Miracle-dz/UniFAR-FTUs huggingface dataset:
Challenges in Spatial Analysis

This is a time of unprecedented opportunity for spatial analysis. More people than ever have access to the Global Positioning System for direct measurement of location on the Earth’s surface; to the products of high-resolution remote-sensing satellites; and to the manipulative power of geographic information systems (GIS). Some of these technologies are encountered in everyday life, through sites such as Google Earth (earth.google.com), Google Maps (maps.google.com), and Microsoft Windows Live Local (live.local.com), and through the widespread use of in-vehicle navigation systems. Several academic disciplines are recording a spatial turn, a new and in some cases renewed interest in space and location as a framework for analysis, understanding, and presentation of results. A recent publication (National Research Council, 2006) has defined and explored spatial thinking as a paradigm for primary and secondary education, and projects have been funded around the world to advance spatial literacy (e.g., www.spatial-literacy.org).
Sentinel Lymph Node Biopsy in Nonmelanoma Skin Cancer Patients

The management of lymph nodes in nonmelanoma skin cancer patients is currently still debated. Merkel cell carcinoma (MCC), squamous cell carcinoma (SCC), pigmented epithelioid melanocytoma (PEM), and other rare skin neoplasms have a well-known risk to spread to regional lymph nodes. The use of sentinel lymph node biopsy (SLNB) could be a promising procedure to assess this risk in clinically N0 patients. Metastatic SNs have been observed in 4.5–28% SCC (according to risk factors), in 9–42% MCC, and in 14–57% PEM. We observed overall 30.8% positive SNs in 13 consecutive patients operated for high-risk nonmelanoma skin cancer between 2002 and 2011 in our institution. These high rates support recommendation to implement SLNB for nonmelanoma skin cancer especially for SCC patients. Completion lymph node dissection following positive SNs is also a matter of discussion especially in PEM. It must be remembered that a definitive survival benefit of SLNB in melanoma patients has not been proven yet. However, because of its low morbidity when compared to empiric elective lymph node dissection or radiation therapy of lymphatic basins, SLNB has allowed sparing a lot of morbidity and could therefore be used in nonmelanoma skin cancer patients, even though a significant impact on survival has not been demonstrated.
Dynamic similarity-based activity detection and recognition within smart homes

Purpose – Within smart homes, ambient sensors are used to monitor interactions between users and the home environment. The data produced from the sensors are used as the basis for the inference of the users' behaviour information. Partitioning sensor data in response to individual instances of activity is critical for a smart home to be fully functional and to fulfil its roles, such as correctly measuring health status and detecting emergency situations. The purpose of this study is to propose a similarity‐based segmentation approach applied on time series sensor data in an effort to detect and recognise activities within a smart home.Design/methodology/approach – The paper explores methods for analysing time‐related sensor activation events in an effort to undercover hidden activity events through the use of generic sensor modelling of activity based upon the general knowledge of the activities. Two similarity measures are proposed to compare a time series based sensor sequence and a generic sensor model.
Enhancing the security of Bloom-filters with blind signatures

In this paper, we propose a privacy-preserving variant of Bloom-filters. The Bloom-filter has many applications in the network. In some of those applications, equipping the Bloom-filter with the privacy-preserving mechanism is crucial for the deployment. In this paper, to make them secure, we propose a new Bloom-filter protocol named privacy-preserving Bloom filter. We propose two protocols based on the unique blind signature and the oblivious pseudorandom function. To show that the protocol is secure, we give a formal security definition and prove the security under the definition.
Why Did the Equity Home Bias Fall During the Financial Panic of 2008?

Investors tend to put most of their wealth in local stocks; theories of portfolio choice and uncertainty aversion jointly predict that this home bias should increase during a financial crisis. Yet, using a sample of 45 countries, I document that the equity home bias fell during the financial panic of 2008. Exploiting bilateral stock holdings, I find that investors actively increased their home bias, but large valuation changes subsumed these trades. Across countries, the change in home bias is consistent with partial portfolio rebalancing and increased information asymmetries during the crisis.
The Design of an Adaptive Content Management Learning System for Children with Autism

The prevalence of autism in children in the world is estimated as one per 62 children, higher levels reported in some countries. These children experience significant problems with the development of social, behavioural and verbal and non-verbal communication skills. The skills impairment levels varies from an individual to another and that made teaching autistics a challenge for caregivers such as teachers and relatives. Hence, there are quite a number of frameworks of a software learning systems which focus on gaining the children’s attention using representational visual illustration as a learning method instead of the textual form. However, majority of these tools are lacking the personalisation ability to suite everyone in the spectrum. Assistive technology offers an alternative way to attract children with autism. Therefore, this research is proposing an Adaptive Content Management Learning System (ACMLS) model to assist caregivers to produce, design and fine-tune or customise the learning materials appropriately so that the system interface and the materials are suitable for every individual in the spectrum according to each child personal profile aiming to make learning attractive and to contribute in improving their social, communication and behavioural skills and nonetheless, their attention level to the delivered educational topics. The ACMLS model design adopts four main components which are: (1) Design component: which covers the visual design, design principles and the mental model of the children with autism. (2) Technology component: which covers the assistive technology tools and the architecture of the ACMLS system. (3) Education component: Which covers the learning objectives, styles, strategies, methods and the cognitive model. (4) Participants component: which covers the main participants who’re playing a role in the ACMLS model such as: caregivers and children with autism.
Practical Concerns for Scalable Synchronization ( author information elided )

With the advent of multi-core chips and simultaneous multithreading technologies, high degrees of parallelism will soon become the norm for both smalland large-scale systems. Operating systems for such highly parallel environments require efficient synchronization. Unfortunately, the ever-increasing overhead of synchronization instructions on modern CPUs has made such efficiency difficult to achieve. This paper evaluates the performance of synchronization strategies on modern CPU architectures. We show that synchronization instructions can be a thousand times more expensive than normal instructions and that formerly scalable synchronization strategies now perform very poorly. We then evaluate several state-of-the-art solutions that combine copy-based update and deferred reclamation to allow lock-free concurrent reading. These solutions exhibit different update management and reclamation strategies, each of which performs well, but offers a unique trade-off between performance, memory consumption and complexity. We present an experimental evaluation of these strategies and discuss the impact of potential future changes in CPU architecture.
Constraints on the coupled quintessence from cosmic microwave background anisotropy and matter power spectrum

We discuss the evolution of linear perturbations in a quintessence model in which the scalar field is nonminimally coupled to cold dark matter. We consider the effects of this coupling on both cosmic microwave background temperature anisotropies and matter perturbations. Because of the modification of the scale of cold dark matter as {rho}{sub c}={rho}{sub c}{sup (0)}a{sup -3+{xi}}, we can shift the turnover in the matter power spectrum even without changing the present energy densities of matter and radiation. This can be used to constrain the strength of the coupling. We find that the phenomenology of this model is consistent with current observations up to the coupling power n{sub c}{<=}0.01 while adopting the current parameters measured by the Wilkinson Microwave Anisotropy Probe. Upcoming cosmic microwave background observations continuing to focus on resolving the higher peaks may put strong constraints on the strength of the coupling.
Little Data, Big Stories: Taking the Pulse of Large-Scaled Events on Twitter

With the proliferation of Big Data, Social Science projects being developed, this work takes a step back to design research avenues that specifically look at smaller, real-time Social Science projects. Building on an already developed platform, called Dynamic Twitter Network Analysis (DTNA), we build out exploration into multiple world event types, which were captured in real-time and used smaller datasets to allow the user the ability to seek location and topic-specific data collections in parallel to events occurring. With these datasets, we first establish what could be learned during the event that mimics larger projects in the same domain. Secondly, we compare the events to help bring awareness to strategies that can evolve as specific events occur. The datasets examined are from a 24-hour period from specific locations of relevance with a focus on polarizing events. This includes: 1)Boston Marathon Bombing, 2) Sandy Hook Elementary Shooting, 3) Gezi Park Riots, 4) Hurricane Sandy, 5) Batkid, Make-a-Wish Foundation, 6) Brazil World Cup Protests, and 7) 2014 NBA Championship (Game 5). These networks will be analyzed both from social network analysis (SNA) and natural language processing (NLP) approaches (including sentiment analysis and part of speech tagging comparing personal pronoun use).
Image-based mapping, global localization and position tracking using VG-RAM weightless neural networks

Humans can easily memorize images of places and labels (road names, addresses, etc.) associated with them, as well as trajectories defined by sequences of images and corresponding positions. Later, they are able to remember places' labels and relative positions when seeing the same images again. In this work, we present an image-based mapping, global localization and position tracking system based on Virtual Generalizing Random Access Memory (VG-RAM) weightless neural networks, dubbed VIBML. VIBML mimics humans ability of learning about a place and of recognizing the same place in a later moment, as well as of tracking self-movement through the environment using images. We evaluated the performance of VIBML on the precise localization of an autonomous car using real-world datasets. Our experimental results showed that VIBML is able to localize car-like robots on large maps of real world environments with accuracy equivalent to that of state-of-the-art methods - VIBML is able to localize an autonomous car with average positioning error of 1.12m and with 75% of the poses with error below 1.5m in a 3.75km path around the main campus of the Federal University of Espírito Santo.
Virtual-Cell Frequency Reuse Scheme to Support Seamless Service in Femtocell Environments

Multiple access technologies based on Orthogonal Frequency Division Multiplexing (OFDM) yield evolutionary advantages for the next generation wireless communication systems. Wireless communication systems based on Orthogonal Frequency Division Multiple Access (OFDMA) increase channel capacity over broad bandwidth and improve the bandwidth efficiency. To maximally use resource in OFDMAbased wireless networks, frequency reuse factor should be one. However, this leads the degradation of performance caused by co-channel interference (CCI). Due to this, we propose an Virtual-Cell Frequency Reuse (VCFR) Scheme to eliminate CCI in cell edge as well as to increase spectral efficiency. Since VCFR allocates the same subset of channel bandwidth in three adjacent sectorized outer regions within three different cells, it is suitable for femtocell environment. Simulation result validates that proposed scheme improves the maximum throughput compared with existing frequency reuse schemes.
Autonomous and Ideological Models of Literacy : approaches from New Literacy Studies

What has come to be termed the ‘New Literacy Studies’ (NLS) (Gee, 1991; Street, 1996) represents a new tradition in considering the nature of literacy, focussing not so much on literacy as a ‘technology of the mind’ (cf Goody, 1968, 1977) or as a set of skills, but rather on what it means to think of literacy as a social practice (Street, 1984). This entails the recognition of multiple literacies, varying according to time and space but also contested in relations of power. In this paper I will outline the main positions offered by NLS, consider their relationship to earlier anthropological interest in literacy and take account of recent critiques regarding ‘the limits of the local’ that have implications for both ethnographies of literacy and for anthropological interest in this field. I conclude with a brief account of the contiguous field of Multimodality and ask how these two fields relate to and offer a way of commenting upon the debates about ‘media’ in the EASA media anthropology network.
Enhancing Group Cohesion in Virtual Communities of Practice

This paper describes software agents that monitor the formation of teams within the System-Oriented UAV simulator SOUL. Operators must be trained in virtual teams, working in communities of practice. According to the evaluation of their collective skills, users can then be placed in communities of competencies. For monitoring the interactions between operators, an agent framework facilitates the collaborative training, by pushing up contracts and aiding cohesion of human-teams. The contracts make the most with the direct use of e-portfolio for operators to gather the social dependencies on knowledge and skills. This allows us to assess operator's actions in real time together with an immediate detection and analysis of misbehavior feedbacks, improving the whole situation awareness.
COTS-Architecture with a Real-Time OS for a Self-Driving Miniature Vehicle

The international competition 2007 DARPA Urban Challenge significantly fostered the research and development on intelligent vehicles. Recent prototypes from Google for instance are able to drive fully autonomously through the San Francisco bay area for more than 450,000km. Even if self-driving vehicles are not yet available for purchase for customers, technology spin-offs are increasingly finding their ways into today's vehicles as pedestrian collision warning system, automated parking, or adaptive cruise control with temporary lane following. Thus, the upcoming generation of software and computer engineers needs to be accordingly educated to be already familiar with the concepts and pitfalls of these complex systems. In this article, we describe the architecture of a self-driving miniature vehicle based solely on commercial-off-the-shelf (COTS) components for the hardware and the real-time operating system ChibiOS/RT for the software/hardware interface.
An expert system for bibliographic retrieval using Prolog

PROBIB-2 is a prototype expert system for online bibliographic retrieval that provides enhanced retrieval capabilities through the application of logic programming. Implemented in Prolog, this system attempts to integrate the capabilities of a human search intermediary into a retrieval system that provides a deductive reasoning capability. Through a natural language interface, the user may retrieve information about the knowledge in the database as well as documents in response to query. User profiles may be used to establish a query environment to enable the system to determine the information need of the user. A concern with using Prolog to perform an online search of a large database is that the response time would be unacceptable. In order to overcome this drawback a special-purpose hardware device is proposed that, given an initial query, scans the disk-based Prolog database and returns to the main computer a subset of predicates relevant to the query. This set of predicates may then be used to resolve the user query in the normal Prolog manner.
Improving driving behavior by allowing drivers to browse their own recorded driving data

In this paper, we present our on-going efforts to develop a self-diagnosis system to improve safe-driving behavior by allowing drivers to review a record of their own driving activity. By employing stochastic driver-behavior modeling, the proposed system is able to detect various types of potentially hazardous situations, which conventional event data recorders are not able to capture, or which drivers themselves are not aware of such latent risky situations. Utilizing these automatically detected hazardous situations, our web-based system offers a user-friendly interface for drivers to navigate and review each hazardous situation in detail (e.g., driving scenes are displayed with corresponding driving signals). Furthermore, the system provides feedback on each risky driving behavior and suggests how the users can appropriately respond to such situations in a safe manner. The proposed system establishes a cooperative relationship between the driver, the vehicle, and the driving environment in order to develop the next-generation of safety systems and pave the way for an alternative form of driver education that could further reduce the number of fatal accidents. The system's potential benefits are demonstrated through experimental evaluation, showing that safe-driving behavior improved significantly after using the proposed system.
A Comparative Evaluation of Two Intrusion-Tolerant Routing Protocols for Wireless Sensor Networks

Wireless Sensor Networks (WSNs) have emerged recently as one of the most attractive research subjects. The resource-constraint characteristics of WSNs limit the secure design and development of security protocols and presumably thwart the integration of an intrusion detection system (IDS). Whilst, sensor nodes which usually operate in unattended and even harsh environments, are prone to failures and vulnerable to malicious attacks. For reliable and secure communications in WSNs, intrusion-tolerant routing becomes possibly a critical attribute that should be integrated into WSNs. However, there was not much research investigated on this topic. In order to provide a useful starting point for researchers interested in the subject, in this paper, we comparatively study two intrusion-tolerant routing protocols for WSNs, namely INSENS and ITSRP. Simulations were especially invested to assess their practicality. We also propose some suggestions to improve these two protocols. Although the feasibility of our suggestions is still questionable and deserves more investigation, we consider this study as the first step to fulfill the design of intrusion-tolerant WSNs as well as to build a complete intrusion-tolerant framework hereafter.
A Simple Pixel Structure Using Polycrystalline-Silicon Thin-Film Transistors for High-Resolution Active-Matrix Organic Light-Emitting Diode Displays

A simple pixel structure comprising two transistors and one capacitor and a novel driving method are proposed for small-sized and high-resolution active-matrix organic light-emitting diode displays on polycrystalline-silicon thin-film transistor (TFT) backplane. The proposed pixel increases the yield due to small number of TFTs and improves image quality by compensating threshold voltage variations of driving TFTs. The proposed pixel is designed as test pixels with 353 pixels per inch, and the measured emission current error range of -74.2% -60.6% is improved to the range of -2.5% -2.1% when the proposed pixel is used.
Autologous Pancreatic Islet Transplantation in Human Bone Marrow

The liver is the current site of choice for pancreatic islet transplantation, even though it is far from being ideal. We recently have shown in mice that the bone marrow (BM) may be a valid alternative to the liver, and here we report a pilot study to test feasibility and safety of BM as a site for islet transplantation in humans. Four patients who developed diabetes after total pancreatectomy were candidates for the autologous transplantation of pancreatic islet. Because the patients had contraindications for intraportal infusion, islets were infused in the BM. In all recipients, islets engrafted successfully as shown by measurable posttransplantation C-peptide levels and histopathological evidence of insulin-producing cells or molecular markers of endocrine tissue in BM biopsy samples analyzed during follow-up. Thus far, we have recorded no adverse events related to the infusion procedure or the presence of islets in the BM. Islet function was sustained for the maximum follow-up of 944 days. The encouraging results of this pilot study provide new perspectives in identifying alternative sites for islet infusion in patients with type 1 diabetes. Moreover, this is the first unequivocal example of successful engraftment of endocrine tissue in the BM in humans.
Factors Influencing The Performance of Image Captioning Model: An Evaluation

Recently, neural network-based methods have shown impressive performances in captioning task. There have been numerous attempts with many proposed architectures to solve this captioning problem. In this paper, we present the evaluation of different alternatives in architecture and optimization algorithms for a neural image captioning model. First, we present the study of a image captioning model that is comprised of two modules -- a convolutional neural network which encodes the input image into a fixed-dimensional feature vector and a recurrent neural network to decode that representation into a sequence of words describing the input image. After that, we consider different alternatives regarding architecture and optimization algorithm to train the model. We conduct a set of experiments on standard benchmark datasets to evaluate different aspects of the captioning system using standard evaluation methods that are utilized in image captioning literatures. Based on the results of those experiments, we propose several suggestions on architecture and optimization algorithm of the image captioning model that is balanced in terms of the performance and the feasibility to be deployed on real-world problems with commodity hardware.