Reviewerly/exHarmony huggingface dataset:
Prevention of vulnerabilities arising from optimization of code with Undefined Behavior

Aggressive optimization in modern compilers may uncover vulnerabilities in program code that did not lead to bugs prior to optimization. The source of these vulnerabilities is in code with undefined behavior. Programmers use such constructs relying on some particular behavior these constructs showed before in their experience, but the compiler is not obliged to stick to that behavior and may change the behavior if it’s needed for optimization since the behavior is undefined by language standard. This article describes approaches to detection and elimination of vulnerabilities arising from optimization in the case when source code is available but its modification is undesirable or impossible. Concept of a safe compiler (i.e. compiler that ensures no vulnerability is added to the program during optimization) is presented and implementation of such a compiler on top of GCC compiler is described. Implementation of safe compiler’s functionality is divided into three security levels whose applicability is discussed in the article. Feasibility of using the safe compiler on real-world codebases is demonstrated and possible performance losses are estimated.
Forecasting and Modelling of Solar Radiation for Photovoltaic (PV) Systems

Solar radiation is characterized by its fluctuation because it depends to different factors such as the day hour, the speed wind, the cloud cover and some other weather conditions. Certainly, this fluctuation can affect the PV power production and then its integration on the electrical micro grid. An accurate forecasting of solar radiation is so important to avoid these problems. In this chapter, the solar radiation is treated as time series and it is predicted using the Auto Regressive and Moving Average (ARMA) model. Based on the solar radiation forecasting results, the photovoltaic (PV) power is then forecasted. The choice of ARMA model has been carried out in order to exploit its own strength. This model is characterized by its flexibility and its ability to extract the useful statistical properties, for time series predictions, it is among the most used models. In this work, ARMA model is used to forecast the solar radiation one year in advance considering the weekly radiation averages. Simulation results have proven the effectiveness of ARMA model to forecast the small solar radiation fluctuations.
Batch-Softmax Contrastive Loss for Pairwise Sentence Scoring Tasks

The use of contrastive loss for representation learning has become prominent in computer vision, and it is now getting attention in Natural Language Processing (NLP).Here, we explore the idea of using a batch-softmax contrastive loss when fine-tuning large-scale pretrained transformer models to learn better taskspecific sentence embeddings for pairwise sentence scoring tasks.We introduce and study a number of variations in the calculation of the loss as well as in the overall training procedure; in particular, we find that a special data shuffling can be quite important.Our experimental results show sizable improvements on a number of datasets and pairwise sentence scoring tasks including classification, ranking, and regression.Finally, we offer detailed analysis and discussion, which should be useful for researchers aiming to explore the utility of contrastive loss in NLP.
Efficient distributed path computation on RDF knowledge graphs using partial evaluation

A key property of Linked Data is the representation and publication of data as interconnected labelled graphs where different resources linked to each other form a network of meaningful information. Searching these important relationships between resources – within single or distributed graphs – can be reduced to a pathfinding or navigation problem, i.e., looking for chains of intermediate nodes. SPARQL1.1, the current standard query language for RDF-based Linked Data defines a construct – called Property Paths (PPs) – to navigate between entities within a single graph. Since Linked Data technologies are naturally aimed at decentralised scenarios, there are many cases where centralising this data is not feasible or even not possible for querying purposes. To address these problems, we propose a SPARQL PP-based graph processing approach – dubbed DpcLD – where users can execute SPARQL PP queries and find paths distributed across multiple, connected graphs exposed as SPARQL endpoints. To execute the distributed path queries we implemented an index-free, cache-based query engine that communicates with a shared algorithm running on each remote endpoint, and computes the distributed paths. In this paper, we highlight the way in which this approach exploits and aggregates partial paths, within a distributed environment, to produce complete results. We perform extensive experiments to demonstrate the performance of our approach on two datasets: One representing 10 million triples from the DBPedia SPARQL benchmark, and another full benchmark dataset corresponding to 124 million triples. We also perform a scalability test of our approach using real-world genomics datasets distributed across multiple endpoints. We compare our distributed approach with other distributed and centralized pathfinding approaches, showing that it outperforms other distributed approaches by orders of magnitude, and provides a good trade-off for cases when the data cannot be centralised.
A-SDF: Learning Disentangled Signed Distance Functions for Articulated Shape Representation

Recent work has made significant progress on using implicit functions, as a continuous representation for 3D rigid object shape reconstruction. However, much less effort has been devoted to modeling general articulated objects. Compared to rigid objects, articulated objects have higher degrees of freedom, which makes it hard to generalize to unseen shapes. To deal with the large shape variance, we introduce Articulated Signed Distance Functions (A-SDF) to represent articulated shapes with a disentangled latent space, where we have separate codes for encoding shape and articulation. We assume no prior knowledge on part geometry, articulation status, joint type, joint axis, and joint location. With this disentangled continuous representation, we demonstrate that we can control the articulation input and animate unseen instances with unseen joint angles. Furthermore, we propose a Test-Time Adaptation inference algorithm to adjust our model during inference. We demonstrate our model generalize well to out-of-distribution and unseen data, e.g., partial point clouds and real-world depth images.
AdvFoolGen: Creating Persistent Troubles for Deep Classifiers

Researches have shown that deep neural networks are vulnerable to malicious attacks, where adversarial images are created to trick a network into misclassification even if the images may give rise to totally different labels by human eyes. To make deep networks more robust to such attacks, many defense mechanisms have been proposed in the literature, some of which are quite effective for guarding against typical attacks. In this paper, we present a new generative attack model termed AdvFoolGen, which can generate attacking images from the same feature space as that of the natural images, so as to keep baffling the network even though state-of-the-art defense mechanisms have been applied. We systematically evaluate our model by comparing with well-established attack algorithms. Through experiments, we demonstrate the effectiveness and robustness of our attack in the face of state-of-the-art defense techniques and unveil the potential reasons for its effectiveness through principled analysis. As such, AdvFoolGen contributes to understanding the vulnerability of deep networks from a new perspective and may, in turn, help in developing and evaluating new defense mechanisms.
Discovering and Exploiting Sparse Rewards in a Learned Behavior Space

Abstract Learning optimal policies in sparse rewards settings is difficult as the learning agent has little to no feedback on the quality of its actions. In these situations, a good strategy is to focus on exploration, hopefully leading to the discovery of a reward signal to improve on. A learning algorithm capable of dealing with this kind of settings has to be able to (1) explore possible agent behaviors and (2) exploit any possible discovered reward. Exploration algorithms have been proposed that require the definition of a low-dimension behavior space, in which the behavior generated by the agent's policy can be represented. The need to design a priori this space such that it is worth exploring is a major limitation of these algorithms. In this work, we introduce STAX, an algorithm designed to learn a behavior space on-the-fly and to explore it while optimizing any reward discovered. It does so by separating the exploration and learning of the behavior space from the exploitation of the reward through an alternating two-step process. In the first step, STAX builds a repertoire of diverse policies while learning a low-dimensional representation of the high-dimensional observations generated during the policies evaluation. In the exploitation step, emitters optimize the performance of the discovered rewarding solutions. Experiments conducted on three different sparse reward environments show that STAX performs comparably to existing baselines while requiring much less prior information about the task as it autonomously builds the behavior space it explores.
An Analysis of Applying Zoom Cloud Meeting Towards EFL Learning in Pandemic Era Covid-19

The objectives of this research are to find out the students’ perspective, the barriers, the advantages and disadvantages as well as the strategies of applying Zoom Cloud Meeting app towards EFL learning of STMIK Handayani in pandemic era Covid-19. The subjects of this research are five students of Manajemen Informatika (MI) and five students of Technical Information Extensive (TI Ext). The research employed descriptive qualitative design by using self-report questionnaire and interview as the research instruments. The result of research showed that (1) the existence of zoom cloud meeting in EFL learning are very effective to stop the spreading of covid 19 in pandemic era but the learning process cannot be perfect than the conventional learning (2) the main barriers of applying zoom cloud meeting are the bad network and the usage of big quota. (3) The advantages are many kinds of good features and high quality access, more flexible, comfortable, and relaxed and disadvantages are the learning condition cannot be felt, the learning is not perfect, make the students lazier and less of togetherness (4) the effective strategies are the methods must be interactive and the materials have to be delivered directly with audio and visual.
Analysis of the Level of Vulnerability to Floods using the Method of Simple Moving Average (A Case Study of the City of Padang)

Flood is the overflow of the river flow due to water exceed the storage capacity of the river, so that overflow and inundate the plains or the lower regions in the vicinity. Floods are natural disasters that often occur in Indonesia because it is located in the tropics. A lot of flood prone areas which have high population density, due to less understanding of the community regarding the geographic region. Floods often occur in the City of Padang caused by high rainfall in the upstream areas of the river and the topography is hilly so increase the speed of the flow of water to the lower plains. In this study, the author examines the factors of vulnerability to flood by using the method of Simple Moving Average with a parameter of the flood that used rainfall and long rainy days.
Gray consistency optical flow algorithm based on mask-R-CNN and a spatial filter for velocity calculation

The optical flow method has been widely used to measure the vehicle velocity by observing the stationary ground with a camera looking-down. However, when there are moving objects on the stationary ground, the interfering optical flow field will be generated, which decreases the velocity measurement accuracy of a vehicle relative to the ground. In order to reduce the effects caused by moving objects, this paper integrates pyramid Lucas–Kanade (LK) algorithm with the gray consistency method to use the information of color images thoroughly. First, a mask region with convolutional neural network (Mask-R-CNN) is used to recognize the objects that have motions relative to the ground, and it covers them with masks to enhance the similarity between pixels and to reduce the impacts of the noisy moving pixels. Then images are decomposed into three channels, red, green, and blue (i.e., <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mi>R</mml:mi> </mml:math> , <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mi>G</mml:mi> </mml:math> , and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mi>B</mml:mi> </mml:math> ), and processed by median filter. Based on the gray consistency method, the optical flow can be obtained by the pyramid LK algorithm. Finally, the velocity is calculated by the optical flow value. The prominent advantages of the proposed algorithm are: (i) increase the velocity measurement accuracy of a vehicle relative to the ground; (ii) use the information of color images acquired with cameras thoroughly and obtain velocity calculation outputs with less fluctuation; (iii) reduce wrong values caused by noises that are from the origin image and introduced by similar color masks. Four experiments are conducted to test the proposed algorithm and results with superior precision and reliability show the feasibility and effectiveness of the proposed method for the velocity measurement accuracy of a vehicle relative to the ground.
Unbiased Filtering of Accidental Clicks in Verizon Media Native Advertising

Verizon Media (VZM) native advertising is one of VZM largest and fastest growing businesses, reaching a run-rate of several hundred million USDs in the past year. Driving the VZM native models that are used to predict event probabilities, such as click and conversion probabilities, is OFFSET - a feature enhanced collaborative-filtering based event-prediction algorithm. In this work we focus on the challenge of predicting click-through rates (CTR) when we are aware that some of the clicks have short dwell-time and are defined as accidental clicks. An accidental click implies little affinity between the user and the ad, so predicting that similar users will click on the ad is inaccurate. Therefore, it may be beneficial to remove clicks with dwell-time lower than a predefined threshold from the training set. However, we cannot ignore these positive events, as filtering these will cause the model to under predict. Previous approaches have tried to apply filtering and then adding corrective biases to the CTR predictions, but did not yield revenue lifts and therefore were not adopted. In this work, we present a new approach where the positive weight of the accidental clicks is distributed among all of the negative events (skips), based on their likelihood of causing accidental clicks, as predicted by an auxiliary model. These likelihoods are taken as the correct labels of the negative events, shifting our training from using only binary labels and adopting a binary cross-entropy loss function in our training process. After showing offline performance improvements, the modified model was tested online serving VZM native users, and provided 1.18% revenue lift over the production model which is agnostic to accidental clicks.
The online computer-assisted translation class getting faster target language

Computer-assisted translation technology is complex and, in many cases, intimidating for students new to CAT software. Thus, CAT technology is best taught using a step-by-step approach with exercises first focusing on the basic features and then on more elaborate software functions (scaffolding). The goal is to equip students with the step-by-step instructions and screencast tutorials needed to complete exercises. Students gain further software experience by working on a larger text of their choice throughout the semester. They first prepare and format this source text for CAT tool use, and they then build a termbase and translate the source text into their target language. Finally, students write two essays reflecting on the advantages and disadvantages of using CAT software for producing their target texts. Translation students must be aware of the context in which translation memory tools are used and the impact that their use has on the translation itself.
Towards Multilingual Image Captioning Models that Can Read

Few current image captioning systems are capable to read and integrate read text into the generated descriptions, none of them was developed to solve the problem from a bilingual approach. The design of image captioning systems that can read, and, also work with different languages involves problems from a great variety of natures. In this work, we propose Multilingual M4C-Captioner, a bilingual architecture that can be easily trained with different languages with minor changes in the configuration. Our architecture is a modified version of the M4C-captioner, which mainly differs in the text embedding module and the OCR’s embedding module, our approach modifies the former in order to use a pre-trained and multilingual version of BERT, and the last by using the pre-trained FastText vectors for the target languages. This paper presents results for the English and Spanish language, however, our proposal can be easily extended to more than 100 languages. Additionally, we provide the first synthetically translated version of the TextCaps dataset for image captioning with reading comprehension.
Hybrid Context-Aware Recommendation System Using Deep Autoencoder

Nowadays, smart devices are revolutionizing mobile computing systems, and recommender system are a major driving force. There are a wide variety of recommender systems, but the most popular is collaborative filtering where recommendation is given based on user preference. But recently, deep learning-based models like RBM and autoencoder are extensively being used in recommender system mainly because of their ability to model complex nonlinear data with activation functions, and moreover, making a deep learning-based model is comparatively much easier as compared to other models as they are extremely flexible. Using deep autoencoder as a model in recommendation is very new and is gaining popularity nowadays, and so we propose a deep autoencoder-based hybrid recommender model using collaborative filtering. We will demonstrate that (a) this model performs better than shallow autoencoders and (b) adding contextual information improves the model performance.
Contrastive Variational Reinforcement Learning for Complex Observations.

Deep reinforcement learning (DRL) has achieved significant success in various robot tasks: manipulation, navigation, etc. However, complex visual observations in natural environments remains a major challenge. This paper presents Contrastive Variational Reinforcement Learning (CVRL), a model-based method that tackles complex visual observations in DRL. CVRL learns a contrastive variational model by maximizing the mutual information between latent states and observations discriminatively, through contrastive learning. It avoids modeling the complex observation space unnecessarily, as the commonly used generative observation model often does, and is significantly more robust. CVRL achieves comparable performance with state-of-the-art model-based DRL methods on standard Mujoco tasks. It significantly outperforms them on Natural Mujoco tasks and a robot box-pushing task with complex observations, e.g., dynamic shadows. The CVRL code is available publicly at this https URL.
Tight Bounds for Simon's Algorithm.

Simon’s algorithm is the first example of a quantum algorithm exponentially faster than any classical algorithm, and has many applications in cryptanalysis. While these quantum attacks are often extremely efficient, they are generally missing some precise cost estimate. This article aims at resolving this issue by computing precise query costs for the different use cases of Simon’s algorithm in cryptanalysis.
Continuous Human Learning Optimization with Enhanced Exploitation

Human Learning Optimization (HLO) is an emerging meta-heuristic with promising potential. Although HLO can be directly applied to real-coded problems as a binary algorithm, the search efficiency may be significantly spoiled due to “the curse of dimensionality”. To extend HLO, Continuous HLO (CHLO) is developed to solve real-values problems. However, the research on CHLO is still in its initial stages, and further efforts are needed to exploit the effectiveness of the CHLO. Therefore, this paper proposes a novel continuous human learning optimization with enhanced exploitation (CHLOEE), in which the social learning operator is redesigned to perform global search more efficiently so that the individual learning operator is relieved to focus on performing local search for enhancing the exploitation ability. Finally, the CHLOEE is evaluated on the benchmark problem and compared with CHLO as well as recent state-of-the-art meta-heuristics. The experimental results show that the proposed CHLOEE has better optimization performance.
Service Portfolios of Leading National Research and Education Networks and Implementation on the Basis of the National Research Computer Network of Russia

The paper systematizes and generalizes well-established practices of operation and development of service platforms for the research and education sphere from leading National Research and Education Networks and network consortia. The current state and planned directions of enhancement of the service platform of the new generation National Research Computer Network of Russia are presented, including the scaling and improving of already operated information technology services and specialized services for the target audience, and the implementation of new, potentially demanded services, taking into account the accumulated world experience and domestic features.
Conformal Prediction for Text Infilling and Part-of-Speech Prediction

Modern machine learning algorithms are capable of providing remarkably accurate point-predictions; however, questions remain about their statistical reliability. Unlike conventional machine learning methods, conformal prediction algorithms return confidence sets (i.e., set-valued predictions) that correspond to a given significance level. Moreover, these confidence sets are valid in the sense that they guarantee finite sample control over type 1 error probabilities, allowing the practitioner to choose an acceptable error rate. In our paper, we propose inductive conformal prediction (ICP) algorithms for the tasks of text infilling and part-of-speech (POS) prediction for natural language data. We construct new ICP-enhanced algorithms for POS tagging based on BERT (bidirectional encoder representations from transformers) and BiLSTM (bidirectional long short-term memory) models. For text infilling, we design a new ICP-enhanced BERT algorithm. We analyze the performance of the algorithms in simulations using the Brown Corpus, which contains over 57,000 sentences. Our results demonstrate that the ICP algorithms are able to produce valid set-valued predictions that are small enough to be applicable in real-world applications. We also provide a real data example for how our proposed set-valued predictions can improve machine generated audio transcriptions.