Reviewerly/exHarmony huggingface dataset:
Ordinos: A Verifiable Tally-Hiding E-Voting System

Modern electronic voting systems (e-voting systems) are designed to provide not only vote privacy but also (end-to-end) verifiability.Several verifiable e-voting systems have been proposed in the literature, with Helios being one of the most prominent ones.Almost all such systems, however, reveal not just the voting result but also the full tally, consisting of the exact number of votes per candidate or even all single votes.There are several situations where this is undesirable.For example, in elections with only a few voters (e.g., boardroom or jury votings), revealing the complete tally leads to a low privacy level, possibly deterring voters from voting for their actual preference.In other cases, revealing the complete tally might unnecessarily embarrass some candidates.Often, the voting result merely consists of a single winner or a ranking of candidates, so revealing only this information but not the complete tally is sufficient.This property is called tallyhiding and it offers completely new options for e-voting.In this paper, we propose the first provably secure endto-end verifiable tally-hiding e-voting system, called Ordinos.We instantiated our system with suitable cryptographic primitives, including an MPC protocol for greater-than tests, implemented the system, and evaluated its performance, demonstrating its practicality.Moreover, our work provides a deeper understanding of tally-hiding in general, in particular in how far tally-hiding affects the levels of privacy and verifiability of e-voting systems.
Leveraging multi-level dependency of relational sequences for social spammer detection

Much recent research has shed light on developing the relation-dependent but the content-independent framework for social spammer detection. This is mainly because the relation among users is difficult to be altered when spammers attempt to conceal their malicious intentions. Our study investigates the spammer detection problem in the context of multi-relation social networks and makes an attempt to fully exploit the sequences of heterogeneous relations for enhancing the detection accuracy. Specifically, we present the Multi-level Dependency Model (MDM). The MDM is able to exploit the user's long-term dependency hidden in their relational sequences along with short-term dependency. Moreover, MDM fully considers short-term relational sequences from the perspectives of individual-level and union-level, due to the fact that the type of short-term sequences is multi-folds. Experimental results on a real-world multi-relational social network demonstrate the effectiveness of our proposed MDM on multi-relational social spammer detection.
Zone Encryption with Anonymous Authentication for V2V Communication

Vehicle-to-vehicle (V2V) communication systems are currently being prepared for real-world deployment, but they face strong opposition over privacy concerns. Position beacon messages are the main culprit, being broadcast in cleartext and pseudonymously signed up to 10 times per second. So far, no practical solutions have been proposed to encrypt or anonymously authenticate V2V messages. We propose two cryptographic innovations that enhance the privacy of V2V communication. As a core contribution, we introduce zone-encryption schemes, where vehicles generate and authentically distribute encryption keys associated to static geographic zones close to their location. Zone encryption provides security against eavesdropping, and, combined with a suitable anonymous authentication scheme, ensures that messages can only be sent by genuine vehicles, while adding only 224 Bytes of cryptographic overhead to each message. Our second contribution is an authentication mechanism fine-tuned to the needs of V2V which allows vehicles to authentically distribute keys, and is called dynamic group signatures with attributes. Our instantiation features unlimited locally generated pseudonyms, negligible credential download-and-storage costs, identity recovery by a trusted authority, and compact signatures of 216 Bytes at a 128-bit security level.
Time‐Warped Foveated Rendering for Virtual Reality Headsets

Abstract Rendering in real time for virtual reality headsets with high user immersion is challenging due to strict framerate constraints as well as due to a low tolerance for artefacts. Eye tracking‐based foveated rendering presents an opportunity to strongly increase performance without loss of perceived visual quality. To this end, we propose a novel foveated rendering method for virtual reality headsets with integrated eye tracking hardware. Our method comprises recycling pixels in the periphery by spatio‐temporally reprojecting them from previous frames. Artefacts and disocclusions caused by this reprojection are detected and re‐evaluated according to a confidence value that is determined by a newly introduced formalized perception‐based metric, referred to as confidence function. The foveal region, as well as areas with low confidence values, are redrawn efficiently, as the confidence value allows for the delicate regulation of hierarchical geometry and pixel culling. Hence, the average primitive processing and shading costs are lowered dramatically. Evaluated against regular rendering as well as established foveated rendering methods, our approach shows increased performance in both cases. Furthermore, our method is not restricted to static scenes and provides an acceleration structure for post‐processing passes.
Visualizing Evolving Software Cities

Visualization approaches that leverage a 3D city metaphor have become popular. Numerous variations, including virtual and augmented reality have emerged. Despite its popularity, the city metaphor falls short when depicting the evolution of a system, which results in buildings and districts moving around in unpredictable ways. We present a novel approach to visualize software systems as evolving cities that treats evolution as a first-class concept. It renders with fidelity not only changes but also refactorings in a comprehensive way. To do so, we developed custom ways to traverse time. We implemented our approach in a publicly accessible web-based platform named m3triCity.
A systematic review of scheduling approaches on multi-tenancy cloud platforms

Scheduling in cloud is complicated as a result of multi-tenancy. Diverse tenants have different requirements, including service functions, response time, QoS and throughput. Diverse tenants require different scheduling capabilities, resource consumption and competition. Multi-tenancy scheduling approaches have been developed for different service models, such as Software as a Service (SaaS), Platform as a service (PaaS), Infrastructure as a Service (IaaS), and Database as a Service (DBaaS). In this paper, we survey the current landscape of multi-tenancy scheduling, laying out the challenges and complexity of software engineering where multi-tenancy issues are involved. This study emphasises scheduling policies, cloud provisioning and deployment with regards to multi-tenancy issues. We conduct a systematic literature review of research studies related to multi-tenancy scheduling approaches on cloud platforms determine the primary scheduling approaches currently used and the challenges for addressing key multi-tenancy scheduling issues. We adopted a systematic literature review method to search and review many major journal and conference papers on four major online electronic databases, which address our four predefined research questions. Defining inclusion and exclusion criteria was the initial step before extracting data from the selected papers and deriving answers addressing our enquiries. Finally, 53 papers were selected, of which 62 approaches were identified. Most of these methods are developed without cloud layers' limitation (43.40%) and on SaaS, most of scheduling approaches are oriented to framework design (43.75%). The results have demonstrated most of multi-tenancy scheduling solutions can work at any delivery layer. With the difference of tenants' requirements and functionalities, the choice of cloud service delivery models is changed. Based on our study, designing a multi-tenancy scheduling framework should consider the following 3 factors: computing, QoS and storage resource. One of the potential research foci of multi-tenancy scheduling approaches is on GPU scheduling.
Approximate rooftop solar PV potential of Indian cities for high-level renewable power scenario planning

Several data-intensive, detailed methodologies for estimating the rooftop PV potential of urban areas have been reported in the literature in the last few years. While such a comprehensive approach is required for working out the implementation aspects, it is not necessary for high-level renewable power scenario planning and generation capacity expansion planning. This paper presents a methodology for generating approximate estimates of the rooftop solar PV potential of cities, for this latter purpose. The focus is on developing a workable estimate, instead of a precise prediction. The methodology is based on a solar-city plan for Mumbai city in India and uses it as the base estimate. The extrapolation factors for extrapolating it to other Indian cities have been identified and modelled using mathematical models and micro-level simulations in PVSyst. The results have been assimilated to estimate the net PV-active rooftop area of a city, which translates directly into the estimated rooftop PV-potential of the city. The methodology has been illustrated by application to thirteen Indian cities, each having a population of more than two million and a total population of 79 million. The total technical rooftop PV potential of these thirteen cities is found to be around 17.8 GWp.
Transformer VQ-VAE for Unsupervised Unit Discovery and Speech Synthesis: ZeroSpeech 2020 Challenge

In this paper, we report our submitted system for the ZeroSpeech 2020 challenge on Track 2019. The main theme in this challenge is to build a speech synthesizer without any textual information or phonetic labels. In order to tackle those challenges, we build a system that must address two major components such as 1) given speech audio, extract subword units in an unsupervised way and 2) re-synthesize the audio from novel speakers. The system also needs to balance the codebook performance between the ABX error rate and the bitrate compression rate. Our main contribution here is we proposed Transformer-based VQ-VAE for unsupervised unit discovery and Transformer-based inverter for the speech synthesis given the extracted codebook. Additionally, we also explored several regularization methods to improve performance even further.
Hyper-Parameter Tuning based on Random Search for DenseNet Optimization

Deep learning is a machine learning technology that is currently experiencing rapid development. One of the deep learning architecture models is Densely Connected Convolutional Networks (DenseNet), which connects each layer along with feature maps to all next layers. In other words, the next layer receives input feature maps from all previous layers. Because of this density architecture, the training time is longer and requires much memory. One of the ways to overcome these problems is to optimize using the batching strategy method. This batching method uses adaptive batch size with the ratio of learning rate during training to get faster training time without reducing accuracy. To obtain the optimal performance of this batching, a proper selection of hyper-parameter learning rate and batch size is needed. In this study, we perform tuning this hyper-parameter to get the optimal value. The selection of hyper-parameters uses a random search to select candidates for learning rate and batch size, and then an experiment is conducted on the candidates. Random search randomly selects the hyper-parameters, then training and scoring to the hyper-parameters. Our experiment results show that the lower limit of the batch size used is 64, while the optimal learning rate range is in the range of 0.1 - 0.3. The average accuracy obtained from using this hyper-parameter is 95%. Our experiments run on GPU RTX 2080 Ti with CIFAR-10 and CIFAR-100 datasets.
A Novel Machine Learning Based Wearable Belt For Fall Detection

Falls are a major cause of hip fractures in elderly people. Such fractures take a lot of time to heal even after a surgery is performed. In addition to this, the majority of such injuries prove to be fatal due to the lack of quick communication and immediate medical response. This situation is pretty common in today's world where the elderly are most of the time unattended at home. Owing to this need, we have designed a system to detect such falls leveraging machine learning and signal processing algorithms deployed over a simple 32-bit micro-controller. To achieve higher accuracy, we prepared our custom dataset of various types of fall as well as other daily routine activities. Our device informs the close relatives/family via a GSM Module when a fall is detected. The main purpose of our system is to detect a fall and trigger the alert system and take immediate action to minimize the impact of the fall. Our system has been able to detect a fall within 0.25 seconds with high accuracy. This can further be used to develop a real-time safety mechanism gear to minimize the injury in case of a fall.
Echo Memory-Augmented Network for time series classification

Echo State Networks (ESNs) are efficient recurrent neural networks (RNNs) which have been successfully applied to time series modeling tasks. However, ESNs are unable to capture the history information far from the current time step, since the echo state at the present step of ESNs mostly impacted by the previous one. Thus, ESN may have difficulty in capturing the long-term dependencies of temporal data. In this paper, we propose an end-to-end model named Echo Memory-Augmented Network (EMAN) for time series classification. An EMAN consists of an echo memory-augmented encoder and a multi-scale convolutional learner. First, the time series is fed into the reservoir of an ESN to produce the echo states, which are all collected into an echo memory matrix along with the time steps. After that, we design an echo memory-augmented mechanism employing the sparse learnable attention to the echo memory matrix to obtain the Echo Memory-Augmented Representations (EMARs). In this way, the input time series is encoded into the EMARs with enhancing the temporal memory of the ESN. We then use multi-scale convolutions with the max-over-time pooling to extract the most discriminative features from the EMARs. Finally, a fully-connected layer and a softmax layer calculate the probability distribution on categories. Experiments conducted on extensive time series datasets show that EMAN is state-of-the-art compared to existing time series classification methods. The visualization analysis also demonstrates the effectiveness of enhancing the temporal memory of the ESN.
SAR-NAS: Skeleton-based action recognition via neural architecture searching

This paper presents a study of automatic design of neural network architectures for skeleton-based action recognition. Specifically, we encode a skeleton-based action instance into a tensor and carefully define a set of operations to build two types of network cells: normal cells and reduction cells. The recently developed DARTS (Differentiable Architecture Search) is adopted to search for an effective network architecture that is built upon the two types of cells. All operations are 2D based in order to reduce the overall computation and search space. Experiments on the challenging NTU RGB+D and Kinectics datasets have verified that most of the networks developed to date for skeleton-based action recognition are likely not compact and efficient. The proposed method provides an approach to search for such a compact network that is able to achieve comparative or even better performance than the state-of-the-art methods.
PESTO: Proactively Secure Distributed Single Sign-On, or How to Trust a Hacked Server

Single Sign-On (SSO) is becoming an increasingly popular authentication method for users that leverages a trusted Identity Provider (IdP) to bootstrap secure authentication tokens from a single user password. It alleviates some of the worst security issues of passwords, as users no longer need to memorize individual passwords for all service providers, and it removes the burden of these service to properly protect huge password databases. However, SSO also introduces a single point of failure. If compromised, the IdP can impersonate all users and learn their master passwords. To remedy this risk while preserving the advantages of SSO, Agrawal et al. (CCS'18) recently proposed a distributed realization termed PASTA (password-authenticated threshold authentication) which splits the role of the IdP across n servers. While PASTA is a great step forward and guarantees security as long as not all servers are corrupted, it uses a rather inflexible corruption model: servers cannot be corrupted adaptively and - even worse - cannot recover from corruption. The latter is known as proactive security and allows servers to re-share their keys, thereby rendering all previously compromised information useless. In this work, we improve upon the work of PASTA and propose a distributed SSO protocol with proactive and adaptive security (PESTO), guaranteeing security as long as not all servers are compromised at the same time. We prove our scheme secure in the UC framework which is known to provide the best security guarantees for password-based primitives. The core of our protocol are two new primitives we introduce: partially-oblivious distributed PRFs and a class of distributed signature schemes. Both allow for non-interactive refreshing of the secret key material and tolerate adaptive corruptions. We give secure instantiations based on the gap one-more BDH and RSA assumption respectively, leading to a highly efficient 2-round PESTO protocol. We also present an implementation and benchmark of our scheme in Java, realizing OAuth-compatible bearer tokens for SSO, demonstrating the viability of our approach.
Tolerance versus synaptic noise in dense associative memories

Abstract The retrieval capabilities of associative neural networks are known to be impaired by fast noise, which endows neuron behavior with some degree of stochasticity, and by slow noise, due to interference among stored memories; here, we allow for another source of noise, referred to as “synaptic noise,” which may stem from i. corrupted information provided during learning, ii. shortcomings occurring in the learning stage, or iii. flaws occurring in the storing stage, and which accordingly affects the couplings among neurons. Indeed, we prove that this kind of noise can also yield to a break-down of retrieval and, just like the slow noise, its effect can be softened by relying on density, namely by allowing p -body interactions among neurons.
Open and closed anatomical surface description via hemispherical area-preserving map

Spherical harmonics, one of the most widely used basis functions for shape description, rely heavily on a spherical parameterization of the given surface. Additionally, spherical harmonics based 3D modeling works well only for closed surfaces, while many anatomical structures are hemisphere-like open objects. Therefore, it is more natural to have a hemisphere-based approach for their shape description. In this work, we propose a novel framework for the shape description of open and closed hemisphere-like surfaces. We first develop two hemispherical area-preserving parameterization methods for simply-connected open and closed surfaces respectively, and then utilize the hemispherical harmonics basis functions to yield an accurate representation of hemisphere-like anatomical surfaces. We assess the performance of the proposed framework for the shape description of human head. In particular, 60 hemispherical anatomical surfaces (20 closed brain surfaces, 20 closed skull surfaces, and 20 open scalp surfaces) constructed from human head MRI scans are utilized for this purpose. For the three types of surfaces, our framework achieves a significant improvement in the surface reconstruction accuracy by 75%, 80% and 50% respectively when compared to the spherical harmonics based approach. This suggests that our new shape description framework can facilitate the biomedical analysis of hemisphere-like anatomical objects.
Employees’ Behavior in Phishing Attacks: What Individual, Organizational, and Technological Factors Matter?

Phishing, as a social engineering attack has become an increasing threat to organizations in cyberspace. To prevent this, a well-designed continuous security training and educational program needs to be established and enforced in organizations. Prior studies have focused on phishing attack from a limited view of technology countermeasure, e-mail's characteristic, information processing, and securing individual's behaviors to tackle existing gaps. In this research, we developed a theoretical model of factors that influence users in the clicking of phishing e-mails from a broader Socio-Technical perspective. We applied Protection Motivation Theory (PMT) and habit theory for investigating individual factors, Theory of Planned Behavior (TPB) and Deterrence Theory for investigating organizational and technological factors accordingly. The findings revealed habit and protective countermeasure positively affect clicking on phishing e-mails, whereas, no effect of the procedural countermeasures was evident. The results of this study can be used to design phishing simulation exercise and embedded training for vulnerable employees.
Adversarial vulnerabilities of human decision-making

Significance “What I cannot efficiently break, I cannot understand.” Understanding the vulnerabilities of human choice processes allows us to detect and potentially avoid adversarial attacks. We develop a general framework for creating adversaries for human decision-making. The framework is based on recent developments in deep reinforcement learning models and recurrent neural networks and can in principle be applied to any decision-making task and adversarial objective. We show the performance of the framework in three tasks involving choice, response inhibition, and social decision-making. In all of the cases the framework was successful in its adversarial attack. Furthermore, we show various ways to interpret the models to provide insights into the exploitability of human choice.
Realistic Virtual Humans from Smartphone Videos

This paper introduces an automated 3D-reconstruction method for generating high-quality virtual humans from monocular smartphone cameras. The input of our approach are two video clips, one capturing the whole body and the other providing detailed close-ups of head and face. Optical flow analysis and sharpness estimation select individual frames, from which two dense point clouds for the body and head are computed using multi-view reconstruction. Automatically detected landmarks guide the fitting of a virtual human body template to these point clouds, thereby reconstructing the geometry. A graph-cut stitching approach reconstructs a detailed texture. Our results are compared to existing low-cost monocular approaches as well as to expensive multi-camera scan rigs. We achieve visually convincing reconstructions that are almost on par with complex camera rigs while surpassing similar low-cost approaches. The generated high-quality avatars are ready to be processed, animated, and rendered by standard XR simulation and game engines such as Unreal or Unity.