Miracle-dz/UniFAR-FTUs huggingface dataset:
A self-calibration method for smart video cameras

The automatic calibration of the intrinsic camera parameters such as the focal length and the camera orientation is an important pre-requisite for many computer vision algorithms in video surveillance. Despite its importance only a few number of methods show their applicability in embedded systems. This paper shows new results of previous work done in camera self-calibration on images of the York Urban data-set. These 102 images show typical urban scenes one might expect in practice. The evaluation shows that in 52 of 102 images the proposed method achieves less than 5% relative error in the focal length at a mean computation time per image of 14.45 s on a standard PC. We believe that these results show a fair balance between accuracy and computational performance and encourage an embedded implementation on a smart camera.
Gestalt-inspired features extraction for object category recognition

We propose a methodology inspired by Gestalt laws to extract and combine features and we test it on the object category recognition problem. Gestalt is a psycho-visual theory of Perceptual Organization that aims to explain how visual information is organized by our brain. We interpreted its laws of homogeneity and continuation in link with shape and color to devise new features beyond the classical proximity and similarity laws. The shape of the object is analyzed based on its skeleton (good continuation) and as a measure of homogeneity, we propose self-similarity enclosed within shape computed at super-pixel level. Furthermore, we propose a framework to combine these features in different ways and we test it on Caltech 101 database. The results are good and show that such an approach improves objectively the efficiency in the task of object category recognition.
Further Evidence of Emotional Allodynia in Unmedicated Young Adults with Major Depressive Disorder

Background Recent evidence suggests that sensitivity to the emotional sequela of experimental thermal pain(measured by emotional unpleasantness) is heightened in individuals with major depressive disorder(MDD), a phenomenon we termed “emotional allodynia”. The aim of this study was to examine whether acute happy and sad mood induction alters emotional allodynia in MDD. We hypothesized that emotional allodynia will be a robust characteristic of individuals with MDD compared to healthy controls. Thus, it would remain following acute mood induction, independent of valence. Methods Twenty-one subjects with current MDD and 21 well-matched healthy subjects(HC) received graded brief temperature stimuli following happy and sad mood inductions procedures(MIP). All subjects rated the intensity and affect(pleasantness/unpleasantness) of each stimulus. Sensory(pain intensity) and affective(pain unpleasantness) thresholds were determined by methods of constant stimuli. Results The MIPs reliably induced happy and sad mood and the resulting induced mood and subjective arousal were not different between the groups at the time of temperature stimulation. Compared to HC, MDD individuals demonstrated emotional allodynia. We found significantly decreased affective pain thresholds whereby significantly lower temperatures became unpleasant in the MDD compared to the HC group. This was not observed for the sensory pain thresholds. Within the MDD, the affective pain thresholds were significantly lower than the corresponding pain intensity thresholds, whereby non-painful temperatures were already unpleasant for the MDD irrespective of the induced mood. This was not observed for the HC groups where the affective and pain intensity thresholds were comparable. Conclusions These findings suggest that emotional allodynia may be a chronic characteristic of current MDD. Future studies should determine if emotional allodynia persists after psychological or pharmacological interventions. Finally, longitudinal work should examine whether emotional allodynia is a result of or vulnerability for depression and the role it plays in the increased susceptibility for pain complaints in this disorder.
Business process management: a boundaryless approach to modern competitiveness

Discusses the concept of business process management as an approach which relies on cross‐functional contributions to organizational performance. Presents arguments on the importance of “hard” and “soft” elements which are all required for determining effective competitiveness. A review of the literature suggests that although the term “process management” is increasingly being used in everyday business language, the practical experience so far is very limited. Presents the concept of business process management through its various key elements, supported by practical examples. Discusses the methodological approaches used in various contexts for creating a culture based on process management. Concludes with some useful guidelines to support efforts in establishing a business process management culture.
Low-Complexity Hybrid Precoding in Massive Multiuser MIMO Systems

Massive multiple-input multiple-output (MIMO) is envisioned to offer considerable capacity improvement, but at the cost of high complexity of the hardware. In this paper, we propose a low-complexity hybrid precoding scheme to approach the performance of the traditional baseband zero-forcing (ZF) precoding (referred to as full-complexity ZF), which is considered a virtually optimal linear precoding scheme in massive MIMO systems. The proposed hybrid precoding scheme, named phased-ZF (PZF), essentially applies phase-only control at the RF domain and then performs a low-dimensional baseband ZF precoding based on the effective channel seen from baseband. Heavily quantized RF phase control up to 2 bits of precision is also considered and shown to incur very limited degradation. The proposed scheme is simulated in both ideal Rayleigh fading channels and sparsely scattered millimeter wave (mmWave) channels, both achieving highly desirable performance.
One Dimensional Systolic Inversion Architecture Based on Modified GF(2^k) Extended Euclidean Algorithm

The need for small chip covered area in most handheld devices with out sacrifices in computational power introduces an interesting problem concerning expensive, computational intensive operations, like GF(2 k ) inversion which is widely used in cryptography. This paper addresses this problem by proposing a systolic inversion architecture for GF(2 k ) fields. This architecture is based on an extended analysis on an optimized version of Modified Extended Euclidean Algorithm (OMEEA) that is using signal reusability and simplification of the control signals with regard to hardware design and manages to make the inversion process less complex. The proposed one dimensional systolic inversion architecture based on OMEEA was measured in terms of hardware components number, latency and critical path delay with very interesting results when compared to other well known designs thus proving the efficiency of the analysis on OMEEA algorithm.
Comparison of Some Shrinkage Estimators and the OLS Estimator for Regression Coefficients under the Pitman Nearness Criterion : A Monte Carlo Study

In this paper, we consider the Stein-rule (SR), positive-part Stein-rule (PSR), minimum mean squared error (MMSE) and adjusted minimum mean squared error (AMMSE) estimators for regression coeffi cients, and we compare each shrinkage estimator and the OLS estimator under the Pitman nearness (PN) criterion. Since an analytical comparison is diffi cult, we compare each shrinkage estimator and the OLS estimator by Monte Carlo experiments. Our Monte Carlo results show that not only the SR estimator but also the MMSE and PSR estimators dominate the OLS estimator in the sense of the PN criterion. In particular, our Monte Carlo results show that the MMSE estimator is most preferred to the OLS estimator among the shrinkage estimators considered here in the sense of the PN criterion, except for some cases.
Publicly verifiable private credentials - a technique for privately signing citizen initiatives

Besides voting, another way of practicing democracy is by signing popular/citizen initiatives or optional referendums of certain types. We address the problems related to the electronic remote signing of support for referendums and popular initiatives (e-referendums). We propose, publicly veriable private credentials (PVPCs), a type of certied pseudonym digital signatures for which one can publicly prove that only eligible users (belonging to a specied group) got them and that no user got two of them. They can be used to sign citizen initiatives. Our technique generates PVPCs that can be publicly proven to belong to an unknown permutation of the eligible users (proving the aforementioned property). We also argue that e-referendum systems can achieve more success than remote e-voting (being more robust to the main weaknesses of the SERVE project, namely denial-of-service, \Man in the middle", and virus attacks). In particular we provide a technique to reduce the risk of exposure to virus attacks by incorporating manually generated passwords into computer-generated random numbers.
A visual sensing platform for creating a smarter multi-modal marine monitoring network

Demands from various scientific and management communities along with legislative requirements at national and international levels have led to a need for innovative research into large-scale, low-cost, reliable monitoring of our marine and freshwater environments. In this paper we demonstrate the benefits of a multi-modal approach to monitoring and how an in-situ sensor network can be enhanced with the use of contextual image data. We provide an outline of the deployment of a visual sensing system at a busy port and the need for monitoring shipping traffic at the port. Subsequently we present an approach for detecting ships in a challenging image dataset and discuss how this can help to create an intelligent marine monitoring network.
Using NLP Techniques for Meaning Negotiation

This paper describes an automatic algorithm of meaning nego tiation that enables semantic interoperability between local overlapp ing and heterogeneous ontologies. Rather than reconciling differences between het erogeneous ontologies, this algorithm searches for mappings between concepts of differ ent ontologies. The algorithm is composed of three main steps: (i) computing the linguistic meaningof the label occurring in the ontologies via natural language proc essing, (ii)contextualization of such a linguistic meaning by considering the context, i.e . th ontologies, where a label occurs; (iii) comparing contextualized linguistic meaning of two ontologies in in order to find a possible matching between them.
A Secure Watermarking and Tampering Detection Technique on RGB Image Using 2 Level DWT

Digital watermarking is a method of combining data into a digital signal. In digital watermarking we work on two images one is for watermark image, which is cover on the original image (secondary image) that gives security to the image. It acts as a digital mark, providing the image a good decision of ownership or authenticity. Digital watermarking method is very inspiring for image authentication or security for attacks. This paper presents digital watermarking for their classification, application, techniques, attacks and also tampering detection in digital watermarking. We proposed a new algorithm for digital watermarking and tampering detection technique, by combining these techniques, we can improve the security of image. We worked on RGB components such as red, green and blue for enhancing security and robustness. 2-DWT applied on RGB components for better results. In the tampering process, we used watermarked image as a reference image for detecting tampering. The experimental results gave good PSNR value which is reached up to 55%.
Biomimetic Receptors for Bioanalyte Detection by Quartz Crystal Microbalances — From Molecules to Cells

A universal label-free detection of bioanalytes can be performed with biomimetic quartz crystal microbalance (QCM) coatings prepared by imprinting strategies. Bulk imprinting was used to detect the endocrine disrupting chemicals (EDCs) known as estradiols. The estrogen 17β-estradiol is one of the most potent EDCs, even at very low concentrations. A highly sensitive, selective and robust QCM sensor was fabricated for real time monitoring of 17β-estradiol in water samples by using molecular imprinted polyurethane. Optimization of porogen (pyrene) and cross-linker (phloroglucinol) levels leads to improved sensitivity, selectivity and response time of the estradiol sensor. Surface imprinting of polyurethane as sensor coating also allowed us to generate interaction sites for the selective recognition of bacteria, even in a very complex mixture of interfering compounds, while they were growing from their spores in nutrient solution. A double molecular imprinting approach was followed to transfer the geometrical features of natural bacteria onto the synthetic polymer to generate biomimetic bacteria. The use of biomimetic bacteria as template makes it possible to prepare multiple sensor coatings with similar sensitivity and selectivity. Thus, cell typing, e.g., differentiation of bacteria strains, bacteria growth profile and extent of their nutrition, can be monitored by biomimetic mass sensors. Obviously, this leads to controlled cell growth in bioreactors.
Composition of Transfer Rules in a Multi-lingual Mt System

The paper describes a software reusability technique relevant to multi-lingualmachine translation systems, which can greatly reduce the eeort needed to construct sets of transfer rules for new language-pairs. If transfer information is coded in a declarative fashion, we show how simple methods borrowed from logic programming can be used to compose two sets of transfer rules for the language-pairs L 1 ! L 2 and L 2 ! L 3 into a new set for the pair L 1 ! L 3. Automatically composed sets of rules are generally of lower quality than hand-coded sets, but can rapidly be improved by hand to an adequate level. We sketch the basic debug-and-test cycle used, and report initial results of experiments in which transfer rule sets for Swedish ! English and English ! French, each of between 1 000 and 1 500 rules, were composed into a set for Swedish ! French. The resulting transfer rules have been used to construct a Swedish-to-French version of the SLT speech translation system.
Sistem Pendukung Keputusan Pemilihan Jurusan Dengan Menggunakan Metode SAW Di SMA 6 Tasikmalaya

SMA Negeri 6 Tasikmalaya merupakan salah satu sekolah di Kota Tasikmalaya yang tiap tahunnya rutin melaksanakan pemilihan jurusan bagi siswanya. Penjurusan ini dimaksudkan agar nanti siswa dapat menyelesaikan sekolah sesuai dengan minat dan kemampuannya sebelum melanjutkan ke jenjang yang lebih tinggi. Proses pemilihan jurusan saat ini memiliki kelemahan diantaranya membutuhkan waktu cukup lama dan juga hasil yang didapat kurang akurat karena bisa saja terjadi banyak kekeliruan karena belum tersedianya aplikasi khusus untuk mendukung perhitungan tersebut. Selain itu unsur subjektifitas pun cukup tinggi karena kriteria yang digunakan masih sedikit dan kurang relevan. Untuk mengatasi masalah tersebut maka dibuatkan sistem penunjang keputusan untuk membantu Guru Bimbingan Konseling(BK) dalam menentukan pemilihan jurusan. Dalam penelitian ini menggunakan metode Simple Additive Weighting (SAW). Adapun kriteria yang digunakan adalah Nilai Rapot Matematika, Nilai Rapot Bahasa Indonesia, Nilai Rapot Bahasa Inggris, Nilai Rapot IPA, Nilai Rapot IPS, Nilai psikotes, Minat Siswa IPA, Minat Siswa IPS, Saran Orang Tua IPA, dan Saran Orang Tua IPS. Hasil akhir dari penelitian ini didapatkan bahwa sistem pendukung keputusan dengan metode SAW mampu mengatasi permasalahan dalam proses pemilihan jurusan di SMA 6 Tasikmalaya.
Evolutionary reinforcement learning of artificial neural networks

In this article we describe EANT, Evolutionary Acquisition of Neural Topologies, a method that creates neural networks by evolutionary reinforcement learning. The structure of the networks is developed using mutation operators, starting from a minimal structure. Their parameters are optimised using CMA-ES, Covariance Matrix Adaptation Evolution Strategy, a derandomised variant of evolution strategies. EANT can create neural networks that are very specialised; they achieve a very good performance while being relatively small. This can be seen in experiments where our method competes with a different one, called NEAT, NeuroEvolution of Augmenting Topologies, to create networks that control a robot in a visual servoing scenario.
Immigrated urn models—theoretical properties and applications

Urn models have been widely studied and applied in both scientific and social science disciplines. In clinical studies, the adoption of urn models in treatment allocation schemes has proved to be beneficial to researchers, by providing more efficient clinical trials, and to patients, by increasing the likelihood of receiving the better treatment. In this paper, we propose a new and general class of immigrated urn (IMU) models that incorporates the immigration mechanism into the urn process. Theoretical properties are developed and the advantages of the IMU models are discussed. In general, the IMU models have smaller variabilities than the classical urn models, yielding more powerful statistical inferences in applications. Illustrative examples are presented to demonstrate the wide applicability of the IMU models. The proposed IMU framework, including many popular classical urn models, not only offers a unify perspective for us to comprehend the urn process, but also enables us to generate several novel urn models with desirable properties.
Morphological and Molecular Discrimination of Fasciola Species Isolated From Domestic Ruminants of Urmia City, Iran

Background: The trematodes of the genus Fasciola (the liver flukes) are among the well-known instances of food-borne parasites worldwide. Differentiation of Fasciola species is important because of their different transmission and epidemiological characteristics. The current study was undertaken to discriminate Fasciola species in the domestic ruminants of Urmia city, Iran. Methods: Adult flukes were isolated from the naturally infected livers of the slaughtered water buffaloes and sheep. The flukes were initially identified based on morphological and morphometric parameters. A 618-bp-long fragment of the 28SrRNA gene of Fasciola was amplified by polymerase chain reaction (PCR). The amplified fragment was digested by DraII or AvaII enzymes for a restriction fragment length polymorphism (RFLP) analysis and sequenced for the phylogenetic tree construction. Results: Based on the morphometric examination, the flukes belonged to F. hepatica, F. gigantica and an intermediate Fasciola form. The PCR-RFLP analysis was able to differentiate F. hepatica from F. gigantica. While the phylogenetic reconstruction justified, to some extent, the morphological diagnosis, it failed to segregate F. hepatica from F. gigantica identified in this and the previous studies. Conclusion: To resolve fully the problem of taxonomy and evolution in Fasciola species, employing a broad range of molecular and morphological approaches is necessary. This is crucial for epidemiological surveys and successful clinical management of their infection.
Genetic consequences of introducing allopatric lineages of Bluestriped Snapper (Lutjanus kasmira) to Hawaii

A half century ago the State of Hawaii began a remarkable, if unintentional, experiment on the population genetics of introduced species, by releasing 2431 Bluestriped Snappers (Lutjanus kasmira) from the Marquesas Islands in 1958 and 728 conspecifics from the Society Islands in 1961. By 1992 L. kasmira had spread across the entire archipelago, including locations 2000 km from the release site. Genetic surveys of the source populations reveal diagnostic differences in the mtDNA control region (d = 3.8%; φST = 0.734, P < 0.001) and significant allele frequency differences at nuclear DNA loci (FST = 0.49; P < 0.001). These findings, which indicate that source populations have been isolated for approximately half a million years, set the stage for a survey of the Hawaiian Archipelago (N = 385) to determine the success of these introductions in terms of genetic diversity and breeding behaviour. Both Marquesas and Society mtDNA lineages were detected at each survey site across the Hawaiian Archipelago, at about the same proportion or slightly less than the original 3.4:1 introduction ratio. Nuclear allele frequencies and parentage tests demonstrate that the two source populations are freely interbreeding. The introduction of 2431 Marquesan founders produced only a slight reduction in mtDNA diversity (17%), while the 728 Society founders produced a greater reduction in haplotype diversity (41%). We find no evidence of genetic bottlenecks between islands of the Hawaiian Archipelago, as expected under a stepping‐stone model of colonization, from the initial introduction site. This species rapidly colonized across 2000 km without loss of genetic diversity, illustrating the consequences of introducing highly dispersive marine species.
An Adapted Parameterization for Smooth Geometry Images

Geometry images are important representations of 3D geometry models. Smooth geometry images contributes to low approximation error and high image compressibility. We present an adapted parameterization method to generate a smooth geometry image. It is quite challenging to directly modify the parameter domain to smooth geometry images. Our novel idea is that we use an indirect way to construct a resulting parameter domain according to the desired geometry image. We first move image pixels according to the current parameter domain to decrease the local linear error. Then we formulate a relationship between the moved image pixels and the current parameter domain. Finally, we use the relationship to update the parameter domain by restituting the image pixels to their original positions. The process will continue until the local linear error is less than a given threshold or the number of iterations is larger than a given threshold. Experimental results illustrate that geometry images generated by our method have low linear errors and low approximation errors under different sampling resolutions.
“Science is not my thing”: Exploring deaf non-science majors’ science identities

Students who are deaf and hard-of-hearing are underrepresented in science majors, yet we know little about why. Students from other underrepresented groups in science— women and people of color—tend to highly value altruistic or communal career goals, while perceiving science as uncommunal. Research suggests that holding stereotypical conceptions about scientists and perceptions of science as uncommunal may strongly hinder recruitment into science majors. This study sought to explore the science identities of students who are deaf, hard-of-hearing, and hearing signers. The study focused on non-science majors in bilingual (American Sign Language and written English) biology laboratory courses. This study is the first step to understanding if stereotypes about scientists and perceptions of science as uncommunal disproportionately affect students who are deaf and hard-of-hearing. Findings suggest that students’ science identities are influenced by stereotypical portrayals of scientists and a preference for people-centered careers, specifically within the Deaf community. Applied research is needed to challenge stereotypes, and identify connections between science and the Deaf community, to support the growth of deaf and hard-of-hearing students’ science identities to increase participation in science careers.