Miracle-dz/UniFAR-FTUs huggingface dataset:
RAPE MYTH ACCEPTANCE: A VIGNETTE APPROACH

OF THESIS RAPE MYTH ACCEPTANCE: A VIGNETTE APPROACH The purpose of this study was to empirically examine the extent to which rape myth acceptance (RMA) varies according to four key contextual factors—race, the victim–perpetrator relationship, resistance strategies, and the decision to report—among those embedded within college and military cultures. Although sexual assault in a university context has been thoroughly investigated, it is typically in comparison to the general population that may not share the same high-risk elements that promote the environment for sexual assault. Therefore, comparisons of college, military, and a general population were sampled to better understand the attitudes that maintain RMA in these high risk environments. Consistent with previous research aimed at understanding attitudes associated with RMA (Carroll et al., 2016; McMahon, 2010), findings from this study indicated that although individuals hold relatively low RMA overall, individuals tend to endorse other rape myths that blame the victim and exonerate the perpetrator. Specifically, race, resistance strategies, and the decision to report all influenced how likely individuals were to attribute some blame to the victim in the vignette.
Epigenetic regulation of fetal brain development and neurocognitive outcome

Twenty-five years ago, the neurodevelopmental hypothesis of schizophrenia opened the way for analyzing potential prenatal influences on late postnatal pathogenetic events (1). Since that time, it has been well-established that hypoxia, infection, and other noxious factors can cause fetal brain injury and intrauterine growth restriction and that prenatal brain lesions can cause alterations in the postnatal brain development (2). A study in PNAS (3) suggests that even the subtle normal variations of intrauterine environment may lead to recognizable differences in postnatal brain structure and cognitive functions.
Fault Estimation and Fault-Tolerant Control for Discrete-Time Dynamic Systems

In this paper, a novel discrete-time estimator is proposed, which is employed for simultaneous estimation of system states, and actuator/sensor faults in a discrete-time dynamic system. The existence of the discrete-time simultaneous estimator is proven mathematically. The systematic design procedure for the derivative and proportional observer gains is addressed, enabling the estimation error dynamics to be internally proper and stable, and robust against the effects from the process disturbances, measurement noise, and faults. Based on the estimated fault signals and system states, a discrete-time fault-tolerant design approach is addressed, by which the system may recover the system performance when actuator/sensor faults occur. Finally, the proposed integrated discrete-time fault estimation and fault-tolerant control technique is applied to the vehicle lateral dynamics, which demonstrates the effectiveness of the developed techniques.
Computer Technologies in Powerful Learning Environments: The Case of Using Animated and Interactive Graphics for Teaching Financial Concepts.

What is a powerful learning environment? Simply stated, it is an environment that generates high learning gains for its users. Does this means that the label “powerful” can only be allocated a posteriori through empirical testing? No, otherwise we would simply call it an “effective” learning environment. The term powerful refers to potential learning outcomes and is hence based on the learning theory that supports this prediction. Subsequently, the meaning of powerful varies according to learning theories. A powerful learning environment is built on a hypothetical causal relationship between the environment features and the learning processes. For instance, a socio-cultural designer would qualify an environment as being powerful according to the forms of social interactions that are supported. Within a mastery learning perspective, it is rather the possibility to adapt instruction to the learner needs that would legitimate the word powerful. For a behaviorist, the controlled delivery of information and the possibility to deliver immediate feedback would justify the same label. The purpose of this book is precisely to articulate the tuning of technology issues with the learning process. From a cognitive science perspective, powerful refers to many aspects, but a core issue is the mapping between the computational model of the domain and the mental model to be constructed by the learner. The relationship between these two models is of course not a simple ‘copy-from-disk-to-brain’ mechanism but a complex process based on interactivity and visualization. Interactivity is the core “powerful” mechanism in pedagogical simulations. Nevertheless, learners have multiple difficulties with efficiently conducting this hypothetico-deductive process (de Jong & van Joolingen 1998).
Netvigator: Scalable Network Proximity Estimation

Network proximity estimation is an important component in discovering and locating services and applications. With the growing number of services and service providers in the largescale Internet, accurately estimating network proximity with minimal probing overhead becomes essential for scalable deployment. Although there exist a number of network distance estimation schemes, they either rely on extensive infrastructure support, require the IP address of the potential targets, falsely cluster distant nodes, or perform poorly with few measurement errors. We propose Netvigator, a scalable network proximity estimation tool that uses information obtained from probing a small number of landmark nodes and intermediate routers (termed milestones) that are discovered en route to the landmarks, to identify the closest nodes. With very little additional probing overhead, Netvigator uses distance information to the milestones to accurately locate the closets nodes. We developed a Netvigator prototype and report our performance evaluation on Planet-Lab and in the intranet of a large enterprise. We also test its scalability using simulations.
Recommendation Based on Trust Diffusion Model

Recommender system is emerging as a powerful and popular tool for online information relevant to a given user. The traditional recommendation system suffers from the cold start problem and the data sparsity problem. Many methods have been proposed to solve these problems, but few can achieve satisfactory efficiency. In this paper, we present a method which combines the trust diffusion (DiffTrust) algorithm and the probabilistic matrix factorization (PMF). DiffTrust is first used to study the possible diffusions of trust between various users. It is able to make use of the implicit relationship of the trust network, thus alleviating the data sparsity problem. The probabilistic matrix factorization (PMF) is then employed to combine the users' tastes with their trusted friends' interests. We evaluate the algorithm on Flixster, Moviedata, and Epinions datasets, respectively. The experimental results show that the recommendation based on our proposed DiffTrust + PMF model achieves high performance in terms of the root mean square error (RMSE), Recall, and F Measure.
The study of routing strategies in vehicular ad-hoc networks

Vehicular ad hoc networks (VANETs) are a particular category of mobile ad hoc networks (MANETs) characterized by a high mobility and a reduced connectivity. As a prerequisite to communication, an efficient route between network nodes must be established, and it must be adapt to the rapidly changing topology of vehicles in motion. This is aim of VANET routing protocols. The simulation scene was set up by means of the NS2 simulation platform in this paper. The performances of ad hoc on-demand distance vector (AODV) and dynamic source routing (DSR), two classical routing protocols of MANET and VANET, are simulated and analyzed, and the conclusion that the AODV protocol is more suitable for VANET than the DSR protocol is derived.
Developing a Mobile Stereo Vision System for Accurate Target Triangulation

In order for mobile robots to truly operate autonomously of human operators, they must be able to accurately observe their environments and make informed decisions based on this information. One of the fundamental tasks that a robot must perform in order to understand its surroundings is target localization that is, determining the location of objects around it. Using this information, the robot can then generate a map of its surroundings, find specific objects, and even determine its own location. In this report, I will demonstrate an autonomous mobile robot system that is capable of recognizing unique markers within its environment using a stereo camera system, and obtain triangulation data accurate to within a centimeter of the ground truth. I then use this stereo camera system to implement a stereo vision algorithm which aims to reduce the quantization noise of the system by solving the next best view problem.
A STUDY OF ALCOHOL RELATED PHYSICAL DISEASES IN GENERAL HOSPITAL PATIENTS

We studied the prevalence of harmful use of alcohol and alcohol related diseases in newly admitted patients in a general hospital. Two hundred and ninety seven patients were screened for alcohol use with Alcohol Use Disorders Identification Test (AUDIT). Sixty one subjects (21%) had harmful pattern of alcohol consumption. Of these twenty eight patients (9.4%) had alcohol-related diseases, while in 33 patients (11.1%) we did not find any association between alcohol use and hospital diagnoses. Physicians′ referral rate for problem drinking was low. The findings indicate the need for an active consultation service to address the specific issue of harmful drinking among patients admitted in general hospitals.
Loop-lifted XQuery RPC with deterministic updates

XRPC is a minimal XQuery extension that enables distributed query execution, combining the Remote Procedure Call (RPC) paradigm with the existing concept of XQuery functions. By calling out of a for-loop to multiple destinations, and by calling functions that themselves perform XRPC calls, complex P2P communication patterns can be achieved. We further propose the use of SOAP as the protocol for XRPC, which allows seamless integration with web services and Service Oriented Architectures (SOA). XRPC is implemented in the open source MonetDB/XQuery system. We show that the technique of loop-lifting, that executes all expressions inside a for-loop in a single bulk operator -- pervasively applied in MonetDB/XQuery to obtain efficient relational query plans -- also benefits XRPC. Loop-lifting enables us to send bulk RPC requests, dramatically reducing the number of SOAP messages, and thus the performance impact of network latency. The XRPC extension is orthogonal to all XQuery language features, including the XQuery Update Facility (XUF). The XUF W3C Draft proposal does not define the order in which multiple update actions to the same node must be applied. We instead choose to make this order deterministic, and show how distributed updates can be made deterministic using a small protocol extension.
Generic ILP vs Specialized 0-1 ILP for Haplotype Inference

Haplotype inference is an important and computationally challenging problem in genetics. A well-known approach to haplotype inference is pure parsimony (HIPP). Despite being based on a simple optimization criterion, HIPP is a computationally hard problem. Recent work has shown that approaches based on Boolean satisfiability namely pseudo-Boolean optimization (PBO), are very effective at tackling the HIPP problem. Extensive work on PBO-based HIPP approaches has been recently developed. Considering that the PBO problem, also known as 0-1 ILP problem, is a particular case of the integer linear programming (ILP) problem, generic ILP solvers can be considered. This paper compares the performance of PBO and ILP solvers on a variety of HIPP models. We conclude that specialized PBO solvers are more suitable than generic ILP solvers.
Practical Concurrent Priority Queues

Priority queues are abstract data structures which store a set of key/value pairs and allow efficient access to the item with the minimal (maximal) key. Such queues are an important element in various areas of computer science such as algorithmics (i.e. Dijkstra's shortest path algorithm) and operating system (i.e. priority schedulers). The recent trend towards multiprocessor computing requires new implementations of basic data structures which are able to be used concurrently and scale well to a large number of threads. In particular, lock-free structures promise superior scalability by avoiding the use of blocking synchronization primitives. Concurrent priority queues have been extensively researched over the past decades. In this paper, we discuss three major ideas within the field: fine-grained locking employs multiple locks to avoid a single bottleneck within the queue; SkipLists are search structures which use randomization and therefore do not require elaborate reorganization schemes; and relaxed data structures trade semantic guarantees for improved scalability.
Computational design of metallophone contact sounds

Metallophones such as glockenspiels produce sounds in response to contact. Building these instruments is a complicated process, limiting their shapes to well-understood designs such as bars. We automatically optimize the shape of arbitrary 2D and 3D objects through deformation and perforation to produce sounds when struck which match user-supplied frequency and amplitude spectra. This optimization requires navigating a complex energy landscape, for which we develop Latin Complement Sampling to both speed up finding minima and provide probabilistic bounds on landscape exploration. Our method produces instruments which perform similarly to those that have been professionally-manufactured, while also expanding the scope of shape and sound that can be realized, e.g., single object chords. Furthermore, we can optimize sound spectra to create overtones and to dampen specific frequencies. Thus our technique allows even novices to design metallophones with unique sound and appearance.
Ieee Transactions on Pattern Analysis and Machine Intelligence 1 an Improved Physically-based Method for Geometric Restoration of Distorted Document Images

— In document digitization through camera-based systems , simple imaging setups often produce geometric distortions in the resultant 2D images because of the non-planar geometric shapes of certain documents such as thick bound books, rolled, folded or crumpled materials, etc. Previous work [1]–[4] has demonstrated that arbitrary warped documents can be successfully restored by flattening a 3D scan of the document. These approaches use physically-based or relaxation-based techniques in their flattening process. While this has been demonstrated to be effective in rectifying the image content and improving OCR, these previous approaches have several limitations in terms of speed and stability. In this paper, we propose a distance-based penalty metric to replace the mass-spring model and introduce additional bending resistance and drag forces to improve the efficiency of the existing approaches. The use of Verlet integration and special plane collision handling schemes also help to achieve better stability without sacrificing efficiency. Experiments on various document images captured from books, brochures and historical documents with arbitrary warpings have demonstrated large improvements over the existing approaches in terms of stability and efficiency.
Learning Probabilistic Non-Linear Latent Variable Models for Tracking Complex Activities

A common approach for handling the complexity and inherent ambiguities of 3D human pose estimation is to use pose priors learned from training data. Existing approaches however, are either too simplistic (linear), too complex to learn, or can only learn latent spaces from "simple data", i.e., single activities such as walking or running. In this paper, we present an efficient stochastic gradient descent algorithm that is able to learn probabilistic non-linear latent spaces composed of multiple activities. Furthermore, we derive an incremental algorithm for the online setting which can update the latent space without extensive relearning. We demonstrate the effectiveness of our approach on the task of monocular and multi-view tracking and show that our approach outperforms the state-of-the-art.
Perceived ethnic discrimination, shame related beliefs and emotional well-being of black Africans compared to white British adults.

The Research Dossier consists of a literature review and two empirical studies. The literature review critically evaluates depression in Black African and Caribbean people living in the UK. The first study utilises a qualitative methodology and explores Nigerian peoples’ understanding of the term depression. The second study adopts a quantitative approach which investigates perceived ethnic discrimination, shame related beliefs and emotional well-being of Black Africans compared to White British adults.
Y-Stent-Assisted Coil Embolization of Wide-Neck Intracranial Aneurysms

This report evaluated the short and midterm results of the safety and effectiveness of the treatment technique with hybrid and non-hybrid Y-configured, dual stent-assisted coil embolization of wide-neck intracranial aneurysms, and reviewed the literature concerning this technique. Nine patients, eight with unruptured and one with ruptured aneurysms were included in the study. Of aneurysms embolized with a hybrid (with two different stents) and non-hybrid (with two identical stents) technique, three were located in the anterior communicating artery, three at the tip and one at the distal site of basilar artery, and two in the middle cerebral artery. All aneurysms included the orifices of bifurcation vessels. All aneurysms were stented and embolized during the same session. While Neuroform and Enterprise stents were used in the hybrid technique, two Enterprise stents were used in the non-hybrid technique. Dual Y-stent assisted coil embolization was performed successfully in eight of nine patients (88.9%), including five patients (55.6%) with hybrid and three patients (33.3%) with non-hybrid technique. No procedural complication, no mortality and no minor or major neurological complications were seen during the angiographic or clinical follow-up. When an attempt was made at passing the second stent through the first Enterprise stent, the stent protruded inside the aneurysm in one patient (11.1%). Hybrid or non-hybrid dual Y-stent-assisted coil embolization in the treatment of ruptured or unruptured wide-neck and complex intracranial aneurysms is a safe and effective method from the viewpoint of short and midterm results.
Structural Adaptations of Decentralized Coordination Processes in Self-Organizing Systems

A key requirement for the realization of modern distributed systems is their ability to dynamically adapt their behavior to changing environmental conditions to preserve their operation even in the presence of uncertain changes. In order to accomplish this, the different parts of such a self-organizing system have to be coordinated to achieve meaningful adaptations. To avoid single point of failures, decentralized control is a key element for the realization of robust and scalable self-adaptation. However, self-organizing systems may, under certain conditions, show unwanted behavior like, e.g. a decrease of performance and/or starvation, even when they are apparently well engineered. A promising concept to overcome such dynamical inefficiencies in self-organizing systems is to realize the dynamic exchange or reconfiguration of the coordination processes responsible for the self-organizing behavior in terms of a structural adaptation. This paper introduces a decentralized coordination framework to realize decentralized control processes for distributed self-organizing systems, that supports the structural exchange (or reconfiguration) of the coordination processes.
Using Feature-Interface Graph for Automatic Interface Recommendation: A Case Study

A great number of open source projects are available for developers to reuse. During software development and maintenance, developers can leverage good interfaces in these open source projects and establish the framework of a new project quickly when reusing these open source projects. However, if developers want to reuse them, they need to read a lot of code files and learn which interfaces can be reused. To help developers better take advantage of the available interfaces used in open source projects, we propose an approach to automatically recommend interfaces by matching feature request with the topics of open source projects. We evaluate the effectiveness of our approach on a famous open source project JEdit. The results show that our approach is able to recommend useful interfaces for developers to reuse.
Nuclear Fragile X Mental Retardation Protein Is localized to Cajal Bodies

Fragile X syndrome is caused by loss of function of a single gene encoding the Fragile X Mental Retardation Protein (FMRP). This RNA-binding protein, widely expressed in mammalian tissues, is particularly abundant in neurons and is a component of messenger ribonucleoprotein (mRNP) complexes present within the translational apparatus. The absence of FMRP in neurons is believed to cause translation dysregulation and defects in mRNA transport essential for local protein synthesis and for synaptic development and maturation. A prevalent model posits that FMRP is a nucleocytoplasmic shuttling protein that transports its mRNA targets from the nucleus to the translation machinery. However, it is not known which of the multiple FMRP isoforms, resulting from the numerous alternatively spliced FMR1 transcripts variants, would be involved in such a process. Using a new generation of anti-FMRP antibodies and recombinant expression, we show here that the most commonly expressed human FMRP isoforms (ISO1 and 7) do not localize to the nucleus. Instead, specific FMRP isoforms 6 and 12 (ISO6 and 12), containing a novel C-terminal domain, were the only isoforms that localized to the nuclei in cultured human cells. These isoforms localized to specific p80-coilin and SMN positive structures that were identified as Cajal bodies. The Cajal body localization signal was confined to a 17 amino acid stretch in the C-terminus of human ISO6 and is lacking in a mouse Iso6 variant. As FMRP is an RNA-binding protein, its presence in Cajal bodies suggests additional functions in nuclear post-transcriptional RNA metabolism. Supporting this hypothesis, a missense mutation (I304N), known to alter the KH2-mediated RNA binding properties of FMRP, abolishes the localization of human FMRP ISO6 to Cajal bodies. These findings open unexplored avenues in search for new insights into the pathophysiology of Fragile X Syndrome.
DIaaS: Data Integrity as a Service in the Cloud

In this paper, we propose a secure cloud storage service architecture with the focus on Data Integrity as a Service (DIaaS) based on the principles of Service-Oriented Architecture and Web services. Our approach not only releases the burdens of data integrity management from a storage service by handling it through an independent third party data Integrity Management Service (IMS), but also reduces the security risk of the data stored in the storage services by checking the data integrity with the help of IMS. We define data integrity protocols for a number of different scenarios, and demonstrate the feasibility of the proposed architecture, service and protocols by implementing them on a public cloud, Amazon S3. We also study the impact of our proposed protocols on the performance of the storage service and show that the benefits of our approach outweigh the little penalty on the storage service performance.