Miracle-dz/UniFAR-FTUs huggingface dataset:
Locally contextualized smoothing of language models for sentiment sentence retrieval

Recently, a number of documents are published on the web. One of the crucial techniques to access to such information is sentiment sentence retrieval. It is very useful to retrieve positive or negative opinions to a specific topic at sentence level. Considering the property that sentiment polarities are often locally consistent in a document, we focus on using local context information for retrieving sentiment-bearing sentences. For this objective, we propose a new smoothing method, extending Dirichlet prior smoothing, to improve effectiveness of the retrieval. We demonstrate through experiments that our proposed smoothing method achieves statistically significant improvements in sentiment sentence retrieval, compared with a conventional smoothing method without local context.
Applications of the Differential Ant-Stigmergy Algorithm on Real-World Continuous Optimization Problems

Ants have always fascinated human beings. What particularly strikes the occasional observer as well as the scientist is the high degree of societal organization that these insects can achieve in spite of very limited individual capabilities (Dorigo et al., 2000). Ants have inspired also a number of optimization algorithms. These algorithms are increasingly successful among researches in computer science and operational research (Blum, 2005; Cordon et al., 2002; Dorigo & Stutzle, 2004). A particular successful metaheuristic—Ant Colony Optimization (ACO)—as a common framework for the existing applications and algorithmic variants of a variety of ant algorithms has been proposed in the early nineties by Marco Dorigo (Dorigo, 1992). ACO takes inspiration from the foraging behavior of some ant species. These ants deposit pheromone on the ground in order to mark some favorable path that should be followed by other members of the colony. ACO exploits a similar mechanism for solving combinatorial optimization problems. In recent years ACO algorithms have been applied to more challenging and complex problem domains. One such domain is continuous optimization. However, a direct application of the ACO for solving continuous optimization problem is difficult. The first algorithm designed for continuous function optimization was continuous ant colony optimization (Bilchev & Parmee, 1995) which comprises two levels: global and local; it uses the ant colony framework to perform local searches, whereas global search is handled by a genetic algorithm. Up to now, there are few other adaptations of ACO algorithm to continuous optimization problems: continuous interacting ant colony (Dreo & Siarry, 2002), ACO for continuous and mixed-variable (Socha, 2004), aggregation pheromone system (Tsutsui, 2006), and multilevel ant-stigmergy algorithm (Korosec & Silc, 2008). In this chapter we will present so-called Differential Ant-Stigmergy Algorithm (DASA), a new approach to the continuous optimization problem (Korosec, 2006). We start with the DASA description followed by three case studies which show real-world application of the proposed optimization approach. Finally, we conclude with discussion of the obtained results. O pe n A cc es s D at ab as e w w w . in te ch w eb . o rg
Interleukin-10 Overexpression Promotes Fas-Ligand-Dependent Chronic Macrophage-Mediated Demyelinating Polyneuropathy

Background Demyelinating polyneuropathy is a debilitating, poorly understood disease that can exist in acute (Guillain-Barré syndrome) or chronic forms. Interleukin-10 (IL-10), although traditionally considered an anti-inflammatory cytokine, has also been implicated in promoting abnormal angiogenesis in the eye and in the pathobiology of autoimmune diseases such as lupus and encephalomyelitis. Principal Findings Overexpression of IL-10 in a transgenic mouse model leads to macrophage-mediated demyelinating polyneuropathy. IL-10 upregulates ICAM-1 within neural tissues, promoting massive macrophage influx, inflammation-induced demyelination, and subsequent loss of neural tissue resulting in muscle weakness and paralysis. The primary insult is to perineural myelin followed by secondary axonal loss. Infiltrating macrophages within the peripheral nerves demonstrate a highly pro-inflammatory signature. Macrophages are central players in the pathophysiology, as in vivo depletion of macrophages using clodronate liposomes reverses the phenotype, including progressive nerve loss and paralysis. Macrophage-mediate demyelination is dependent on Fas-ligand (FasL)-mediated Schwann cell death. Significance These findings mimic the human disease chronic idiopathic demyelinating polyneuropathy (CIDP) and may also promote further understanding of the pathobiology of related conditions such as acute idiopathic demyelinating polyneuropathy (AIDP) or Guillain-Barré syndrome.
Inference of Euler angles for single particle analysis by using Genetic Algorithms.

Single particle analysis is one of the methods for structural studies of protein and macromolecules developed in image analysis on electron microscopy. Reconstructing 3D structure from microscope images is not an easy analysis because of the low resolution of images and lack of the directional information of images in 3D structure. To improve the resolution, different projections are aligned, classified and averaged. Inferring the orientations of these images is so difficult that the task of reconstructing 3D structures depends upon the experience of researchers. But recently, a method to reconstruct 3D structures is automatically devised. In this paper, we propose a new method for determining Euler angles of projections by applying Genetic Algorithms (i.e., GAs). We empirically show that the proposed approach has improved the previous one in terms of computational time and acquired precision.
A theoretical e-government portals' benchmarking framework

E-government benchmarking is the process of ranking e-government according to some agreed best practices. It can be used not only to benchmark but also to assess achievements and identify missing best practices for stakeholders. The purpose of this paper is to propose guidelines to build a new benchmarking framework for e-government portals. This framework is based on measurement of best practices using a best practice model. For this purpose, we have first identified and presented five examples of the benchmarking frameworks available in the literature. Based on the conducted comparison, the findings show that although the benchmarking frameworks are serving their intended purposes, they still suffer from some limitations. The paper also explains how the new framework overcomes these limitations.
Compact label-free biosensor using VCSEL-based measurement system

We report an ultracompact label-free biosensor that uses a vertical-cavity surface-emitting laser (VCSEL)-based measurement system for the characterization of biomolecular interactions. It consists of a VCSEL, a plastic guided-mode resonant filter, and two p-i-n detectors. The system has demonstrated very high sensitivity to molecules on top of the sensor.
Network traffic analysis using clustering ants

This paper exploits a newish self-organizing clustering method to analyze network data. The method is based on cooperative behaviour of ants. Collective intelligence of an ant colony rests on interactions of individual ants and the environment. Hence no central control is needed and the whole process is performed unsupervised by simple agents. The data used in the analysis was recorded from a border gateway of a campus network. The theory of clustering ants is introduced before the clustering process takes place. The target of the research is outlined and preprocessing of the data is described in detail. Several clustering procedures are performed and the results are visualized. Daily variation in network traffic is discovered and the method is found to be working. A way to detect long range changes is presented and some changes are observed in the data. Future work and lacks of the clustering method are discussed.
Contextualised service delivery in the Internet of Things: Parking recommender for smart cities

The Internet of Things (IoT) plays an important role in the development of smart cities. In this paper we focus on the development of IoT-based smart services for solving urban problems that involve IoT-enabled Observation, Orientation, Decision, and Action (OODA) loops. We also focus on how to efficiently support such OODA loops in situations where such loops involve internet-scale data. More specifically, IoT supports Observation via the discovery of sensors and the integration of their data. It supports Orientation via a contextualisation process that refines such data to include only those that are relevant to the situation and/or activities of each specific individual or group. As IoT contextualisation potentially involves internet-scale data, performing this process efficiently allows for fast decision making, and this in turn permits carrying out a timely Action. In this paper we propose an approach and related techniques for performing internet-scale data contextualisation. In particular, we propose IoT-based contextualisation techniques that effectively consider the entire range of data that is being collected in smart cities and use such data to provide hyper-personalised information to each user, i.e., information that best suits the context of each user in the Smart City. We exemplify the proposed contextualisation solution in a smart parking space recommender application/service, and provide an experimental evaluation of this service to illustrate the benefits of our solution.
Density-based Cluster Algorithms in Low-dimensional and High-dimensional Applications Second International Workshop on Text-based Information Retrieval (tir 05)

Cluster analysis is the art of detecting groups of similar objects in large data sets— without having specified these groups by means of explicit features. Among the various cluster algorithms that have been developed so far the density-based algorithms count to the most advanced and robust approaches. However, this paper shows that density-based cluster analysis embodies no principle with clearly defined algorithmic properties. We contrast the density-based cluster algorithms DBSCAN and MajorClust, which have been developed having different clustering tasks in mind, and whose strengths and weaknesses can be explained against the background of the dimensionality of the data to be clustered. Our motivation for this analysis comes from the field of information retrieval, where cluster analysis plays a key role in solving the document categorization problem. The paper is organized as follows: Section 1 recapitulates the important principles of cluster algorithms, Section 2 discusses the density-based algorithms DBSCAN and MajorClust, and Section 3 illustrates the strengths and weaknesses of both algorithms on the basis of geometric data analysis and document catego-rization problems. 1 Cluster Algorithms This section gives a short introduction to the problem of cluster analysis and outlines existing cluster approaches along the classical taxonomy. Moreover, it presents an alternative view to cluster algorithms , which is suited to explain different characteristics of their behavior. Definition 1 (Clustering). Let D be a set of objects. A clustering C ⊆ {C | C ⊆ D} of D is a division of D into sets for which the following conditions hold: With respect to the set of objects D the following shall be stipulated: – |D| = n – The objects in D represent points in the Euclidean space of dimension m. – Based on a metric d : D × D → R, the similarity or the dissimilarity between any two points in D can be stated.
The Cancer-Associated Virus Landscape in HIV Patients with Oral Hairy Leukoplakia, Kaposi's Sarcoma, and Non-Hodgkin Lymphoma

Although HIV-positive patients are at higher risk for developing a variety of infection-related cancers, the prevalence of infections with the seven known cancer-associated viruses has not been studied. Luciferase immunoprecipitation systems were used to evaluate antiviral antibodies in four 23-person groups: healthy blood donors and HIV-infected patients with oral hairy leukoplakia (OLP), Kaposi's sarcoma (KS), or non-Hodgkin lymphoma (NHL). Antibody profiling revealed that all HIV-positive individuals were strongly seropositive for anti-gp41 and antireverse transcriptase antibodies. However, anti-p24 HIV antibody levels were highly variable and some OLP and KS patients demonstrated weak or negative responses. Profiling two EBV antigens revealed no statistical difference in antibody levels among the three HIV-infected groups. A high frequency of KSHV infection was detected in HIV patients including 100% of KS, 78% of OLP, and 57% of NHL patients. Most HIV-infected subjects (84%) showed anti-HBV core antibodies, but only a few showed antibodies against HCV. MCV seropositivity was also common (94%) in the HIV-infected individuals and KS patients showed statistically higher antibody levels compared to the OLP and NHL patients. Overall, 68% of the HIV-infected patients showed seropositivity with at least four cancer-associated viruses. Antibody profiles against these and other infectious agents could be useful for enhancing the clinical management of HIV patients.
Improving overlapping speaker detection using multiple speaker tracking information

Traditionally, multiple speaker tracking consists of two stages, namely, 1) detection of location measurements, followed by 2) a multiple object tracking approach. In general, these two steps are performed separately, and the tracking performance is highly dependent on the measurement detection rate. The performance of the widely used Steered Response Power (SRP)-based measurement detectors, however, drastically decreases in the overlapping speech scenario, where the dominant speaker frequently masks the low-energy speakers. To overcome this problem, we propose an approach that enhances the probabilistic SRP-based measurement detector, using the multiple speaker information obtained in the tracking step. In doing so, this approach tightly couples the two stages, and increases the detection rate of low-energy speakers during overlapping speech segments. Experiments conducted on the AV16.3 corpus showed a significant improvement of the detection and tracking performance, when the proposed approach is integrated into a Kalman-based multiple speaker tracking framework.
Linear discriminant analysis as EEG features reduction technique for brain-computer interfaces

BCI systems analyze the EEG signal and translate patient intentions into simple commands. Signal processing methods are very important in such systems. Signal processing covers: preprocessing, feature extraction, feature selection and classification. In the article authors present the results of implementing linear discriminant analysis as a feature reduction technique for BCI systems. Streszczenie: Systemy BCI analizują sygnal EEG i tlumaczą intencje uzytkownika na proste polecenia. Waznym elementem systemow BCI jest przetwarzanie sygnalu. Obejmuje ono: przetwarzanie wstepne, ekstrakcje cech, selekcje cech i klasyfikacje. W artykule autorzy prezentują wyniki badan z zastosowaniem liniowej analizy dyskryminacyjnej jako narzedzia do redukcji cech. (Liniowa analiza dyskryminacyjna jako narzedzie redukcji cech sygnalu EEG)
‘Singled out because of skin color … ’: exploring ethnic minority female teachers’ embodiment in physical education

ABSTRACT There is a significant lack of diversity within the teaching population nationwide that reflects historical, political, and institutional racialized inequality. In the context of physical education, ethnic minority teachers often report feeling ‘different,’ marginalized, and struggle to negotiate the dominant school culture they feel they do not belong to. Purpose: To explore how race and gender intersect in the lived experiences of ethnic minority female PE teachers in predominantly white schools in the United States. Methods: This study used narrative and visual research methods. Results: Participants often felt isolated and uncomfortable in their educational contexts, actively seeking out other ethnic minorities to make meaningful connections and validate their lived experiences. Discussion: The intersection of race and gender in participants’ embodied identities reflects sexist and racist systems in which white privilege is positioned as normal or universal. PE and PE teacher education programs must actively work to disrupt and destabilize these norms.
The case for a network protocol isolation layer

Network protocols are typically designed and tested individually. In practice, however, applications use multiple protocols concurrently. This discrepancy can lead to failures from unanticipated interactions between protocols. In this paper, we argue that sensor network communication stacks should have an isolation layer, whose purpose is to make each protocol's perception of the wireless channel independent of what other protocols are running. We identify two key mechanisms the isolation layer must provide: shared collision avoidance and fair channel allocation. We present an example design of an isolation layer that builds on the existing algorithms of grant-to-send and fair queueing. However, the complexities of wireless make these mechanisms insufficient by themselves. We therefore propose two new mechanisms that address these limitations: channel decay and fair cancellation. Incorporating these new mechanisms reduces the increase in end-to-end delivery cost associated with concurrently operating two protocols by more than 60%. The isolation layer improves median protocol fairness from 0.52 to 0.96 in Jain's fairness index. Together, these results show that using an isolation layer makes protocols more efficient and robust.
CUIS at the NTCIR-12 MobileClick2 Task

We present our approach for tackling the iUnit ranking and iUnit summarization subtasks of MobileClick2. We ﬁrst conduct intent discovery based on latent topic modeling. Our iUnit ranking method exploits the discovered intents and considers the importance of an iUnit in each Web content document. We further develop our iUnit summarization model using the outcome from the iUnit ranking subtask. Our result submitted to the iUnit ranking subtask demonstrates superior performance.
The Wisconsin Card Sorting Test in the detection of personality disorders

The official categories of personality disorders were debated entities in the last decades, and researchers have used validity indicators for the proposed categories. Among other measures, the Wisconsin Card Sorting Test (WCST) has been an instrument mainly used in validation studies of the schizotypal personality disorder. The purpose of this study was to test the usefulness of the WCST to determine whether any personality disorder is present. The results indicate that only perseveration errors show sensibility for discriminating between presence or absence of a personality disorder; even that its specificity is doubtful. WCST measures, on the contrary, fail to show significant differences among the categories of personality disorders.
Network function virtualization in the multi-tenant cloud

With more and more tenants launching their applications on the cloud, various requirements have been posed regarding the cloud's performance, security, and management. In the face of tenant demands, the cloud provider deploys different hardware middleboxes, carrying out different network functions, and enhancing the cloud's capability in serving tenant requirements. While middleboxes are crucial to the cloud, concerns have been raised regarding their costs, manageability, and performance overhead. To tackle these problems, researchers have proposed an alternative to hardware middleboxes: network function virtualization. Software applications are deployed in place of hardware middleboxes, offering equivalent functionalities while greatly improving flexibility, manageability, and cost-efficiency. In this paper we discuss opportunities and challenges that network function virtualization brings to the multi-tenant cloud. We also propose a cloud architecture that exploits virtual network functions. Our contributions can serve as an enlightener for future efforts in this area.
Practical Antenna Selection for Spatial Multiplexing MIMO Systems with Decoding Ordering

A critical factor in the deployment of spatial multiplexing multiple-input multiple-output (MIMO) systems is the cost of multiple analog transmit/receive chains at the mobile terminal side. To mitigate this problem, antenna subset selection at the transmitter/receive has been proposed. With antenna selection, a small number of analog chains are multiplexed between a larger number of transmit/receive antenna elements. In this paper, we derive simple and robust criteria to antenna selection for V-BLAST error rate performance maximization. Both cases, where channel state information (CSI) at the transmitter is available and unavailable, are studied. Simulation results show that the proposed algorithm provides a very close performance to the optimum exhaustive search performance.
A-posteriori error estimate for a heterogeneous multiscale finite element method for advection-diffusion problems with rapidly oscillating coefficients and large expected drift

In this paper, we are engaged with an a-posteriori error estimate for a heterogeneous multiscale finite elements method (HMM) for solving linear advection-diffusion problems with rapidly oscillating coefficient functions and a large expected drift. The estimate is obtained in the L-norm and the achieved global error indicator can be localized with regard to the elements of the computational grid. This enables us to establish adaptive mesh refinement strategies for solving the problem. Even though the suggested estimate is only proved under the assumption of periodic coefficients, we show that it is also applicable to a heterogeneous setting. This claim is demonstrated by means of numerical experiments.
A Novel Wireless Sensor Network Frame for Urban Transportation

The rapid progress in the research and development of electronics, sensing, signal processing, and communication networks has significantly advanced the state of applications of intelligent transportation systems (ITSs). However, efficient and low-cost methods for gathering information in large-scale roads are lacking. Consequently, wireless sensor network (WSN) technologies that are low cost, low power, and self-configuring are a key function in ITS. The potential application scenarios and design requirements of WSN for urban transportation (WSN-UT) are proposed in this work. A customized network topology is designed to meet the special requirements, and WSN-UT is specifically tailored for UT applications. WSN-UT enables users to obtain traffic and road information directly from the local WSN within its wireless scope instead of the remote ITS data center. WSN-UT can be configured according to different scenario requirements. A three-level subsystem and a configuration and service subsystem constitute the WSN-UT network frame, and the service/interface and protocol algorithms for every subsystem level are designed for WSN-UT.