nils-herrmann/clean_scirepeval huggingface dataset:
Towards native code offloading based MCC frameworks for multimedia applications: A survey

A number of resource-intensive applications, such as augmented reality, natural language processing, object recognition, and multimedia-based software are pushing the computational and energy boundaries of smartphones. Cloud-based services augment the resource-scare capabilities of smartphones while offloading compute-intensive methods to resource-rich cloud servers. The amalgam of cloud and mobile computing technologies has ushered the rise of Mobile Cloud Computing (MCC) paradigm which envisions operating smartphones and modern mobile devices beyond their intrinsic capabilities. System virtualization, application virtualization, and dynamic binary translation (DBT) techniques are required to address the heterogeneity of smartphone and cloud architectures. However, most of the current research work has only focused on the offloading of virtualized applications while giving limited consideration to native code offloading. Moreover, researchers have not attended to the requirements of multimedia based applications in MCC offloading frameworks. In this study, we present a survey and taxonomy of state-of-the-art MCC frameworks, DBT techniques for native offloading, and cross-platform execution techniques for multimedia based applications. We survey the MCC frameworks from the perspective of offload enabling techniques. We focus on native code offloading frameworks and analyze the DBT and emulation techniques of smartphones (ARM) on a cloud server (x86) architectures. Furthermore, we debate the open research issues and challenges to native offloading of multimedia based smartphone applications.
A "Paradoxical" Indentity-Based Signature Scheme Resulting from Zero-Knowledge

At EUROCRYPT’88, we introduced an interactive zero-howledge protocol ( G ~ O U and Quisquater [13]) fitted to the authentication of tamper-resistant devices (e.g. smart cads , Guillou and Ugon ). Each security device stores its secret authentication number, an RSA-like signature computed by an authority from the device identity. Any transaction between a tamperresistant security device and a verifier is limited to a unique interaction: the device sends its identity and a random test number; then the verifier teUs a random large question; and finally the device answers by a witness number. The transaction is successful when the test number is reconstructed from the witness number, the question and the identity according to numbers published by the authority and rules of redundancy possibly standardized. This protocol allows a cooperation between users in such a way that a group of cooperative users looks like a new entity, having a shadowed identity the product of the individual shadowed identities, while each member reveals nothing about its secret. In another scenario, the secret is partitioned between distinkt devices sharing the same identity. A group of cooperative users looks like a unique user having a larger public exponent which is the greater common multiple of each individual exponent. In this paper, additional features are introduced in order to provide: firstly, a mutual interactive authentication of both communicating entities and previously exchanged messages, and, secondly, a digital signature of messages, with a non-interactive zero-knowledge protocol. The problem of multiple signature is solved here in a very smart way due to the possibilities of cooperation between users. The only secret key is the factors of the composite number chosen by the authority delivering one authentication number to each smart card. This key is not known by the user. At the user level, such a scheme may be considered as a keyless identity-based integrity scheme. This integrity has a new and important property: it cannot be misused, i.e. derived into a confidentiality scheme.
Video Surveillance Online Repository (ViSOR): an integrated framework

The availability of new techniques and tools for Video Surveillance and the capability of storing huge amounts of visual data acquired by hundreds of cameras every day call for a convergence between pattern recognition, computer vision and multimedia paradigms. A clear need for this convergence is shown by new research projects which attempt to exploit both ontology-based retrieval and video analysis techniques also in the field of surveillance. This paper presents the ViSOR (Video Surveillance Online Repository) framework, designed with the aim of establishing an open platform for collecting, annotating, retrieving, and sharing surveillance videos, as well as evaluating the performance of automatic surveillance systems. Annotations are based on a reference ontology which has been defined integrating hundreds of concepts, some of them coming from the LSCOM and MediaMill ontologies. A new annotation classification schema is also provided, which is aimed at identifying the spatial, temporal and domain detail level used. The ViSOR web interface allows video browsing, querying by annotated concepts or by keywords, compressed video previewing, media downloading and uploading. Finally, ViSOR includes a performance evaluation desk which can be used to compare different annotations.
OCEANUS: a spatio-temporal data stream system prototype

Recent advances in wireless communication, miniaturization of spatially enabled devices and global navigation satellite systems (GNSS) services have resulted in a large number of novel application domains. Applications in these novel domains (moving objects tracking, sensor networks, fleet management, real-time intelligent transportation systems, etc.) process huge volume of continuous streaming data, i.e. data that is produced incrementally over time, rather than being available in full before processing. Data stream management systems (DSMS) have been developed to manage continuous data streams. Usually based on relational paradigm, they have rudimentary support for spatial data. Recent research efforts in data stream management systems focus mainly on processing continuous queries over traditional data streams, and only a few papers addressed spatio-temporal continuous queries. In this paper we present OCEANUS, an ongoing effort to extend TelegraphCQ DSMS with spatial support providing a platform for spatio-temporal streaming applications.
Why are VMPFC patients more utilitarian? A dual-process theory of moral judgment explains

Koenigs, Young and colleagues  recently tested patients with emotion-related damage in the ventromedial prefrontal cortex (VMPFC) usingmoral dilemmas used in previous neuroimaging studies . These patients made unusually utilitarian judgments (endorsing harmful actions that promote the greater good). My collaborators and I have proposed a dual-process theory of moral judgment [2,3] that we claim predicts this result. In a Research Focus article published in this issue of Trends in Cognitive Sciences, Moll and de Oliveira-Souza [4] challenge this interpretation. Our theory aims to explain some puzzling patterns in commonsense moral thought. For example, people usually approve of diverting a runaway trolley thatmortally threatens five people onto a side-track, where it will kill only one person. And yet people usually disapprove of pushing someone in front of a runaway trolley, where this will kill the person pushed, but save five others . Our theory, in a nutshell, is this: the thought of pushing someone in front of a trolley elicits a prepotent, negative emotional response (supported in part by the medial prefrontal cortex) that drives moral disapproval [2,3]. People also engage in utilitarian moral reasoning (aggregate cost–benefit analysis), which is likely subserved by the dorsolateral prefrontal cortex (DLPFC) . When there is no prepotent emotional response, utilitarian reasoning prevails (as in the first case), but sometimes prepotent emotions and utilitarian reasoning conflict (as in the second case). This conflict is detected by the anterior cingulate cortex, which signals the need for cognitive control, to be implemented in this case by the anterior DLPFC [Brodmann’s Areas (BA) 10/46]. Overriding prepotent emotional responses requires additional cognitive control and, thus, we find increased activity in the anterior DLPFC when people make difficult utilitarian moral judgments . More recent studies support this theory: if negative emotions make people disapprove of pushing the man to his death, then inducing positive emotion might lead to more utilitarian approval, and this is indeed what happens . Likewise, patients with frontotemporal dementia (known for their ‘emotional blunting’) should more readily approve of pushing the man in front of the trolley, and they do [7]. This finding directly foreshadows the hypoemotional VMPFC patients’ utilitarian responses to this and other cases . Finally, we’ve found that cognitive load selectively interferes with utilitarian moral judgment,
Flexible and efficient Gaussian process models for machine learning

Gaussian process (GP) models are widely used to perform Bayesian nonlinear regression and classification — tasks that are central to many machine learning problems. A GP is nonparametric, meaning that the complexity of the model grows as more data points are received. Another attractive feature is the behaviour of the error bars. They naturally grow in regions away from training data where we have high uncertainty about the interpolating function. In their standard form GPs have several limitations, which can be divided into two broad categories: computational difficulties for large data sets, and restrictive modelling assumptions for complex data sets. This thesis addresses various aspects of both of these problems. The training cost for a GP hasO(N3) complexity, whereN is the number of training data points. This is due to an inversion of the N × N covariance matrix. In this thesis we develop several new techniques to reduce this complexity to O(NM2), whereM is a user chosen number much smaller thanN . The sparse approximation we use is based on a set of M ‘pseudo-inputs’ which are optimised together with hyperparameters at training time. We develop a further approximation based on clustering inputs that can be seen as a mixture of local and global approximations. Standard GPs assume a uniform noise variance. We use our sparse approximation described above as a way of relaxing this assumption. By making a modification of the sparse covariance function, we can model input dependent noise. To handle high dimensional data sets we use supervised linear dimensionality reduction. As another extension of the standard GP, we relax the Gaussianity assumption of the process by learning a nonlinear transformation of the output space. All these techniques further increase the applicability of GPs to real complex data sets. We present empirical comparisons of our algorithms with various competing techniques, and suggest problem dependent strategies to follow in practice.
Augmented Reality Oculus Rift

This paper covers the whole process of developing an Augmented Reality Stereoscopig Render Engine for the Oculus Rift. To capture the real world in form of a camera stream, two cameras with fish-eye lenses had to be installed on the Oculus Rift DK1 hardware. The idea was inspired by Steptoe . After the introduction, a theoretical part covers all the most neccessary elements to achieve an AR System for the Oculus Rift, following the implementation part where the code from the AR Stereo Engine is explained in more detail. A short conclusion section shows some results, reflects some experiences and in the final chapter some future works will be discussed. The project can be accessed via the git repository https: // github. com/ MaXvanHeLL/ ARift. git .
Wireless Power Transfer in Loosely Coupled Links: Coil Misalignment Model

A novel analytical model of inductively coupled wireless power transfer is presented. For the first time, the effects of coil misalignment and geometry are addressed in a single mathematical expression. In the applications envisaged, such as radio frequency identification (RFID) and biomedical implants, the receiving coil is normally significantly smaller than the transmitting coil. Formulas are derived for the magnetic field at the receiving coil when it is laterally and angularly misaligned from the transmitting coil. Incorporating this magnetic field solution with an equivalent circuit for the inductive link allows us to introduce a power transfer formula that combines coil characteristics and misalignment factors. The coil geometries considered are spiral and short solenoid structures which are currently popular in the RFID and biomedical domains. The novel analytical power transfer efficiency expressions introduced in this study allow the optimization of coil geometry for maximum power transfer and misalignment tolerance. The experimental results show close correlation with the theoretical predictions. This analytic technique can be widely applied to inductive wireless power transfer links without the limitations imposed by numerical methods.
Modulation Scheme for Improved Operation of an RB-IGBT-Based Resonant Inverter Applied to Domestic Induction Heating

Domestic induction appliances require power converters that feature high efficiency and accurate power control in a wide range of operating conditions. To achieve this modulation techniques play a key role to optimize the power converter operation. In this paper, a series resonant inverter featuring reverse-blocking insulated gate bipolar transistors and an optimized modulation technique are proposed. An analytical study of the converter operation is performed, and the main simulation results are shown. The proposed topology reduces both conduction and switching losses, increasing significantly the power converter efficiency. Moreover, the proposed modulation technique achieves linear output power control, improving the final appliance performance. The results derived from this analysis are tested by means of an experimental prototype, verifying the feasibility of the proposed converter and modulation technique.
Measuring IT Service Management Capability: Scale Development and Empirical Validation

This paper conceptualizes IT service management (ITSM) capability, a key competence of today’s IT provider organizations, and presents a survey instrument to facilitate the measurement of an ITSM capability for research and practice. Based on the review of four existing ITSM maturity models (CMMISVC, COBIT 4.1, SPICE, ITIL v3), we first develop a multi-attributive scale to assess maturity on an ITSM process level. We then use this scale in a survey with 205 ITSM key informants who assessed IT provider organizations along a set of 26 established ITSM processes. Our exploratory factor analysis and measurement model assessment results support the validity of an operationalization of ITSM capability as a second-order construct formed by ITSM processes that span three dimensions: service planning, service transition, and service operation. The practical utility of our survey instrument and avenues for future research on ITSM capability are outlined.
Effective and Efficient Global Context Verification for Image Copy Detection

To detect illegal copies of copyrighted images, recent copy detection methods mostly rely on the bag-of-visual-words (BOW) model, in which local features are quantized into visual words for image matching. However, both the limited discriminability of local features and the BOW quantization errors will lead to many false local matches, which make it hard to distinguish similar images from copies. Geometric consistency verification is a popular technology for reducing the false matches, but it neglects global context information of local features and thus cannot solve this problem well. To address this problem, this paper proposes a global context verification scheme to filter false matches for copy detection. More specifically, after obtaining initial scale invariant feature transform (SIFT) matches between images based on the BOW quantization, the overlapping region-based global context descriptor (OR-GCD) is proposed for the verification of these matches to filter false matches. The OR-GCD not only encodes relatively rich global context information of SIFT features but also has good robustness and efficiency. Thus, it allows an effective and efficient verification. Furthermore, a fast image similarity measurement based on random verification is proposed to efficiently implement copy detection. In addition, we also extend the proposed method for partial-duplicate image detection. Extensive experiments demonstrate that our method achieves higher accuracy than the state-of-the-art methods, and has comparable efficiency to the baseline method based on the BOW quantization.
A 2.3-mW, 5-Gb/s Low-Power Decision-Feedback Equalizer Receiver Front-End and its Two-Step, Minimum Bit-Error-Rate Adaptation Algorithm

This paper presents a low-power decision-feedback equalizer (DFE) receiver front-end and a two-step minimum bit-error-rate (BER) adaptation algorithm. A high energy efficiency of 0.46 mW/Gbps is made possible by the combination of a direct-feedback finite-impulse-response (FIR) DFE, an infinite-impulse-response (IIR) DFE, and a clock-and-data recovery (CDR) circuit with adjustable timing offsets. Based on this architecture, the power-hungry stages used in prior DFE receivers such as the continuous-time linear equalizer (CTLE), the current-mode summing circuit for a multitap DFE, and the fast selection logic for a loop-unrolling DFE can all be removed. A two-step adaptation algorithm that finds the equalizer coefficients minimizing the BER is described. First, an extra data sampler with adjustable voltage and timing offsets measures the single-bit response (SBR) of the channel and coarsely tunes the initial coefficient values in the foreground. Next, the same circuit measures the eye-opening and bit-error rates and fine tunes the coefficients in background using a stochastic hill-climbing algorithm. A prototype DFE receiver fabricated in a 65-nm LP/RF CMOS dissipates 2.3 mW and demonstrates measured eye-opening values of 174 mV pp and 0.66 UIpp while operating at 5 Gb/s with a -15-dB loss channel.
Security and Protocol Exploit Analysis of the 5G Specifications

The third generation partnership project released its first 5G security specifications in March 2018. This paper reviews the proposed security architecture and its main requirements and procedures and evaluates them in the context of known and new protocol exploits. Although security has been improved from previous generations, our analysis identifies potentially unrealistic 5G system assumptions and protocol edge cases that can render 5G communication systems vulnerable to adversarial attacks. For example, null encryption and null authentication are still supported and can be used in valid system configurations. With no clear proposal to tackle pre-authentication message-based exploits, mobile devices continue to implicitly trust any serving network, which may or may not enforce a number of optional security features, or which may not be legitimate. Moreover, several critical security and key management functions are considered beyond the scope of the specifications. The comparison with known 4G long-term evolution protocol exploits reveals that the 5G security specifications, as of Release 15, Version 1.0.0, do not fully address the user privacy and network availability challenges.
Initial Coin Offering and Platform Building ∗

In an initial coin offering (ICO), a company (or an open-source project) pre-sells tokens which will serve as an internal medium of exchange within a peer-to-peer platform yet to be built. We present a model that rationalizes the use of ICOs for launching such platforms: by adding dynamics to a platform launch, ICOs can 1) solve a coordination failure inherent in many platforms with network effects; and 2) harness the “wisdom of the crowd” by aggregating dispersed information about platform quality. Through either mechanism, an ICO increases platform value, makes the launch of a valuable platform more likely, and thus increases social welfare. We use our model to provide guidance to regulators: We analyze under what circumstances ICOs should be banned or allowed, and discuss governance mechanisms that they should include.
V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation

Convolutional Neural Networks (CNNs) have been recently employed to solve problems from both the computer vision and medical image analysis fields. Despite their popularity, most approaches are only able to process 2D images while most medical data used in clinical practice consists of 3D volumes. In this work we propose an approach to 3D image segmentation based on a volumetric, fully convolutional, neural network. Our CNN is trained end-to-end on MRI volumes depicting prostate, and learns to predict segmentation for the whole volume at once. We introduce a novel objective function, that we optimise during training, based on Dice coefficient. In this way we can deal with situations where there is a strong imbalance between the number of foreground and background voxels. To cope with the limited number of annotated volumes available for training, we augment the data applying random non-linear transformations and histogram matching. We show in our experimental evaluation that our approach achieves good performances on challenging test data while requiring only a fraction of the processing time needed by other previous methods.
Show, Control and Tell: A Framework for Generating Controllable and Grounded Captions

Current captioning approaches can describe images using black-box architectures whose behavior is hardly controllable and explainable from the exterior. As an image can be described in infinite ways depending on the goal and the context at hand, a higher degree of controllability is needed to apply captioning algorithms in complex scenarios. In this paper, we introduce a novel framework for image captioning which can generate diverse descriptions by allowing both grounding and controllability. Given a control signal in the form of a sequence or set of image regions, we generate the corresponding caption through a recurrent architecture which predicts textual chunks explicitly grounded on regions, following the constraints of the given control. Experiments are conducted on Flickr30k Entities and on COCO Entities, an extended version of COCO in which we add grounding annotations collected in a semi-automatic manner. Results demonstrate that our method achieves state of the art performances on controllable image captioning, in terms of caption quality and diversity. Code and annotations are publicly available at: https://github.com/ aimagelab/show-control-and-tell.
Optic disc and cup segmentation methods for glaucoma detection with modification of U-Net convolutional neural network

Glaucoma is the second leading cause of blindness all over the world, with approximately 60 million cases reported worldwide in 2010. If undiagnosed in time, glaucoma causes irreversible damage to the optic nerve leading to blindness. The optic nerve head examination, which involves measurement of cup-todisc ratio, is considered one of the most valuable methods of structural diagnosis of the disease. Estimation of cup-to-disc ratio requires segmentation of optic disc and optic cup on eye fundus images and can be performed by modern computer vision algorithms. This work presents universal approach for automatic optic disc and cup segmentation, which is based on deep learning, namely, modification of U-Net convolutional neural network. Our experiments include comparison with the best known methods on publicly available databases DRIONS-DB, RIM-ONE v.3, DRISHTI-GS. For both optic disc and cup segmentation, our method achieves quality comparable to current state-of-the-art methods, outperforming them in terms of the prediction time.