Reviewerly/exHarmony huggingface dataset:
Content Management Systems Performance and Compliance Assessment Based on a Data-Driven Search Engine Optimization Methodology

While digitalization of cultural organizations is in full swing and growth, it is common knowledge that websites can be used as a beacon to expand the awareness and consideration of their services on the Web. Nevertheless, recent research results indicate the managerial difficulties in deploying strategies for expanding the discoverability, visibility, and accessibility of these websites. In this paper, a three-stage data-driven Search Engine Optimization schema is proposed to assess the performance of Libraries, Archives, and Museums websites (LAMs), thus helping administrators expand their discoverability, visibility, and accessibility within the Web realm. To do so, the authors examine the performance of 341 related websites from all over the world based on three different factors, Content Curation, Speed, and Security. In the first stage, a statistically reliable and consistent assessment schema for evaluating the SEO performance of LAMs websites through the integration of more than 30 variables is presented. Subsequently, the second stage involves a descriptive data summarization for initial performance estimations of the examined websites in each factor is taking place. In the third stage, predictive regression models are developed to understand and compare the SEO performance of three different Content Management Systems, namely the Drupal, WordPress, and custom approaches, that LAMs websites have adopted. The results of this study constitute a solid stepping-stone both for practitioners and researchers to adopt and improve such methods that focus on end-users and boost organizational structures and culture that relied on data-driven approaches for expanding the visibility of LAMs services.
A survey on genomic data by privacy-preserving techniques perspective

Nowadays, the purpose of human genomics is widely emerging in health-related problems and also to achieve time and cost-efficient healthcare. Due to advancement in genomics and its research, development in privacy concerns is needed regarding querying, accessing and, storage and computation of the genomic data. While the genomic data is widely accessible, the privacy issues may emerge due to the untrusted third party (adversaries/researchers), they may reveal the information or strategy plans regarding the genome data of an individual when it is requested for research purposes. To mitigate this problem many privacy-preserving techniques are used along with cryptographic methods are briefly discussed. Furthermore, efficiency and accuracy in a secure and private genomic data computation are needed to be researched in future.
Parallel Corpora Preparation for English-Amharic Machine Translation

In this paper, we describe the development of an English-Amharic parallel corpus and Machine Translation (MT) experiments conducted on it. Two different tests have been achieved. Statistical Machine Translation (SMT) and Neural Machine Translation (NMT) experiments. The performance using the bilingual evaluation understudy metric (BLEU) shows 26.47 and 32.44 respectively for SMT and NMT. The corpus was collected from the Internet using automatic and semi automatic techniques. The harvested corpus concerns domains coming from Religion, Law, and News. Finally, the corpus, we built is composed of 225,304 parallel sentences, it will be shared for free with the community. In our knowledge, this is the biggest parallel corpus so far concerning the Amharic language.
Exploring the Representation of Word Meanings in Context: A Case Study on Homonymy and Synonymy

This paper presents a multilingual study of word meaning representations in context.We assess the ability of both static and contextualized models to adequately represent different lexical-semantic relations, such as homonymy and synonymy.To do so, we created a new multilingual dataset that allows us to perform a controlled evaluation of several factors such as the impact of the surrounding context or the overlap between words, conveying the same or different senses.A systematic assessment on four scenarios shows that the best monolingual models based on Transformers can adequately disambiguate homonyms in context.However, as they rely heavily on context, these models fail at representing words with different senses when occurring in similar sentences.Experiments are performed in Galician, Portuguese, English, and Spanish, and both the dataset (with more than 3,000 evaluation items) and new models are freely released with this study.
The One-More Discrete Logarithm Assumption in the Generic Group Model

The one more-discrete logarithm assumption (OMDL) underlies the security analysis of identification protocols, blind signature and multi-signature schemes, such as blind Schnorr signatures and the recent MuSig2 multi-signatures. As these schemes produce standard Schnorr signatures, they are compatible with existing systems, e.g. in the context of blockchains. OMDL is moreover assumed for many results on the impossibility of certain security reductions.Despite its wide use, surprisingly, OMDL is lacking any rigorous analysis; there is not even a proof that it holds in the generic group model (GGM). (We show that a claimed proof is flawed.) In this work we give a formal proof of OMDL in the GGM. We also prove a related assumption, the one-more computational Diffie-Hellman assumption, in the GGM. Our proofs deviate from prior GGM proofs and replace the use of the Schwartz-Zippel Lemma by a new argument.
Learning with Safety Constraints: Sample Complexity of Reinforcement Learning for Constrained MDPs.

Many physical systems have underlying safety considerations that require that the policy employed ensures the satisfaction of a set of constraints. The analytical formulation usually takes the form of a Constrained Markov Decision Process (CMDP). We focus on the case where the CMDP is unknown, and RL algorithms obtain samples to discover the model and compute an optimal constrained policy. Our goal is to characterize the relationship between safety constraints and the number of samples needed to ensure a desired level of accuracy---both objective maximization and constraint satisfaction---in a PAC sense. We explore two classes of RL algorithms, namely, (i) a generative model based approach, wherein samples are taken initially to estimate a model, and (ii) an online approach, wherein the model is updated as samples are obtained. Our main finding is that compared to the best known bounds of the unconstrained regime, the sample complexity of constrained RL algorithms are increased by a factor that is logarithmic in the number of constraints, which suggests that the approach may be easily utilized in real systems.
Selective Replay Enhances Learning in Online Continual Analogical Reasoning

In continual learning, a system learns from non-stationary data streams or batches without catastrophic forgetting. While this problem has been heavily studied in supervised image classification and reinforcement learning, continual learning in neural networks designed for abstract reasoning has not yet been studied. Here, we study continual learning of analogical reasoning. Analogical reasoning tests such as Raven's Progressive Matrices (RPMs) are commonly used to measure non-verbal abstract reasoning in humans, and recently offline neural networks for the RPM problem have been proposed. In this paper, we establish experimental baselines, protocols, and forward and backward transfer metrics to evaluate continual learners on RPMs. We employ experience replay to mitigate catastrophic forgetting. Prior work using replay for image classification tasks has found that selectively choosing the samples to replay offers little, if any, benefit over random selection. In contrast, we find that selective replay can significantly outperform random selection for the RPM task <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Policy Transfer across Visual and Dynamics Domain Gaps via Iterative Grounding

The ability to transfer a policy from one environment to another is a promising avenue for efficient robot learning in realistic settings where task supervision is not available. This can allow us to take advantage of environments well suited for training, such as simulators or laboratories, to learn a policy for a real robot in a home or office. To succeed, such policy transfer must overcome both the visual domain gap (e.g. different illumination or background) and the dynamics domain gap (e.g. different robot calibration or modelling error) between source and target environments. However, prior policy transfer approaches either cannot handle a large domain gap or can only address one type of domain gap at a time. In this paper, we propose a novel policy transfer method with iterative “environment grounding”, IDAPT, that alternates between (1) directly minimizing both visual and dynamics domain gaps by grounding the source environment in the target environment domains, and (2) training a policy on the grounded source environment. This iterative training progressively aligns the domains between the two environments and adapts the policy to the target environment. Once trained, the policy can be directly executed on the target environment. The empirical results on locomotion and robotic manipulation tasks demonstrate that our approach can effectively transfer a policy across visual and dynamics domain gaps with minimal supervision and interaction with the target environment. Videos and code are available at https://clvrai.com/idapt
CoCoNets: Continuous Contrastive 3D Scene Representations

This paper explores self-supervised learning of amodal 3D feature representations from RGB and RGB-D posed images and videos, agnostic to object and scene semantic content, and evaluates the resulting scene representations in the downstream tasks of visual correspondence, object tracking, and object detection. The model infers a latent 3D representation of the scene in the form of 3D feature points, where each continuous world 3D point is mapped to its corresponding feature vector. The model is trained for contrastive view prediction by rendering 3D feature clouds in queried viewpoints and matching against the 3D feature point cloud predicted from the query view. Notably, the representation can be queried for any 3D location, even if it is not visible from the input view. Our model brings together three powerful ideas of recent exciting research work: 3D feature grids as a neural bottleneck for view prediction, implicit functions for handling resolution limitations of 3D grids, and contrastive learning for unsupervised training of feature representations. We show the resulting 3D visual feature representations effectively scale across objects and scenes, imagine information occluded or missing from the input viewpoints, track objects over time, align semantically related objects in 3D, and improve 3D object detection. We outperform many existing state-of-the-art methods for 3D feature learning and view prediction, which are either limited by 3D grid spatial resolution, do not attempt to build amodal 3D representations, or do not handle combinatorial scene variability due to their non-convolutional bottlenecks.
Using deep Q-network for selection hyper-heuristics

Hyper-Heuristics is an active research field that aims to automatically select (or generate) the best low-level heuristic in each step of the search process. This work investigates a Hyper-Heuristic with a Deep Q-Network (DQN) selection strategy and compares it with two state-of-the-art approaches, namely the Dynamic MAB and the Fitness-Rate-Rank MAB. The experiments conducted on two domains from the HyFlex framework showed that the DQN approach outperformed the others on the Vehicle Routing Problem and was competitive on the Traveling Salesman Problem. This indicates that the DQN is a robust selection strategy that is less sensitive to the domain than the MAB based approaches.
Distance-based Hyperspherical Classification for Multi-source Open-Set Domain Adaptation

Vision systems trained in closed-world scenarios fail when presented with new environmental conditions, new data distributions, and novel classes at deployment time. How to move towards open-world learning is a long-standing research question. The existing solutions mainly focus on specific aspects of the problem (single domain Open-Set, multi-domain Closed-Set), or propose complex strategies which combine several losses and manually tuned hyperparameters. In this work, we tackle multi-source Open-Set domain adaptation by introducing HyMOS: a straightforward model that exploits the power of contrastive learning and the properties of its hyperspherical feature space to correctly predict known labels on the target, while rejecting samples belonging to any unknown class. Hy-MOS includes style transfer among the instance transformations of contrastive learning to get domain invariance while avoiding the risk of negative-transfer. A self-paced threshold is defined on the basis of the observed data distribution and updates online during training, allowing to handle the known-unknown separation. We validate our method over three challenging datasets. The obtained results show that HyMOS outperforms several competitors, defining the new state-of-the-art. Our code is available at https://github.com/silvia1993/HyMOS.
What we can learn from how programmers debug their code

Researchers have developed numerous debugging approaches to help programmers in the debugging process, but these approaches are rarely used in practice. In this paper, we investigate how programmers debug their code and what researchers should consider when developing debugging approaches. We conducted an online questionnaire where 102 programmers provided information about recently fixed bugs. We found that the majority of bugs (69.6 %) are semantic bugs. Memory and concurrency bugs do not occur as frequently (6.9 % and 8.8 %), but they consume more debugging time. Locating a bug is more difficult than reproducing and fixing it. Programmers often use only IDE build-in tools for debugging. Furthermore, programmers frequently use a replication-observation-deduction pattern when debugging. These results suggest that debugging support is particularly valuable for memory and concurrency bugs. Furthermore, researchers should focus on the fault localization phase and integrate their tools into commonly used IDEs.
Heuristic Evaluation of an African-centric Mobile Persuasive Game for Promoting Safety Measures Against COVID-19

The COVID-19 pandemic affected the whole world, including the African Population. As the lockdown rules against the spread of COVID-19 pandemic are being eased off and Africans have begun going about their normal daily activities, there is a need for interventions and measures to ensure that they continue to observe the safety guidelines to prevent a second wave of the pandemic. While several interventions are emerging, there is a limited number of games or gamified interventions aimed at raising awareness about these safety guidelines with a specific focus on Africans. Games and gamified applications are popular among Africans especially young people due to their entertainment value. Therefore, we present the design, implementation and heuristic evaluation of a mobile persuasive game, titled COVID Dodge, aimed at raising awareness on the importance of social distancing and other precautionary measures against the spread of the COVID-19. This persuasive game strategically employs popular persuasive features and strategies to increase the attention of Africans towards social distancing and other precautionary measures. The results of the heuristic evaluation (Heuristic Evaluation for Playability) revealed that the game possessed a high level of playability which implies that it would be engaging and enjoyed by users. The result of the persuasive strategy evaluation revealed that 13 out of the 15 strategies we implemented were strongly present in the game. Based on the evaluators' comments, we provided some design consideration and insights for developing persuasive games.
Modeling unaccounted-for gas among residential natural gas consumers using a comprehensive fuzzy cognitive map

Residential natural gas consumption depends on several factors. Available tools and methods to identify, categorize, and validate effective factors have some limitations, making consumption modeling more complex. Once a comprehensive model of effective consumption factors is developed for residential gas consumers, it can predict consumption. In addition, such a model could be used to verify the accuracy of measuring devices in order to reduce unaccounted for gas (UFG). The key factors affecting residential gas consumption were identified based on previous studies and their mutual effects were analyzed using a fuzzy cognitive mapping (FCM) method. The most significant factors and their effects on natural gas consumption in the residential sector were determined. In this study, for the first time, the expected consumption for each consumer was estimated using a consumption index. Generally, if the estimated consumption is significantly different from the amount recorded by the meter, it could suggest a potential source of UFG. The proposed method was applied to the data collected from the residential gas consumers of a small region in Iran (Dasht-e Arjan region, Fars province), and the results demonstrate the effectiveness of the proposed method.
One-shot deflectometry for high-speed inline inspection of specular quasi-plane surfaces

Deflectometry is a non-contact optical technique which uses a simple system setup to measure and inspect specular surfaces. Conventional deflectometry methods usually need very complicated system calibration and time-consuming phase-shifting methods, which are not practical for high-speed measurement. But if the measurement target is a quasi-plane surface where the local slope is directly modulated by phase, the complicated system calibration can be neglected. In this study, to reduce error, we investigated and analyzed error contribution when measuring quasi-plane surfaces. A simple and high speed one-shot phase retrieval method is proposed for quickly measuring surface phase. We used a single high carrier-frequency composite pattern for pattern projection, and derivation of the captured fringe pattern for phase retrieval. A phase-related slope is obtained for slope calculation and the 3D shape is reconstructed by integrating the surface slopes. We verified our proposed method by measuring representative quasi-plane surfaces and comparing results with conventional methods.
Products-6K: A Large-Scale Groceries Product Recognition Dataset

Product recognition is a task that receives continuous attention by the computer vision/deep learning community mainly with the scope of providing robust solutions for automatic checkout supermarkets. One of the main challenges is the lack of images that illustrate in realistic conditions a high number of products. Here the product recognition task is perceived slightly differently compared to the automatic checkout paradigm but the challenges encountered are the same. The setting under which this dataset is captured is with the aim to help individuals with visual impairment in doing their daily grocery in order to increase their autonomy. In particular, we propose a large-scale dataset utilized to tackle the product recognition problem in a supermarket environment. The dataset is characterized by (a) large scale in terms of unique products associated with one or more photos from different viewpoints, (b) rich textual descriptions linked to different levels of annotation and, (c) images acquired both in laboratory conditions and in a realistic supermarket scenario portrayed in various clutter and lighting conditions. A direct comparison with existing datasets of this category demonstrates the significantly higher number of the available unique products, as well as the richness of its annotation enabling different recognition scenarios. Finally, the dataset is also benchmarked using various approaches based both on visual and textual descriptors
Performance Evaluation of RSA-based Secure Cloud Storage Protocol using OpenStack

In this paper, the implementation of the General Secure Cloud Storage Protocol is carried out and instantiated by a multiplicatively Homomorphic Encryption Scheme (HES). The protocol provides a system for secure storage of data over the cloud, thereby allowing the client to carry out the operational tasks on it efficiently. The work focuses on the execution of five major modules of the protocol. We also evaluate the performance of the protocol with respect to the computation cost of these modules on the basis of different security parameters and datasets by conducting a series of experiments. The cloud was built using OpenStack and the data were outsourced from the client’s system to the cloud to study the security features and performance metrics when adopting the cloud environment.
HiQ-ProjectQ: Towards user-friendly and high-performance quantum computing on GPUs

In this work, we present some of the latest efforts made at Huawei Research in order to improve the overall performance of ProjectQ, the quantum computing framework used as the foundation of our quantum research. Since a few years, performance assessment of the framework using profiling tools has shown that a significant portion of the compilation time is spent in doing memory management linked to the lifetime and access of Python objects. The main purpose of this work is therefore aimed at addressing some of these limitations by introducing a new C++ processing backend for ProjectQ, as well as starting a complete rewrite of the simulator code already written in C++. The core of this work is centered around on a new C++ API for ProjectQ that moves most of the compiler processing into natively compiled code. We achieve this by providing a new compiler engine to perform the conversion from Python to C++, which ensures that we retain maximum compatibility with existing user code while providing significant speed-ups. We also introduce some of our work aimed at porting the existing C++ code to offload the more demanding calculations onto GPUs for better performance. We then investigate the performance of this new API by comparing it with the original ProjectQ implementation, as well as some other existing quantum computing frameworks. The preliminary results show that the C++ API is able to considerably reduce the cost of compilation, to the point that the compilation process becomes mostly limited by the performance of the simulator. Unfortunately, due to the some last minutes developments and the time frame required for the submission of the present manuscript, we are unable to provide conclusive benchmark results for the GPU-capable implementation of the simulator. These will most likely be presented during the keynote presentation at the DATE conference in 2021.