Miracle-dz/UniFAR-FTUs huggingface dataset:
A/P(rivacy) Testing: Assessing Applications for Social and Institutional Privacy

The way information systems are designed has a crucial effect on users' privacy, but users are rarely involved in Privacy-by-Design processes. To bridge this gap, we investigate how User-Centered Design (UCD) methods can be used to improve the privacy of systems' designs. We present the process of developing A/P(rivacy) Testing, a platform that allows designers to compare several privacy designs alternatives, eliciting end-users' privacy perceptions of a tested system or a feature (Figure 1). We describe three online experiments, with 959 participants, in which we created and validated the reliability of a scale for Users' Perceived Systems' Privacy (UPSP), and used it to compare between privacy designs alternatives by using scenarios and different variants. We show that A/B testing is applicable for privacy purposes and that our scale is differentiating between designs that perceived as legitimate and designs that may violate users' expectations.
Spatial-Spectral Squeeze-and-Excitation Residual Network for Hyperspectral Image Classification

Jointly using spectral and spatial information has become a mainstream strategy in the field of hyperspectral image (HSI) processing, especially for classification. However, due to the existence of noisy or correlated spectral bands in the spectral domain and inhomogeneous pixels in the spatial neighborhood, HSI classification results are often degraded and unsatisfactory. Motivated by the attention mechanism, this paper proposes a spatial–spectral squeeze-and-excitation (SSSE) module to adaptively learn the weights for different spectral bands and for different neighboring pixels. The SSSE structure can suppress or motivate features at a certain position, which can effectively resist noise interference and improve the classification results. Furthermore, we embed several SSSE modules into a residual network architecture and generate an SSSE-based residual network (SSSERN) model for HSI classification. The proposed SSSERN method is compared with several existing deep learning networks on two benchmark hyperspectral data sets. Experimental results demonstrate the effectiveness of our proposed network.
Inundation Extent Mapping by Synthetic Aperture Radar: A Review

Recent flood events have demonstrated a demand for satellite-based inundation mapping in near real-time (NRT). Simulating and forecasting flood extent is essential for risk mitigation. While numerical models are designed to provide such information, they usually lack reference at fine spatiotemporal resolution. Remote sensing techniques are expected to fill this void. Unlike optical sensors, synthetic aperture radar (SAR) provides valid measurements through cloud cover with high resolution and increasing sampling frequency from multiple missions. This study reviews theories and algorithms of flood inundation mapping using SAR data, together with a discussion of their strengths and limitations, focusing on the level of automation, robustness, and accuracy. We find that the automation and robustness of non-obstructed inundation mapping have been achieved in this era of big earth observation (EO) data with acceptable accuracy. They are not yet satisfactory, however, for the detection of beneath-vegetation flood mapping using L-band or multi-polarized (dual or fully) SAR data or for urban flood detection using fine-resolution SAR and ancillary building and topographic data.
An efficient spatio-temporal architecture for animation rendering

Producing high quality animations featuring rich object appearance and compelling lighting effects is very time consuming using traditional frame-by-frame rendering systems. In this paper we present a rendering architecture for computing multiple frames at once by exploiting the coherence between image samples in the temporal domain. For each sample representing a given point in the scene we update its view-dependent components for each frame and add its contribution to pixels identied through the compensation of camera and object motion. This leads naturally to a high quality motion blur and signicantly reduces the cost of illumination computations. The required visibility information is provided using a custom ray tracing acceleration data structure for multiple frames simultaneously. We demonstrate that precise and costly global illumination techniques such as bidirectional path tracing become affordable in this rendering architecture.
From Motions to Emotions: Classification of Affect from Dance Movements using Deep Learning

This work investigates classification of emotions from MoCap full-body data by using Convolutional Neural Networks (CNN). Rather than addressing regular day to day activities, we focus on a more complex type of full-body movement - dance. For this purpose, a new dataset was created which contains short excerpts of the performances of professional dancers who interpreted four emotional states: anger, happiness, sadness, and insecurity. Fourteen minutes of motion capture data are used to explore different CNN architectures and data representations. The results of the four-class classification task are up to 0.79 (F1 score) on test data of other performances by the same dancers. Hence, through deep learning, this paper proposes a novel and effective method of emotion classification, which can be exploited in affective interfaces.
An experience report on using architectural models within distributed scrum teams contexts

Model artifacts in agile software development (or agile modeling) are used to establish a shared understanding on the requirements that the items from the product backlog will address. A common challenge is to have a proper balance between the project's agility and architecture. This shared understanding gains more importance in large-scale contexts composed by multiple agile teams. Teams must clearly define their work scope and interactions with other teams, and architects must design a proper amount of design. This paper describes the application of a process for modeling a candidate logical architecture to be used within a distributed Scrum teams context. The process encompasses agile modeling of a candidate architecture and traceability tools for delivering user stories in a team backlog and usage in inter-team coordination.
What influences software startups to use lean startup?

Software startups develop new products or service using software development under extreme uncertainty conditions. Agile methodologies are considered the most suitable for these teams since they accomodate changes easier than traditional software development methodologies. A very well-known methodology for startups is Lean Startup that is considered to be a variant of agile methodologies. Nevertheless, prior studies found that Lean Startup adoption by software startups are still low. This research will focus to understand why the Lean Startup adoption is not higher despite its fame and history of good results. To achieve that, this research will try to develop a conceptual framework using determinants borrowed from technology adoption theories well-used in information systems literature and also on prior studies on agile methodology adoption. The conceptual framework will then be evaluated through a questionnaire with software startups practioners. It is expected that this conceptual framework could answer why more software startups are not using Lean Startup.
Comparison of Two Evolvable Systems in the Automated Analog Circuit Synthesis

This paper presents the comparison of two different algorithms: a Univariate Marginal Distribution Algorithm for Analog Circuits (UMDA-AC) and a Genetic Algorithm for Analog Circuits (GA-AC). These algorithms are compared in performing the synthesis of topology and sizing of an analog low pass filter. Modeling of circuits is made by means of a linear representation technique with a variable length chromosome. Evaluation of circuits’ functionality is carried out by Simulation Program with Integrated Circuits Emphasis (Spice), since one of the objectives is to keep as low as possible the amount of non Spice-Simulable circuits while keep elements’ values within preferred ones. Experiments show the effectiveness of a set of evolvable mechanisms in both algorithms, and while GA-AC and its three genetic operators are more able to keep low the rate of non Spice-Simulable circuits; UMDA-AC performs less evaluations by means of its estimated distribution
Frequency-adaptive Virtual Flux estimation for grid synchronization under unbalanced conditions

This paper proposes a new and explicitly frequency-adaptive method for Virtual Flux estimation and voltage sensor-less grid synchronization under unbalanced conditions. The proposed system is based on using Second Order Generalized Integrators, arranged to simultaneously fulfill the purposes of frequency-adaptive band-pass filtering, integration and quadrature signal generation. This results in a simple and efficient structure for combined Virtual Flux estimation and separation into positive and negative sequence components. The properties of the proposed Virtual Flux model is analyzed theoretically, first as an integrator for implementing generic Virtual Flux estimation, and then with respect to sequence separation. The dynamic performance of the proposed estimation method is tested by simulations for the case of an unbalanced voltage drop in the grid and for a step in grid frequency. The simulations verify the performance to be as expected, with similar dynamics as synchronization based on voltage measurements.
Non-incremental inference of 0 L-systems with positive sample

Many algorithms of grammatical inference were developed for several types of grammars (Adriaans, Fernau and Van Zaanen, 2002). The inference problem consists of finding, from a set of strings, a grammar that produces all the strings of this set (Miclet and Cornuéjols, 2002). We are interested here by the inference of a particular class of L-systems, noted the 0L-systems (Yokomori, 1992). We present a heuristic algorithm, which only uses positive sample corresponding to either independent or developmental biological structures. The positive sample noted I + is the set of all strings that the inferred 0L-system must produce. It is a non-incremental algorithm in the sense that the positive sample becomes unchanged (new strings cannot be added progressively) (Miclet and Cornuéjols, 2002). Developmental biology and inference: The problem of inductive inference has in recent years been extensively investigated. It has particular significance for developmental languages in which the description of the developmental stages of an organism is formally defined as a series of strings of symbols. The problem is to devise developmental rules which transform the strings of symbols in a way consistent with observations for a particular species. In this sense, there is an overlap between model building in developmental biology and the grammatical inference problem. This problem enters the realm of developmental biology in the following way. The biologist interested in a particular plant is confronted with a large number of experimental observations. His task is to explain on the basis of such experimental results the way in which the particular plant develops (Feliciangeli, Gabor and Herman, 1973). Studying self-similarities in plant structures: In the growth processes of many living organisms, especially plants, regularly repeated appearances of closely related biological structures are readily noticeable. This phenomenon of self-similarity has been tentatively captured by several botanical notions (Prusinkiewicz 2004), (Ferraro, Godin, and Prusinkiewicz, 2005). Our main objective is to use the grammatical inference in the detection of this self-similarity from a symbolic representation of a tree structure in development, or from several tree structures corresponding to different varieties in the aim to compare them. Complementary arguments for such a biological motivation, as well as some similar problems, are discussed in (Herman and Walker, 1972). Specificities of the proposed method Inference of L-systems was recently studied using genetic programming (Jacob, 1998). By proposing this new heuristic method we avoid some of the drawbacks of the genetic algorithms, i.e.: uncertainty on the …
Optimum selection of features for 2D (color) and 3D (depth) face recognition using modified PCA (2D)

The paper proposes a Modified Principal Component Analysis coined as 2DPCA to compare 2D and 3D face recognition. In 2DPCA a covariance matrix of image is obtained directly from the original image and is used to find the eigenvectors for image feature extraction. Here the Texas 3D [1] face recognition database was considered, which has 1149 pairs of high resolution, preprocessed and pose normalized color and range images. These images are pixel-to-pixel registered and of resolution of 751×501 pixels. The experiment performed using the images reconstructed from feature vectors demonstrated that depth information was beneficial in representing and recognizing the face with least number of principal components.
Puerarin Attenuates Ovalbumin-Induced Lung Inflammation and Hemostatic Unbalance in Rat Asthma Model

Aim. We aimed to investigate and evaluate the preventive activity of puerarin on the ovalbumin-induced asthma rat model. Materials and Methods. Male Wistar rats were sensitized intraperitoneally on days 0, 7, and 14 and challenged to ovalbumin intratracheally on day 21. Groups of sensitized rats were treated randomly either with placebo, puerarin, dexamethasone, or puerarin combined with dexamethasone, from days 15 to 20. Inflammatory markers, including cell counts in bronchoalveolar lavage fluid (BALF), inflammatory cytokines, histopathology, and coagulation parameters, such as coagulation tests and the activity of coagulation factors, were analyzed. Results. Puerarin significantly inhibited the recruitment of inflammatory cells in BALF and lung tissue. At the same time, the release of IL-4, IL-10, and IFN-γ in serum and the expression of mRNAs in lung tissue homogenate were changed by puerarin. Administration of puerarin also effectively rectified the coagulation disorder in asthmatic rats, such as prothrombin time (PT) (P < 0.01), thrombin time (TT) (P < 0.05), fibrinogen (FIB) (P < 0.01),the activity of factor II (FII) (P < 0.01), the activity of factor V (FV) (P < 0.05), the activity of factor VII (FVII) (P < 0.05), the activity of factor X (FX) (P < 0.05), the activity of factor VIII (FVIII) (P < 0.01), the activity of factor IX (FIX) (P < 0.05), and the activity of factor XII (FXII) (P < 0.05). Conclusions. Our results provide a clue that puerarin was useful for the preventive of allergic airway disease in rodents.
A Nonmonotonic Lag Function for False Alarms to Associates.

In a continuous-recognition paradigm, undergraduates were asked whether they had already heard in the series each of the 300 words. The rate of false alarms to semantic associates of prior words depended strongly on the lag between the word and its associate: at no lag, there was absolutely no increase over the general rate; at a greater lag, the false alarms to associates increased; at still greater lags, they returned toward the general rate. This nonmonotonic lag function disconfirms all one-step notions of generalization, including those based on implicit associative responses or on feature overlap, whether activated during study or test, and it suggests several two-step models of forgetting during the lag.
Snowball Sampling Applied to Opiate Addicts Outside the Treatment System

Sampling opiate addicts outside the treatment system (hidden population) is considered difficult because there is no appropriate sampling frame. Several methods to achieve some form of random selection have been reviewed by earlier authors. One of these is 'snowballing' or snowball sampling. This term, however, covers various methods based on the central concept of locating new respondents through introductions from initial contact persons. In this publication theoretical aspects of one particular type of snowball sampling are described: snowballing with random nominee selection. Experiences with applying this type of snowballing in a medium sized Dutch city will be reported. The characteristics of the resulting study group of opiate addicts outside the treatment system are described together with different kinds of bias that may affect the representativeness of the study group.
RobuRec: Robust Sybil attack defense in online recommender systems

With the growth of Internet usage and online social networks, the online Recommender Systems are becoming popular among system users. Although the influence of the recommender systems is expanding, the possibility of residing fake identities (Sybils) from nefarious users increase due to various reasons. To mitigate the impact of such users, several approaches are proposed. However, the need for robust algorithms is still necessary regarding recommender systems since the small portion of Sybils can distort the accuracy of predictions extremely. We propose a novel robust recommendation algorithm (RobuRec) using information level and admission control. The performance of RobuRec is experimented on various recommendation datasets with all possible Sybil attacks. The evaluation result shows that RobuRec can improve prediction error by 21% and 49% compared to two comparable schemes (LTSMF [23] and PCA [24], respectively). On all datasets and against various attack strategies, in turn, our RobuRec scheme shows the best peformance in terms of prediction shift.
MC1R variants affect the expression of melanocortin and melanogenic genes and the association between melanocortin genes and coloration

The melanocortin‐1 receptor (MC1R) gene influences coloration by altering the expression of genes acting downstream in the melanin synthesis. MC1R belongs to the melanocortin system, a genetic network coding for the ligands that regulate MC1R and other melanocortin receptors controlling different physiological and behavioural traits. The impact of MC1R variants on these regulatory melanocortin genes was never considered, even though MC1R mutations could alter the influence of these genes on coloration (e.g. by decreasing MC1R response to melanocortin ligands). Using barn owl growing feathers, we investigated the differences between MC1R genotypes in the (co)expression of six melanocortin and nine melanogenic‐related genes and in the association between melanocortin gene expression and phenotype (feather pheomelanin content). Compared to the MC1R rufous allele, responsible for reddish coloration, the white allele was not only associated with an expected lower expression of melanogenic‐related genes (TYR, TYRP1, OCA2, SLC45A2, KIT, DCT) but also with a lower MC1R expression and a higher expression of ASIP, the MC1R antagonist. More importantly, the expression of PCSK2, responsible for the maturation of the MC1R agonist, α‐melanocyte‐stimulating hormone, was positively related to pheomelanin content in MC1R white homozygotes but not in individuals carrying the MC1R rufous allele. These findings indicate that MC1R mutations not only alter the expression of melanogenic‐related genes but also the association between coloration and the expression of melanocortin genes upstream of MC1R. This suggests that MC1R mutations can modulate the regulation of coloration by the pleiotropic melanocortin genes, potentially decoupling the often‐observed associations between coloration and other phenotypes.
Multipitch Estimation of Piano Sounds Using a New Probabilistic Spectral Smoothness Principle

A new method for the estimation of multiple concurrent pitches in piano recordings is presented. It addresses the issue of overlapping overtones by modeling the spectral envelope of the overtones of each note with a smooth autoregressive model. For the background noise, a moving-average model is used and the combination of both tends to eliminate harmonic and sub-harmonic erroneous pitch estimations. This leads to a complete generative spectral model for simultaneous piano notes, which also explicitly includes the typical deviation from exact harmonicity in a piano overtone series. The pitch set which maximizes an approximate likelihood is selected from among a restricted number of possible pitch combinations as the one. Tests have been conducted on a large homemade database called MAPS, composed of piano recordings from a real upright piano and from high-quality samples.
Fixed-Beam Frequency-Tunable Phase-Reversal Coplanar Stripline Antenna Array

A fixed-beam frequency-tunable coplanar stripline (CPS) antenna array using the phase reversal technique and varactor diodes tuning is presented. The antenna array is composed of plurality of half-wavelength CPS sections interconnected by phase reversing crossovers. These crossovers have two functions: they serve as the radiators (small dipoles) of the array and also provide their own required in-phase excitations for broadside radiation. Two microstrip-to-CPS transitions for excitation of the balanced input/output antenna array are implemented and compared. A six-element array is demonstrated theoretically and experimentally, with 9.3 dBi of gain for bidirectional radiation and 12.5 dBi of gain for unidirectional radiation using a back reflector. Moreover, shunt varactor diodes are incorporated along the CPS structure to achieve frequency tuning through a bias voltage control while broadside radiation patterns remain fixed. The return loss and the radiation patterns at several frequencies are presented, and up to 50% tuning range is obtained. The proposed antenna array is simple and uniplanar with small lateral size, high radiation efficiency and high directivity. In addition, it uses a very simple biasing circuit with high DC-RF isolation. Its balanced input/output makes it suitable for RF system integration and active integrated antenna design.
Similarity Ranking as Attribute for Machine Learning Approach to Authorship Identification

In the authorship identification task, examples of short writings of N authors and an anonymous document written by one of these N authors are given. The task is to determine the authorship of the anonymous text. Practically all approaches solved this problem with machine learning methods. The input attributes for the machine learning process are usually formed by stylistic or grammatical properties of individual documents or a defined similarity between a document and an author. In this paper, we present the results of an experiment to extend the machine learning attributes by ranking the similarity between a document and an author: we transform the similarity between an unknown document and one of the N authors to the order in which the author is the most similar to the document in the set of N authors. The comparison of similarity probability and similarity ranking was made using the Support Vector Machines algorithm. The results show that machine learning methods perform slightly better with attributes based on the ranking of similarity than with previously used similarity between an author and a document.
Single-Inductor Multi-Output (SIMO) DC-DC Converters With High Light-Load Efficiency and Minimized Cross-Regulation for Portable Devices

A load-dependant peak-current control single-inductor multiple-output (SIMO) DC-DC converter with hysteresis mode is proposed. It includes multiple buck and boost output voltages. Owing to the adaptive adjustment of the load-dependant peak-current control technique and the hysteresis mode, the cross-regulation can be minimized. Furthermore, a new delta-voltage generator can automatically switch the operating mode from pulse width modulation (PWM) mode to hysteresis mode, thereby avoiding inductor current accumulation when the total power of the buck output terminals is larger than that of the boost output terminals. The proposed SIMO DC-DC converter was fabricated in TSMC 0.25 mum 2P5M technology. The experimental results show high conversion efficiency at light loads and small cross-regulation within 0.35%. The power conversion efficiency varies from 80% at light loads to 93% at heavy loads.