Miracle-dz/UniFAR-FTUs huggingface dataset:
Fast subsequence matching in time-series databases

We present an efficient indexing method to locate 1-dimensional subsequences within a collection of sequences, such that the subsequences match a given (query) pattern within a specified tolerance. The idea is to map each data sequences into a small set of multidimensional rectangles in feature space. Then, these rectangles can be readily indexed using traditional spatial access methods, like the R*-tree [9]. In more detail, we use a sliding window over the data sequence and extract its features; the result is a trail in feature space. We propose an efficient and effective algorithm to divide such trails into sub-trails, which are subsequently represented by their Minimum Bounding Rectangles (MBRs). We also examine queries of varying lengths, and we show how to handle each case efficiently. We implemented our method and carried out experiments on synthetic and real data (stock price movements). We compared the method to sequential scanning, which is the only obvious competitor. The results were excellent: our method accelerated the search time from 3 times up to 100 times.
Simulation and visualization of a market-based model for logistics management in transportation

Distributed logistics and transportation is an important and emerging area of application for multi-agent systems, which has recently attracted a lot of research interest. In previous research ([1], [2]) we have proposed and developed novel techniques to deal with some of the challenges and problems in this application domain. In this paper we describe the software system which was built to visualize and demonstrate our multi-agent model.
Learning to Adjust and Refine Gait Patterns for a Biped Robot

In this paper, a reinforced learning method for biped walking is proposed, where the robot learns to appropriately modulate an observed walking pattern. The biped robot was equipped with two Q -learning mechanisms. First, the robot learns a policy to adjust a defective walking pattern, gait-by-gait, into a more stable one. To avoid the complexity of adjusting too many joints of a humanoid robot and to speed up the learning process, the dimensionality of the action space was reduced. In turn, the other learning mechanism trained the robot to walk in a refined pattern, allowing it to walk faster without the loss of other required criteria, such as walking straight. This approach was implemented with both a simulated robot model and an actual biped robot. The results from the simulations and experiments show that successful walking policies were obtained. The learning system works quickly enough so that the robot was able to continually adapt to the terrain as it walked.
Innovative behaviour: the effect of structural and psychological empowerment on nurses.

AIM This paper is a report of a study to investigate the relationship between structural empowerment/psychological empowerment and innovative behaviour. BACKGROUND Innovative behaviour is essential for nurses in today's dynamic healthcare systems. Empowerment has become an increasingly important factor in predicting innovative behaviour. Structural empowerment refers to power based on the employee's position in the organization, while psychological empowerment consists of the fundamental personal convictions that employees have about their role in the organization. METHODS A cross-sectional correlational survey was conducted in The Netherlands in 2007 with 519 Registered Nurses. The instruments used were the Conditions of Work Effectiveness Questionnaire II, the Psychological Empowerment Instrument and the Questionnaire Innovative Behaviour. Four hypotheses were tested using descriptive statistics, bivariate and multiple regression and one-way analysis of variance. RESULTS Structural and psychological empowerment were statistically significant predictors of innovative behaviour. Informal power and impact were the most relevant determinants of innovative behaviour, the latter the strongest. Psychological empowerment functioned as a mediator between structural empowerment and innovative behaviour. The expected moderating effect of structural empowerment was not proven. CONCLUSION Organizations need to create the right conditions to be able to strengthen nurses' empowerment. Networking is an important skill which should be incorporated in the nursing education. Future research should take into consideration nursing culture and personality traits as determining factors. Replication of this study in another setting and a study examining causality are recommended so that additional empirical evidence may be obtained.
Design and validation of standardized clinical and functional remission criteria in schizophrenia

Background International Remission Criteria (IRC) for schizophrenia were developed recently by a group of internationally known experts. The IRC detect only 10%–30% of cases and do not cover the diversity of forms and social functioning. Our aim was to design a more applicable tool and validate its use – the Standardized Clinical and Functional Remission Criteria (SCFRC). Methods We used a 6-month follow-up study of 203 outpatients from two Moscow centers and another further sample of stable patients from a 1-year controlled trial of atypical versus typical medication. Diagnosis was confirmed by International Classification of Diseases Version 10 (ICD10) criteria and the Mini-International Neuropsychiatric Interview (MINI). Patients were assessed by the Positive and Negative Syndrome Scale, including intensity threshold, and further classified using the Russian domestic remission criteria and the level of social and personal functioning, according to the Personal and Social Performance Scale (PSP). The SCFRC were formulated and were validated by a data reanalysis on the first population sample and on a second independent sample (104 patients) and in an open-label prospective randomized 12-month comparative study of risperidone long-acting injectable (RLAI) versus olanzapine. Results Only 64 of the 203 outpatients (31.5%) initially met the IRC, and 53 patients (26.1%) met the IRC after 6 months, without a change in treatment. Patients who were in remission had episodic and progressive deficit (39.6%), or remittent (15%) paranoid schizophrenia, or schizoaffective disorder (17%). In addition, 105 patients of 139 (51.7%), who did not meet symptomatic IRC, remained stable within the period. Reanalysis of data revealed that 65.5% of the patients met the SCFRC. In the controlled trial, 70% of patients in the RLAI group met the SCFRC and only 19% the IRC. In the routine treatment group, 55.9% met the SCFRC and only 5.7% the IRC. Results of the further independent sample demonstrated that 35% met the IRC, 65% the SCFRC, and 56% of patients met both the symptomatic and functional criteria. In the controlled trial of RLAI and olanzapine, 40% and 35% of patients, respectively, met the IRC, while 70% and 55%, respectively, met the SCFRC. Conclusion In schizophrenia outpatients, a greater proportion of stable cases is detected in remission by SCFRC in comparison with IRC. The SCFRC were more sensitive to the full spectrum of schizophrenia. The SCFRC appear to be valid as a tool and clinically useful as they produce a comprehensive assessment of treatment effectiveness for a wide range of patients.
Texture-Energy-Analysis-Based Image Recognition Algorithm for Identifying the Early Stage Tumor

A new algorithm (image registration and texture energy analysis - ITEAR), which can accurately identify the early stage tumor in CT images, is developed in this paper. The ITEAR algorithm aligns the corresponding PET and CT images by the mutual information registration method and applies the adaptive threshold segmentation algorithm to segmenting the region of interest (ROI) in PET images. Then, the texture energy measurement method and edge detection processes are implemented to locate the accurate position of the early stage tumor and extract its edge. It has been proved by experiments that the ITEAR algorithm can be used to accurately locate the early stage tumor in any part of human body.
Information Propagation Speed and Patterns in Social Networks: A Case Study Analysis of German Tweets

In this paper, we present our experiences in analyzing Twitter data. The analysis has shown that information diffuses over time through the Twitter network in certain patterns. Furthermore, it has shown those friend relationships significantly influence the information propagation speed on Twitter. Since it was launched in 2006, the microblogging service grew tremendously. Tweets are sent by users all around the world. Results show that there are two major patterns. While these patterns accommodate us to understand the diffusion of information through Twitter in an even better plan, the analysis of friend networks provides information on who influences the network, concerning the number of re-tweets and the time between a tweet and its re-tweets. The approaches have been evaluated both technically, based on how certain a topic matches one of the patterns and how prominent friends are compared to other users, and conceptually, based on existing, well-known approaches in measuring the speed and scale of information diffusion on Twitter.
Weakly supervised parsing with rules

This work proposes a new research direction to address the lack of structures in traditional n-gram models. It is based on a weakly supervised dependency parser that can model speech syntax without relying on any annotated training corpus. La- beled data is replaced by a few hand-crafted rules that encode basic syntactic knowledge. Bayesian inference then samples the rules, disambiguating and combining them to create complex tree structures that maximize a discriminative model's posterior on a target unlabeled corpus. This posterior encodes sparse se- lectional preferences between a head word and its dependents. The model is evaluated on English and Czech newspaper texts, and is then validated on French broadcast news transcriptions.
Levels of 4-Hydroxynonenal and Malondialdehyde Are Increased in Brain of Human Chronic Users of Methamphetamine

Animal studies suggest that the widely used psychostimulant drug methamphetamine (MA) can harm brain dopamine neurones, possibly by causing oxidative damage. However, evidence of oxidative damage in brain of human MA users is lacking. We tested the hypothesis that levels of two “gold standard” products generated from lipid peroxidation, 4-hydroxynonenal (one of the most reactive lipid peroxidation aldehyde products) and malondialdehyde, would be elevated in post mortem brain of 16 dopamine-deficient chronic MA users compared with those in 21 matched control subjects. Derivatized aldehyde concentrations were determined by gas chromatography-mass spectrometry. In the MA group, we found significantly increased levels of 4-hydroxynonenal and malondialdehyde in the dopamine-rich caudate nucleus (by 67 and 75%, respectively) and to a lesser extent in frontal cortex (48 and 36%, respectively) but not in the cerebellar cortex. Approximately half of the MA users had levels of 4-hydroxynonenal falling above the upper limit of the control range in caudate and frontal cortex. A subgroup of MA users with high brain drug levels had higher concentrations of the aldehydes. Our data suggest that MA exposure in human causes, as in experimental animals, above-normal formation of potentially toxic lipid peroxidation products in brain. This provides evidence for involvement of oxygen-based free radicals in the action of MA in both dopamine-rich (caudate) and -poor (cerebral cortex) areas of human brain.
Implementing surgical services in a rural, resource-limited setting: a study protocol

Introduction There are well-established protocols and procedures for the majority of common surgical diseases, yet surgical services remain largely inaccessible for much of the world's rural poor. Data on the process and outcome of surgical care expansion, however, are very limited, and the roll-out process of rural surgical implementation in particular has never been studied. Here, we propose the first implementation research study to assess the surgical scale-up process in the rural district of Achham, Nepal. Methods and analysis Based primarily on the protocols of the WHO's Integrated Management for Emergency and Essential Surgical Care (IMEESC), this study's threefold implementation strategy will include: (1) the core IMEESC surgical care program, (2) community-based follow-up via health workers, and (3) hospital-based quality improvement programs. The implementation program will employ additional emergency and surgical care protocols developed collaboratively by physicians, nurses and the authors. This strategy will be referred to as IMEESC-Plus. This study will employ both qualitative and quantitative research methodologies to collect clinical data and information on the reception and utilisation of services. The first 18 months of the implementation process will be studied and divided into an initial phase (first 6 months) and a consolidation phase (subsequent 12 months). Discussion This study aims to describe the logistics of the implementation process of IMEESC-Plus, and assess the quality of the resulting IMEESC-Plus services during the course of the implementation process. Using data generated from this study, larger, multi-site implementation studies can be planned that assess the scale-up of surgical services worldwide in resource-limited areas.
Knowledge discovery on extracted outlook pst files and emails

Emails organize the bulk of all official communications in an organization. Email repositories are implicit warehouses of knowledge about projects, people and processes. There would be value to several domains in visualizing and discovering sentiments in Email data. This proposed work describes how to extract personal storage tables, also known as . pst files, to back up pst data, created by using Microsoft Office Outlook. And also Extract contacts, messages, appointments, notes, tasks, journal entries and calendars from pst files and store it into database. The summary consists of a list of action items extracted from the inbox message of Gmail, Microsoft Account, YahooMail An email analytics structure that combines data analytics and text-mining principles to mine email repositories for useful insights. The email text analysis can be used to estimate communication of interactions among the members. It also supports identify candidates of leaders acting as a hub of information channel of the communication network. And also it presents Sentiment Analysis, an interactive visualization system that objective to analyze the email data of an organization.
A Brief Introduction to the GeM Annotation Schema for Complex Document Layout

In this paper we sketch the design, motivation and use of the GeM annotation scheme: an XML-based annotation framework for preparing corpora involving documents with complex layout of text, graphics, diagrams, layout and other navigational elements. We set out the basic organizational layers, contrast the technical approach with some other schemes for complex markup in the XML tradition, and indicate some of the applications we are pursuing.
Spiritual health, intellectual disability and health care

Our focus has been the field of intellectual disability (ID), where there are few studies on religion, religious services or activities, prayer and health in the population of persons with disability and especially intellectual disability. The Institute for Health, Health Care Policy and Aging Research at Rutgers University (2,3) has studied health practices, social activities, well-being and attendance or religious services as a predictor of the course of disability in a population of non -institutionalised community dwelling elderly persons (2,812 persons with an average age of 74.5 years) from New Haven, who were interviewed annually from 1982 through 1989. Disability (like stroke, diabetes, broken bones, amputation etc) was composed of 15 items and rated from “some difficulty” to “unable to do”. The studies showed that religious involvement was tied to a broad array of behavioural and psychosocial resources, that these resources were associated with attendance at services and some of these associations were especially pronounced among disabled elderly persons. Their longitudinal study with a 12 years followup showed that attendance at services was a strong predictor of better functioning, that health practices, social ties and indicators of wellbeing reduced, but did not eliminate these effects, and that disability had minimal effects on subsequent attendance. The studies showed that religious participation had an impact on health and well-being of elderly people and especially these with an disability.
Hepatitis C viraemia in adults with type 2 autoimmune hepatitis

Sera from 14 patients with type 2 autoimmune hepatitis (anti‐LKM, positive) were investigated for evidence of hepatitis C virus (HCV) infection. Antibodies to HCV were detected in 13 patients by both commercial and “in‐house” ELlSAs and also by a second generation recombinant immunoblot assay. Nine of the 13 (69%) anti‐HCV positive patients were shown to be viraemic by a polymerase chain reaction‐based assay for serum HCV RNA. Neither anti‐HCV nor serum HCV RNA were detected in any of 6 controls with primary biliary cirrhosis or in 39 healthy blood donors. These findings strongly suggest a role for HCV in the pathogenesis of type 2 autoimmune hepatitis.
CTX-M-14 and CTX-M-15 enzymes are the dominant type of extended-spectrum β-lactamase in clinical isolates of Escherichia coli from Korea

This study was performed to assess the prevalence and genotypes of plasmid-borne extended-spectrum β-lactamases (ESBLs) and AmpC β-lactamases in Escherichia coli in Korea. A total of 576 isolates of E. coli was collected from 12 Korean hospitals during May and July 2007. A phenotypic confirmatory test detected ESBLs in 82 (14.2 %) of the 576 E. coli isolates. The most common types of ESBLs identified were CTX-M-14 (n=32) and CTX-M-15 (n=27). The prevalence and diversity of the CTX-M mutants, including CTX-M-15, CTX-M-27 and CTX-M-57, with significant hydrolytic activity against ceftazidime were increased. PCR experiments detected genes encoding plasmid-borne AmpC β-lactamases in 15/56 cefoxitin-intermediate or cefoxitin-resistant isolates, and the most common type of AmpC β-lactamase identified was DHA-1 (n=10). These data suggest that the incidence of ESBLs in E. coli has increased as a result of the dissemination of CTX-M enzymes in Korea. In addition, CTX-M-22, CTX-M-27 and CTX-M-57 have appeared in Korea.
VLSI architecture for dynamic time-warp recognition of handwritten symbols

The method of dynamic time warping is a well-established technique for time alignment and comparison of speech and image patterns. It has found extensive application in speech recognition and related areas of pattern matching. Comparing the handwritten symbol to the set of training symbols (called reference symbols), we can recognize the input handwritten symbol by computing the distances among the input symbol and the reference symbols in the training set. In this paper we propose a VLSI architecture based on the space-time domain expansion which can compute the symbol distance and also give the index pairs which correspond to the warp function. The time complexity is O(max(m, n)) by using m × n processing elements array, where m is the length of the input symbol and n is the length of the reference symbol. With a uniprocessor, the matching process will have the time complexity O(m × n). If there are p reference symbols, using the proposed architecture, the recognition problem can be solved in time O(max(m, n, p)). With a uniprocessor, the time complexity will be O(m × n × p). The algorithm partition problems are discussed. Verification of the proposed VLSI architecture is also given.
Adaptive display algorithm for interactive frame rates during visualization of complex virtual environments

We describe an adaptive display algorithm for interactive frame rates during visualization of very complex virtual environments. The algorithm relies upon a hierarchical model representation in which objects are described at multiple levels of detail and can be drawn with various rendering algorithms. The idea behind the algorithm is to adjust image quality adaptively to maintain a uniform, user-specified target frame rate. We perform a constrained optimization to choose a level of detail and rendering algorithm for each potentially visible object in order to generate the “best” image possible within the target frame time. Tests show that the algorithm generates more uniform frame rates than other previously described detail elision algorithms with little noticeable difference in image quality during visualization of complex models. CR
Memory-Based Learning for Article Generation

Article choice can pose difficult problems in applications such as machine translation and automated summarization. In this paper, we investigate the use of corpus data to collect statistical generalizations about article use in English in order to be able to generate articles automatically to supplement a symbolic generator. We use data from the Penn Treebank as input to a memory-based learner (TiMBL 3.0; Daelemans et al., 2000) which predicts whether to generate an article with respect to an English base noun phrase. We discuss competitive results obtained using a variety of lexical, syntactic and semantic features that play an important role in automated article generation.
Help me help you: Interfaces for personal robots

The communication bottleneck between robots and people [1] presents an enormous challenge to the human-robot interaction community. Rather than exclusively focusing on improving robot object learning, task learning, and natural language understanding, we propose also designing interfaces that make up for low communication bandwidth by thoughtfully accounting for the constrained capabilities of robots [2]. People are adept at compensating for communication limitations, changing their communicative strategies for talking to pets, babies [3], foreigners [4], and robots [5]. Communicative accommodation already exists. Thus, instead of requiring robots to perfectly understand natural language, gestures, etc., there is a wide variety of research and design to be done in the space of alternative communicative modalities. We propose to approach this problem by accounting for limitations in robot abilities and taking advantage of already familiar human-computer interaction models, leveraging a communication model based upon Information Theory. Using this design perspective, we present three different mobile user interfaces that were fully developed and implemented on a PR2 (Personal Robot 2) [6] for task domains in navigation, perception, learning and manipulation.
Harmonic distortion correction for 8-bit delay line ADC using gray code

Harmonic distortion correction (HDC) is an effective digital calibration technique to estimate and correct errors and distortions in an analog circuit. However, the convergence time is still a concern. In this paper, we propose the injection of a periodic 3-bit gray code sequence for HDC to digitally calibrate an 8-bit delay line ADC. In our simulation results, digital calibration with the gray code injection improves SNDR and SFDR to 42.5 dB and 45.4 dB, respectively, compared with the original SNDR of 25.6 dB and the original SFDR of 25.7 dB, with a 13.5 milliseconds calibration time, which is 64X faster than with injection of pseudorandom numbers (860 milliseconds). Also the SNDR converges to 41.6 dB after averaging 224 samples, while the SNDR with injection of pseudorandom numbers converges to 41.5 dB after 237 samples.