Miracle-dz/UniFAR-FTUs huggingface dataset:
Designing Disaster Information Management Systems 2.0: Connecting communities and responders

Information and supporting information systems is a key element in an effective emergency response. From creating situational awareness to informed decision making, information enables responders to optimize their decisions and operations. Today, with the increased availability of information technology around the globe, a new active player in the field of information management is emerging as communities are becoming increasingly active in the field of information gathering, analyzing and sharing. However, communities may have specific requirements and approaches to using information systems in crisis situations. Moreover, connecting information systems between communities and responder pose specific challenges due to the different information needs, capacities and incentives to use them. In this paper we build on the DERMIS premises and explore through a case study if and how these principles apply to inclusive information systems. We present the initial findings of this work of designing information systems involving both communities and formal responders.
A Speaker-Dependent Approach to Separation of Far-Field Multi-Talker Microphone Array Speech for Front-End Processing in the CHiME-5 Challenge

We propose a novel speaker-dependent speech separation framework for the challenging CHiME-5 acoustic environments, exploiting advantages of both deep learning based and conventional preprocessing techniques to prepare data effectively for separating target speech from multi-talker mixed speech collected with multiple microphone arrays. First, a series of multi-channel operations is conducted to reduce existing reverberation and noise, and a single-channel deep learning based speech enhancement model is used to predict speech presence probabilities. Next, a two-stage supervised speech separation approach, using oracle speaker diarization information from CHiME-5, is proposed to separate speech of a target speaker from interference speakers in mixed speech. Given a set of three estimated masks of the background noise, the target speaker and the interference speakers from single-channel speech enhancement and separation models, a complex Gaussian mixture model based generalized eigenvalue beamformer is then used for enhancing the signal at the reference array while avoiding the speaker permutation issue. Furthermore, the proposed front-end can generate a large variety of processed data for an ensemble of speech recognition results. Experiments on the development set have shown that the proposed two-stage approach can yield significant improvements of recognition performance over the official baseline system and achieved top accuracies in all four competing evaluation categories among all systems submitted to the CHiME-5 Challenge.
Tech Report: A Virtualized Scratchpad-Based Architecture for Real-time Event-Triggered Applications

Systems-on-a-Chip (SoC) devices integrating hard processing cores with programmable logic (PL) are becoming increasingly available. While these platforms have been originally designed for high performance computing applications, their rich feature set can be exploited to efficiently implement mixed criticality domains serving both critical hard real-time tasks, as well as soft real-time tasks. In this paper, we take a deep look at COTS-based heterogeneous SoCs that incorporates PL and a multicore processor. We show how one can tailor these processors to support a mixed/full criticality system, where cores are strictly isolated to avoid contention on shared resources such as Last-Level Cache (LLC) and main memory. In order to avoid conflicts in LLC, we propose the use of cache coloring, implemented in the Jailhouse hypervisor. In addition, we employ ScratchPad Memory (SPM) inside the PL to support a multi-phase execution model for realtime tasks that avoids conflicts in shared memory. We provide a full-stack, working implementation on a latest-generation SoC platform, and show results based on both a set of data intensive tasks, as well as a complete case study based on an anomaly detection application for an autonomous vehicle.
A Min-Max Scheduling Load Balanced Approach to Enhance Energy Efficiency and Performance of Mobile ADHOC Networks

Energy efficiency and traffic management in Mobile Ad hoc Networks (MANETs) is a complex process due to the self-organizing nature of the nodes. Quality of service (QoS) of the network is achieved by addressing the issues concerned with load handling and energy conservation. This manuscript proposes a min-max scheduling (M2S) algorithm for energy efficiency and load balancing (LB) in MANETs. The algorithm operates in two phases: neighbor selection and load balancing. In state selection, the transmission of the node is altered based on its energy and packet delivery factor. In the load balancing phase, the selected nodes are induced by queuing and scheduling the process to improve the rate of load dissemination. The different processes are intended to improve the packet delivery factor (PDF) by selecting appropriate node transmission states. The transmission states of the nodes are classified through periodic remaining energy update; the queuing and scheduling process is dynamically adjusted with energy consideration. A weight-based normalized function eases neighbor selection by determining the most precise neighbor that satisfies transmission and energy constraints. The results of the proposed M2SLB (Min-Max Scheduling Load Balancing) proves the consistency of the proposed algorithm by improving the network throughput, packet delivery ratio and minimizing delay and packet loss by retaining higher remaining energy.
Improving fuzzy c -means method for unbalanced dataset

Traditional fuzzy c-means method (FCM) is a famous clustering algorithm, but has a poor clustering performance for unbalanced dataset. To tackle this defect, a new FCM is presented by introducing cluster size into the formula of determining the membership values in every iteration. Experimental results on synthetic and UCI datasets showed that the proposed method has a better clustering performance than traditional FCM in terms of dealing with datasets with unbalanced clusters.
Automatic Detection of Exudates in Color Fundus Retinopathy Images

Objective: The aim of this paper is to design a computationally intelligent method to determine exudates, the Non-Proliferative Diabetic Retinopathy (NPDR) symptom which is considered to be the initial stage of retinopathy disease. If NPDR is not identified at its earlier stage, it may lead to Proliferative Diabetic Retinopathy (DR), the complicated stage of retinal symptom that may leads to blindness. Methods: It is proposed to develop an automatic computer aided detection system that screen a large number of people to identify the DR in its earlier stage for proper treatments. In this work, images are taken from publicly available e-optha database. Analysis mainly considers three stages which include removal of optic disc and normalization done by histogram processing; texture information extracted using Gray Level Co-Occurrence Matrix (GLCM) and classification is done with the help of Support Vector Machines (SVM). Findings: Preprocessing method used in this work enhances the contrast of the low or poor quality images. Here, optic disc segmentation is performed, which helps in providing better result by avoiding the misclassification of optic disc as lesions. GLCM method provides different texture features to SVM o provide better result in identifying exudate lesions. Conclusion: The intelligent machine learning approaches aid the ophthalmologists with accurate and efficient detection of abnormalities in fundus images. Through this system, the abnormal retinal images can be identified in its initial stage and an accurate assessment of retinal disease is possible.
Performance Base Empirical Analysis of Mutual Fund of Nepal

The paper analyzes the returns earned by the sample mutual funds benchmarking with market return. It also assesses whether they are taking advantages of diversification, market timing and selectivity of securities to their investors. Secondary data of eight sample mutual funds’ have been used from 2015 to 2018 published by Nepal Stock Exchange and respective fund manager. Risk adjusted performance measures Jensen alpha, Treynor ratio and Sharpe ratio have been used to analyze return in terms of risk and Co-efficient of Determination (R2), Quadratic Regression of Treynor and Mazuy and Famade composition model are employed to assess diversification, market timing and selectivity ability of fund manager. The result explores that funds that are operated from 36 months over-perform benchmark market index and those funds operated for 16 months are suffering from very low return. Further evidence shows that low amount of diversification, moderate level of selectivity and no significant relationship between timing skill and return of funds.
Acute Pancreatitis: An Exploratory, Cross-sectional, Single-center Study of the Epidemiological Features of the Disease in a Sample of Saudi Patients

Epidemiological studies on Acute Pancreatitis (AP) are significantly scarce in the Saudi Arabian Literature. In this paper, we aim to explore the current trends of AP in a sample of Saudi patients. This is a cross-sectional study in which we reviewed AP-related admissions from 2014 to 2017. Data collected included demographics, clinical presentation, investigations, severity, complications, and the outcome at the end of hospitalization. During the study period, 107 patients were admitted due to AP. Fifty-seven (53%) were males. Biliary pancreatitis was the most common etiology found among our patients (39.3%; 95% CI: 30.5–48.7), followed by alcoholic pancreatitis (11.2%; 95% CI: 6.5–18.6) and hypertriglyceridemia (8%; 95% CI: 4.5–15.2). Pancreatic pseudocysts were the most common complication we found in this series (15%; 95% CI: 9.4–23). Of all the hospitalized patients in this study, eight patients (7.9%) died (95% CI: 3.8–14.1). The number of AP-related admissions and mortality rate appear to have increased as compared with the numbers in earlier national studies. The etiological groups have also changed. As compared with Western/Asian studies, however, there was almost no difference in the epidemiological patterns except for the mortality rate.
THE IMPACT OF CORPORATE OWNERSHIP ON UNITARY BOARD INDEPENDENCE: EVIDENCE FROM BANGLADESH

Board independence is an important corporate governance element andwithout good governance it is impossible to form a unitary board system.After the crash in Bangladesh in 2011, it becomes a major issue for publiclisted companies in Bangladesh to follow a unitary system. This studyaddresses the issue by examining statistical relationship between corporateownership and board independence. Descriptive statistics, bivariate analysisand multiple regression techniques are applied in analyzing the attitudesof family, public and institutional owners towards board composition, thedegree of influence each type of owner has on board and the presence goesbeyond board independence. The results show that board are lacking inindependence and family ownership has a significant, negative effect onboard independence, in which the results indicate that public and institutionalownership have a positive, nonsignificant influence. It is recommended forprofessionals to be included on board and that nonexecutive directors shouldbe selected by a search committee to ensure that their competency matchesthe firm needs. It is further recommended that there should be a minimumof two independent directors and that management should be motivated,in line with the positive reinforcement theory. The German or Japanesemodel (two-tier board) can also be considered. This study also suggeststhat regulators should rethink how an independent and professional boardcan be formed when the barrier of concentrated ownership is removed.Keywords: Corporate governance, board independence, family ownership,corporate ownership, Bangladesh
Power consumption and performance comparative study of logarithmic-time CMOS adders

We present a comparative study of two types of high performance adders: the Brent-Kung (1982) and the ELM adder. The evaluation is carried out with the goal of reducing power consumption, while trying to keep the influence on performance to a minimum. A variety of approaches to achieve this, such as delay balancing and pipelining, are studied and their effectiveness is assessed for adders of multiple word lengths. Experiments demonstrate that relative power savings as large as 12%, and performance enhancements as large as 6% are possible. It is also shown that the use of these classic techniques by themselves is not as effective as it was initially thought, since the required overhead consumes more power than it is saved. Then, to obtain some real improvements, some new techniques have to be used.
QUALITY AND NOVEL INDICATORS OF COMMERCIAL LIFE IN Valencia ORANGES (Citrus sinensis Osbeck)

Mexico is the fifth largest producer of fresh oranges in the world, and Tepalcingo in Morelos has recently become an important area of cultivation for cv Valencia. The objectives of this work were to assess fruit quality in the oranges from this region, and to determine their commercial life during storage. Significant differences occurred in the juice volume of fruits during all three seasons evaluated; however, no differences were detected in their weight, size, shape, rind thickness, or flavedo color (L*, C*, h). TSS/TA (total soluble solids/titratable acidity) ratios were high (10.3-12.9) in fruits collected during spring and summer. Overall, the oranges met the specifications of both national and international quality standards. Changes during storage indicated that compression force, % weight loss, and sensorial flavor were negatively impacted by storage at 24⁰C while TSS and TA of juice remained unaffected at 11 and 24 ⁰C. The commercial life of oranges was 21 d at 24 ⁰C and > 28 d at 11 ⁰C as determined from changes in the concentrations of certain aroma compounds and from differences in the firmness of their rinds. Such changes correlate well with both weight loss and the sensorial perception of quality attributes.
Tick Size Change and Market Quality in the U.S. Treasury Market

This paper studies the effects of a recent tick size reduction in the U.S. Treasury securities market. Employing difference-in-differences regressions, we find significantly narrower bid-ask spreads and increased trading activity. Market depth declines overall, but depth close to the top of the book changes little. The smaller tick size enables prices to adjust more easily to information and allows traders to quickly capture their information advantage, resulting in greater price efficiency and an information shift toward the smaller-tick cash market from the futures market. Overall, we conclude that the tick size reduction improves market quality and the information environment.
OutdoorSent: Can Semantic Features Help Deep Learning in Sentiment Analysis of Outdoor Images?

Opinion mining in outdoor images posted by users during day-to-day or leisure activities, for example, can provide valuable information to better understand urban areas. In this work, we propose a framework to classify the sentiment of outdoor images shared by users on social networks. We compare the performance of state-of-the-art ConvNet architectures, namely, VGG-16, Resnet50, and InceptionV3, as well as one specifically designed for sentiment analysis. The combination of such classifiers, a strategy known as ensemble, is also considered. We also use different experimental setups to evaluate how the merging of deep features and semantic information derived from the scene attributes can improve classification performance. The evaluation explores a novel dataset, namely OutdoorSent, of geolocalized urban outdoor images extracted from Instagram related to three sentiment polarities (positive, negative, and neutral), as well as another dataset publicly available (DeepSent). We observe that the incorporation of knowledge related to semantics features tend to improve the accuracy of low-complex ConvNet architectures. Furthermore, we also demonstrated the applicability of our results in the city of Chicago, United States, showing that they can help to understand the subjective characteristics of different areas of the city. For instance, particular areas of the city tend to concentrate more images of a specific class of sentiment. The ConvNet architectures, trained models, and the proposed outdoor image dataset will be publicly available at http://dainf.ct. utfpr.edu. br/outdoorsent.
Cross-Layer Capacity Optimization In Ofdma Systems: Wimax And Lte

Given the broad range of applications supported, high data rate required and low latency promised; dynamic radio resource management is becoming vital for newly emerging air interface technologies such as Wireless interoperability for Microwave Access (WiMAX) and Long Term Evolution (LTE) adopted by international standards. This thesis considers Orthogonal Frequency Division Multiple Access (OFDMA) system, which has been implemented in both WiMAX and LTE technologies as their air interface multiple access mechanism. A framework for optimized resource allocation with Quality of Service (QoS) support that aims to balance between service provider’s revenue and subscriber’s satisfaction is proposed. A cross-layer optimization design for subchannel, for WiMAX, and Physical Resource Block (PRB), for LTE, and power allocations with the objective of maximizing the capacity (in bits/symbol/Hz) subject to fairness parameters and QoS requirements as constraints is presented. An Adaptive Modulation and Coding (AMC)-based cross-layer scheme has also been proposed in this thesis by adopting an AMC scheme together with the cross-layer scheme to realize a more practical and viable resource allocation. The optimization does not only consider users channel conditions but also queue status of each user as well as different QoS requirements. In the proposed framework, the problem of power allocation is solved analytically while the subchannel/PRB allocation is solved using integer programming exhaustive search. The simulation and numerical results obtained in this thesis have shown improved system performance as compared to other optimization schemes known in literature.
Large scale data management for the sciences

Traditional enterprises and novel scientific applications are accumulating petabyte-scale datasets, which makes the need for large-scale data management more pressing than ever. Geographic distribution of the datasets accompanied by complex demands on data makes large-scale data management challenging. This is especially true for sciences that model complex physical and biological phenomena using data from multiple sources. This dissertation addresses two critical problems for data management of scientific datasets: combining large number of diverse data sources for execution of scientific queries and executing data-intensive scientific queries efficiently, in terms of both network and I/O. As a first step towards scientific data management, this thesis describes design and specification of SkyQuery, a system that federates data seamlessly from several petabyte size, autonomous and heterogeneous Astronomy databases scattered worldwide. Using SkyQuery, scientists can write declarative queries that compare and merge multiple astronomical datasets. For efficient query execution and scalability, we propose Bypass-Yield Caching—a novel caching framework for database systems that dramatically reduces the network bandwidth requirements of data-intensive federations such as SkyQuery making them good network citizens. Our description of the bypass-yield cache includes novel cache evaluation metrics and several innovative algorithms. Distributed applications such as the Bypass Yield Cache often rely on a-priori knowledge of query cardinalities to make cache optimization decisions. In this context, we present a black-box approach to cardinality estimation that is suitable for distributed applications. All our techniques are general in that they can be adapted to different scientific domains such as life and earth sciences where similar data management problems abound. The success of SkyQuery and its adoption by the National Virtual Observatory (NVO) is an example of data management systems enabling scientific endeavors.
Optimal Sensor Placement Strategy for the Identification of Local Bolted Connection Failures in Steel Structures

Failure of bolted connections in steel structures may result in catastrophic effects. Many algorithms in existing literature use modal information of a structure to identify damage in that structure, based on the data acquired from accelerometers which record the vibration time histories at different points on the structure. The location of these points may have significant effects on the quality of the acquired data, and thus the identified modal information. In this paper, a distance measure based Markov chain Monte Carlo algorithm is proposed to determine the optimal locations for the accelerometers, and the optimal location of the impact hammer if need. Different damage cases with various combinations of bolt failures are considered in this study. Failures at various levels are simulated by loosening the bolts in a predefined order. To compare the efficiency of the proposed method, the total effect of various damage cases on the accelerations at the optimal locations are calculated for the proposed method and a state-of-the-art method from the existing literature. The results demonstrate the efficiency of the proposed strategy in locating the accelerometers, which can produce data that are more sensitive to the bolted connection failures.
Innovative use of optical coherence tomography catheter via nipple orifice: a case report of first intraductal images of florid ductal hyperplasia.

The incidence of malignancy in patients with pathologic nipple discharge (PND) varies from 1% to 23% (1). Currently, methylene blue dye or lacrimal probe guided isolated duct excision is still the standard treatment of PND. Majority of those patients are not with cancer-involved ducts and have unnecessary surgery to reveal the definitive histopathology. With conventional imaging, it is not possible to understand whether those ducts involve cancer or not (2). It is well established that ductoscopy is a novel technique that could diagnose a single papilloma; however, it is insufficient to diagnose an in situ or invasive cancer (3,4). Currently, optical coherence tomography (OCT) is used as a non-invasive imaging test that utilizes light waves to capture cross-sectional pictures of retina or coronary arteries (5). In our novel approach, a miniaturized OCT catheter is inserted into the breast ductal system via the nipple orifices to provide detailed real-time imaging of breast ductal epithelial anatomy.