Miracle-dz/UniFAR-FTUs huggingface dataset:
Enabling low bitrate mobile visual recognition: a performance versus bandwidth evaluation

The rapid development of technologies in both hardware and software have made content-based multimedia services feasible on mobile devices such as smartphones and tablets; and the strong needs for mobile visual search and recognition have been emerging. While many real applications of visual recognition require a large scale recognition systems, the same technologies that support server-based scalable visual recognition may not be feasible on mobile devices due to the resource constraints. Although the client-server framework ensures the scalability, the real-time response subjects to the limitation on network bandwidth. Therefore, the main challenge for mobile visual recognition system should be the recognition bitrate, which is the amount of data transmission under the same recognition performance. For this work, we exploit and compare various strategies such as compact features, feature compression, feature signatures by hashing, image scaling, etc., to enable low bitrate mobile visual recognition. We argue that thumbnail image is a competitive candidate for low bitrate visual recognition because it carries multiple features at once and multi-feature fusion is important as the size of semantic space increases. Our evaluations on two subsets of ImageNet, both contain more than 10,000 images with 19 and 137 categories, verify the efficacy of thumbnail images. We further suggest a new strategy that combines single (local) feature signature and the thumbnail image, which achieves significant bitrate reduction from (average) 102,570 to 4,661 bytes with merely (overall) 10% performance degradation.
Charged Tachyons and Gauge Symmetry Breaking

We discuss the condensation of charged tachyons in the heterotic theory on the Kaluza-Klein Melvin background. The arguments are based on duality relations which are expected to hold from the adiabatic argument. It is argued that in many cases the rank of the gauge group is not changed by the condensation, as opposed to naive expectations.
Poly[di-μ3-hydroxy[μ4-5-(4-carboxyphenyl)pyridine-2-carboxylato-κ5 N,O 2:O 2′:O 4:O 4′]dicadmium]

The asymmetric unit of the title polymeric complex, [Cd2(C13H7NO4)(OH)2]n, consists of two independent CdII atoms, one 5-(4-carboxyphenyl)pyridine-2-carboxylate ligand and two hydroxy groups. One CdII atom is six-coordinated by two O atoms from two ligand molecules and by four μ3-OH groups in a distorted trigonal–prismatic geometry. The other is five-coordinated by one N and two O atoms from two ligands and by two μ3-OH groups, forming a distorted square-pyramidal geometry. The two independent CdII atoms are connected by the ligand molecules and the OH groups into a three-dimensional framework. O—H⋯O hydrogen bonds between the OH groups and the carboxylate O atoms are observed.
SVM and graphical algorithms: a cooperative approach

We present a cooperative approach using both support vector machine (SVM) algorithms and visualization methods. SVM are widely used today and often give high quality results, but they are used as "black-box" (it is very difficult to explain the obtained results) and cannot treat easily very large datasets. We have developed graphical methods to help the user to evaluate and explain the SVM results. The first method is a graphical representation of the separating frontier quality, it is then linked with other visualization tools to help the user explaining SVM results. The information provided by these graphical methods is also used for SVM parameter tuning, they are then used together with automatic algorithms to deal with very large datasets on standard computers. We present an evaluation of our approach with the UCI and the Kent Ridge Bio-medical data sets.
On the use of array learners towards Automatic Speech Recognition for dysarthria

Providing Automatic Speech Recognition (ASR) systems for dysarthria is a challenging task since the normal and the disabled speech have different attributes; hence, using ASR systems designed and trained for normal speakers is not an effective approach. It is important to craft ASR technologies specifically for the speech disabled. Nonetheless, because of the complexity and variability of dysarthric speech, previous studies failed to achieve adequate performance. In this paper we investigated the applications of array learners towards dysarthric speech recognition. The array was implemented by several neural networks that configured to work in parallel. The proposed approach was verified by using the speech materials of seven dysarthric subjects with speech intelligibility from 2% to 86%. For comparison, the results were compared with a dysarthric ASR based on the legacy single-learner approach as the reference model. It is shown that the array learner-based dysarthric ASR improved the mean word recognition rate of 10.41% over the reference model, and decreased the error rate of 4.84%.
Learning to walk before you run : Financial Behavior and mobile banking in Madagascar

(english) In Madagascar, Orange introduced its mobile banking services in September 2010. Mobile-banking (m-banking) is a system that allows users to conduct a number of financial transactions through a mobile phone. The existing body of literature suggests that the use of m-banking services may have a positive impact on individual savings, affect money transfer behavior and/or encourage financial inclusion. In 2012, we conducted a survey of 598 randomly selected Orange clients in Antananarivo. We use the matching methodology to assess the impacts of m-banking on clients' financial behavior. The results show that the use of m-banking services increases the number of national remittances sent and received. It is in line with the conclusions of the existing literature devoted to M-Pesa in Kenya. Yet we find that using of m-banking services has no significant impact on the sums saved by users or the sums of remittances sent and received, which appears to contradict the users' perceptions. This result may, however, be explained by a learning-by-doing process: users need to first learn to trust the e-money system before making any significant changes to their financial behavior. _________________________________ (francais) En septembre 2010, l’operateur Orange a introduit les services de banque mobile appeles Orange Money a Madagascar. Ils permettent d’effectuer des operations de depot et de retrait d’argent, de transferts nationaux et de paiements de marchandises. Selon la litterature existante, l’utilisation de ces services engendrerait une augmentation de l’epargne individuelle, pourrait modifier les comportements de transferts et/ou favoriser la bancarisation des plus pauvres. Afin d’analyser les consequences du m-banking sur les comportements financiers des populations concernees a Madagascar, nous procedons a une etude d’impact reposant sur des donnees originales. En mars 2012, nous avons realise une enquete aupres de 196 clients Orange utilisateurs reguliers des services Orange money et 402 clients Orange non utilisateurs de ces services. Afin de comparer rigoureusement les comportements financiers de ces deux groupes, nous apparions les individus sur la base de leurs scores de propension respectifs. Nos resultats montrent alors que l’utilisation des services Orange Money conduit a accroitre significativement la frequence des transferts envoyes et recus. Ce resultat est corrobore par l’approche subjective puisque 55% des utilisateurs Orange Money declarent que ce service les a encourages a effectuer des transferts plus frequemment. En revanche, nous montrons qu’Orange Money n’a d’impact significatif ni sur les montants epargnes ni sur les montants transferes (a l’envoi comme a la reception), ce qui tend a contredire le sentiment des utilisateurs. La temporalite des effets des services de m-banking apparait alors. Les modifications de montants transferes et epargnes s’inscrivent probablement davantage dans la duree alors que la frequence des transferts serait plus rapidement affectee eu egard au moindre cout et a la facilite d’utilisation d’Orange Money.
Monopoles and four-manifolds

Recent developments in the understanding of $N=2$ supersymmetric Yang-Mills theory in four dimensions suggest a new point of view about Donaldson theory of four manifolds: instead of defining four-manifold invariants by counting $SU(2)$ instantons, one can define equivalent four-manifold invariants by counting solutions of a non-linear equation with an abelian gauge group. This is a ``dual'' equation in which the gauge group is the dual of the maximal torus of $SU(2)$. The new viewpoint suggests many new results about the Donaldson invariants.
Audio-visual scene understanding utilizing text information for a cooking support robot

This paper addresses multimodal “scene understanding” for a robot using audio-visual and text information. Scene understanding is defined by extracting six-W information such as What, When, Where, Who, Why, and hoW on the surrounding environment. Although scene understanding for a robot has been studied in the fields of robot vision and audition, only the first four Ws except for why and how information were considered. We, thus, focus on extracting how information, in particular, on cooking scenes. In cooking scenes, we define how information as a cooking procedure, and it is useful that a robot gives appropriate advice for cooking. To realize such cooking support, we propose a multi-modal cooking procedure recognition framework consisting of Convolutional Neural Network (CNN), and Hierarchical Hidden Markov Model (HHMM). CNN is knows as one of the most advanced classifiers, and it is applied to recognize a cooking events from audio and visual information. HHMM models a cooking procedure represented by a sequence of cooking events, which is defined as a relationship between cooking events using text data obtained from web, and the cooking events classified with CNN. Therefore, our proposed framework integrates these three types of modalities. We constructed an interactive cooking support system based on the proposed framework, which advice a next step in the current cooking procedure through human-robot communication. Preliminary results with simulated and real recorded multi-modal scenes showed the robustness of the proposed framework in a noisy and/or occluded situation.
An Examination of the Role of Ethics in Tax Compliance Decisions

The relationship between ethical beliefs and tax compliance is well documented, but extant research has not explored the relationships among general ethical beliefs, contextual ethical beliefs, and tax compliance behavior. In this study we propose a model that is intended to clarify the mechanisms through which ethical beliefs impact tax compliance. In the model, contextual ethical beliefs represent the mechanism through which individuals' general ethical beliefs impact tax compliance behavior. The model is tested using participants' ethical orientations as measures of their general ethical beliefs and using participants' ethical evaluations of others' tax compliance decisions as measures of their contextual ethical beliefs. Tax compliance behavior is inferred from participants' estimates of the likelihood that they would evade. Overall, the findings from our study support the proposed model. Ethical orientations are directly related to ethical evaluations; ethical evaluations directly predict tax complia.
High Order Contractive Runge-Kutta Methods for Volterra Functional Differential Equations

A contractivity theory of Runge-Kutta methods for stiff nonlinear Volterra functional differential equations (VFDEs) is established, by means of which a family of high order contractive Runge-Kutta methods for VFDEs are identified. The application of the general theory for stiff nonlinear VFDEs to the special cases of stiff nonlinear delay differential equations and delay integro-differential equations is also presented.
Globally Consistent Indoor Mapping via a Decoupling Rotation and Translation Algorithm Applied to RGB-D Camera Output

This paper presents a novel RGB-D 3D reconstruction algorithm for the indoor environment. The method can produce globally-consistent 3D maps for potential GIS applications. As the consumer RGB-D camera provides a noisy depth image, the proposed algorithm decouples the rotation and translation for a more robust camera pose estimation, which makes full use of the information, but also prevents inaccuracies caused by noisy depth measurements. The uncertainty in the image depth is not only related to the camera device, but also the environment; hence, a novel uncertainty model for depth measurements was developed using Gaussian mixture applied to multi-windows. The plane features in the indoor environment contain valuable information about the global structure, which can guide the convergence of camera pose solutions, and plane and feature point constraints are incorporated in the proposed optimization framework. The proposed method was validated using publicly-available RGB-D benchmarks and obtained good quality trajectory and 3D models, which are difficult for traditional 3D reconstruction algorithms.
A quantitative technique for assessing the change in severity over time in psoriatic lesions using computer aided image analysis

Psoriasis is a chronic skin disease affecting an estimated 125 million people worldwide. One of the key problems in the management of this condition is the objective measurement of lesion severity over time. Currently, severity is scored by clinicians using visual protocols leading to intra and inter observer variability that makes measurement of treatment efficacy subjective. In this paper, an automatic computer aided image analysis system is proposed that quantitatively assess the changes of erythema and scaling severity of psoriatic lesions in long-term treatment. The algorithm proposed in this paper works on 2D digital images by selecting features that can be used to accurately segment erythema and scaling in psoriasis lesions and assess their changes in severity, according to the popular psoriasis area and severity index (PASI). The algorithms are validated by developing linear models that correlate well with changes in severity scores given by dermatologists. To the best of our knowledge, no such computer assisted method for psoriasis severity assessment in a long-term treatment exists.
A Success Story: Togo Is Moving toward Becoming the First Sub-Saharan African Nation to Eliminate Lymphatic Filariasis through Mass Drug Administration and Countrywide Morbidity Alleviation

Lymphatic filariasis (LF) is a debilitating vector-borne disease predominantly caused by the helminths Wuchereria bancrofti and Brugia malayi [1], [2]. Endemic in 72 countries, LF is responsible for 5.9 million DALYs lost and is implicated as the second leading cause of disability worldwide by the World Health Organization (WHO) [3]–[5]. Although 70% of those infected do not exhibit symptoms, almost all persons infected have subclinical damage to the lymphatic vessels [6], [7]. An estimated 40 million people are symptomatic with the predominant morbidities associated with LF: lymphedema and/or hydrocele [8]. In recognition of the worldwide burden of LF, in 1997, the World Health Assembly passed the resolution WHA 50.29 calling for collaborative efforts by member states to eliminate the disease as a public health problem [9]. In 2000, the Global Programme to Eliminate Lymphatic Filariasis (GPELF) was formed in response to the WHA resolution and aimed to eliminate the disease by 2020. The program adopted a two-pronged strategy: (1) to interrupt transmission of the causal parasite and (2) to alleviate morbidities associated with the disease [10]. The two pillars of the GPELF's strategy form the basic framework for any successful LF program. Togo is one of the 34 African countries endemic for lymphatic filariasis and is surrounded by the endemic countries of Benin, Ghana, and Burkina Faso [11]. The National Program to Eliminate Lymphatic Filariasis (NPELF) was founded in 2000 and is one of the few LF programs that address the dual goals of the global elimination program on a national scale. Togo is the first sub-Saharan country to achieve probable interruption of transmission and to move to the post-MDA surveillance phase as defined by the WHO [12]. Here we describe the elements that proved successful in the national strategy to address LF in Togo.
Sample-Based Vegetation Distribution Information Synthesis

In constructing and visualizing a virtual three-dimensional forest scene, we must first obtain the vegetation distribution, namely, the location of each plant in the forest. Because the forest contains a large number of plants, the distribution of each plant is difficult to obtain from actual measurement methods. Random approaches are used as common solutions to simulate a forest distribution but fail to reflect the specific biological arrangements among types of plants. Observations show that plants in the forest tend to generate particular distribution patterns due to growth competition and specific habitats. This pattern, which represents a local feature in the distribution and occurs repeatedly in the forest, is in line with the “locality” and “static” characteristics in the “texture data”, making it possible to use a sample-based texture synthesis strategy to build the distribution. We propose a vegetation distribution data generation method that uses sample-based vector pattern synthesis. A sample forest stand is obtained first and recorded as a two-dimensional vector-element distribution pattern. Next, the large-scale vegetation distribution pattern is synthesized automatically using the proposed vector pattern synthesis algorithm. The synthesized distribution pattern resembles the sample pattern in the distribution features. The vector pattern synthesis algorithm proposed in this paper adopts a neighborhood comparison technique based on histogram matching, which makes it efficient and easy to implement. Experiments show that the distribution pattern synthesized with this method can sufficiently preserve the features of the sample distribution pattern, making our method meaningful for constructing realistic forest scenes.
2-DOF based novel controller design for DC voltage regulation of LCL interfaced three phase grid connected solar PV system

The dedicated PI controller for dc voltage regulation and control has been implemented with control technique for operation of grid-connected PV system. Generally, dedicated PI controller have been used for the dc link voltage control due to ease of implementation. The PI controller is having constraint regarding response time and overshoot with only proportional and one integral gain. The conventional PI controller is having one degree of freedom (DOF). In this paper the 2-DOF PI controller in PI-P configuration is been proposed for the dc link voltage control. It is based upon the concept to increase the DOF in the system. The system with PI-P controller with 2 DOF having two proportional gains and one integral gain. The performance of the PI-P controller is compared with the PI controller while PV system connected to grid (considering grid current response) and under disturbance in reference dc voltage. The system transient during interconnection have been improved and showing better response for dc voltage control.
Enabling trust-aware semantic web service selection a flexible and personalized approach

In today's online markets, consumers need support in finding providers that offer the products or services they need and that are trustworthy. While Semantic Web Services (SWS) research addresses the first problem (discovering functionally suitable service providers), it neglects the second. Hence, several attempts have been made to complement service retrieval techniques based on semantic matchmaking with trust-establishing techniques that leverage collaborative consumer feedback. However, the diversity and multi-faceted nature of SWS impose special requirements on the underlying feedback mechanism, in particular w.r. t. their flexibility and expressiveness. Existing approaches only partially meet those requirements. In this paper, we will therefore propose a trust-establishing mechanism for Semantic Web Services that allows to assess a service provider's trustworthiness with respect to various service aspects and is flexible enough to adjust to various kinds of services and consumer requirements.
Cooperative output feedback design for networks of strict-feedback systems having high relative degrees

This paper studies semi-global robust output consensus of multi-agent uncertain nonlinear systems of a strict-feedback normal form. The concerned agents may have high relative degrees with actuating disturbances, thus strictly extended from the previous study [17]. We develop a Lyapunov function based approach to establishing a cooperative output feedback protocol. Specifically, each local protocol consists of a distributed observer and a networked internal model. For an illustration, the proposed result is applied for a network of microelectromechanical-system (MEMS) oscillators.
Developing a feedback system to augment monitoring performance of aircraft pilots

As a supervisor of a highly automated technical system, a human operator is the “ultima ratio” in abnormal situations that cannot be handled by the automation. Therefore, a human operator must adequately monitor the automated system throughout the entire operation. Unfortunately, humans tend to fall prey to a couple of demons leading to inadequate monitoring behavior, such as boredom, attentional tunneling or perservation. As a consequence, the human might lose situation awareness and be unable to detect and handle an abnormal situation in the given amount time. In this paper, we present a feedback system to augment monitoring performance on the part of an aircraft pilot. The feedback system has been developed under the umbrella of the European research and development project A-PiMod, where novel team-centered concepts for pilot-automation interaction are investigated.
Needed Concepts in the Study of Gender Identity

On the 18th-century frontiers of the Virginia highland, weary travelers were fortunate to arrive by nightfall at a log cabin called a “necessary. ” The “necessary” provided the bare essentials of shelter, food and drink, enabling them to continue the journey on the next day into the wilderness. The concepts I shall discuss are needed in psychology’s “necessary” to enable the difficult journey toward understanding the psychology of being woman in a social context. Today’s psychology is more like a luxury hotel, offering the traveler a superabundance of conceptual tools. I would perform no service by adding new concepts to the bewildering array that already includes gender identity, sex identity, sex roles and sex role identities, masculinity, femininity, sex-reversed identity, androgyny (high and “undifferentiated”), etc. The plethora of concepts is complicated by the several definitions given to each by different authors. “Gender identity” means different things to different people. Some broad definitions encompass everything it means to feel, think and act like a woman or man. More specific definitions are modeled on social stereotypes of masculinity or femininity and on androgyny. Other normative definitions require acceptance of one’s biological sexuality, hence presumably of certain social meanings attached to it. Our concepts should be capable of handling such evaluative issues, but they should not be evaluative concepts. Therefore, I will start naively with the first needed concept, and ask what gender is. Gender is a