user’s email data on Google’s remote servers and guarantees that Google is unable to interpret it. When managing their email, the user still enjoys the full capabilities of the Gmail web client, including composing, reading, and robustly searching email by message metadata. The user is able to do all of this without revealing their usage habits to Google. The user is able to do all of this without revealing their emails to Google. Using SEED, the user benefits from the conveniences of webmail and preserves the integrity of their private information stored online.