Miracle-dz/UniFAR-FTUs huggingface dataset:
Information driven association rule hiding algorithms

Privacy is one of the most important properties an information system must satisfy. A relatively new trend shows that classical access control techniques are not sufficient to guarantee privacy when datamining techniques are used. Privacy Preserving Data Mining (PPDM) algorithms have been recently introduced with the aim of sanitizing the database in such a way to prevent the discovery of sensible information (e.g. association rules). A drawback of such algorithms is that the introduced sanitization may disrupt the quality of data itself. In this paper we introduce a new methodology and algorithms for performing useful PPDM operations, while preserving the data quality of the underlying database.
Using a non-immersive virtual reality approach to assess cognition in post-stroke aphasia: validating the Cognitive Assessment for Aphasia App (C3A)

Background : As part of the symposium on Cognition and Aphasia, this paper examines a new approach to assessing cognition in aphasia. Traditional pen-and-paper tests of cognition are often dependent on language skills for completion and this creates a barrier for individuals with aphasia, as results may be confounded if individuals experience di ﬃ culty responding to questions and/or understanding complex instructions. To address this issue, we developed the Cognitive Assessment for Aphasia App (C3A) – a non-immersive virtual reality cognitive assessment designed to be inclusive of individuals with post-stroke aphasia (Wall et al., 2017). This assessment incorporates computerised audio and visual feedback, practice opportunities, and intuitive functional tasks to minimise dependency on language skills for completion. Developing the C3A required the input from a multidisciplinary clinical and research team and stroke survivors. We targeted cognitive domains that are commonly a ﬀ ected in stroke and in ﬂ uence functional outcomes. The C3A included a simple reaction time task to assess psychomotor skills and attention, a sequence copy task to assess visual memory, and a kitchen task to assess executive functioning. Aims : To (
Separating Subsurface Scattering from Photometric Images

While subsurface scattering is common in many real objects, almost all separation algorithms focus on extracting specular and diffuse components from real images. In this paper, we present a model-less approach derived from the bi-directional surface scattering reflectance distribution function (BSSRDF). In our approach, we show that an illumination image is composed by the Lambertian diffuse and subsurface scattering images. By converting the separation problem into one of two-layer separation in the illumination domain, a Bayesian framework is used to solve the optimization problem which incorporates spatial and illumination constraints, the latter of which are captured as a set of diffuse priors. We present the detailed mathematical formulation and experimental results.
An In Vitro Study of The Antifungal Activity of Silver/Chitosan Nanoformulations Against Important Seed Borne Pathogens.

This research is reports the fungicidal properties of nano-size silver/chitosan nanoformulations (NFs) used as an agent for antifungal treatment of various seed borne plant pathogens. Fungal phytopathogens, especially seed borne disease causing species, Rhizoctonia solani, Aspergillus flavus, Alternaria alterneta were isolated form chickpea seeds. Differences were observed in the antifungal activity of the silver nanoparticle, chitosan nanoparticles and silver /chitosan nanocomposite, upon the mycelial growth and zone of inhibition of the fungi. Extent of fungal inhibition was derived using the above information, in order to evaluate the antifungal efficacy of these nanoformulations against pathogens. Tests for the fungal growth revealed that the NFs showed significant inhibition effectiveness. This study suggests the possibility to use silver/chitosan NFs as an alternative to fungicides for controlling seed borne phytopathogens.
Predicting the Emotional Reaction of the Learner with a Machine Learning Technique

Emotions play an important role in cognitive processes and specially in learning tasks. Online learning is no exception. Detecting a learner’s emotional reaction for a given situation is an essential element for every Distant Learning Environment. Nevertheless, inferring a learner’s emotional reaction in those environments is not a trivial task. In this paper, we present an agent capable of predicting a learner’s emotional reaction in a distant learning environment based on the learner’s personal and non-personal traits using a machine learning technique, namely the ID3 algorithm. We then describe the agent’s method for predicting the learner’s emotional reaction and discuss the obtained results.
Leadership for differentiated instruction: teachers’ engagement with on-site professional learning at an Australian secondary school

ABSTRACT Increasing numbers of students with identified special needs and disabilities attending Australian schools have enhanced the expectation that mainstream teachers will demonstrate competence in planning and teaching for an academically diverse range of students. A qualitative action research study explored how teachers at an Australian secondary campus understood and addressed learner diversity in the context of a whole-school effort towards more inclusive practices. Data were collected over a one-year period as the principal researcher, acting as participant/observer, sought to identify how engagement with professional learning influenced teachers’ understandings, attitudes and practices related to differentiated instruction. Findings highlight the role of school leaders in providing sustained, contextual professional learning and shifting a school culture towards more inclusive teaching practices.
Attacking Android smartphone systems without permissions

Android requires third-party applications to request for permissions when they access critical mobile resources, such as users' personal information and system operations. In this paper, we present the attacks that can be launched without permissions. We first perform call graph analysis, component analysis and data-flow analysis on various parts of Android framework to retrieve unprotected APIs. Unprotected APIs provide a way of accessing resources without any permissions. We then exploit selected unprotected APIs and launch a number of attacks on Android phones. We discover that without requesting for any permissions, an attacker can access to device ID, phone service state, SIM card state, Wi-Fi and network information, as well as user setting information, such as airplane, location, NFC, USB and power modes of mobile devices. An attacker can also disturb Bluetooth discovery services, and block the incoming emails, calendar events, and Google documents. Moreover, an attacker can set volumes of devices and trigger alarm tones and ringtones that users personally set for their devices. An attacker can also launch camera, mail, music and phone applications even when the devices are locked. We compare our research on two Android versions, and discover that as platform providers incorporate more APIs, the number of unprotected APIs increases and new attacks become possible. We thus suggest platform providers to inspect Android frameworks systematically before releasing new versions.
Conceptual Theory for Workflow Management Support Systems

Workflow Management is a relatively new field which has aroused much interest over the past few years. Largely driven by industrial developments (products, case studies), the field lacks a theory in which workflow can be founded. This paper develops a conceptual framework based on empirical evidence and literature. It provides the concepts and the relations between concepts that constitute the “research area of workflow”. It clarifies the content of the field and its position towards other fields of research.
Formal Methods in the Robin project: Specification and verification of the Nova microhypervisor

The objective of the Robin project is to develop an open robust computing infrastructure. The Nova micro hypervisor is currently being developed as a basis for this robust infrastructure. One workpackage of Robin concentrates on the application of formal methods to this newly developed micro hypervisor. The goals within Robin are (1) to verify some properties of a selected hypervisor module and (2) to develop a formal specification for the hypervisor interface. This paper presents our approach for the verification of Nova. I will especially discuss the challenges and some solutions of operating-system kernel verification.
‘A really hostile environment’: Adiaphorization, global policing and the crimmigration control system

This article examines institutional practices designed to control criminalized migrants in the UK and advances three arguments. First, these practices have evolved, since the early 1970s, into a bespoke ‘crimmigration control system’ distinct from the domestic criminal justice system. Second, this system is directed exclusively at efficient exclusion and control; through a process of adiaphorization, moral objections to the creation of a ‘really hostile environment’ have been disabled. Third, the pursuit of the criminalized immigrant—a globally recognized ‘folk devil’—provides a vital link between domestic and global systems of policing, punishment and exclusion. The UK crimmigration control system is an example of wider processes that are taking place in institutions concerned with the control of suspect populations across the globe.
Quantum algorithms revisited

Quantum computers use the quantum interference of different computational paths to enhance correct outcomes and suppress erroneous outcomes of computations. A common pattern underpinning quantum algorithms can be identified when quantum computation is viewed as multiparticle interference. We use this approach to review (and improve) some of the existing quantum algorithms and to show how they are related to different instances of quantum phase estimation. We provide an explicit algorithm for generating any prescribed interference pattern with an arbitrary precision.
UCFrame: A Use Case Framework for Crowd-Centric Requirement Acquisition

To build needed mobile applications in specific domains, requirements should be collected and analyzed in holistic approach. However, resource is limited for small vendor groups to perform holistic requirement acquisition and elicitation. The rise of crowdsourcing and crowdfunding gives small vendor groups new opportunities to build needed mobile applications for the crowd. By finding prior stakeholders and gathering requirements effectively from the crowd, mobile application projects can establish sound foundation in early phase of software process. Therefore, integration of crowd-based requirement engineering into software process is important for small vendor groups. Conventional requirement acquisition and elicitation methods are analyst-centric. Very little discussion is in adapting requirement acquisition tools for crowdcentric context. In this study, several tool features of use case documentation are revised in crowd-centric context. These features constitute a use case-based framework, called UCFrame, for crowd-centric requirement acquisition. An instantiation of UCFrame is also presented to demonstrate the effectiveness of UCFrame in collecting crowd requirements for building two mobile applications.
Multi balanced trees for face retrieval from image database

We are interested here in retrieving images containing a specific person in image database. Due to large variations in illumination conditions, hairstyles, facial expressions, etc. and the factors like occlusion, sunglasses, profile, etc., robust face matching has been a challenging problem. On the other hand, the speed of search is also a considerable issue, especially for the dataset with millions of face images. Inspired by face tracks in video retrieval which take advantages from the abundance of frames to get multiple exemplars, we present an approach named multi balanced trees for face retrieval from image dataset in this paper. Face images in the dataset are efficiently organized by the trees produced for persons. Multi sampling on the facial components employs the rich local information, which can help to differentiate different persons. Given a query face, a sorted face set with similarities is obtained by inserting the query into a tree. It is easy and fast to get the search results in respect that it avoids calculating the distances between query and elements in the cluster. In addition, a rectification strategy is given in the query process to rectify the error occurred in the generation of trees, resulting in a significant improvement of retrieval quality. Experimental results show the better face grouping ability in comparison with traditional methods. The speed of searching is improved as well.
Personality and work-home interaction among dual-earner couples in South Africa: Testing an actor-partner interdependence model

This study aimed to investigate the effect of personality on work-home interaction crossover effects among dual-earner couples in South Africa. The participants were dual-earner couples (n = 264; 132 couples) with young children. They completed measures on work-home interaction and personality. Multilevel modelling was used to analyse predictions for six personality variables and four work-home interaction outcome variables. Additionally, we examined interaction effects between husbands’ and wives’ actor effects, as well as between their partner effects. Results suggest only wives’ Extraversion, Emotional Stability, and Openness positively influenced husbands’ experience of work-home interaction. Work-home personality crossover actor effects were more pronounced for wives. Partner effects were stronger for husbands in that an increase in Extraversion in wives will increase husbands’ experience of positive home-work interaction. A gendered effect appears to influence work-home interaction among South African working couples; this knowledge is important for interventions to establish balanced work and home lives.
Image Variational Denoising Using Gradient Fidelity on Curvelet Shrinkage

A new variational image model is presented for image restoration using a combination of the curvelet shrinkage method and the total variation (TV) functional. In order to suppress the staircasing effect and curvelet-like artifacts, we use the multiscale curvelet shrinkage to compute an initial estimated image, and then we propose a new gradient fidelity term, which is designed to force the gradients of desired image to be close to the curvelet approximation gradients. Then, we introduce the Euler-Lagrange equation and make an investigation on the mathematical properties. To improve the ability of preserving the details of edges and texture, the spatial-varying parameters are adaptively estimated in the iterative process of the gradient descent flow algorithm. Numerical experiments demonstrate that our proposed method has good performance in alleviating both the staircasing effect and curvelet-like artifacts, while preserving fine details.
RESULTS FROM A DECADE-LONG VLBI ASTROMETRIC MONITORING OF THE PAIR OF QUASARS 1038+528 A AND B

Abstract. We present results from three epochs, spanningnearly a decade, of VLBI monitoring of the angular separa-tionbetweenthequasars1038+528AandB.Ourobservations,madesimultaneouslyatλ 3. 6cmandλ 13cm,achievesubmil-liarcsecond(submas)precisioninthemeasurementofthe∼33”separationbetweenthetwoquasars.Wedetectedchangesinthisangular separation of nearly 0.2 mas. These changes can be ex-plainedwholly,ormostly,bythemotionofthereferencefeatureused in quasar B which appears to be a component in the jet,separating from the core at a speed near c. We also concludethat any proper motion of quasar A is less than 10 µas yr −1 . There is an intriguing hint of a “jitter” in the position of thepeak brightness of this quasar, but the signal-to-noise ratio istoo low for us to draw a ﬁrm conclusion.Key words: astrometry and celestial mechanics: instrumenta-tion: interferometers – astrometry – galaxies: quasars – radiocontinuum: galaxies1. IntroductionThe application of Very-Long-Baseline Interferometry (VLBI)tothedeterminationofpositionsofobjectsonthesky,andteles-copesontheEarth(i.e.,astrometryandgeodesy)routinelypro-vides precisions at the milli-arcsecond (mas) and centimeterlevels, respectively. VLBI astrometry has allowed a celestialreferenceframetobebuiltonthestablepositionsofextragalac-ticradiosourcesdeterminedwithmasaccuracy(Maetal.1990);thistechniqueisnowalsoaccurateenoughforgeodeticdetermi-nations which are geophysically signiﬁcant (see, for example,Herringetal.1986;Robertson1991;Haasetal.1995). Itsinher-ent resolution makes the VLBI technique ideal for the study ofthe kinematics of the inner parts of radio sources. The standardtheoryofextragalacticradiosources(relativisticjettheory;e.g.,
A 3D Generic Inverse Dynamic Method using Wrench Notation and Quaternion Algebra

In the literature, conventional 3D inverse dynamic models are limited in three aspects related to inverse dynamic notation, body segment parameters and kinematic formalism. First, conventional notation yields separate computations of the forces and moments with successive coordinate system transformations. Secondly, the way conventional body segment parameters are defined is based on the assumption that the inertia tensor is principal and the centre of mass is located between the proximal and distal ends. Thirdly, the conventional kinematic formalism uses Euler or Cardanic angles that are sequence-dependent and suffer from singularities. In order to overcome these limitations, this paper presents a new generic method for inverse dynamics. This generic method is based on wrench notation for inverse dynamics, a general definition of body segment parameters and quaternion algebra for the kinematic formalism.
Cognitive Sorting

Sorting or ordering a list of items is one of the tasks that occur frequently in majority of computer programs. In our work we incorporated certain aspects of human cognition into sorting algorithms that will improve their performance. This paper presents two new variants of Selection sort. The first algorithm has a time complexity of O(1) in the best case. The second algorithm outperforms Selection sort and its variants when sorting a list of items that has a large number of duplicates present in it.
A Directed Binding Mechanism of Processive Motion for the Kinesin Motor Protein Families

A novel physical mechanism is discussed for the processive propagation of two-headed motor proteins such as kinesin along protein filaments. Our model uses the fact that the binding of each head must be directionality oriented to the protein filament. The binding sites are realized by a 2D periodic potential due to the filament’s surface. The deviation of the geometry of the kinesin from the relaxed state to the state where both motor domains are simultaneously bound to the filament results in an internal stress of the molecule. Un-binding of one of the motor domains from the filament, which is due to the release of chemical energy from ATP hydrolysis, results in a mechanical movement until the relaxed state is reached again. We develop a simple mathematical and mechanical model in which directed binding of the heads to the filament results in a directed twist away from its relaxed state of the molecule, occurring probably in the neck linker region. Un-binding of the head from the filament relaxes the twist and defines the propagation direction. We show that there must be at least one torsional spring for every head to store elastic energy. It is the internal structure both of the relaxed and tensed-up state that defines the walking direction of kinesin. Calculations based on the model are in good quantitative agreement with experimental observations.
Optimal clock synchronization in networks

Having access to an accurate time is a vital building block in all networks; in wireless sensor networks even more so, because wireless media access or data fusion may depend on it. Starting out with a novel analysis, we show that orthodox clock synchronization algorithms make fundamental mistakes. The state-of-the-art clock synchronization algorithm FTSP exhibits an error that grows exponentially with the size of the network, for instance. Since the involved parameters are small, the error only becomes visible in midsize networks of about 10--20 nodes. In contrast, we present PulseSync, a new clock synchronization algorithm that is asymptotically optimal. We evaluate PulseSync on a Mica2 testbed, and by simulation on larger networks. On a 20 node network, the prototype implementation of PulseSync outperforms FTSP by a factor of 5. Theory and simulation show that for larger networks, PulseSync offers an accuracy which is several orders of magnitude better than FTSP. To round off the presentation, we investigate several optimization issues, e.g. media access and local skew.
Automatic Traffic Sign Detection and Recognition Using SegU-Net and a Modified Tversky Loss Function With L1-Constraint

Traffic sign detection is a central part of autonomous vehicle technology. Recent advances in deep learning algorithms have motivated researchers to use neural networks to perform this task. In this paper, we look at traffic sign detection as an image segmentation problem and propose a deep convolutional neural network-based approach to solve it. To this end, we propose a new network, the SegU-Net, which we form by merging the state-of-the-art segmentation architectures–SegNet and U-Net to detect traffic signs from video sequences. For training the network, we use the Tversky loss function constrained by an L1 term instead of the intersection over union loss traditionally used to train segmentation networks. We use a separate network, inspired by the VGG-16 architecture, to classify the detected signs. The networks are trained on the challenge free sequences of the CURE-TSD dataset. Our proposed network outperforms the state-of-the-art object detection networks, such as the Faster R-CNN inception Resnet V2 and R-FCN Resnet 101, by a large margin and obtains a precision and recall of 94.60% and 80.21%, respectively, which is the current state of the art on this part of the dataset. In addition, the network is tested on the German Traffic Sign Detection Benchmark (GTSDB) dataset, where it achieves a precision and recall of 95.29% and 89.01%, respectively. This is on a par with the performance of the aforementioned object detection networks. These results prove the generalizability of the proposed architecture and its suitability for robust traffic sign detection in autonomous vehicles.