Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact of different morphological profiles on the classification accuracy of urban hyperspectral data

We present a detailed study on the classification of urban hyperspectral data with morphological profiles (MP). Although such a spectral-spatial classification approach may significantly increase achieved accuracy, the computational complexity as well as the increased dimensionality and redundancy of such data sets are potential drawbacks. This can be overcome by feature selection. Moreover it is useful to derive detailed information on the contribution of different components from MP to the classification accuracy by evaluating these subsets. We apply a wrapper approach for feature selection based on support vector machines (SVM) with sequential feature forward selection (FFS) search strategy to two hyperspectral data sets that contain the first principal components (PC) and various corresponding MP from an urban area. In doing so, we identify feature subsets of increasing size that perform best in terms of kappa for the given setup. Results clearly demonstrate that maximum classification accuracies are achieved already on small feature subsets with few morphological profiles.
Achievable Capacity in Hybrid DS-CDMA/OFDM Spectrum-Sharing

In this paper, we consider DS-CDMA/OFDM spectrum sharing systems and obtain the achievable capacity of the secondary service under different subchannel selection policies in the fading environment. Subchannel selection policies are divided into two categories: uniform subchannel selection, and nonuniform subchannel selection. Uniform subchannel selection is preferred for cases where a priori knowledge on subchannels state information is not available at the secondary transmitter. For cases with available a priori knowledge on subchannels state information, we study various nonuniform subchannel selection policies. In each case, we obtain the optimum secondary service power allocation and the corresponding maximum achievable capacity. Then we present results on the scaling law of the opportunistic spectrum sharing in DS-CDMA/OFDM systems with multiple users. Numerical results show that the optimal subchannel selection is based on the minimum value of the subchannel gain between the secondary transmitter and the primary receiver.
Benchmark of feature selection techniques with machine learning algorithms for cancer datasets

Classification is a technique based on machine learning used to classify each item in a set of data into a set of predefined classes or group. It is widely used in medical field to classify the medical data. In producing better classification result, feature selection been applied in many of the classification work as part of preprocessing step, where a subset of feature been used rather than the whole features from particular dataset. Feature selection eliminates irrelevant attribute to obtain high quality features that may contribute in enhancing classification process and producing better classification results. This study is conducted with the intention to focus on feature selection techniques as a method that helps classifiers producing better classification performance with the most significant features. During the experiments, a comparison between benchmark feature selection methods based on three cancer datasets and four well recognized machine learning algorithms has been made. This paper then analyzes the performance of all classifiers with and without feature selection in term of ROC and F-Measure. The study found that although there are no single feature selection method can satisfy all datasets, the results still effectively support the fact that feature selection helps in increasing the classifier performance with existence of minimum number of features.
Robust Vision-Based Target Tracking Control System for an Unmanned Helicopter Using Feature Fusion

We present in this paper a robust vision-based tracking control system for an unmanned helicopter to track a moving ground target. It integrates a real-time vision-based target detection algorithm with a tracking control law in a closed loop. First, the proposed target detection algorithm extracts geometry, color and motion features from captured images. Based on these features, a finite-state machine is then introduced to dynamically coordinates the work of decision making under the Bayes framework, and a tracking control law is designed to minimize a certain tracking error function. Experimental results obtained from actual flight tests are also presented and demonstrate the effectiveness and robustness of our vision-based tracking control system in real scenes.
Sparsity-Driven Frequency Diverse MIMO Radar Imaging for Moving Targets

 Abstract—By synthesizing multiple frequency signals into a wideband signal, the frequency diverse multiple-input-multiple- output (FD-MIMO) radar can obtain higher resolution than ordinary MIMO radar. Furthermore, FD-MIMO radar can obtain higher resolution and better recovery performance by exploiting the sparsity of the targets. For moving targets with unknown velocities, the recovery performance based on compressive sensing (CS) would significantly degrade if the estimation accuracy of velocities is not enough. We propose a new approach of adaptive sparse recovery via velocity estimation (ASR-VE) for multiple moving targets FD-MIMO radar imaging. ASR-VE estimates the scattering coefficients as well as the unknown velocities involved in the sensing matrix in the loop iterations. Numerical simulations are carried out to verify the effectiveness of the proposed method.
An objective rationale for the choice of regularisation parameter with application to global multiple-frequency S -wave tomography

Abstract. In a linear ill-posed inverse problem, the regularisation parameter (damping) controls the balance between minimising both the residual data misfit and the model norm. Poor knowledge of data uncertainties often makes the selection of damping rather arbitrary. To go beyond that subjectivity, an objective rationale for the choice of damping is presented, which is based on the coherency of delay-time estimates in different frequency bands. Our method is tailored to the problem of global multiple-frequency tomography (MFT), using a data set of 287 078 S-wave delay times measured in five frequency bands (10, 15, 22, 34, and 51 s central periods). Whereas for each ray path the delay-time estimates should vary coherently from one period to the other, the noise most likely is not coherent. Thus, the lack of coherency of the information in different frequency bands is exploited, using an analogy with the cross-validation method, to identify models dominated by noise. In addition, a sharp change of behaviour of the model e∞-norm, as the damping becomes lower than a threshold value, is interpreted as the signature of data noise starting to significantly pollute at least one model component. Models with damping larger than this threshold are diagnosed as being constructed with poor data exploitation. Finally, a preferred model is selected from the remaining range of permitted model solutions. This choice is quasi-objective in terms of model interpretation, as the selected model shows a high degree of similarity with almost all other permitted models (correlation superior to 98% up to spherical harmonic degree 80). The obtained tomographic model is displayed in the mid lower-mantle (660–1910 km depth), and is shown to be compatible with three other recent global shear-velocity models. A wider application of the presented rationale should permit us to converge towards more objective seismic imaging of Earth's mantle.
Recovery and analysis of transaction scope from scattered information in Java Enterprise Applications

Java Enterprise Applications (JEAs) are large systems that integrate multiple technologies and programming languages. Transactions in JEAs simplify the development of code that deals with failure recovery and multi-user coordination by guaranteeing atomicity of sets of operations. The heterogeneous nature of JEAs, however, can obfuscate conceptual errors in the application code, and in particular can hide incorrect declarations of transaction scope. In this paper we present a technique to expose and analyze the application transaction scope in JEAs by merging and analyzing information from multiple sources. We also present several novel visualizations that aid in the analysis of transaction scope by highlighting anomalies in the specification of transactions and violations of architectural constraints. We have validated our approach on two versions of a large commercial case study.
Investigation of Factors Affecting Apathy in Three Major Types of Dementia.

Introducion Apathy is an important factor in the clinical management of dementia, as it has been associated with poor disease outcome, reduced daily functioning and caregiver distress. Considering apathy as a problem that needs to be managed and knowing the factors affecting apathy will enable appropriate initiatives to be planned. This study was conducted to compare apathy across three types of dementia and determine the factors affecting apathy for each of the three types of dementia. Methods The sample consisted of 46 patients with Alzheimer's disease (AD), 31 patients with frontotemporal dementia (FTD) and 29 patients with vascular dementia (VaD). Apathy was assessed using the Neuropsychiatric Inventory-apathy subscale (NPI), dementia severity was assessed using the Clinical Dementia Rating Scale (CDR), cognitive status was assessed using the Mini Mental Status Examination (MMSE) functional ability was measured with the Katz Index of Independence in Activities of Daily Living (ADL) and the Lawton-Brody Instrumental Activities of Daily Living (IADL). This is a descriptive and cross-sectional study. Results Significant differences were found between the apathy score of three types of dementia. Cognitive impairment correlated significantly with the apathy score in AD and VaD. Functionality scores and severity of dementia showed a significant correlation with apathy in each group. No statistically significant relationship was detected between age, gender and apathy. Multiple regression analyses show that apathy scores correlated with IADL in patients with AD. Conclusion This study demonstrated that apathy is very common symptom in patients with FTD as well as patients with AD and VaD. Health professionals need to be aware of recognize apathy. Patients should be assessed for apathy regardless of dementia types, age and gender.
The Sources of Uncertainty in Disorders of Consciousness

This article considers recent developments in neurotechnology and their potential to either mitigate or exacerbate the uncertainty of diagnosis and prognosis of DoCs, as well as the potential impact of replacing the diagnosis of vegetative state with unresponsive wakefulness syndrome. The main reasons for abolishing the term “vegetative state” are (1) the unacceptably high misclassification rate of patients with covert awareness, (2) the undesirable prescriptive connotations of the term, and (3) the unwarranted prognostic connotations. While improving prognosis and diagnosis may help surrogates as they face decisions concerning treatment, more accurate diagnosis and prognosis will not by themselves provide decisive answers. Significant diagnostic and prognostic uncertainties are likely to persist and may in fact be ineliminable. We argue that this uncertainty has to be acknowledged, communicated, and factored together with patient values, desires, and preferences in ethically complex, value-laden decisions about the treatment of brain-injured patients.
Searching for clinical prediction rules in MEDLINE.

OBJECTIVES Clinical prediction rules have been advocated as a possible mechanism to enhance clinical judgment in diagnostic, therapeutic, and prognostic assessment. Despite renewed interest in the their use, inconsistent terminology makes them difficult to index and retrieve by computerized search systems. No validated approaches to locating clinical prediction rules appear in the literature. The objective of this study was to derive and validate an optimal search filter for retrieving clinical prediction rules, using the National Library of Medicine's MEDLINE database. DESIGN A comparative, retrospective analysis was conducted. The "gold standard" was established by a manual search of all articles from select print journals for the years 1991 through 1998, which identified articles covering various aspects of clinical prediction rules such as derivation, validation, and evaluation. Search filters were derived, from the articles in the July through December issues of the journals (derivation set), by analyzing the textwords (words in the title and abstract) and the medical subject heading (from the MeSH Thesaurus) used to index each article. The accuracy of these filters in retrieving clinical prediction rules was then assessed using articles in the January through June issues (validation set). MEASUREMENTS The sensitivity, specificity, positive predictive value, and positive likelihood ratio of several different search filters were measured. RESULTS The filter "predict$ OR clinical$ OR outcome$ OR risk$" retrieved 98 percent of clinical prediction rules. Four filters, such as "predict$ OR validat$ OR rule$ OR predictive value of tests," had both sensitivity and specificity above 90 percent. The top-performing filter for positive predictive value and positive likelihood ratio in the validation set was "predict$. ti. AND rule$. " CONCLUSIONS Several filters with high retrieval value were found. Depending on the goals and time constraints of the searcher, one of these filters could be used.
On Network Proximity in Web Applications

In this paper, we discuss one approach for development and deployment of web sites (web pages) devoted to the description of objects (events) with a precisely delineated geographic scope. This article describes the usage of context-aware programming models for web development. In our paper, we propose mechanisms to create mobile web applications which content links to some predefined geographic area. The accuracy of such a binding allows us to distinguish individual areas within the same indoor space. Target areas for such development are applications for Smart Cities and retail.
An adaptable constrained locking protocol for high data contention environments

Multiversions of data are used in database systems to increase concurrency and to provide efficient recovery. Data versions improve the concurrency by allowing the concurrent execution of non-conflicting read-write lock requests on different versions of data in an arbitrary fashion. A transaction that accesses a data item version which is later diagnosed to lead to an incorrect execution, is aborted. This act is reminiscent of the validation phase in the optimistic concurrency control schemes. Various performance studies suggest that these schemes perform poorly in high data contention environments where the excessive transaction aborts result due to the failed validation. We propose an adaptable constrained two-version two-phase locking (C2V2PL) scheme in which these non-conflicting requests are allowed only in a constrained manner. C2V2PL schemes assume that a lock request failing to satisfy the specific constraints will lead to an incorrect execution and hence, must be either rejected or blocked. This eliminates the need for a separate validation phase. When the contention for data among the concurrent transactions is high, the C2V2PL scheduler in aggressive state rejects such lock requests. The deadlock free nature of C2V2PL scheduler adapts to the low data contention environments by accepting the lock requests that have failed the specific constraints but contrary to the assumption, will not lead to an incorrect execution. Thus improving the performance due to reduced transaction aborts in this conservative state.
The Precarious Balance: State and Society in Africa

The conceptual distance which separates the title of this book from that of the international workshop (at the Truman Research Institute of the Hebrew University of Jerusalem) that gave birth to it, 'The Reordering of the State in Africa', is an indication of the intellectual growth of its participants and a tribute to the contribution of Donald Rothchild and Naomi Chazan, who have edited the proceedings. The main line of argument to be found in The Precarious Balance: state and society in Africa can be summarised succinctly. The continent has experienced a deepening economic crisis during the last decade or so, which has led to a serious weakening of the capacity of the state to control and direct change in society; that, in turn, has led to deteriorating standards of living, a feeling of neglect, and a loss of faith on the part of African populations; and finally, as a result of the above, the latter have delinked (or disengaged) from the state or, more correctly, from the formal sector dominated by the state, to engage in a variety of activities that have resulted in the growth of a huge ' informal' economy.
Joint channel-physical layer network coding in multi-way wireless relay systems

Multi-way relaying is a widely considered network configuration, where multiple communication nodes exchange their information via a single relay. In this configuration, a pairwise information exchange and a channel condition based node pair selection for simultaneous information transfer provide the best performance. For such schemes, the relationship between the minimum number of time slots required for complete information exchange among communication nodes and the number of communication nodes is derived. Furthermore, this paper proposes an improved joint channel decoding-network coding algorithm, which is to be employed at the relay node. In the proposed joint decoding algorithm, the soft information of the information bits is exchanged between the channel decoders corresponding to different node pairs transmitting simultaneously. We also propose three different schemes for soft information exchange between the different channel decoders. The proposed algorithm is highly scalable with respect to the number of communication nodes. Simulation results demonstrate that our proposed joint decoding algorithm offers improved error performance over the existing joint decoding algorithms for a three-way relay system under the three different information exchanging schemes. The simulation results verify that the performance of the proposed decoding algorithm increases with the number of overlapping information pairs.
The Relevance of Genetically Altered Mouse Models of Human Disease

The impetus to develop useful models of human disease and toxicity has resulted in a number of large-scale mouse mutagenesis programmes. This, in turn, has stimulated considerable concern regarding the scientific validity and welfare of genetically altered mice, and the large numbers of mice that are required by such programmes. In this paper, the scientific advantages and limitations of genetically altered mice as models of several human diseases are discussed. We conclude that, while the use of some such mouse models has contributed considerably to an understanding of human disease and toxicity, other genetically altered mouse models have limited scientific relevance, and fewer have positively contributed to the development of novel human medicines. Suggestions for improving this unsatisfactory situation are made.
The Mario AI Benchmark and Competitions

This paper describes the Mario AI benchmark, a game-based benchmark for reinforcement learning algorithms and game AI techniques developed by the authors. The benchmark is based on a public domain clone of Nintendo's classic platform game Super Mario Bros, and completely open source. During the last two years, the benchmark has been used in a number of competitions associated with international conferences, and researchers and students from around the world have contributed diverse solutions to try to beat the benchmark. The paper summarizes these contributions, gives an overview of the state of the art in Mario-playing AIs, and chronicles the development of the benchmark. This paper is intended as the definitive point of reference for those using the benchmark for research or teaching.
Automatic detection of pronunciation errors in CAPT systems based on confidence measure

Computer aided pronunciation training (CAPT) systems aim at listening to a learner's utterance, judging the overall pronunciation quality and pointing out any pronunciation errors in the utterance. However, the performance of the current error detection techniques can not satisfy the users' expectation. In this paper, we introduce confidence measures and anti-models into the error detection algorithm. The basic theory and the roles of confidence measures and anti-models are discussed. We then propose a new algorithm, and evaluate it on an English learning system. Experiments are conducted based on the TIMIT database and an adaptive database, which involves 40 Chinese undergraduates. The results show that confidence measures can be utilized to effectively improve the performance of the CAPT system.
A Review on Sarcasm Detection from Machine-Learning Perspective

In this paper, we want to review one of the challengingproblems for the opinion mining task, which is sarcasm detection. To be able to do that, many researchers tried to explore suchproperties in sarcasm like theories of sarcasm, syntacticalproperties, psycholinguistic of sarcasm, lexical feature, semanticproperties, etc. Studies done in the last 15 years not only madeprogress in semantic features, but also show increasing amount ofmethod of analysis using a machine-learning approach to processdata. Because of this reason, this paper will try to explain currentmostly used method to detect sarcasm. Lastly, we will present aresult of our finding, which might help other researchers to gaina better result in the future.
HIV-1 Replication in the Central Nervous System Occurs in Two Distinct Cell Types

Human immunodeficiency virus type 1 (HIV-1) infection of the central nervous system (CNS) can lead to the development of HIV-1-associated dementia (HAD). We examined the virological characteristics of HIV-1 in the cerebrospinal fluid (CSF) of HAD subjects to explore the association between independent viral replication in the CNS and the development of overt dementia. We found that genetically compartmentalized CCR5-tropic (R5) T cell-tropic and macrophage-tropic HIV-1 populations were independently detected in the CSF of subjects diagnosed with HIV-1-associated dementia. Macrophage-tropic HIV-1 populations were genetically diverse, representing established CNS infections, while R5 T cell-tropic HIV-1 populations were clonally amplified and associated with pleocytosis. R5 T cell-tropic viruses required high levels of surface CD4 to enter cells, and their presence was correlated with rapid decay of virus in the CSF with therapy initiation (similar to virus in the blood that is replicating in activated T cells). Macrophage-tropic viruses could enter cells with low levels of CD4, and their presence was correlated with slow decay of virus in the CSF, demonstrating a separate long-lived cell as the source of the virus. These studies demonstrate two distinct virological states inferred from the CSF virus in subjects diagnosed with HAD. Finally, macrophage-tropic viruses were largely restricted to the CNS/CSF compartment and not the blood, and in one case we were able to identify the macrophage-tropic lineage as a minor variant nearly two years before its expansion in the CNS. These results suggest that HIV-1 variants in CSF can provide information about viral replication and evolution in the CNS, events that are likely to play an important role in HIV-associated neurocognitive disorders.