Reviewerly/exHarmony huggingface dataset:
The impact of blockchain adoption on competitive performance: the mediating role of process and relational innovation

(2020). The impact of blockchain adoption on competitive performance: the mediating role of process and relational innovation. Logistique & Management: Vol. 28, Numéro Spécial : MOSIM 2018 session 'Supply Chain 4.0'. Guest Editors: Ridha Derrouiche and Samir Lamouri, pp. 88-96.
PowerHammer: Exfiltrating Data From Air-Gapped Computers Through Power Lines

In this article, we provide an implementation, evaluation, and analysis of PowerHammer - an attack that uses power lines to exfiltrate data from air-gapped computers. A malicious code running on a compromised computer intentionally controls the utilization of the CPU cores. The CPU utilization is electromagnetically conducted and propagated through the power lines in the form of a parasitic signal that is modulated, encoded, and transmitted on top of the current flow fluctuations. This electromagnetic phenomenon is known as `conducted emission'. In this attack, the attacker taps the indoor electrical power wiring that is connected to the electrical outlet of the compromised computer. The conducted electromagnetic emission of the compromised computer is analyzed and the exfiltrated data is decoded. The proposed attack is then experimentally evaluated and characterized. The communication performance is discussed and a set of defensive countermeasures is presented. A crucial aspect of the proposed covert communication scheme is that it fully conforms to civilian and military conductive emission standards.
Symmetry and block structure of the Liouvillian superoperator in partial secular approximation

We address the structure of the Liouvillian superoperator for a broad class of bosonic and fermionic Markovian open systems interacting with stationary environments. We show that the accurate application of the partial secular approximation in the derivation of the Bloch-Redfield master equation naturally induces a symmetry on the superoperator level, which may greatly reduce the complexity of the master equation by decomposing the Liouvillian superoperator into independent blocks. Moreover, we prove that, if the steady state of the system is unique, one single block contains all the information about it, and that this imposes a constraint on the possible steady-state coherences of the unique state, ruling out some of them. To provide some examples, we show how the symmetry appears for two coupled spins interacting with separate baths, as well as for two harmonic oscillators immersed in a common environment. In both cases the standard derivation and solution of the master equation is simplified, as well as the search for the steady state. The block diagonalization does not appear when a local master equation is chosen.
Balance

Financial deposits are fundamental to the security of cryptoeconomic protocols as they serve as insurance against potential misbehaviour of agents. However, protocol designers and their agents face a trade-off when choosing the deposit size. While substantial deposits might increase the protocol security, for example by minimising the impact of adversarial behaviour or risks of currency fluctuations, locked-up capital incurs opportunity costs. Moreover, some protocols require over-collateralization in anticipation of future events and malicious intentions of agents. We present Balance, an application-agnostic system that reduces over-collateralization without compromising protocol security. In Balance, malicious agents receive no additional utility for cheating once their deposits are reduced. At the same time, honest and rational agents increase their utilities for behaving honestly as their opportunity costs for the locked-up deposits are reduced. Balance is a round-based mechanism in which agents need to continuously perform desired actions. Rather than treating agents' incentives and behaviour as ancillary, we explicitly model agents' utility, proving the conditions for incentive compatibility. Balance improves social welfare given a distribution of honest, rational, and malicious agents. Further, we integrate Balance with a cross-chain interoperability protocol, XCLAIM, reducing deposits by 10% while maintaining the same utility for behaving honestly. Our implementation allows any number of agents to be maintained for at most 55,287 gas (~ USD 0.07) to update all agents' scores, and at a cost of 54,948 gas (~ USD 0.07) to update the assignment of all agents to layers.
A novel framework for inverse procedural texture modeling

Procedural textures are powerful tools that have been used in graphics for decades. In contrast to the alternative exemplar-based texture synthesis techniques, procedural textures provide user control and fast texture generation with low-storage cost and unlimited texture resolution. However, creating procedural models for complex textures requires a time-consuming process of selecting a combination of procedures and parameters. We present an example-based framework to automatically select procedural models and estimate parameters. In our framework, we consider textures categorized by commonly used high level classes. For each high level class we build a data-driven inverse modeling system based on an extensive collection of real-world textures and procedural texture models in the form of node graphs. We use unsupervised learning on collected real-world images in a texture class to learn sub-classes. We then classify the output of each of the collected procedural models into these sub-classes. For each of the collected models we train a convolutional neural network (CNN) to learn the parameters to produce a specific output texture. To use our framework, a user provides an exemplar texture image within a high level class. The system first classifies the texture into a sub-class, and selects the procedural models that produce output in that sub-class. The pre-trained CNNs of the selected models are used to estimate the parameters of the texture example. With the predicted parameters, the system can generate appropriate procedural textures for the user. The user can easily edit the textures by adjusting the node graph parameters. In a last optional step, style transfer augmentation can be applied to the fitted procedural textures to recover details lost in the procedural modeling process. We demonstrate our framework for four high level classes and show that our inverse modeling system can produce high-quality procedural textures for both structural and non-structural textures.
Online Hate Does Not Stay Online – How Implicit and Explicit Attitudes Mediate the Effect of Civil Negativity and Hate in User Comments on Prosocial Behavior

Incivility and hateful language in user comments are met with growing concern among politicians, the general public, and scholars. There are fears that such comments may decrease social cohesion and ultimately result in less prosocial behavior among citizens. We investigate whether hate, or even civil negativity in user comments alone, inhibit actual prosocial behavior through recipients' explicit and implicit attitudes. In an online experiment, 253 participants read user comments (neutral, civil-negative, hateful) about refugees and received five Euros which they could donate for a refugee aid organization or keep for themselves. The results show that participants confronted with hateful or negative user comments donated less money. The effect was mediated by both explicit and implicit attitudes toward refugees with hate having a stronger influence via implicit attitudes. The results are discussed in light of possible measures for reducing the behavioral impact of negative and hateful user comments.
Deep Elastic Networks With Model Selection for Multi-Task Learning

In this work, we consider the problem of instance-wise dynamic network model selection for multi-task learning. To this end, we propose an efficient approach to exploit a compact but accurate model in a backbone architecture for each instance of all tasks. The proposed method consists of an estimator and a selector. The estimator is based on a backbone architecture and structured hierarchically. It can produce multiple different network models of different configurations in a hierarchical structure. The selector chooses a model dynamically from a pool of candidate models given an input instance. The selector is a relatively small-size network consisting of a few layers, which estimates a probability distribution over the candidate models when an input instance of a task is given. Both estimator and selector are jointly trained in a unified learning framework in conjunction with a sampling-based learning strategy, without additional computation steps. We demonstrate the proposed approach for several image classification tasks compared to existing approaches performing model selection or learning multiple tasks. Experimental results show that our approach gives not only outstanding performance compared to other competitors but also the versatility to perform instance-wise model selection for multiple tasks.
Hyperparameter Optimization of LSTM Network Models through Genetic Algorithm

Next word prediction is an important problem in the domain of NLP, hence in modern artificial intelligence. It draws both scientific and industrial interest, as it consists the core of many processes, like autocorrection, text generation, review prediction etc. Currently, the most efficient and common approach used is classification, using artificial neural networks (ANNs). One of the main drawbacks of ANNs is fine - tuning their hyperparameters, a procedure which is essential to the performance of the model. On the other hand, the approaches usually used for fine - tuning are either computationally unaffordable (e.g. grid search) or of uncertain efficiency (e.g. trial & error). As a response to the above, through the current paper is presented a simple genetic algorithm approach, which is used for the hyperparameter tuning of a common language model and it achieves tuning efficiency without following an exhaustive search.
Transparency Logs via Append-Only Authenticated Dictionaries

Transparency logs allow users to audit a potentially malicious service, paving the way towards a more accountable Internet. For example, Certificate Transparency (CT) enables domain owners to audit Certificate Authorities (CAs) and detect impersonation attacks. Yet, to achieve their full potential, transparency logs must be bandwidth-efficient when queried by users. Specifically, everyone should be able to efficientlylook up log entries by their keyand efficiently verify that the log remainsappend-only. Unfortunately, without additional trust assumptions, current transparency logs cannot provide both small-sizedlookup proofs and small-sizedappend-only proofs. In fact, one of the proofs always requires bandwidth linear in the size of the log, making it expensive for everyone to query the log. In this paper, we address this gap with a new primitive called anappend-only authenticated dictionary (AAD). Our construction is the first to achieve (poly)logarithmic size for both proof types and helps reduce bandwidth consumption in transparency logs. This comes at the cost of increased append times and high memory usage, both of which remain to be improved to make practical deployment possible.
Impact of use of technology on student learning outcomes: Evidence from a large-scale experiment in India

One of the Sustainable Development Goals (SDG-4) adopted by the United Nations focuses on ensuring inclusive and equitable quality education for all. Most research on impact of technology on learning outcomes depends on designs that require low student-to-computer ratio and extensive retraining of teachers. These requirements make the designs difficult to implement on a large scale and hence are limited in terms of inclusivity and ability to 'provide equitable opportunity for all'. Our paper is the first to evaluate an intervention design that is aimed at dealing with these concerns. We conduct a large-scale randomised field experiment in 1823 rural government schools in India that uses technology-aided teaching to replace one-third of traditional classroom teaching. Even with high student-to-computer ratios and minimal teacher training, we observe a positive impact on student learning outcomes. The study thus presents a low cost, resource-light design, which can be implemented in a developing country on a large scale to address the problem of poor learning outcomes, thereby making the intervention inclusive and equitable in line with the spirit of SDG-4.
"The Togetherness that We Crave"

In this paper we explore the possibility to leverage the emerging social VR platforms in hopes of addressing the limitations in prior work concerning design technologies to support Long Distance Relationships. Using 650 social media posts and comments, we present three themes emerging in long distance couples' experiences of social VR applications. This study represents our first endeavor to explore how engaging with social VR affects some of the deepest and most meaningful aspects of human experiences such as romantic relationships and emotional connections, so as to inform the design of more socially and emotionally satisfactory VR systems in the future.
Region-based Fitting of Overlapping Ellipses and its application to cells segmentation

We present RFOVE, a region-based method for approximating an arbitrary 2D shape with an automatically determined number of possibly overlapping ellipses. RFOVE is completely unsupervised, operates without any assumption or prior knowledge on the object's shape and extends and improves the Decremental Ellipse Fitting Algorithm (DEFA) [1]. Both RFOVE and DEFA solve the multi-ellipse fitting problem by performing model selection that is guided by the minimization of the Akaike Information Criterion on a suitably defined shape complexity measure. However, in contrast to DEFA, RFOVE minimizes an objective function that allows for ellipses with higher degree of overlap and, thus, achieves better ellipse-based shape approximation. A comparative evaluation of RFOVE with DEFA on several standard datasets shows that RFOVE achieves better shape coverage with simpler models (less ellipses). As a practical exploitation of RFOVE, we present its application to the problem of detecting and segmenting potentially overlapping cells in fluorescence microscopy images. Quantitative results obtained in three public datasets (one synthetic and two with more than 4000 actual stained cells) show the superiority of RFOVE over the state of the art in overlapping cells segmentation.
GraphAIR: Graph representation learning with neighborhood aggregation and interaction

Graph representation learning is of paramount importance for a variety of graph analytical tasks, ranging from node classification to community detection. Recently, graph convolutional networks (GCNs) have been successfully applied for graph representation learning. These GCNs generate node representation by aggregating features from the neighborhoods, which follows the "neighborhood aggregation" scheme. In spite of having achieved promising performance on various tasks, existing GCN-based models have difficulty in well capturing complicated non-linearity of graph data. In this paper, we first theoretically prove that coefficients of the neighborhood interacting terms are relatively small in current models, which explains why GCNs barely outperforms linear models. Then, in order to better capture the complicated non-linearity of graph data, we present a novel GraphAIR framework which models the neighborhood interaction in addition to neighborhood aggregation. Comprehensive experiments conducted on benchmark tasks including node classification and link prediction using public datasets demonstrate the effectiveness of the proposed method.
Timing Attacks on Error Correcting Codes in Post-Quantum Schemes

While error correcting codes (ECC) have the potential to significantly reduce the failure probability of post-quantum schemes, they add an extra ECC decoding step to the algorithm. Even though this additional step does not compute directly on the secret key, it is susceptible to side-channel attacks. We show that if no precaution is taken, it is possible to use timing information to distinguish between ciphertexts that result in an error before decoding and ciphertexts that do not contain errors, due to the variable execution time of the ECC decoding algorithm. We demonstrate that this information can be used to break the IND-CCA security of post-quantum secure schemes by presenting an attack on two round 1 candidates to the NIST Post-Quantum Standardization Process: the Ring-LWE scheme LAC and the Mersenne prime scheme Ramstake. This attack recovers the full secret key using a limited number of timed decryption queries and is implemented on the reference and the optimized implementations of both submissions. It is able to retrieve LAC's secret key for all security levels in under 2 minutes using less than $2^16 $ decryption queries and Ramstake's secret key in under 2 minutes using approximately $2400$ decryption queries. The attack generalizes to other lattice-based schemes with ECC in which any side-channel information about the presence of errors is leaked during decoding.
GALACTICS

In this paper, we propose a constant-time implementation of the BLISS lattice-based signature scheme. BLISS is possibly the most efficient lattice-based signature scheme proposed so far, with a level of performance on par with widely used pre-quantum primitives like ECDSA. It is only one of the few postquantum signatures to have seen real-world deployment, as part of the strongSwan VPN software suite. The outstanding performance of the BLISS signature scheme stems in large part from its reliance on discrete Gaussian distributions, which allow for better parameters and security reductions. However, that advantage has also proved to be its Achilles' heel, as discrete Gaussians pose serious challenges in terms of secure implementations. Implementations of BLISS so far have included secret-dependent branches and memory accesses, both as part of the discrete Gaussian sampling and of the essential rejection sampling step in signature generation. These defects have led to multiple devastating timing attacks, and were a key reason why BLISS was not submitted to the NIST postquantum standardization effort. In fact, almost all of the actual candidates chose to stay away from Gaussians despite their efficiency advantage, due to the serious concerns surrounding implementation security. Moreover, naive countermeasures will often not cut it: we show that a reasonable-looking countermeasure suggested in previous work to protect the BLISS rejection sampling can again be defeated using novel timing attacks, in which the timing information is fed to phase retrieval machine learning algorithm in order to achieve a full key recovery. Fortunately, we also present careful implementation techniques that allow us to describe an implementation of BLISS with complete timing attack protection, achieving the same level of efficiency as the original unprotected code, without resorting on floating point arithmetic or platform-specific optimizations like AVX intrinsics. These techniques, including a new approach to the polynomial approximation of transcendental function, can also be applied to the masking of the BLISS signature scheme, and will hopefully make more efficient and secure implementations of lattice-based cryptography possible going forward.
OmniMVS: End-to-End Learning for Omnidirectional Stereo Matching

In this paper, we propose a novel end-to-end deep neural network model for omnidirectional depth estimation from a wide-baseline multi-view stereo setup. The images captured with ultra wide field-of-view (FOV) cameras on an omnidirectional rig are processed by the feature extraction module, and then the deep feature maps are warped onto the concentric spheres swept through all candidate depths using the calibrated camera parameters. The 3D encoder-decoder block takes the aligned feature volume to produce the omnidirectional depth estimate with regularization on uncertain regions utilizing the global context information. In addition, we present large-scale synthetic datasets for training and testing omnidirectional multi-view stereo algorithms. Our datasets consist of 11K ground-truth depth maps and 45K fisheye images in four orthogonal directions with various objects and environments. Experimental results show that the proposed method generates excellent results in both synthetic and real-world environments, and it outperforms the prior art and the omnidirectional versions of the state-of-the-art conventional stereo algorithms.
Generating Diverse and Descriptive Image Captions Using Visual Paraphrases

Recently there has been significant progress in image captioning with the help of deep learning. However, captions generated by current state-of-the-art models are still far from satisfactory, despite high scores in terms of conventional metrics such as BLEU and CIDEr. Human-written captions are diverse, informative and precise, but machine-generated captions seem to be simple, vague and dull. In this paper, aimed at improving diversity and descriptiveness characteristics of generated image captions, we propose a model utilizing visual paraphrases (different sentences describing the same image) in captioning datasets. We explore different strategies to select useful visual paraphrase pairs for training by designing a variety of scoring functions. Our model consists of two decoding stages, where a preliminary caption is generated in the first stage and then paraphrased into a more diverse and descriptive caption in the second stage. Extensive experiments are conducted on the benchmark MS COCO dataset, with automatic evaluation and human evaluation results verifying the effectiveness of our model.
Efficient Autonomy Validation in Simulation with Adaptive Stress Testing

During the development of autonomous systems such as driverless cars, it is important to characterize the scenarios that are most likely to result in failure. Adaptive Stress Testing (AST) provides a way to search for the most-likely failure scenario as a Markov decision process (MDP). Our previous work used a deep reinforcement learning (DRL) solver to identify likely failure scenarios. However, the solver's use of a feed-forward neural network with a discretized space of possible initial conditions poses two major problems. First, the system is not treated as a black box, in that it requires analyzing the internal state of the system, which leads to considerable implementation complexities. Second, in order to simulate realistic settings, a new instance of the solver needs to be run for each initial condition. Running a new solver for each initial condition not only significantly increases the computational complexity, but also disregards the underlying relationship between similar initial conditions. We provide a solution to both problems by employing a recurrent neural network that takes a set of initial conditions from a continuous space as input. This approach enables robust and efficient detection of failures because the solution generalizes across the entire space of initial conditions. By simulating an instance where an autonomous car drives while a pedestrian is crossing a road, we demonstrate the solver is now capable of finding solutions for problems that would have previously been intractable.