Miracle-dz/UniFAR-FTUs huggingface dataset:
Internal Social Media: A New Kind of Participatory Organizational Communication?: Two Explorative Studies of Coworkers as Communicators on Internal Social Media

Purpose – The aim of the article is to explore the challenges associated with introducing internal social media (ISM) into organizations in order to help them reap the benefits of coworker communication on ISM. Design/methodology/approach – The article is based on an exploratory study in ten organizations. The data was collected in semi-structured interviews with ISM coordinators in spring 2014. Findings – According to the ISM coordinators, four challenges were associated with introducing ISM: (1) Coworkers could perceive communication on ISM as not work-related. (2) Coworkers might not understand the informal nature of communication on ISM, and self-censorship might stop them communicating on ISM. (3) ISM was not considered a ‘natural’ part of the daily routines in the organizations, and (4) top managers mainly supported ISM in words, not in action. Research limitations/implications – The study is based on the perceptions of ISM coordinators. Further research is called for to explore both coworker perceptions and actual communication on ISM. Practical implications – Practitioners introducing ISM should be aware of these four challenges, and should help coworkers to make sense of communication on ISM as work-related communication among coworkers. ISM coordinators’ perceptions of their own role in relation to coworker communication on ISM make a difference. Originality/value – The study provides insights into the key challenges associated with introducing ISM, as well as the role of ISM coordinators as community facilitators and sense-givers.
CLASSIFICADOR ADAPTATIVO PARA DADOS IMAGEM EM ALTA DIMENSIONALIDADE COM UM CONJUNTO PEQUENO DE AMOSTRAS DE TREINAMENTO

Neste trabalho é testado um classificador adaptativo que visa suavizar os efeitos causados por um número insuficiente de amostras de treinamento, fato este que pode degradar severamente a acurácia dos resultados obtidos por um classificador paramétrico utilizando dados com dimensão alta. O classificador adaptativo adiciona amostras semi-rotuladas ao conjunto das amostras de treinamento com o objetivo de reduzir os efeitos causados pelo pequeno número de amostras. O efeito das amostras semi-rotuladas é controlado por meio de um peso menor do que o peso atribuído as amostras originais. Os experimentos desenvolvidos mostram que este procedimento é eficiente na redução dos efeitos do fenômeno de Hughes contribuindo para aumentar a acurácia da imagem temática produzida.
Construction of Word Network from Traditional Chinese Medicine Corpus

: In this paper, we created an automatic quanticized traditional Chinese medicine (TCM) term network with the measurement of cosine distance. After scanning over the corpus, we got a set of word vectors whose relationships could be measured. After clustering, we obtained a three-level network as a category tree. Leaves stand for different types of words and we got clusters like herbs, diseases, theories of medicine etc. Of all categories, we selected words nearest to the center of each cluster and invited our experts to evaluate whether a word is a correct uncollected TCM term and got a new word extraction rate of around 70%. Our network was almost completely machine-generated so that it is much more efficient and might lead us to several new approaches of TCM with the knowledge from our network.
Building Model Object Classification for Semantic Enrichment Using Geometric Features and Pairwise Spatial Relations

Semantic enrichment is a process of supplementing/correcting information in a poorly prepared BIM model. Object classifications are essential information, but are commonly missing or incorrectly represented when transferring a BIM model or creating a model using tools customized for other domains in design. Automated compilation of 'as-is' BIM models from point cloud data also requires object classification, as well as 3D reconstruction. We present a systematic approach to classifying objects in a BIM model, for use in future semantic enrichment systems. Previous work on object classification in BIM model enrichment was restricted by its limited ability to accurately interpret geometric and spatial features and by the constraints of Boolean logic rules and the rule compilation process. To address these issues, we propose a procedure for establishing a knowledge base that associates objects with their features and relationships, and a matching algorithm based on a similarity measurement between the knowledge base and facts. An implementation on a synthetic bridge model shows that whereas some objects can be classified by shape features alone, most objects require the use of spatial relations for unique classification. Spatial context is more likely to uniquely identify an object than shape features are.
A CMOS Charge Pump Circuit with Short Turn-on Time for Low-spur PLL Synthesizers

A charge pump circuit with very short turn-on time is presented for minimizing reference spurs in CMOS PLL frequency synthesizers. In the source switching charge pump circuit, applying proper voltages to the source nodes of the current source FETs can significantly reduce the unwanted glitch at the output current while not degrading the rising time, thus resulting in low spur at the synthesizer output spectrum. A 1.1-1.6 GHz PLL synthesizer employing the proposed charge pump circuit is fabricated in 65 nm CMOS. The current consumption of the charge pump is 490 μA from 1 V supply. Compared to the conventional charge pump, it is shown that the reference spur is improved by 7.5 dB through minimizing the turn-on time. Theoretical analysis is described to show that the measured results agree well with the theory.
Statistical Analysis of Functional Magnetic Resonance Imaging Data

Human beings have long been interested in how we ourselves work and function. Of particular fascination has been the working of the human brain. Through the ages we have had opportunity to learn about how our brain functions, mostly haphazardly as the result of illness or accident: a Roman gladiator who suffered amnesia after a blow to the skull; an elderly person who had a stroke, lost the use of one side of the body, but gradually recovered much of the original functionality; savants who were unable to speak but were musical or mathematical prodigies. From all of these isolated incidents, together with postmortem dissections of the brains of healthy individuals, scientists were able to build models of how the brain functions, how information is processed, and what specific regions in the brain are responsible for different types of tasks. It is only relatively recently, however, that technological advances have allowed us to study the function of the human brain – healthy or diseased – in something closer to real time. Neuroimaging techniques such as positron emission tomography (PET), electroencephalography (EEG), magnetoencephalography (MEG), and functional magnetic resonance imaging (fMRI) have brought a wealth of new knowledge and new understanding to cognitive neuroscientists. Statisticians have been an important part of this exciting endeavor. Statistical Analysis of Functional Magnetic Resonance Imaging Data
Computer-assisted detection of 90% of EFL student errors

ABSTRACT Software can facilitate English as a Foreign Language (EFL) students’ self-correction of their free-form writing by detecting errors; this article examines the proportion of errors which software can detect. A corpus of 13,644 words of written English was created, comprising 90 compositions written by Spanish-speaking students at levels A2-B2 (inclusive) of the Common European Framework. A total of 1,310 language errors were detected by the researcher. It was found that approximately 21% of these errors were spelling errors. A further 58% were characterised as either two-word phrases (45%), three-word phrases (9%), or four- and five-word phrases (4%) which are either absent from or rare in a large corpus of English which is known to be correct. The nature of software which can detect such words and phrases and bring them to students’ attention with a view to self-correction is briefly described. Of the remaining 21% of errors not detected by such software, most were found to be either errors of tense (7%), misuse of false friends (4%) or problems with determiners (3%). Again, software which can help students detect and correct such errors is outlined. The limitations and pedagogical significance of the research are then briefly discussed.
Passive Capture for Ubiquitous Learning Log Using SenseCam

In our previous works, we developed a system named SCROLL in order to log, organize, recall and evaluate the learning log. However up to now, we just use an active mode to record logs. This means that a learner must take a capture of learned contents consciously and most of learning chances be lost unconsciously. In order to solve this problem, we started a project named PACALL (Passive Capture for Learning Log) in order to have a passive capture using SenseCam. With the help of SenseCam, learner's activity can be captured as a series of images. We also developed a system to help a learner find the important images by analyzing sensor data and images processing technology. Finally, the selected images will be uploaded to the current SCROLL system as ubiquitous learning logs. This research suggests that SenseCam can be used to do passive capture of learning experiences and workload of reflection can be reduced by analyzing sensor data of SenseCam.
Digital Competence in the Kindergarten Sector

The kindergarten project at the The Norwegian Centre for ICT in Education will help to promote the development of digital competence in Early Childhood Education and Care and in pre-school teacher-training in a secure and professionally-oriented way. The main goal of the kindergarten project is to develop and strengthen use of ICT for improved quality, a better learning outcome and learning strategies for children in kindergartens and for students in pre-school teacher training. The key aim is to strengthen the digital competence of the entire kindergarten sector in the future. In December 2011 in Norway, 282,732 children attended kindergartens, and there were 88,823 employees in the kindergarten sector (Utdanningsdirektoratet 2012).
Allocating Police Resources While Limiting Racial Inequality

Abstract Police targeting hot spots of crime tends to disproportionately burden minorities via stops and arrests. This work attempts to reduce disproportionate minority contact by formulating a crime hot spots spatial allocation strategy for police that prioritizes areas of high crime, but constrains the targeted hot spots given different levels of acceptable racial inequality. This racial inequality constraint is measured as the proportion of minorities likely to be stopped in those areas prioritized by police. Using data on stops and crime in New York City, I show that police stops can be more equitably distributed according to race, but there are fundamental trade-offs. One cannot gain a racial distribution of stops proportionately equal to the residential population without large decreases in the efficiency of targeting high crime areas. More modest gains can be had though in reducing the proportion of minorities stopped while still targeting high crime locations.
Implementasi Teknik Seleksi Fitur Pada Klasifikasi Malware Android Menggunakan Support Vector Machine

Android Malware has grown significantly along with the advance of the times and the increasing variety of technique in the development of Android. Machine Learning technique is a method that now we can use in the modeling the pattern of a static and dynamic feature of Android Malware. In the level of accuracy of the Malware type classification, the researcher connect between the application feature with the feature required by each type of Malware category. The category of malware used is a type of Malware that many circulating today, to classify the type of Malware in this study used Support Vector Machine (SVM). The SVM type will be used is class SVM one against one using the RBF Kernel. The feature will be used in this classification are the Permission and Broadcast Receiver. To improve the accuracy of the classification result in this study used Feature Selection method. Selection of feature used is Correlation-based Feature Selection (CFS), Gain Ratio (GR) and Chi-Square (CHI). A result from Feature Selection will be evaluated together with result that not use Feature Selection. Accuracy Classification Feature Selection CFS result accuracy of 90.83%, GR and CHI of 91.25% and data that not use Feature Selection of 91.67%. The result of testing indicates that permission and broadcast receiver can be used in classifying type of Malware, but the Feature Selection method that used have accuracy is a little below the data that are not using Feature Selection.
Restraining Technical Debt when Developing Large-Scale Ajax Applications

Addressing technical debt during the software development process relies heavily on a refactoring phase, in which automatic code transformations are used as a crucial mechanism to reduce a system's technical debt. However, automatic refactoring is not an option when developing Ajax applications. Therefore, an approach that restrains the accumulation of a system's technical debt is needed. In this paper, we present and evaluate such an approach and its reification as a framework. We conclude that our proposed framework enables restraining technical debt in a large-scale Ajax application without the need for automatic code
APPLICATION OF RADIAL BASIS FUNCTION NEURAL NETWORK TO PREDICT EXCHANGE RATE WITH FINANCIAL TIME SERIES

Abstract The literature indicates that exchange rates are largely unforecastable from the fact that the overwhelming majority of studies have employed linear models in forecasting exchange rates. In this paper, we applied Radial Basis Function Neural Network (RBFNN) to predict exchange rate, motived by the fact that RBFNN have the ability to implicitly detect complex nonlinear relationships between dependent and independent variables as it “learns” the relationship inherent in the exchange rate data presented to it. The model learning algorithm uses a diverse data set for training so as to adapt itself quickly for new exchange rate data. We apply the RBFNN to panel data of the exchange rates (USD/EUR, JPN/USD, USD/GBP, USD/CHY) are examined and optimized to be used for time-series predictions, some experiments testified the proposed method is effective and feasible.
Exploring Situation of Accountability Relationship of Councilors in Multiparty Politics

This research paper explored the situation and challenges facing politically elected Councilors from different political parties on how to implementing accountability in multiparty politics. Accountability relationship plays an important role in understanding behavior and attitude of the local actors from different political institutional background, and experience towards implementation of accountability practices. Qualitative methods and approaches were utilized in data collection and analysis. The study adopted a case study approach where four (3) municipalities of Kinondoni, Temeke, and Ilala of the Dar es Salaam city council in Tanzania were involved in the study. The overall sample of 44 respondents involved in the study through in-depth interviews. Documentary review was considered necessary, where different documents complementarily to the study were rationally reviewed. The results of this research paper revealed that there was a poor relationship that resulted into accountability disagreement practice in most of political governance matters due to patronage politics and policy preferences. This research paper recommends reforms on accountability framework in Dar es Salaam municipalities.
Corpus-based Modelling of Grammar Variation

Current research in natural language learning and processing supports the view that grammatical competence consists in mastering and integrating multiple, parallel “constraints” (Seidenberg and MacDonald 1999, MacWhinney 2004, Burzio 2005). During language comprehension, constraints are defined as a set of cues through which a speaker can successfully map a complex linguistic unit (a word form, a phrase, an utterance etc.) to its intended function in discourse. For example, in the Italian sentence arriva il treno (‘the train is approaching’), the noun phrase il treno is understood to play the role of subject due to an integrated cluster of cues concerning the position of the subject relative to the verb, noun-verb agreement, compliance of predicate selectional restrictions and general knowledge about the world. Conversely, in language production, constraints consist in discourse functions that are jointly mapped onto linguistic forms. If we want to translate arriva il treno into English, we have to be aware of the peculiar functional constraint positioning the subject before the verb in the target language.
Kera: A Unified Storage and Ingestion Architecture for Efficient Stream Processing

Big Data applications are rapidly moving from a batch-oriented execution to a real-time model in order to extract value from the streams of data just as fast as they arrive. Such stream-based applications need to immediately ingest and analyze data and in many use cases combine live (i.e., real-time streams) and archived data in order to extract better insights. Current streaming architectures are designed with distinct components for ingestion (e.g., Kafka) and storage (e.g., HDFS) of stream data. Unfortunately, this separation is becoming an overhead especially when data needs to be archived for later analysis (i.e., near real-time): in such use cases, stream data has to be written twice to disk and may pass twice over high latency networks. Moreover, current ingestion mechanisms offer no support for searching the acquired streams in real time, an important requirement to promptly react to fast data. In this paper we describe the design of Kera: a unified storage and ingestion architecture that could better serve the specific needs of stream processing. We identify a set of design principles for stream-based Big Data processing that guide us in designing a novel architecture for streaming. We design Kera in order to reduce the storage and network utilization significantly, which can lead to reduced times for stream processing and archival. To this end, we propose a set of optimization techniques for handling streams with a log-structured (in memory and on disk) approach. On top of our envisioned architecture we devise the implementation of an efficient interface for data ingestion, processing, and storage (DIPS), an interplay between processing engines and smart storage systems, with the goal to reduce the end-to-end stream processing latency.
The predictors of occupational disability in obsessive-compulsive disorder in a large clinical sample

Aim: Obsessive-compulsive disorder (OCD) is a common mental disorder leading to severe loss of functioning. We aimed to investigate socio-demographic and clinical factors affecting occupational disability in OCD. Material and Methods: It is a cross sectional study with individuals recruited from an outpatient psychiatry clinic. A total of 393 patients were given the Yale-Brown Obsessive Compulsive Scale (Y-BOCS), Hamilton Depression Rating Scale-17 Item (HAM-D), Beck Anxiety Inventory, Barratt Impulseness Scale-11 and Wender Utah Rating Scale. Occupational disability was defined as the inability to work over the past month due to pychopathology associated with OCD. Housewives, students and retired people were excluded. Results: The rates of occupational disability were 52.9% in the whole sample, 44.3% in men and 60% in women. Higher Y-BOCS scores, higher HAM-D scores, being single, female gender, younger age at first treatment, less school years and previous suicide attempt were associated with a higher risk of occupational disability. The BAI scores, previous hospitalization, age at onset, smoking, childhood ADHD and past or present tic disorder did not statistically affect occupational status. Conclusions: OCD is associated with serious occupational disability causing inability to work in more than half of patients. The severity of OCD and depressive symptoms, marital status, gender, education level, age at first treatment and history of suicide attempt predict occupational disability.
FBC: a flat binary code scheme for fast Manhattan hash retrieval

Hash coding is a widely used technique in approximate nearest neighbor (ANN) search, especially in document search and multimedia (such as image and video) retrieval. Based on the difference of distance measurement, hash methods are generally classified into two categories: Hamming hashing and Manhattan hashing. Benefitting from better neighborhood structure preservation, Manhattan hashing methods outperform earlier methods in search effectiveness. However, due to using decimal arithmetic operations instead of bit operations, Manhattan hashing becomes a more time-consuming process, which significantly decreases the whole search efficiency. To solve this problem, we present an intuitive hash scheme which uses Flat Binary Code (FBC) to encode the data points. As a result, the decimal arithmetic used in previous Manhattan hashing can be replaced by more efficient XOR operator. The final experiments show that with a reasonable memory space growth, our FBC speeds up more than 80% averagely without any search accuracy loss when comparing to the state-of-art Manhattan hashing methods.
Sistema de monitorización de puertas y ventanas de un centro de datos con IoT

En este trabajo se presenta la construcción de un prototipo de un sistema de monitorización de puertas y ventanas a través de una plataforma IoT. El objetivo fue diseñar un sistema que reporte a un servidor en la nube el cambio de estado, abierta o cerrada, de tres puertas y dos ventanas de la sala de equipos del centro de datos. Los cambios de estado son registrados por el servidor y por medio de una interfaz de usuario se muestran en línea los estados de las puertas y ventanas. La arquitectura del sistema se basa en una red inalámbrica de sensores integrada por un nodo central y cinco nodos de monitorización. Los nodos de monitorización están compuestos por una tarjeta PyBoard, dos sensores digitales de efecto Hall y una interfaz inalámbrica wifi. Al detectar un cambio de estado en puertas y ventanas, los nodos de monitorización lo notifican al nodo central, y este lo transmite al servidor por medio de un punto de acceso wifi. Cuando una puerta o ventana permanece abierta más de un período de tiempo configurable, se envía un mensaje SMS y de WhatsApp a un teléfono móvil. El alcance logrado en la transmisión wifi en la red fue 47 metros con línea de vista.