Miracle-dz/UniFAR-FTUs huggingface dataset:
Computer aided detection via asymmetric cascade of sparse hyperplane classifiers

This paper describes a novel classification method for computer aided detection (CAD) that identifies structures of interest from medical images. CAD problems are challenging largely due to the following three characteristics. Typical CAD training data sets are large and extremely unbalanced between positive and negative classes. When searching for descriptive features, researchers often deploy a large set of experimental features, which consequently introduces irrelevant and redundant features. Finally, a CAD system has to satisfy stringent real-time requirements.This work is distinguished by three key contributions. The first is a cascade classification approach which is able to tackle all the above difficulties in a unified framework by employing an asymmetric cascade of sparse classifiers each trained to achieve high detection sensitivity and satisfactory false positive rates. The second is the incorporation of feature computational costs in a linear program formulation that allows the feature selection process to take into account different evaluation costs of various features. The third is a boosting algorithm derived from column generation optimization to effectively solve the proposed cascade linear programs.We apply the proposed approach to the problem of detecting lung nodules from helical multi-slice CT images. Our approach demonstrates superior performance in comparison against support vector machines, linear discriminant analysis and cascade AdaBoost. Especially, the resulting detection system is significantly sped up with our approach.
Self-adjusting scheme for high speed routers

Many schemes of high-performance IP address lookup have been proposed recently. But most of them do not process dynamically the packets and give no specific interest in the skewness of the traffic. Hence, the lack of dynamic packet routing algorithms has been the motivation for this research. In this paper, we have conceived a self-adjusting tree filter, by combining the scheme of binary search on prefix length with the splay tree model. Consequently, we have at most 2 hash accesses for all consecutive values. We give also a special interest in the amount of packets treated then routed through the default route entry. Those packets present an important part of the traffic treated by the routers and they might cause more harm than others as they traverse a long decision path before they are finally sent to the default route.
All-optical, low-power 2R regeneration of 10Gb/s NRZ signals using a III–V on SOI microdisk laser

We demonstrate an all-optical low-power 2R regenerator of 10Gb/s NRZ data based on a 10-μm diameter microdisk laser, heterogeneously integrated onto silicon-on-insulator and processed in a CMOS pilot-line. The scheme works for sub-milliWatt input signals.
A Review on Eye-Gaze Tracking, GCDs, and Implemented Foveated Imaging Systems Using Optimized Methods

— Eye-gaze tracking has been an important and interesting area of research for quite some time now. In day-to-day applications, eye-gaze tracking can be used as a computer interface for both industrial and nonindustrial applications, which require hands-free installations. It can also be used to help disabled people to use computers for communication and for environmental control. To track the eye gaze we have to deal with three principle problems: detecting the eye, tracking the eye and detecting the gaze of the eye on the screen where a user is looking at. Furthermore, developing a foveated imaging system, implemented on a general purpose computer, greatly reduces the transmission bandwidth of images. By taking advantage of this fact, it is possible to create an image that is almost perceptually indistinguishable from a constant resolution image, but requires substantially less information to code it. This is accomplished by degrading the resolution of the image so that it matches the space-variant degradation in the resolution of the human eye. Gaze-Contingent Displays (GCDs) attempt to balance the amount of information displayed against the visual information processing capacity of the observer through real-time eye movement sensing. Based on the location of the observer’s focus of attention, GCD content can be “tuned” through several display processing means: Screen-based displays, model-based, and At-tentive User Interfaces (AUIs) managing object-level entities (e.g., windows, applications). This paper briefly reviews past and pre-sent display techniques as well as emerging graphics and eye tracking technology for Gaze-Contingent Display development, and finally the state-of-the-art methods to implement the fove-ated imaging system utilizing the hardware advancement.
Metrics for Graph Drawing Aesthetics

Graph layout algorithms typically conform to one or more aesthetic criteria (e.g. minimizing the number of bends, maximizing othrogonality). Determining the extent to which a graph drawing conforms to an aesthetic criterion tends to be done informally, and varies between different algorithms. This paper presents formal metrics for measuring the aesthetic presence in a graph drawing for seven common aesthetic criteria, applicable to any graph drawing of anysize. The metrics are useful for determining the aesthetic quality of a given graph drawing, or for defining a cost function for genetic algorithms or simulated annealing programs. The metrics are continous, so that aesthetic quality is not stated as a binary conformance decision (i.e. the drawing either conforms to the aesthetic or not), but can be stated as the extent of aesthetic conformance using a number between 0 and 1. The paper presents the seven metric formulae. The application of these metrics is demonstrated through the aesthetic analysis of example graph drawings produced by common layout algorithms.
Robustness of complex communication networks under link attacks

Recent research results show that some most important complex communication systems, which usually can be modeled as scale-free networks with a power-law nodal degree distribution, may be fragile under intentional attacks that take down network hubs. We study the robustness of these networks under deliberate attacks which remove network links. Specifically, we evaluate the extreme case where an efficient graph-partitioning algorithm is applied, based on accurate network-topology information, to decide on the links to be removed. Simulation results show that even such type of calculated link-removal attack cannot easily split a complex communication network. Moreover, among the two split parts, the larger one generally remains as a scale-free network with a very small network diameter. We also consider the case where a specific set of nodes have to be split away from the major part of the network. Simulation results show that applying a graph-partitioning algorithm generally does not lead to a significantly more cost-effective solution than simply removing the given set of nodes together with the links connected to them.
Adaptive Support of Range Queries via Push-Pull Algorithms

In many real time interactive simulations the problem of interest management is that of filtering updates through application-specified range queries. This "associative' form of memory access is often implemented more simply as a reference memory access pattern by discretising the n-dimensional space in to "cells". Range queries then being mapped on to this discretised form. In this paper we leverage previous work on optimising communication in reference memory access systems to provide the same optimisation to associative memory access systems. We define possible semantics of range queries and present three designs along with an analysis of the problem in various applications domains and a discussion of the viability of our designs for each problem domain.
Towards multi-tenant cache management for ISP networks

The decreasing cost of storage and the advent of virtualization technology can allow Internet Service Providers (ISPs) to deploy multi-tenant caching infrastructures and lease them to content producers and Content Delivery Networks (CDNs). Serving content requests directly from the ISP network does not only reduce the delivery time, but also allows the ISP to optimize the network resources by controlling the placement and routing of content items. In this paper, we introduce a multi-tenant cache management approach that significantly reduces the bandwidth utilization of ISPs networks by pro-actively allocating caching space, leased by content producers and/or CDNs, and intelligently routing content to the end users. Using real content request traces, we show that the optimal solution to this problem can increase the cache hit ratio by 70.64% while reducing the bandwidth usage by 57.17% on average, compared to a commonly used reactive cache management scheme. These results provide a benchmark for the development of novel multi-tenant cache management strategies.
Adaptive firearm recognition algorithm in a dynamic background

The modern-day video-surveillance system is a significant advancement in technology used in various applications with the aim of automatizing the recognition of dangerous situations and helping human security officers to make the most appropriate decisions in order to ensure the safety of the general population. With the ever-growing crime rate in the Philippines, the government made counter measures to ensure the safety of the citizens. CCTV (closed-circuit television) cameras were installed in public places in which the government aims it to help deter crime and solve investigations. However, index crimes is still growing even though the CCTV cameras were installed in numerous places since current surveillance and control systems still require human supervision and intervention. This study proposes to empower the CCTV cameras with the use of an automatic firearm detection system in videos appropriate for surveillance purposes. With the adaptation of existing object detection techniques, an algorithm will be designed according to the given specifications of the CCTV cameras installed in the Philippines. The proposed algorithm will trigger an alarm signal when a firearm was to be detected. This study aims to help reduce the number of crimes that involves firearms and to ensure a safer community for the people with the use of the proposed algorithm.
Aerial Search Optimization Model (ASOM) for UAVs in Special Operations

Abstract : We construct an optimization model that assists commanders, operators, and planners to effectively deploy and employ unmanned aerial vehicles "UAVs" in special operations missions. Specifically, we consider situations where targets "e.g., insurgents" operate in a region of interest and a small special operations team is assigned to search and detect these targets. The special operations team is equipped with short-range surveillance UAVs. We combine intelligence regarding the targets with availability and capability of UAVs in an integer linear programming model. The goal is to detect the largest possible number of targets with the given resources. The model prescribes optimal deployment locations for the ground units and optimal time-phased search areas for the UAVs. The model has been implemented successfully in four field experiments. Preliminary empirical evidence indicates that the model provides 50% increase in detection opportunities compared with a plan manually generated by experienced commanders.
Summarizing and Searching Hidden-Web Databases Hierarchically Using Focused Probes

Many valuable text databases on the web have non-crawlable contents that are “hidden” behind search interfaces. Metasearchers are helpful tools for searching over many such databases at once through a unified query interface. A critical task for a metasearcher to process a query efficiently and effectively is the selection of the most promising databases for the query, a task that typically relies on statistical summaries of the database contents. Unfortunately, web-accessible text databases do not generally export content summaries. In this paper, we present an algorithm to derive content summaries from “uncooperative” databases by using “focused query probes,” which adaptively zoom in on and extract documents that are representative of the topic coverage of the databases. The content summaries that result from this algorithm are efficient to derive and more accurate than those from previously proposed probing techniques for content-summary extraction. We also present a novel database selection algorithm that exploits both the extracted content summaries and a hierarchical classification of the databases, automatically derived during probing, to produce accurate results even for imperfect content summaries. Finally, we evaluate our techniques thoroughly using a variety of databases, including 50 real web-accessible text databases.
Shigella Isolates From the Global Enteric Multicenter Study Inform Vaccine Development

Shigella case isolates from the Global Enteric Multicenter Study were serotyped to guide vaccine development. A quadrivalent vaccine that includes O antigens from S. sonnei, S. flexneri 2a, S. flexneri 3a, and S. flexneri 6 should provide broad protection.
Depression analysis: a multimodal approach

Depression is a severe mental health disorder causing high societal costs. Current clinical practice depends almost exclusively on self report and clinical opinion, risking a range of subjective biases. It is therefore useful to design a diagnostic aid to assist clinicians. This project aims at developing a novel multimodal framework for depression analysis. In this PhD work, it is hypothesized that a multimodal affective sensing system can better capture what characterises a person's affective state than single modality systems. The project will explore facial dynamics, head movements, upper body gestures, EEG measures and speech characteristics related to affect, in subjects with major depressive disorders. Integrating the individual sensing modalities, a multimodal approach that show improved performance characteristics over single modality approaches will be developed.
1 RELATIONSHIPS WITH CUSTOMERS , SUPPLIERS AND COMPETITORS – IMPLICATIONS FOR FIRM ’ S EXPORT PERFORMANCE

Markets ́ globalisation and competition made foreign markets increasingly viable and natural opportunities for growth-oriented firms. This fact is even more important for firms originating from small national markets. In the global market atmosphere these firms are forced to build their export marketing strategy based on relationships with their customers, suppliers or even competitors in order to be successful in their export market performance. Considering this fact the basic research question of the paper was to what extent the relationships can influence the firm’s performance in foreign markets. Our research framework postulated that the firm’s export performance is determined by relationships in addition to the type of industry and firm’s internal forces. Three types of relationships were analysed: with customers, suppliers and with competitors. Several industries were compared in terms of their export performance characteristics. The general conclusion of the research was that the indications were not sufficient to claim that firms from a small country developed relationships with customers, suppliers or competitors intensively in order to improve their export market performance.
Technostress: Technological Antecedents and Implications

With the proliferation and ubiquity of information and communication technologies (ICTs), it is becoming imperative for individuals to constantly engage with these technologies in order to get work accomplished. Academic literature, popular press, and anecdotal evidence suggest that ICTs are responsible for increased stress levels in individuals (known as technostress). However, despite the influence of stress on health costs and productivity, it is not very clear which characteristics of ICTs create stress. We draw from IS and stress research to build and test a model of technostress. The person-environment fit model is used as a theoretical lens. The research model proposes that certain technology characteristics--like usability (usefulness, complexity, and reliability), intrusiveness (presenteeism, anonymity), and dynamism (pace of change)--are related to stressors (work overload, role ambiguity, invasion of privacy, work-home conflict, and job insecurity). Field data from 661 working professionals was obtained and analyzed. The results clearly suggest the prevalence of technostress and the hypotheses from the model are generally supported. Work overload and role ambiguity are found to be the two most dominant stressors, whereas intrusive technology characteristics are found to be the dominant predictors of stressors. The results open up new avenues for research by highlighting the incidence of technostress in organizations and possible interventions to alleviate it.
Monitoring Extreme Agricultural Drought over the Horn of Africa (HOA) Using Remote Sensing Measurements

The Horn of Africa ((HOA), including Djibouti, Eritrea, Ethiopia, and Somalia) has been slammed by extreme drought within the past years, and has become one of the most food-insecure regions in the world. Millions of people in the HOA are undernourished and are at risk of famine. Meanwhile, global climate change continues to cause more extreme weather and climate events, such as drought and heat waves, which have significant impacts on crop production and food security. This study aimed to investigate extreme drought in the Horn of Africa region, using satellite remote sensing data products from the Moderate Resolution Imaging Spectroradiometer (MODIS), a key instrument onboard the National Aeronautics and Space Administration (NASA) satellites Terra and Aqua, as well as Tropical Rainfall Measuring Mission (TRMM) precipitation data products. Normalized Difference Vegetation Index (NDVI), Vegetation Condition Index (VCI), Temperature Condition Index (TCI), and Vegetation Health Index (VHI) data from 2000 to 2017 were derived from the MODIS measurements and analyzed for assessments of the temporal trend of vegetation health and the impacts of extreme drought events. The results demonstrated the severity of vegetation stress and extreme drought during the past decades. From 1998 to 2017, monthly precipitation over major crop growth seasons decreased significantly. From 2001 to 2017, the mean VHI anomaly of HOA cropland decreased significantly, at a trend of −0.2364 ± 0.1446/year, and the mean TCI anomaly decreased at a trend of −0.2315 ± 0.2009/year. This indicated a deterioration of cropland due to drought conditions in the HOA. During most of the crop growth seasons in 2015 and 2016, the VHI values were below the 10-year (2001–2010) average: This was caused by extreme drought during the 2015–2016 El Niño event, one of the strongest El Niño events in recorded history. In addition, monthly VHI anomalies demonstrated a high correlation with monthly rainfall anomalies in July and August (the growth season of major crops in the HOA), and the trough points of the monthly rainfall and VHI anomaly time series of July and August were consistent with the timing of drought events and El Niño events.
Distributed Information Retrieval and Automatic Identification of Music Works in SAPIR

The Search in Audio-visual content using Peer-to-peer Information Retrieval (SAPIR) project is an EU IST FP6 research project that aims at developing theories and technologies for the next-generation search techniques, which will effectively and efficiently deliver relevant information from exponentially growing distributed and very dynamic multimedia databases. The SAPIR consortium includes experts from industry and academia, and it will provide major innovations for powerful peer-to-peer (P2P) search on audiovisual content. It will be based on a scalable, completely decentralized, largely self-organizing P2P system where peers act both as client and servers and the users produce audio-visual content using multiple devices – ultra-peers may act as service providers which maintain indexes and provide search capabilities. The Information Management System research group of the Department of Information Engineering of the University of Padua is partner of the SAPIR consortium and it is mainly concerned with (1) media analysis and enrichment for search, and (2) complex search, retrieval and ranking in distributed environments, such as P2P networks. This work reports on the results that have been reached so far and that have been mapped on an initial prototype.
Performance analysis of IEEE 802.11e enhanced distributed channel access

Most of the recent analytical studies for IEEE802.11e quality of service (QoS)-aware enhanced distributed coordination function require a large degree of complexity that are difficult to apply to a wide range of parameter settings for the evaluation of service differentiation mechanisms supported in enhanced distributed channel access (EDCA), including the contention window (CW) and arbitrary inter-frame space (AIFS) mechanisms. The authors propose a novel analytical model to analyse the throughput of EDCA with AIFS and CW differentiation schemes. The model is simplified by decomposing the problem into two easily solved Markov chains that can jointly be solved by numerical method. The authors present simulation and analytical results over a broad range of system parameters to demonstrate the accuracy of the proposed model. Compared to the previous models, our model is simple to implement and can be applied to a general configuration circumstances for the evaluation of EDCA. The results are valuable to facilitate proper design of parameters in 802.11e EDCA for the QoS support required by specific applications.
Measurement of Efficiency Level in Nigerian Seaport after Reform Policy Implementation . Case Study of Onne and Rivers Seaport , Nigeria .

This paper focuses on the impact of reforms on port performance using Onne and Rivers ports as a reference point. It analyses the pre and post reform eras of the ports in terms of their performance. The reforms took effect from 1996 after the Federal Government of Nigeria concessioned the ports to private investors. Parameters such as Ship traffic, Cargo throughput, Ship turn round time, Berth Occupancy and personnel were used as variables for the assessment. Secondary Data were collected from the Nigerian Ports Authority and Integrated Logistic Services Nigeria (Intels) for the period 2001 to 2010 and analyzed using Data Envelopment Analysis to assess the efficiency of the port. Analysis revealed a continuous improvement in the overall efficiency of both Ports Since 2006 when the new measure was introduced. Average Ship turn-around time improved in the ports due to modern and fast cargo handling equipment and more cargo handling space which were provided. There is an increase in Ship traffic calling at the ports, resulting in increased cargo throughput and berth occupancy rate at ports of Onne and Rivers. The reform also led to more private investment in the ports’ existing and new facilities and the introduction of a World Class service in port operation. This study concludes that the Ports of Onne and Rivers are performing better under the reform programme of the Federal Government of Nigeria. It finally recommends the urgent need for a regulator to appraise the performance of the reform programme from time to time as provided by the agreement and for the full adoption and utilization of management information system (MIS) to aid performance efficiency.