Reviewerly/exHarmony huggingface dataset:
Exploiting OpenMP and OpenACC to accelerate a geometric approach to molecular docking in heterogeneous HPC nodes

In drug discovery, molecular docking is the task in charge of estimating the position of a molecule when interacting with the docking site. This task is usually used to perform screening of a large library of molecules, in the early phase of the process. Given the amount of candidate molecules and the complexity of the application, this task is usually performed using high-performance computing (HPC) platforms. In modern HPC systems, heterogeneous platforms provide a better throughput with respect to homogeneous platforms. In this work, we ported and optimized a molecular docking application to a heterogeneous system, with one or more GPU accelerators, leveraging a hybrid OpenMP and OpenACC approach. The target application focuses on the virtual screening phases in the drug discovery process, and it is based on geometric transformations of the target ligands. We prove that our approach has a better exploitation of the node compared to pure CPU/GPU data splitting approaches, reaching a throughput improvement up to 25% while considering the same computing node.
Evaluating Blockchain for the Governance of the Plasma Derivatives Supply Chain: How Distributed Ledger Technology Can Mitigate Plasma Supply Chain Risks

Objective: This exploratory study examines how distributed ledger technologies could be used within the plasma derivatives supply chain. The plasma derivatives are used increasingly in the pharmaceutical market and the supply chain is global. However, there are significant risks relating to the governance of the supply. The risks include unclear origin of plasma and the propagation of contaminated or poor-quality blood to the pharmaceutical production process. From an ethical perspective, the risk is that vulnerable individuals are exploited in the donation process. Finally, the plasma supply chain currently depends on only a few exporters of plasma, which presents a supply chain risk. Design: The blockchain technology is piloted in other areas of pharmaceutical supply chains and in this study we examine those solutions and conceptualize how a similar solution can be applied to the plasma supply chain. We identify risks within the plasma supply chain and discuss how blockchain-based solutions can mitigate those risks. Results: Drawing on existing literature within the pharmaceutical blockchain arena, we introduce a solution to verify the origin of plasma. We also model how the blockchain technology can be used to tackle ethical and supply chain risks. Conclusions: Blockchain can have a role in mitigating plasma supply chain risks. The area is, however, novel and requires more research.
3D printed perforated QR codes

QR codes are machine-readable optical 2D matrix barcodes which can be easily displayed physically on printed media or digitally on a screen. In recent years, artists and researchers have paid much attention to beautify and enhance QR codes with visual features beyond their plain standard format. We propose a novel method for carving a QR code on a surface in a perforated manner and fabricating it with single material via ordinary 3D printing techniques. However, it is nontrivial to directly fabricate such artistic QR codes because there are a large amount of disconnected components and small details that are difficult to be fully realized. To this end, the embedded information must be consistent and the 3D printed QR code should be readable with standard decoders, like mobile phones. With the original code, we introduce a QR evaluator to estimate its readability and then optimize the code in terms of the printability and robustness. We demonstrate the effectiveness of our techniques with a wide range of examples printed with homogeneous materials via consumer-level 3D printers.
A Convolutional Encoder-Decoder Network With Skip Connections for Saliency Prediction

In this paper, we propose a novel convolutional encoder-decoder network with skip connections, named CEDNS, to improve the performance of saliency prediction. The encoder network utilizes the DenseNet model as the stem network to extract abundant hierarchical features from input images. Subsequently, a decoder network is designed to sufficiently fuse the hierarchical features to predict saliency more accurately. Between the encoder and decoder, skip connections are employed to transfer hierarchical features produced by the former to the latter. Furthermore, the model can be trained in an end-to-end manner which is beneficial for both training and inference. The experimental results on various benchmark datasets, SALICON, MIT300, and CAT2000, show that the proposed model achieves state-of-the-art performance on several key metrics.
Determinants for Successful Agile Collaboration between UX Designers and Software Developers in a Complex Organisation

Agile and User centred design processes have been reported to frequently putting contradictory demands on people working within these methodological frameworks.The current research addresses this point by focussing on the crucial relationship between software developer and designer.An online survey, a contextual inquiry and a diary study were employed with 107 developers and designers in a large media organisation to determine the factors for success in agile development cycles.The results from the survey show that while developers and designers have similar levels of satisfaction with agile processes, there are differences in the factors predicting those ratings.Developers are happier with the wider teamwork but want more access to and close collaboration with designers, while the latters' concern was the quality of the wider teamwork.Additional contextual inquiries and a diary study with pairs of designers and developers reflected the survey findings that close cooperation (and proximity) was essential for improving communication, reducing inefficiencies, and avoiding suboptimal products being released.However, organisational processes, the setup of the work-environment, and managerial traditions meant that this close collaboration and localised decision-making was found difficult to maintain.Results from the survey, the contextual inquiry, and the diary study found six factors for success from collaborations emerged. 1
Opico: A Study of Emoji-first Communication in a Mobile Social App

In the last two decades, Emoji have become a mainstay of digital communication, allowing ordinary people to convey ideas, concepts, and emotions with just a few Unicode characters. While emoji are most often used to supplement text in digital communication, they comprise a powerful and expressive vocabulary in their own right. In this paper, we study the affordances of "emoji-first" communication, in which sequences of emoji are used to describe concepts without textual accompaniment.
Event Detection using Hierarchical Multi-Aspect Attention

Classical event encoding and extraction methods rely on fixed dictionaries of keywords and templates or require ground truth labels for phrase/sentences. This hinders widespread application of information encoding approaches to large-scale free form (unstructured) text available on the web. Event encoding can be viewed as a hierarchical task where the coarser level task is event detection, i.e., identification of documents containing a specific event, and where the fine-grained task is one of event encoding, i.e., identifying key phrases, key sentences. Hierarchical models with attention seem like a natural choice for this problem, given their ability to differentially attend to more or less important features when constructing document representations. In this work we present a novel factorized bilinear multi-aspect attention mechanism (FBMA) that attends to different aspects of text while constructing its representation. We find that our approach outperforms state-of-the-art baselines for detecting civil unrest, military action, and non-state actor events from corpora in two different languages.
Pushing the speed limit of constant-time discrete Gaussian sampling. A case study on the Falcon signature scheme

Sampling from a discrete Gaussian distribution has applications in lattice-based post-quantum cryptography. Several efficient solutions have been proposed in recent years. However, making a Gaussian sampler secure against timing attacks turned out to be a challenging research problem. In this work, we present a toolchain to instantiate an efficient constant-time discrete Gaussian sampler of arbitrary standard deviation and precision. We observe an interesting property of the mapping from input random bit strings to samples during a Knuth-Yao sampling algorithm and propose an efficient way of minimizing the Boolean expressions for the mapping. Our minimization approach results in up to 37% faster discrete Gaussian sampling compared to the previous work. Finally, we apply our optimized and secure Gaussian sampler in the lattice-based digital signature algorithm Falcon, which is a NIST submission, and provide experimental evidence that the overall performance of the signing algorithm degrades by at most 33% only due to the additional overhead of 'constant-time' sampling, including the 60% overhead of random number generation. Breaking a general belief, our results indirectly show that the use of discrete Gaussian samples in digital signature algorithms would be beneficial.
"Hopefully We Are Mostly Secure": Views on Secure Code in Professional Practice

Security of software systems is of general concern, yet breaches caused by common vulnerabilities still occur. Software developers are routinely called upon to "do more" to address this situation. However there has been little focus on the developers' point of view, and understanding how security features in their day-to-day activities. This paper reports preliminary findings of semi-structured interviews taken during an ethnographic study of professional software developers in one organization who are not security experts. The overall study aims to understand how security features in day-to-day practice, while analysis of the interview data asks whether developers are responsible for security. The study reveals that awareness around security matters is raised through several paths including processes, standards, practices and company training and that a focus on security is driven by contextual factors. Security is taken care of with policies and through safeguards, and is handled differently depending on whether a team is developing new features, and hence "looking forward", or working with existing code and hence "looking back". Developers take and share responsibility for security in the code, but suggest that their responsibility has limits, and relies on collective practice.
Designated-Verifier Pseudorandom Generators, and Their Applications

We provide a generic construction of non-interactive zero-knowledge (NIZK) schemes. Our construction is a refinement of Dwork and Naor's (FOCS 2000) implementation of the hidden bits model using verifiable pseudorandom generators (VPRGs). Our refinement simplifies their construction and relaxes the necessary assumptions considerably. As a result of this conceptual improvement, we obtain interesting new instantiations:
Automatic Video Object Segmentation Based on Visual and Motion Saliency

We present an approach to extract the salient object automatically in videos. Given an unannotated video sequence, the proposed method first computes the visual saliency to identify object-like regions in each frame based on the proposed weighted multiple manifold ranking algorithm. We then compute motion cues to estimate the motion saliency and localization prior. Finally, adopting a new energy function, we estimate a superpixel-level object labeling across all frames, where 1) the data term depends on the visual saliency and localization prior, and 2) the smoothness term depends on the constraints in time and space. Compared to the existing counterparts, the proposed approach automatically segments the persistent foreground object meanwhile preserving the potential shape. Experiments show its promising results on the challenging benchmark videos in comparison with the existing counterparts.
An Improved TF-IDF Algorithm Based on Class Discriminative Strength for Text Categorization on Desensitized Data

The TF-IDF algorithm is one of the most common methods of text representation. The basic tasks of natural language processing, for example, text classification, generally use it to represent text. However, the traditional TF-IDF algorithm doesn't work well and has many shortcomings. Actually, many improved methods have achieved good results, but they are ineffective for desensitized data or encrypted data. In this paper, we propose a novel notion, class discriminative strength, and make use of it to improve TF-IDF. The new algorithm is named TF-IDF-ρ and we utilize it to represent desensitized data for text classification. It's worth mentioning that experimental results from the validation set, such as the recall rate, the precision rate and F1 measure, illustrate it is effective. At last, experiments on the desensitized test set indicate that, related to the traditional TF-IDF, the TF-IDF-ρ increase F1 measure by 4.07% at most.
Scene-Aware Deep Networks for Semantic Segmentation of Images

Scene classification and semantic segmentation are two important research directions in computer vision.They are widely used in the research of automatic driving and human-computer interaction.The purpose of the scene classification is to use the image classification to determine the category of the scene in an image by analyzing the background and the target object, while semantic segmentation aims to classify the image at the pixel level and mark the position and semantic information of the scene unit.In this paper, we aimed to train the semantic segmentation neural network in different scenarios to obtain the models with the same number of scene categories, which they are used to process the images.During the process of the actual test, the semantic segmentation dataset was firstly divided into three categories based on the scene classification algorithm.Then the semantic segmentation neural network is trained under three scenarios, and three semantic segmentation network models are obtained accordingly.To test the property of our methods, the semantic segmentation models we got were selected to treat other pictures, and the results obtained from the performance of scene-aware semantic segmentation were much better than semantic segmentation without considering categories.Our study provided an essential improvement of semantic segmentation by adding category information into consideration, which will be helpful to obtain more precise models for further picture analysis.
Augmenting Television With Augmented Reality

This paper explores the effects of adding augmented reality (AR) artefacts to an existing TV programme. A prototype was implemented augmenting a popular nature documentary. Synchronised content was delivered over a Microsoft HoloLens and a TV. Our preliminary findings suggest that the addition of AR to an existing TV programme can result in creation of engaging experiences. However, presenting content outside the traditional TV window challenges traditional storytelling conventions and viewer expectations. Further research is required to understand the risks and opportunities presented when adding AR artefacts to TV.
Search by lackadaisical quantum walks with nonhomogeneous weights

The lackadaisical quantum walk, which is a quantum walk with a weighted self-loop at each vertex, has been shown to speed up dispersion on the line and improve spatial search on the complete graph and periodic square lattice. In these investigations, each self-loop had the same weight, owing to each graph's vertex-transitivity. In this paper, we propose lackadaisical quantum walks where the self-loops have different weights. We investigate spatial search on the complete bipartite graph, which can be irregular with $N_1$ and $N_2$ vertices in each partite set, and this naturally leads to self-loops in each partite set having different weights $l_1$ and $l_2$, respectively. We analytically prove that for large $N_1$ and $N_2$, if the $k$ marked vertices are confined to, say, the first partite set, then with the typical initial uniform state over the vertices, the success probability is improved from its non-lackadaisical value when $l_1 = kN_2/2N_1$ and $N_2 > (3 - 2\sqrt{2}) N_1$, regardless of $l_2$. When the initial state is stationary under the quantum walk, however, then the success probability is improved when $l_1 = kN_2/2N_1$, now without a constraint on the ratio of $N_1$ and $N_2$, and again independent of $l_2$. Next, when marked vertices lie in both partite sets, then for either initial state, there are many configurations for which the self-loops yield no improvement in quantum search, no matter what weights they take.
Fast Scale-Adaptive Bilateral Texture Smoothing

In the classical bilateral filter, a range kernel is used together with a spatial kernel for smoothing out fine details while simultaneously preserving edges. More recently, it has been demonstrated that even coarse textures can be smoothed using joint bilateral filtering. In this paper, we demonstrate that the superior texture filtering results can be obtained by adapting the spatial kernel at each pixel. To the best of our knowledge, spatial adaptation (of the bilateral filter) has not been explored for texture smoothing. The rationale behind adapting the spatial kernel is that one cannot smooth beyond a certain level using a fixed spatial kernel, no matter how we manipulate the range kernel. In fact, we should simply aggregate more pixels using a sufficiently wide spatial kernel to locally enhance the smoothing. Based on this reasoning, we propose to use the classical bilateral filter for texture smoothing, where we adapt the width of the spatial kernel at each pixel. We describe a simple and efficient gradient-based rule for the latter task. The attractive aspect is that we are able to develop a fast algorithm that can accelerate the computations by an order without visibly compromising the filtering quality. We demonstrate that our method outperforms classical bilateral filtering, joint bilateral filtering, and other filtering methods, and is competitive with the optimization methods. We also present some applications of texture smoothing using the proposed method.
Protecting RISC-V against Side-Channel Attacks

Software (SW) implementations of cryptographic algorithms are vulnerable to Side-channel Analysis (SCA) attacks, basically relinquishing the key to the outside world through measurable physical properties of the processor like power consumption and electromagnetic radiation. Protected SW implementations typically have a significant timing and code size overhead as well as a substantially long development time because hands-on testing the result is crucial. Plenty of scientific publications offer solutions for this problem for all kinds of algorithms but they are not straightforward to implement as they rely on device assumptions which are rarely met, nor do these solutions take micro-architecture related leakages into account. We present a solution to this problem by integrating side-channel analysis countermeasures into a RISC-V implementation. Our solution protects against first-order power or electromagnetic attacks while keeping the implementation costs as low as possible. We made use of state of the art masking techniques and present a novel solution to protect memory access against SCA. Practical results are provided that demonstrate the leakage results of various cryptographic primitives running on our protected hardware platform.
Degree-biased random walk for large-scale network embedding

Network embedding aims at learning node representation by preserving the network topology. Previous embedding methods do not scale for large real-world networks which usually contain millions of nodes. They generally adopt a one-size-fits-all strategy to collect information, resulting in a large amount of redundancy. In this paper, we propose DiaRW, a scalable network embedding method based on a degree-biased random walk with variable length to sample context information for learning. Our walk strategy can well adapt to the scale-free feature of real-world networks and extract information from them with much less redundancy. In addition, our method can greatly reduce the size of context information, which is efficient for large-scale network embedding. Empirical experiments on node classification and link prediction prove not only the effectiveness but also the efficiency of DiaRW on a variety of real-world networks. Our algorithm is able to learn the network representations with millions of nodes and edges in hours on a single machine, which is tenfold faster than previous methods.
Cryptanalysis and Enhancement of an Image Encryption Scheme Based on Bit-Plane Extraction and Multiple Chaotic Maps

Recently, an image encryption scheme combining bit-plane extraction with multiple chaotic maps (IESBC) was proposed. The scheme extracts binary bit planes from the plain-image and performs bit-level permutation and confusion, which are controlled by a pseudo-random sequence and a random image generated by the Logistic map, respectively. As the rows and columns of the four MSBPs are permuted with the same pseudo-random sequence and the encryption process does not involve the statistical characteristics of the plain-image, the equivalent secret key of IESBC can be disclosed in the scenario of known/chosen-plaintext attacks. This paper analyzes the weak points of IESBC and proposes a known-plaintext attack and a chosen-plaintext attack on it. Furthermore, we proposed an enhanced scheme to fix the shortcomings and resist the proposed plaintext attacks. The experimental simulation results demonstrated that the enhanced scheme is excellent in terms of various cryptographic metrics.
Enhancing IoT Data Dependability through a Blockchain Mirror Model

The Internet of Things (IoT) is a remarkable data producer and these data may be used to prevent or detect security vulnerabilities and increase productivity by the adoption of statistical and Artificial Intelligence (AI) techniques. However, these desirable benefits are gained if data from IoT networks are dependable—this is where blockchain comes into play. In fact, through blockchain, critical IoT data may be trusted, i.e., considered valid for any subsequent processing. A simple formal model named “the Mirror Model” is proposed to connect IoT data organized in traditional models to assets of trust in a blockchain. The Mirror Model sets some formal conditions to produce trusted data that remain trusted over time. A possible practical implementation of an application programming interface (API) is proposed, which keeps the data and the trust model in synch. Finally, it is noted that the Mirror Model enforces a top-down approach from reality to implementation instead of going the opposite way as it is now the practice when referring to blockchain and the IoT.