Miracle-dz/UniFAR-FTUs huggingface dataset:
Demo : BACChat : A Building Automation Control Client for Sensor Data Collection

Nowadays, many buildings are equipped with a building management system (BMS), which plays a key role in maintaining the operation, energy efficiency, and safety of a building. With the rising concerns in energy conservation and the fact that buildings are major energy consumers, there is an increasing interest in studies on building management systems in computing society. One key difficulty we found in BMS research is that a BMS system is very complex, and there is very few tools for researchers to flexibly the interaction with a BMS system. We developed Building Automation Control Chat (BACChat) for a BMS system. The BACChat is the client side of BMS, which can interact with the sensing devices of a BMS system. Such operation was not possible because all the BMS operation software are developed by private vendors. We released BACChat as an open source. It can be used for many research problems. For example, we plan to redesign part of the BMS system using wireless sensors; so as to reduce deployment effort and costs. As such, our BACChat is an indispensable component for our system. We also found that data loss occurs when we are using existing software; but we are short of tools to diagnose the problem, therefore, BACChat is useful in such situation. In this demo, we will help our audience from the computing field to better understand the existing BMS system. We will show how BACChat can be used for research studies.
ICASE OPTIMISTIC BARRIER SYNCHRONIZATION

Barrier synchronization is a fundamental operation in parallel computation. In many contexts, at the point a processor enters a barrier it knows that it has already processed all work required of it prior to the synchronization. This paper treats the alternative case, when a processor cannot enter a barrier with the assurance that it has already performed all necessary pre-synchronization computation. The problem arises when the number of pre-synchronization messages to be received by a processor is unknown, for example, in a parallel discrete simulation or any other computation that is largely driven by an unpredictable exchange of messages. We describe an optimistic O(log 2 P) barrier algoritlun for such problems, study its performance on a large-scale parallel system, and consider extensions to general associative reductions, as well as associative parallel prefix computations. 1This research was supported by the National Aeronautics and Space Administration under NASA Contract No. NAS1-18605 while the author was in residence at the Institute for Computer Applications in Science and Engineering (ICASE), NASA Langley Research Center, Hampton, VA 23665. Research was also supported in part by NASA grants NAG-l-1060, NAG-l-If32, and NAG-I-995, and NSF Grants ASC 8819373 and CCR-9201195.
A study on cepstral sub-band normalization for robust ASR

In this paper, we propose a cepstral subband normalization (CSN) approach for robust speech recognition. The CSN approach first applies the discrete wavelet transform (DWT) to decompose the original cepstral feature sequence into low and high frequency band (LFB and HFB) parts. Then, CSN normalizes the LFB components and zeros out the HFB components. Finally, an inverse DWT is applied on LFB and HFB components to form the normalized cepstral features. When using the Haar functions as the DWT bases, the calculation of CSN can be processed efficiently with a 50% reduction on the amount of feature components. In addition, our experimental results on the Aurora-2 task show that CSN outperforms the conventional cepstral mean subtraction (CMS), cepstral mean and variance normalization (CMVN), and histogram equalization (HEQ). We also integrate CSN with advanced frontend (AFE) for feature extraction. Experimental results indicate that the integrated AFE+CSN achieves notable improvements over the original AFE. The simple calculation, compact in form, and effective noise robustness properties enable CSN to perform suitably for mobile applications.
A conjecture in the arithmetic theory of differential equations

This article discusses a conjectural description of the Lie algebra of the differential Galois group attached to a linear differential equation as being the smallest algebraic Lie algebra whose reduction mod p contains, for almost all p, the p-curvature of the reduction mod p of the differential equation in question. RESUME. On discute une description conjecturale de Falgebre de Lie du groupe de Galois differentiel d'une equation differentielle lineaire comme etant la plus petite algebre de Lie algebrique dont la reduction modulo p contient, pour presquc tout p. la p-courbure de la reduction modulo p de Inequation differentielle dont il s'agit.
Clinical Practice

change characterize health care delivery and policy today. Care can no longer be delivered without regard to efficiency of care systems, qualifications of health care ’ providers, appropriateness and effectiveness of care given, or especially cost of services rendered. However, in times of transition we can learn from history while developing strategies for moving forward. Napoleon and General Patton did. From nursing’s history, the indomitable strategist is Florence Nightingale. Nightingale lived in tumultuous times, too. Without prior nursing experience she was called upon to make formidable changes in staffing and mechanical improvements in a facility for care of invalid women. She was sent to Scutari to take quick, corrective action in caring for the casualties of the Crimean War; within the first six months she decreased the mortality from 42.7% to 2.2% by forcing change in medicine and supply delivery and storage, as well as by improving the ratio of nurses to patients. She was instrumental in raising funds for the development of the Nightingale School of Nursing at St. Thomas’ Hospital in London: she reformed nursing education and practice. However, improving nursing was only a small part of her influence on the larger health care system. Among her many accomplishments, she also made significant changes in public sanitation, hospital ventilation, patient nutrition, midwifery, workhouse nursing, Army sanitation reform, uniformity of hospital patient registration as a mechanism for model hospital statistics, and design of sewer drainage, water supply and barracks construction (Baly, 1986). Nightingale was resolute. She was driven by accomplishments and a sense of responsibility and account-
Contextual Relations of Words in Grimm Tales, Analyzed by Self-Organizing Map

Semantic roles of words in natural languages are re(cid:13)ected by the contexts in which they occur. These roles can explicitly be visualized by the Self-Organizing Map (SOM). In the experiments reported in this work the source data consisted of the raw text of Grimm fairy tales without any prior syntactic or semantic categorization of the words. The algorithm was able to create diagrams that seem to comply reasonably well with the traditional syntactical categorizations and human intuition about the semantics of the words.
Crowd-sourcing world models with OpenRoomMap

The construction of world models typically requires laborious survey and cataloguing of a building's contents. OpenRoomMap attempts to crowd-source this information by allowing all building occupants to contribute to the model. Our initial deployment of OpenRoomMap has considered our office building that has a floor area of approximately 10000 square meters over 3 floors. Building occupants have mapped approximately 70% of the building and placed over 4000 objects. We show how the accuracy of placement depends on the physical restrictions that constrain the positions of objects and discuss the strategies we are using to encourage ongoing update and maintenance of the data.
Somatic copy number alterations detected by ultra-deep targeted sequencing predict prognosis in oral cavity squamous cell carcinoma

Background Ultra-deep targeted sequencing (UDT-Seq) has advanced our knowledge on the incidence and functional significance of somatic mutations. However, the utility of UDT-Seq in detecting copy number alterations (CNAs) remains unclear. With the goal of improving molecular prognostication and identifying new therapeutic targets, we designed this study to assess whether UDT-Seq may be useful for detecting CNA in oral cavity squamous cell carcinoma (OSCC). Methods We sequenced a panel of clinically actionable cancer mutations in 310 formalin-fixed paraffin-embedded OSCC specimens. A linear model was developed to overcome uneven coverage across target regions and multiple samples. The 5-year rates of secondary primary tumors, local recurrence, neck recurrence, distant metastases, and survival served as the outcome measures. We confirmed the prognostic significance of the CNA signatures in an independent sample of 105 primary OSCC specimens. Results The CNA burden across 10 targeted genes was found to predict prognosis in two independent cohorts. FGFR1 and PIK3CAamplifications were associated with prognosis independent of clinical risk factors. Genes exhibiting CNA were clustered in the proteoglycan metabolism, the FOXO signaling, and the PI3K-AKT signaling pathways, for which targeted drugs are already available or currently under development. Conclusions UDT-Seq is clinically useful to identify CNA, which significantly improve the prognostic information provided by traditional clinicopathological risk factors in OSCC patients.
Dealing with unknown words in statistical machine translation

In Statistical Machine Translation, words that were not seen during training are unknown words, that is, words that the system will not know how to translate. In this paper we contribute to this research problem by profiting from orthographic cues given by words. Thus, we report a study of the impact of word distance metrics in cognates' detection and, in addition, on the possibility of obtaining possible translations of unknown words through Logical Analogy. Our approach is tested in the translation of corpora from Portuguese to English (and vice-versa).
Designing a DRM System

The digital rights management (DRM) systems aim at protecting and enforcing the legal rights associated with the use of digital content distributed on the Internet. Most of such systems use watermarking techniques to implement the content protection. Although the effectiveness of the implemented protection strictly depends on the adopted watermarking techniques, an important role is also played by watermarking protocols. They define the scheme of the interactions that have to take place among the entities involved in the processes of content protection and Web-based distribution governed by the DRM systems. This paper presents a DRM system developed as a Web software platform to implement the copyright protection of multimedia digital content distributed on the Internet. The DRM system is based on a watermarking protocol previously developed by the authors, and has been designed as a service oriented architecture composed of a federation of coordinated Web entities that play distinct roles and interact within a trusted environment.
Proton motive force-driven and ATP-dependent drug extrusion systems in multidrug-resistant Lactococcus lactis

Three mutants of Lactococcus lactis subsp. lactis MG1363, termed EthR, DauR, and RhoR, were selected for resistance to high concentrations of ethidium bromide, daunomycin, and rhodamine 6G, respectively. These mutants were found to be cross resistant to a number of structurally and functionally unrelated drugs, among which were typical substrates of the mammalian multidrug transporter (P-glycoprotein) such as daunomycin, quinine, actinomycin D, gramicidin D, and rhodamine 6G. The three multidrug-resistant strains showed an increased rate of energy-dependent ethidium and daunomycin efflux compared with that of the wild-type strain. This suggests that resistance to these toxic compounds is at least partly due to active efflux. Efflux of ethidium from the EthR strain could occur against a 37-fold inwardly directed concentration gradient. In all strains, ethidium efflux was inhibited by reserpine, a well-known inhibitor of P-glycoprotein. Ionophores which selectively dissipate the membrane potential or the pH gradient across the membrane inhibited ethidium and daunomycin efflux in the wild-type strain, corresponding with a proton motive force-driven efflux system. The ethidium efflux system in the EthR strain, on the other hand, was inhibited by ortho-vanadate and not upon dissipation of the proton motive force, which suggests the involvement of ATP in the energization of transport. The partial inhibition of ethidium efflux by ortho-vanadate and nigericin in the DauR and RhoR strains suggest that a proton motive force-dependent and an ATP-dependent system are expressed simultaneously. This is the first report of an ATP-dependent transport system in prokaryotes which confers multidrug resistance to the organism.
Learning to use a text processing system: Evidence from “thinking aloud” protocols

There is growing interest in cognitive science in the the mental processes that underly learning and using computer systems (e. g., Bott {1}; Mayer, {2}; Card, Moran & Newell {3}). In this paper we report generalizations about the problems people who are not experienced with computers have learning to use a text-processing system. We are especially interested in unaided self-instruction, because of the practical interest in reducing the role of experienced personel in the training process. We analyze these difficulties in terms of the interaction between the cognitive characteristics of the learner, and the design of self-instruction, and the interface. Finally, we are also interested in implications of these problems for designing better training methods and computer interfaces that are easier to learn.
Effects of Heavy Tailed Distribution on Statistical and Neural Network Based Control Charts

Artificial Neural Networks (ANN) had been used for the detection and classification of patterns in control charts. It has been shown that neural network can detect smaller shifts better than statistical control charts. However, nearly all studies in this area assume that the in-control process data in the control charts follow a normal distribution. In our study, we focus on the effects of heavy tailed distributions on the performance of neural network based control chart and statistical control charts. Statistical control charts like Shewhart X control chart, Exponentially Weighted Moving Average (EWMA) control chart and Cumulative Sum (CUSUM) control chart are presented to make the comparison of the effects of heavy tailed distribution with Neural network based control chart. The criterion to compare the performance of both types of control charts is the average run length (ARL). From the results, the neural network is less robust than the statistical based control charts in the presence of heavy tailed data.
A Review on Digital Image Enhancement Using Histogram Techniques

Image enhancement has significance in image processing. This technique is used as a preprocessing step in speech recognition, texture synthesis, Computer graphics, intelligent transportation systems, Digital camera images etc. To enhance the image we use histogram process that is simple and easy to apply and use. However, histogram equalization (HE) usually results in excessive contrast enhancement, which sometimes gives the processed image an unnatural look and creates visual artifacts but this is one of the best technique to enhance the image. Large number of research work is proposed for pixel and window based image fusion in histogram equalization. This paper introduces comparative study of different histograms.
Detecting falls at homes using a network of low-resolution cameras

In a smart home system, a camera-based fall detector at elderly homes leads to immediate alarming and helping. In this paper we propose an approach for the detection of falls based on multiple cameras. Based on semantic driven features, fall detection is done in 3D and fuzzy logic is used to estimate confidence values for different human postures as well as for the incidence of a fall/no fall. Emphasis is given on simplicity, low computational effort and fast processing. Therefore, based on an evaluation on 73 test sequences, we show the applicability of the method for videos with low spatial resolution and frame rate.
Comparison of GARCH and neural network methods in financial time series prediction

Many researchers have already published huge number of papers comparing Autoregressive (AR) model, a model based on Box-Jenkins methodology, and Back Propagation Artificial Neural Network (BPANN) in financial time-series forecasting. Among them, some compared SVMs and BPs taking AR as a benchmark in forecasting the six major Asian stock markets. They showed that both the SVMs and BPs outperform the traditional models, ARs. They did prediction of transformed data, but not level data. They did not take account of GARCH model, specially developed to model financial time series. Generalized Autoregressive Conditional Heteroskedastic (GARCH) model is needed to capture high volatility for better forecasts. This article applies GARCH model instead AR or ARMA model to compare with standard BP in forecasting of the four international including two Asian stock markets indices. Our fitted GARCH models give better forecasts than the fitted standard BP models in forecasting of the four international markets indices except one market.
EdgeLap : Identifying and discovering features from overlapping sets in networks

Microbes are single celled organisms that can be found everywhere in the world from the air to the soil on the ground. Generally speaking, microbes will be found in extremely close proximity to other species of microbes due to mutual beneficial relationships where one species produces something that another species requires for survival; these groups of microbes are called a community. A widespread question in the microbiological community is trying to identify what microbial interactions are commonly found or not found throughout different communities. To address this question, EdgeLap was developed. EdgeLap is a tool that creates visualizations to help its end users, microbiologists, easily identify and discover common microbial interactions. Although EdgeLap is specifically designed with microbial communities in mind, it can be applied to many other biological networks or even social networks where examining the edges in a network is a question of interest.
Affective Empathy in Children: Measurement and Correlates

Empathy is a construct that plays a pivotal role in the development of interpersonal relationships, and thus ones ability to function socially and often professionally. The development of empathy in children is therefore of particular interest to allow for further understanding of normative and atypical developmental trajectories. This thesis investigated the assessment of affective empathy in children aged 5-12, through the development and comparison of a multimethod assessment approach. Furthermore this thesis evaluated the differential relationships between affective empathy and global behavioural problems in children versus the presence of early psychopathic traits, such as callous-unemotional traits. The first component of this study incorporated; a measure of facial expression of affective empathy, and self-reported experience of affective empathy, as measured by the newly designed Griffith Empathy Measure – Video Observation (GEM-VO) and the Griffith Empathy Measure – Self Report (GEMSR); the Bryant’s Index of Empathy for Children and Adolescents (1982) which is a traditional child self-report measure; and a newly designed parent-report of child affective empathy (Griffith Empathy Measure – Parent Report; GEM-PR). Using a normative community sample of 211 children from grades 1, 3, 5, and 7 (aged 5-6, 7-8, 9-10, & 11-12, respectively), the GEM-PR and the Bryant were found to have moderate to strong internal consistency. As a measure of concurrent validity, strong positive correlations were found between the mother and father reports (GEM-PR) of their child’s affective empathy, for grades 5 and 7, and for girls of all age groups. Using a convenience sample of 31 parents and children aged 5 to 12, the GEM-PR and the Bryant
Adapting Translation Models to Translationese Improves SMT

Translation models used for statistical machine translation are compiled from parallel corpora; such corpora are manually translated, but the direction of translation is usually unknown, and is consequently ignored. However, much research in Translation Studies indicates that the direction of translation matters, as translated language (translationese) has many unique properties. Specifically, phrase tables constructed from parallel corpora translated in the same direction as the translation task perform better than ones constructed from corpora translated in the opposite direction. We reconfirm that this is indeed the case, but emphasize the importance of using also texts translated in the 'wrong' direction. We take advantage of information pertaining to the direction of translation in constructing phrase tables, by adapting the translation model to the special properties of translationese. We define entropy-based measures that estimate the correspondence of target-language phrases to translationese, thereby eliminating the need to annotate the parallel corpus with information pertaining to the direction of translation. We show that incorporating these measures as features in the phrase tables of statistical machine translation systems results in consistent, statistically significant improvement in the quality of the translation.
A Virtual Campus Tour Service Using Remote Control Robots on Robot Service Network Protocol

Recently, there are increasing interests of integration between robot technologies and Internet services, and various cloud-based service platforms for robots are proposed. From these backgrounds, in this paper, we propose a virtual campus tour service which enables remote users to control robots in school campuses via the Internet. In implementing the service, we use Robot Service Network Protocol (RSNP), which is a common protocol specification for robot services. Therefore, users can operate various remote robots in various campuses via the proposed service. Furthermore, we conduct subjective and objective assessments on camera functions for the service, and verify the effectiveness of the proposed one.
An Intelligent City Guide with a "Metal Detector" Interface

In this paper, we propose an intelligent location-aware city guide system which uses a “metal detector” interface for navigating users. Our system allows users to detect shops that match their preferences, in the same way an actual metal detector would be used to find metal objects. The procedure for finding shops that match users’ preferences includes a novel algorithm for learning frequented shops. Evaluation test results validate our overall approach. ACM Classification H5.m [Information interfaces and presentation]: Miscellaneous.
Development of Realistic Digital Expression of Human Avatars through Pupillary Responses based on Heart Rate

The eyes of a virtual human avatar are the fundamental means for communicating language and affective feelings in a virtual environment. For this purpose, this study evaluates user's visual feeling according to the changes pupillary responses based on the heart rate for a representing a human avatar; this is considered as a new factor for representing a realistic avatar. We construct a human avatar in which the pupillary response is delivered real-time based on the heart rate. The results can be regarded as the basis for designing a realistic human avatar system by supporting a new visual realistic representing factor.