Reviewerly/exHarmony huggingface dataset:
DSmith: Compiler Fuzzing through Generative Deep Learning Model with Attention

Compiler fuzzing is a technique to test the functionalities of compiler. It requires well-formed test cases (i.e., programs) that have correct lexicons and syntax to pass the parsing stage of a compiler. Recently, advanced compiler fuzzing methods generate effective test cases by deep neural networks, which learn the language model of regular programs to guarantee test case quality. However, most of these methods fail to capture long-distance dependencies of syntax (e.g., paired curly braces) in a program. As a result, they may generate test cases with syntax errors, which cannot pass the parsing stage to test the compiler functionality. In this paper, we propose a framework, namely DSmith, to capture long-distance dependencies of syntax for a robust test case generation. Specifically, DSmith memorizes the hidden state of each token in a program and leverages the interactions of these hidden states to embed the long-distance dependencies between tokens. It then adopts an encoder-decoder architecture with the embedding of these long-distance dependencies to build a language model of regular programs. Finally, DSmith uses the built language model to generate test cases according to four novel generation strategies, which significantly increase the diversity of test cases. Extensive experiments show that DSmith increases the parsing pass rate of the generated programs by an average of 19% and significantly improves the code coverage of the compiler, compared with state-of-the-art methods. Benefiting from the high pass rate and broad code coverage, DSmith has found eleven brand new bugs in currently supported GCC compiler versions.
Model-Based Warp Overlapped Tiling for Image Processing Programs on GPUs

Domain-specific languages that execute image processing pipelines on GPUs, such as Halide and Forma, operate by 1)~dividing the image into overlapped tiles, and 2)~fusing loops to improve memory locality. However, current approaches have limitations: 1)~they require intra thread block synchronization, which has a nontrivial cost, 2)~they must choose between small tiles that require more overlapped computations or large tiles that increase shared memory access (and lowers occupancy), and 3) their autoscheduling algorithms use simplified GPU models that can result in inefficient global memory accesses.
Correlation diagrams: Graphical visualization of geochemical associations using the EzCorrGraph app

The use of multielement analytical techniques with ever-increasing power of discrimination and extremely low detection limits generates big and trustful geochemical datasets. To impress a geological meaning to these data, exploratory data analysis must follow an approach of gradual increase of complexity, beginning with simple techniques and then following to more high-end ones. Correlation diagram is a graphical method that couple groups of associated variables from a correlation matrix. Since its first proposal in 1965 by Krumbein and Graybill in the book "An Introduction to Statistical Model in Geology" this diagram remained almost forgotten in the literature, possibly due to wearisome and time consuming manual plotting procedures. We present in this paper the EzCorrGraph app, written in HTML5 and JavaScript language, which generates the correlation diagram from a correlation matrix or a list of pairs with a simple and straightforward approach. To validate the results and routines, we compared diagrams generated by the app with some existing diagrams in the literature. The app is freely available on https://ezcorrgraph.firebaseapp.com/.
TuVe

We propose TuVe, a novel shape-changing display consisting of a flexible tube and fluids, in which the droplets flowing through the tube compose the display medium that represents information. In this system, every colored droplet is flowed by controlling valves and a pump connected to the tube. The display part employs a flexible tube that can be shaped to any structure (e.g., wrapped around a specific object), which is achieved by a calibration made to capture the tube structure using image processing with a camera. A performance evaluation reveals that our prototype succeeds in controlling each droplet with a positional error of 2 mm or less, which is small enough to show such simple characters as alphabetic characters using a 7 × 7-pixel resolution display. We also discuss example applications, such as large public displays and flow-direction visualization, that illustrate the characteristics of the TuVe display.
Channel Pruning Via Gradient Of Mutual Information For Light-Weight Convolutional Neural Networks

Channel pruning for light-weighting networks is very effective in reducing memory footprint and computational cost. Many channel pruning methods assume that the magnitude of a particular element corresponding to each channel reflects the importance of the channel. Unfortunately, such an assumption does not always hold. To solve this problem, this paper proposes a new method to measure the importance of channels based on gradients of mutual information. The proposed method computes and measures gradients of mutual information during back-propagation by arranging a module capable of estimating mutual information. By using the measured statistics as the importance of the channel, less important channels can be removed. Finally, the fine-tuning enables robust performance restoration of the pruned model. Experimental results show that the proposed method provides better performance with smaller parameter sizes and FLOPs than the conventional schemes.
Towards the Probabilistic Fusion of Learned Priors into Standard Pipelines for 3D Reconstruction

The best way to combine the results of deep learning with standard 3D reconstruction pipelines remains an open problem. While systems that pass the output of traditional multi-view stereo approaches to a network for regularisation or refinement currently seem to get the best results, it may be preferable to treat deep neural networks as separate components whose results can be probabilistically fused into geometry- based systems. Unfortunately, the error models required to do this type of fusion are not well understood, with many different approaches being put forward. Recently, a few systems have achieved good results by having their networks predict probability distributions rather than single values. We propose using this approach to fuse a learned single-view depth prior into a standard 3D reconstruction system. Our system is capable of incrementally producing dense depth maps for a set of keyframes. We train a deep neural network to predict discrete, nonparametric probability distributions for the depth of each pixel from a single image. We then fuse this "probability volume" with another probability volume based on the photometric consistency between subsequent frames and the keyframe image. We argue that combining the probability volumes from these two sources will result in a volume that is better conditioned. To extract depth maps from the volume, we minimise a cost function that includes a regularisation term based on network predicted surface normals and occlusion boundaries. Through a series of experiments, we demonstrate that each of these components improves the overall performance of the system.
ABC-NET: Avoiding Blocking Effect &amp; Color Shift Network for Single Image Dehazing Via Restraining Transmission Bias

In recent years, single image dehazing methods based on Atmospheric Scattering Model (ASM) have achieved state-of-the-art results. But the dehazing outputs of those methods suffer from color shift and blocking effect. Our preliminary experiments show that the negative bias of the estimated transmission and the bias of tiny transmission value will cause serious color shift. Therefore, in this study, a new loss function (TransLoss) and a new natural activation function (NAF) are proposed to restrain negative bias of transmission and avoid tiny transmission value from being activated, respectively. Moreover, it is noted that the block effect is caused by patch-level transmission estimation mechanism in existing dehazing models. To address this issue, a new pixel-level transmission estimation module (ETM) is dedicated designed to avoid blocking effect. In the end, an end-to-end CNN dehazing network avoiding color shift and blocking effect is developed, termed as ABC-Net. Experimental results indicate that the ABC-Net outperforms four comparison methods on both synthetic and real-world images.
Adversarial Attacks on Deep Learning Models of Computer Vision: A Survey

Deep learning plays a significant role in academic and commercial fields. However, deep neural networks are vulnerable to adversarial attacks, which limits its applications in safety-critical areas, such as autonomous driving, surveillance, and drones and robotics. Due to the rapid development of adversarial examples in computer vision, many novel and interesting adversarial attacks are not covered by existing surveys and could not be categorized according to existing taxonomies. In this paper, we present an improved taxonomy for adversarial attacks, which subsumes existing taxonomies, and investigate and summarize the latest attacks in computer vision comprehensively with respect to the improved taxonomy. Finally, We also discuss some potential research directions.
Intelligent Data Analysis with Classical Machine Learning

In this advance era, artificial intelligence (AI) is the basic building block for numerous innovation opportunities. The basic concept behind all artificial intelligence applications is classical machine learning (CML) technique. The intelligent data analysis (IDA) is used to analyze data in advanced reasoning systems. In this paper, our motto is to analyze the data intelligently, i.e., intelligent data analysis process using classical machine learning algorithms based on experimental data set empirical hidden regularities. It is concluded that machine learning process is the basic concept to implement any problem based on artificial intelligence, whereas intelligent data analysis uses classical machine learning concept and helps in deciding the next best available step during data analysis.
A Privacy-Preserving Distributed Architecture for Deep-Learning-as-a-Service

Deep-learning-as-a-service is a novel and promising computing paradigm aiming at providing machine/deep learning solutions and mechanisms through Cloud-based computing infrastructures. Thanks to its ability to remotely execute and train deep learning models (that typically require high computational loads and memory occupation), such an approach guarantees high performance, scalability, and availability. Unfortunately, such an approach requires to send information to be processed (e.g., signals, images, positions, sounds, videos) to the Cloud, hence having potentially catastrophic-impacts on the privacy of users. This paper introduces a novel distributed architecture for deep-learning-as-a-service that is able to preserve the user sensitive data while providing Cloud-based machine and deep learning services. The proposed architecture, which relies on Homomorphic Encryption that is able to perform operations on encrypted data, has been tailored for Convolutional Neural Networks (CNNs) in the domain of image analysis and implemented through a client-server REST-based approach. Experimental results show the effectiveness of the proposed architecture.
CNN Patch Pooling for Detecting 3D Mask Presentation Attacks in NIR

Presentation attacks using 3D masks pose a serious threat to face recognition systems.Automatic detection of these attacks is challenging due to hyper-realistic nature of masks.In this work, we consider presentations acquired in near infrared (NIR) imaging channel for detection of mask-based attacks.We propose a patch pooling mechanism to learn complex textural features from lower layers of a convolutional neural network (CNN).The proposed patch pooling layer can be used in conjunction with a pretrained face recognition CNN without fine-tuning or adaptation.The pretrained CNN, in fact, can also be trained from visual spectrum data.We demonstrate efficacy of the proposed method on mask attacks in NIR channel from WMCA and MLFP datasets.It achieves near perfect results on WMCA data, and outperforms existing benchmark on MLFP dataset by a large margin.
Virtual Reality as A Tool for Foreign Students’ Anti-Stress Adaptation to A New Educational Environment

In an effort to strengthen their status in world rankings, universities in developed countries are actively fighting to attract talented students from different countries. Despite the prestige of obtaining education abroad, the process of adapting foreign freshmen to the cultural and educational realities of the host country is often accompanied by complex psychological, cultural and social adaptation, often followed by serious psychological problems. The creation of effective tools to level these problems will provide universities with extra points in the struggle for leadership in the export of education. Extensive opportunities in this regard are provided by virtual reality tools, due to its versatility and flexibility. In the framework of this paper, the potential of the virtual reality tool “Emotional Experience Designer”, which was created as a tool to help people who are in crisis life situations, is analyzed. The results of its validation on a group of volunteers (n = 38) are presented, they recorded the statistical significance of the indicators of the participants’ psychological stress decrease as a result of therapy through the “Emotional Experience designer”. The result of validation has led to the creation of a modified version of the Designer, developed by the authors in order to level all its weak aspects and achieve its maximum effectiveness. The modified version was called “Learning without stress” training device. The results of its pilot study will be presented to the scientific community in the near future.
Retrospective and Prospective Mixture-of-Generators for Task-oriented Dialogue Response Generation

Dialogue response generation (DRG) is a critical component of task-oriented dialogue systems (TDSs). Its purpose is to generate proper natural language responses given some context, e.g., historical utterances, system states, etc. State-of-the-art work focuses on how to better tackle DRG in an end-to-end way. Typically, such studies assume that each token is drawn from a single distribution over the output vocabulary, which may not always be optimal. Responses vary greatly with different intents, e.g., domains, system actions. We propose a novel mixture-of-generators network (MoGNet) for DRG, where we assume that each token of a response is drawn from a mixture of distributions. MoGNet consists of a chair generator and several expert generators. Each expert is specialized for DRG w.r.t. a particular intent. The chair coordinates multiple experts and combines the output they have generated to produce more appropriate responses. We propose two strategies to help the chair make better decisions, namely, a retrospective mixture-of-generators (RMoG) and a prospective mixture-of-generators (PMoG). The former only considers the historical expert-generated responses until the current time step while the latter also considers possible expert-generated responses in the future by encouraging exploration. In order to differentiate experts, we also devise a global-and-local (GL) learning scheme that forces each expert to be specialized towards a particular intent using a local loss and trains the chair and all experts to coordinate using a global loss. We carry out extensive experiments on the MultiWOZ benchmark dataset. MoGNet significantly outperforms state-of-the-art methods in terms of both automatic and human evaluations, demonstrating its effectiveness for DRG.
Toward Interference-aware GPU Container Co-scheduling Learning from Application Profiles

Issues related to operating Graphic Processing Unit (GPU) applications efficiently and improving overall system throughput in a GPU cluster environment exist. The platform may not utilize resource of a GPU fully depending on application characteristics because a conventional cluster orchestration platform using GPUs only supports a single execution of an application on a GPU. However, co-execution of GPU applications causes interference coming from resource contention among the applications. If various resource usage of GPU applications is not reflected, it could lead to an unbalanced usage of computing resources and consequently reduce performance in a GPU cluster. This study proposes interference-aware architecture, Co-scheML and evaluates case studies with it for workload execution of GPU applications such as High Performance Computing (HPC), Deep Learning (DL) Training, and DL Inference. Diverse resource usage is profiled to identify various degree of their interference of applications. Due to the difficulty of predicting the interference using these characteristics, interference model is generated by applying a Machine Learning (ML) model with defined GPU metrics. Proposed architecture predicts interference and deploys an application which is co-executed with a running application. Experimental results of case studies with Co-ScheML show that average job completion time is improved by 22%, and the makespan is shortened by 32% in average, as compared to baseline schedulers.
Learning dynamic weights for an ensemble of deep models applied to medical imaging classification

An ensemble of deep models is commonly used to provide more robust and accurate performance for medical image classification. A drawback of the most common ensemble aggregation operators is that they give the same importance to all models in the ensemble. As a consequence, they cannot identify weak models that may negatively influence the ensemble performance. In this work, we propose a new method based on the Dirichlet distribution and Mahalanobis distance to learn dynamic weights to an ensemble of deep learning models. Through this method, it is possible to reduce the influence of weak models for each new sample evaluated by the ensemble and perform online ensemble pruning. We evaluate this method for an ensemble of six well-known deep models applied to four medical imaging datasets. The experiments show that our method achieves the best balanced accuracy for 2 out of 4 datasets and increases the confidence of the ensemble predictions.
Improving Barnes-Hut t-SNE Scalability in GPU with Efficient Memory Access Strategies

The t-Distributed Stochastic Neighbor Embedding (t-SNE) is a widely used technique for dimensionality reduction, however, its application to large datasets is still an issue. In this sense, BH-tSNE was proposed, which is a successful approximation where the Barnes-Hut algorithm is used instead of computing a step of the t-SNE with quadratic computational time complexity. Even so, this improvement still has limitations to process large data volumes (millions of records). Late studies, such as t-SNE-CUDA, have used GPUs to implement highly parallel BH-tSNE. In this research, a new GPU BH-tSNE implementation is proposed using efficient memory access strategies and recent acceleration techniques. Moreover, the embedding of multidimensional data points into three-dimensional space is applied. We examine scalability issues in one of the most expensive steps of GPU BH-tSNE. Our design allows up to 340% faster execution when compared to the t-SNE-CUDA implementation.
Prepping the New Normal: The Readiness of Higher Education Institution in Cordillera on a Flexible Learning

The emergence of COVID-19 pandemic brought unprecedented disruptions in the education sector. For higher education institutions, avoiding and limiting the risks of infection of the academic community has become a primordial concern. Thus, it has become an urgent need to explore innovative learning modalities that will facilitate migration from traditional to flexible teaching and learning options. The paradigm shift therefore in the teaching and learning process in the higher education institutions in Cordillera Administrative Region (CAR) necessitates assessment on the readiness of colleges and universities in the Cordillera as part of this transformation towards new normal. Descriptive research was employed in this study that systematically described the situations of the various Higher Education Institutions (HEIs) of CAR. Online survey was conducted among 28 participating schools in the region. Basic statistical tool like frequency count, weighted mean, averaging and percentages were used to analyze the gathered data. Findings show that, adjusting to the new normal, 25 or 89.3% of the participating HEIs answered that they have been utilizing flexible learning system (FLS) and 16 (57.1%) mentioned that FLS is covered in their institutional policies. Infrastructure to support the delivery of FLS is present among majority of the HEIs. 24 institutions have the capability to develop instructional materials. Faculty and students were also evaluated on their readiness towards the transformation to the new normal and it was revealed that there are challenges that both faculty and students must consider in the adaptation of online distance learning or flexible learning. With the results of the study, school readiness is a big factor in making a decision in shifting from face to face to flexible learning system or online distance learning to achieve quality education. HEIs should therefore develop their own framework, continuity plan or game plan and creation of consortium among HEIs in the region for continuous learning anchored on the existing tools and resources of the institution, capability of staff and faculty members, and capacity of students. Regardless of the modalities of teaching, schools must always aim for the best education that they could give to all students. Equal and quality education must be shared among students amidst this pandemic.
Learning Transferable Domain Priors for Safe Exploration in Reinforcement Learning

Prior access to domain knowledge could significantly improve the performance of a reinforcement learning agent. In particular, it could help agents avoid potentially catastrophic exploratory actions, which would otherwise have to be experienced during learning. In this work, we identify consistently undesirable actions in a set of previously learned tasks, and use pseudo-rewards associated with them to learn a prior policy. In addition to enabling safer exploratory behaviors in subsequent tasks in the domain, we show that these priors are transferable to similar environments, and can be learned off-policy and in parallel with the learning of other tasks in the domain. We compare our approach to established, state-of-the-art algorithms in both discrete as well as continuous environments, and demonstrate that it exhibits a safer exploratory behavior while learning to perform arbitrary tasks in the domain. We also present a theoretical analysis to support these results, and briefly discuss the implications and some alternative formulations of this approach, which could also be useful in certain scenarios.