Miracle-dz/UniFAR-FTUs huggingface dataset:
Scoring in crowdsourced annotation tasks

The use of citizen science to obtain annotations from multiple annotators has been shown to be an effective methodforannotatingdatasetsinwhichcomputationalmeth- ods alone are not feasible. The way in which the annotations are obtained is an important consideration which affects the quality of the resulting consensus annotation. In this paper, we examine three separate approaches to obtaining consen- sus scores for instances rather than merely binary classifica- tions. To obtain a consensus score, annotators were asked to make annotations in one of three paradigms: classification, scoring and ranking. A web-based citizen science experi- ment is described which implements the three approaches as crowdsourced annotation tasks. The tasks are evaluated in relation to the accuracy and agreement among the par- ticipants using both simulated and real-world data from the experiment. The results show a clear difference in perfor- mance between the three tasks, with the ranking task obtain- ing the highest accuracy and agreement among the partici- pants. We show how a simple evolutionary optimiser may be used to improve the performance by reweighting the impor- tance of annotators.
Adopting Cloud Computing Technique for Efficient Event Forwarding Service To Users

Forwarding large volume of live content to relevant users in scalable efficient way in emergency application is main challenging task. One of the model named as the publisher and subscribe is used to forward the event service to users. but it fails to provide relevant event match services to subscribers another major problem is server fail . To handle this problem adopting cloud computing which provides complex computing and reliable communication. In this project proposing distributed overlay SkipCloud to organize servers giving efficient scalable and reliable event matching services to subscribers. To partition the similar subscriptions in same server for each event hybrid multi-dimensional space partition technique is used. Finally proposed techniques shows efficiency in high matching event service and reduces the event forwarding delay.
Sensing enhanced mobile computing with smart devices

Today’s mobile devices not only serve as the key communication tools, but become important sensing and computing platforms as well. Sensing capacities promise the emergence of sensing enhanced mobile computing on smart devices, which leverages various on-board sensors, applies sophisticated machine learning techniques for intelligent reasonings according to the context inference of surrounding environments. In this report, we introduce two systems we built based on this idea, which are PDS, a Phantom Data Usage Detection System and Memento, an Emotion Driven Lifelogging System. First we introduce PDS, a phantom data usage detection system. With the wide development of smartphones, mobile data usage has enjoyed rapid growth in recent years. Unfortunately many users are plagued with Phantom Data Usage (PDU), which refers to the unexpected mobile data usage that does not accord with user perception. We investigate real PDU issues and find the causes of PDU are not only the exceptions of applications, e.g., software bugs or malware, but also the user’s personalized misuse. Based on the observations that each user preserves specific data usage patterns under particular environmental context, we present PDS, which automatically detects whether the current data usage is consumed as expected. Then we present Memento, an emotion driven lifelogging system on wearables. Due to the increasing popularity of mobile devices, the usage of lifelogging has been dramatically expanded. People collect their daily memorial moments and share with friends on the social network, which has been an emerging lifestyle. We see great potential of lifelogging applications along with rapid growth of recent wearable market, where more sensors are introduced to wearables, i.e., electroencephalogram (EEG) sensors, that can further sense the user’s mental activities, e.g., emotions. We present the design and implementation of Memento, an emotion driven lifelogging system on wearables. Memento integrates
Closest Neighbor Search for Hotel Recommendation by Multiple Keywords

The searching of nearest neighbor is based on basically k-NN algorithm. In this system the k-NN algorithm is used for finding the closest location which is satisfying the user requirements by fetching the data from the stored database. In the earlier days the nearest neighbor retrieval was based on spatial index. The spatial index algorithm was having some drawbacks like space and time complexities. Also there was less speed for execution and not much user friendly. Our system uses Boyer Moore Horspool algorithm for pattern matching that is matching the keywords given by user with existing database. The main goal of this system is to find out the closest location from the user’s current location with fulfilling all the user entered menus for the hotels. For example instead of mentioning the names of the hotels search for menus required. For example if user searches for “Biryani, Paneer tikka, Chinese” then the system searches for these keywords and matches with database. And recommends the list of hotels with highest priorities. This system deals with multidimensional and give the response of all nearest neighbors in real time.
Analyzing Repetitive Evaluations of Active Rules Within a Transaction

An active database system automatically triggers rules in response to certain events occuring. Events are issued by transactions or action parts of rules. Repeated executions of rules can be caused by the structure of the initial triggering transaction program and by the structure and execution semantics of rules. Repeated calculations of rules may incur costly redundant computations in rule conditions or actions. The central contribution of this paper is to propose technics for analyzing the behaviour of a transaction and a set of rules triggered by this transaction in order to derive: (i) if a given rule is processed more than once, and (ii) a fine indication of the database changes that may occur between two consecutive executions of the rule. Knowing these changes, it is possible to use existing algorithms that compute useful intermediate expressions in a rule that can be cached and incrementally maintained in order to avoid redundant computations. A notable property of our analysis technics is that they are parametrized by a few essential semantics parameters that define the execution semantics of an active rule language. Thus, our analysis apply to a large class of existing active rule systems.
Energy Optimization in Cryptographic Protocols for the Cloud

In this chapter, we study the energy consumption by various modern cryptographic protocols for the cloud from the algorithmic perspective. The two categories of protocols we consider are (1) hash functions and (2) symmetric key encryption protocols. We identify various parameters that moderate energy consumption of these hashes and protocols. Our work is directed towards redesigning or modifying these algorithms to make them consume lesser energy. As a first step, we try to determine the applicability of the asymptotic energy complexity model by Roy on these hashes and protocols. Specifically, we try to observe whether parallelizing the access of blocks of data in these algorithms reduces their energy consumption based on the energy model. Our results confirm the applicability of the energy model on these hashes and protocols. Our work is motivated by the importance of cryptographic hashes and symmetric key protocols for the cloud. Hence the design of more energy efficient hashes and protocols will contribute in reducing the cloud energy consumption that is continuously increasing. Energy Optimization in Cryptographic Protocols for the Cloud
Neuroglobins: a look into the future

ut of the various novel insights in the ever expanding knowledge about neurology, neuroglobin has come up as an important concept. It is a recently discovered endogenous molecule of the human body with its distribution in various tissues. Staying in apparent in healthy state, it is expressed in conditions like acute hypoxia or ischemia. In the initial studies, it has shown promise as a neuroprotective agent as well as a treatment option for conditions like carbon monoxide poisoning. A brief introduction has been presented here. There have been many recent developments in the field of neurology in the last two decades. One of them has been the discovery of neuroglobin in humans in 2000 by Burmester [1]. Neuroglobin is a third type of newly discovered vertebrate globin alongside hemoglobin and myoglobin. It is an intracellular hemoprotein expressed in both central and peripheral nervous system, cerebrospinal fluid, retina and endocrine tissues and is involved in cellular oxygen homeostasis. Neuroglobin is a monomer that reversibly binds oxygen with an affinity higher than that of hemoglobin. It also increases oxygen availability to brain tissue and provides protection under hypoxic or ischemic conditions, potentially limiting brain damage. It is of ancient evolutionary origin, and is homologous to nerve globins of invertebrates [2]. Neuroglobin expression is low and may be at a ‘rest state’ under normoxia. Upon hypoxia, neuroglobin is up-regulated by transcriptional factors. However, the elevation of neuroglobin level does not persist when being exposed to prolonged hypoxia/ischemia, which indicates that neuroglobin induction is mainly an acute response of hypoxia or ischemia. Moreover, prolonged hypoxia or ischemia may promote neuroglobin degradation through oxidation and ubiquitination. Upon hypoxia or oxidative stress, neuroglobin tends to transfer to penta-coordinated ferrous status, which facilitates nitric oxide production. Nitric oxide, in turn binds to cytochrome c oxidase to inhibit cellular mitochondrial respiration, oxygen consumption and reactive oxygen species production [3]. Various studies are coming up with role of neuroglobin in different conditions. Apart from acute hypoxic states, human neuroglobin overexpression has been hypothesized to protect neurons from mitochondrial dysfunctions and neurodegenerative disorders such as Alzheimer's disease, and to play a shielding role in cancer cells where neuroglobin binds to Raf-1, suppresses Raf/ extracellular signal-regulated kinase (Erk) signaling and functions as a tumour suppressor in hepatoclellular cancer [4]. Neuroglobin is abundant in the ganglion cell layer. It’s knockdown leads to reduced activities of respiratory chain complexes I and O LETTER TO THE EDITOR
How to move away from the silos of business management education?

ABSTRACT Business management education is criticized for being too theoretical and fractional. Despite the numerous efforts to build integrated and experiential business curricula, learning is still organized in disciplinary silos. The curriculum integration efforts are carried out in separate sections of the curriculum rather than the core. There are theoretical, holistic models, but a lack of concrete examples of holistic business curriculum implementations. The authors bring the separate sections together by developing a holistic core curriculum model with three perspectives: a structure to bring intellectual coherence, people organized in learning communities, and an enterprise resource planning–supported learning environment to bring the practical training ground. The authors present a concrete implementation in a case study with first-year undergraduate business students and present their lessons learned.
Visual Servo Control by Optimizing Hybrid Objective Function with Visibility and Path Constraints

This paper presents a new stable hybrid visual servo controller to complete the task of robotarm positioning. Our method is generic hybrid method and superior to the state of the art. The objectivefunction is designed to include the full but weighted 2D and 3D available information. The positioningtask has been formulated as a minimization problem. Here, each of the 2D and 3D error functions is usedto control the six degrees of freedom. The importance weights are computed to satisfy a set of constraintsdefined by the visual servoing process. Stability analysis is also presented for the proposed control law.The experimental evaluation is done in order to show the enhanced performance of the visual servoingprocess. Simulation results show that this method provides an efficient solution to the camera retreat andfeatures visibility problems. Performance of the visual servoing system is evaluated by its ability to keepfeatures visible in the image and the Cartesian trajectory within the robot workspace during the process.
Seeking control in Modern Standard Arabic

Modern Standard Arabic (MSA) has several embedded clause constructions, some of which resemble control in English (and other languages). However, these constructions exhibit some notable differences. Chief among them is the fact that the embedded verb carries agreement features that can indicate both coreference and disjoint reference between a matrix argument and the subject of the complement clause. Through a corpus-based investigation, informed by previous insights regarding the distinction between control and no control, we found no evidence of obligatory control predicates in MSA; these findings contradict accepted generalizations (and predictions) proposed by state-of-the-art theories of control. Nevertheless, although no obligatory control predicates were found, the backward pattern, where the single expressed subject occurs in the embedded clause, revealed morphosyntactic reflexes of the control vs. no control distinction. Coreference between the expressed embedded subject and the unexpressed matrix subject was found to be restricted to a set of predicates. The existence of backward control and its relation to the backward raising construction, also found in MSA, are especially relevant for current debates regarding the theory of control. We propose an analysis that ties together control, raising and restructuring.
COMPARISON OF DIFFERENT CLASSIFICATION TECHNIQUES TO IDENTIFY COCONUT GROWING AREAS IN KOZIKHODE DISTRICT, KERALA

Abstract. This study was envisaged to map the coconut growing areas in Kerala state of India, using multidate NDVI obtained from sentinel 2A MSI data, having spatial resolution as 10 m. 95% Cloud free satellite images were taken for classification and date of pass considered for the study were 16th February, 2017 and 18th December, 2017 for Kozhikode district of Kerala. In this study bio-window of coconut plantation was identified using NDVI images of two dates. It was observed that interclass variations were more prominent in February image. Forest, dense and moderately dense coconut plantations have significantly different NDVI values in February image whereas in December image all three features have similar values. Hence, February image was classified using three classification methods i.e. ISODATA, maximum likelihood and random forest classification to assess which method is better to distinguish coconut plantation from other classes. Random Forest classification technique was found to be more accurate in identifying coconut plantation. Area was also estimated for Kozhikode district and compared with the government statistics. Google Earth was taken as reference to identify coconut plantation as it has a unique star shaped canopy, which is clearly visible in high-resolution imagery.
Smart Response Concept for Fire Emergency Response in Surabaya City

The city of Surabaya is one of the second major cities in Indonesia. At present the growth and development of the city of Surabaya is very rapid in various fields. Increased population density, the number of office building developments, residential areas, industries that are increasingly developing, causing vulnerability to fires. Fire events in the city of Surabaya tend to fluctuate. But the number of events is quite large, especially in 2017. So it is necessary to have innovative efforts to reduce losses due to fires in the city of Surabaya. This fire incident certainly gives a big loss not only materially but also casualties. In this case the Government of Surabaya has begun to implement smart city as one of the steps to deal with various problems in the city of Surabaya, one of which is fire. Through the concept of smart city, it is expected to help reduce losses caused by fires. In line with this, the aim of this research is to formulate a concept of smart fire response to reduce fire losses that occur and as an innovation in fire management. To support the implementation of the research, a triangulation analysis of stakeholders will be carried out (confirmation process from the formulation of researchers related to intelligent response concepts based on existing conditions and results of literature review of expert theories) which then results in stakeholder triangulation as the final result of this intelligent response concept formulation. Keywords—Fire; management fire, smart response, smart fire response
Prototyping Workload-based Resource Coordination for Cloud-leveraged HPC/BigData Cluster Sharing

Recently high-performance computing (HPC) and BigData workloads are increasingly running over cloud-leveraged shared resources, meanwhile traditionally dedicated clusters have been configured only for specific workloads. That is, in order to improve resource utilization efficiency, shared resource clusters are required to support both HPC and BigData workloads. Thus, in this paper, we discuss about a prototyping effort to enable workload-based resource coordination for cloud-leveraged shared HPC/BigData cluster. By taking OpenStack cloud-leveraged shared cluster as an example, we demonstrate the possibility of workload-based bare-metal cluster reconfiguration with interchangeable cluster provisioning and associated monitoring support.
Extracción de patrones para la Industria 4.0 a través de un modelo predictivo

El Internet de las Cosas ha venido incorporándose a nuestras vidas de forma progresiva, trayendo consigo grandes beneficios para la humanidad como lo es disponer de infraestructuras y servicios más interconectados y eficientes, generación de empleo, reducción de costos operativos e incremento de ganancias. En este sentido, el desarrollo e instalación de sensores avanzados para recolección de datos, las soluciones informáticas de conexión remota y otras tecnologías disruptivas están marcando un proceso de transformación en la industria; dando inicio a lo que diversos sectores han denominado cuarta revolución industrial o Industria 4.0. En este artículo se presenta un modelo predictivo para la extracción de patrones utilizando técnicas de fusión de datos que permitan el diseño de un modelo de mantenimiento predictivo, a través de un enfoque de entrenamiento supervisado, realizar la clasificación de datos y probabilísticamente valores predictivos.
Exploring the Optimal Learning Technique for IBM Neurosynaptic System to Overcome Quantization Loss

Inspired by the fact that human brain is much more efficient than any nowadays computers, neuromorphic computing is aim at performing near human brain ability of processing huge amount of data in an extreme short time. For the hardware part, neuromorphic computing is also extended to systems facilitating the computation of neural network and machine learning algorithms. Recently, IBM Neurosynaptic system is one of the well-known project that dedicated on energy-efficient neural network applications. However, However, one of the known issues in TrueNorth design is the limited precision of synaptic weights, each of which can be selected from only four integers. To improve the computation accuracy and reduce the incurred hardware cost, in this work, we investigate seven different regularization functions in the cost function of the learning process on TrueNorth platform. Our experimental results proved that the proposed techniques considerably improve the computation accuracy of TrueNorth platform and reduce the incurred hardware and performance overheads.
The analysis by synthesis of speech melody: from data to models.

This paper describes the application of the analysis by synthesis paradigm to the melody of speech. A complete chain of processes is described from the acoustic analysis of fundamental frequency (f0), via the phonetic modelling of f0 using the Momel algorithm, to the surface phonological representation of the curves using the INTSINT alphabet. Each step of the chain is designed as a reversible process which can be used to generate an acoustic output allowing an objective evaluation of the analysis. Finally, the current implementation of ProZed, a prosody editor for linguists, is described. It is argued that an explicit set of modelling tools like this will allow linguists to test different models of phonological structure which, it is hoped, will result in the availability of more and better data on a wide variety of languages.
Rendering OWL in LaTeX for Improved Readability: Extensions to the OWLAPI

Shimizu, Cogan M. M.S., Department of Computer Science and Engineering, Wright State University, 2017. Rendering OWL in LaTeX for Improved Readability: Extensions to the OWLAPI. As ontology engineering is inherently a multidisciplinary process, it is necessary to utilize multiple vehicles to present an ontology to a user. In order to examine the content of an ontology, formal logic renderings of the axioms appear to be a very helpful approach for some. This thesis introduces a number of incremental improvements to the OWLAPI’s LTEX rendering framework in order to improve the readability, concision, and correctness of OWL files translated into Description Logic and First Order Logic. In addition, we examine the efficacy of these renderings as vehicles for understanding an ontology.
Reconocimiento de lenguaje Signo-Escritura mediante Deep Learning

Este proyecto consiste en la deteccion de la SignoEscritura mediante tecnicas de deep learning. Esta tarea consta de dos partes: la clasificacion de los simbolos y su localizacion dentro de una imagen. Para su realizacion, en primer lugar se ha construido una red neuronal convolucional (CNN) para la clasificacion de los simbolos. Esta CNN se ha desarrollado usando la arquitectura de LeNet5, la cual ha proporcionado resultados satisfactorios. En segundo lugar, se ha llevado a cabo un detector de imagenes que ha combinado las tareas de clasificacion y de localizacion mediante el uso del algoritmo YOLO. Los resultados obtenidos en este segundo punto tambien han sido prometedores. El codigo asociado al desarrollo de este proyecto puede encontrarse publico en la plataforma GitHub [https://github.com/NILGroup/TFG-1819-SignoEscritura]