Reviewerly/exHarmony huggingface dataset:
Learning-based prioritization of test cases in continuous integration of highly-configurable software

Continuous Integration (CI) is a practice widely adopted in the industry to allow frequent integration of code changes. During the CI process, many test cases are executed multiple times a day, subject to time constraints. In this scenario, a learning-based approach, named COLEMAN, has been successfully applied. COLEMAN allows earlier execution of the most promising test cases to reveal faults. This approach considers CI particularities such as time budget and volatility of test cases, related to the fact that test cases can be added/removed along the CI cycles. In the CI of Highly Configuration System (HCS), many product variants must be tested, each one with different configuration options, but having test cases that are common to or reused from other variants. In this context, we found, by analogy, another particularity, the volatility of variants, that is, some variants can be included/discontinued along CI cycles. Considering this context, this work introduces two strategies for the application of COLEMAN in the CI of HCS: the Variant Test Set Strategy (VTS) that relies on the test set specific for each variant, and the Whole Test Set Strategy (WST) that prioritizes the test set composed by the union of the test cases of all variants. Both strategies are evaluated in a real-world HCS, considering three test budgets. The results show that the proposed strategies are applicable regarding the time spent for prioritization. They perform similarly regarding early fault detection, but WTS better mitigates the problem of beginning without knowledge, and is more suitable when a new variant to be tested is added.
Color Image Retrieval Algorithm Fusing Color and Principal Curvatures Information

Aiming at the problems of high dimensional features, poor viewpoint robustness and long retrieval time of existing algorithms in the image retrieval system, this paper presents a new image retrieval algorithm by integrating image color information and surface geometry principal curvatures information.In the proposed method, the color image is first quantized and counted to obtain its color histogram.Simultaneously, the Hessian matrix is used to extract the texture information and the joint histogram of oriented gradient with mix-sampling and multi-scale is constructed.And then, the obtained color histogram and histogram of oriented gradient are fused to obtain the final joint histogram.Experiments are performed on public datasets, and comparison and analysis with representative algorithms based on a single visual feature or set of visual features to verify the performance of our algorithm.The experimental results show that the proposed method has the advantages of low dimensionality, fastness, strong viewpoint robustness and high precision, and can realize image retrieval efficiently.
Deep recursive network for image denoising with global non-linear smoothness constraint prior

Image denoising is a longstanding research topic in low-level visions. The model-based methods mainly depend on certain handcrafted prior terms to regularize the image denoising problem. However, designing a prior with significantly higher denoising performance than the existing priors is challenging, and reconstructing a clean image via the traditional iterative framework is also time-consuming. Recently, deep neural networks-based denoising methods have achieved tremendous advances. Despite their good denoising performance, some of current architectures face weak interpretability to some extent as their network design is empirical. In this paper, we propose a novel deep neural network for the denoising task, which is designed by following the optimization process of a model-based denoising method. Specifically, by incorporating a novel global non-linear smoothness constraint prior term into a maximum a posteriori (MAP)-based cost function, a model-based denoising method can be obtained. After that, inspired by the powerful modelling ability of deep learning techniques, we exploit the proposed denoising method to inform the network design, leading to a novel end-to-end trainable and interpretable deep network, called GNSCNet. In GNSCNet, each network module corresponds to the processing step of the proposed model-based method. Furthermore, to boost the performance, GNSCNet performs denoising in a high-dimensional transforms space (i.e., feature domain). Experimental results demonstrate that the proposed network is superior to other state-of-the-art denoising methods.
Improved Modified Chaotic Invasive Weed Optimization Approach to Solve Multi-Target Assignment for Humanoid Robot

The paper presents an improved modified chaotic invasive weed optimization (IMCIWO) approach for solving a multi-target assignment for humanoid robot navigation. MCIWO is improved by utilizing the Bezier curve for smoothing the path and replaces the conventional split lines. In order to efficiently determine subsequent locations of the robot from the present location on the provided terrain, such that the routes to be specifically generated for the robot are relatively small, with the shortest distance from the barriers that have been generated using the IMCIWO approach. The MCIWO approach designed the path based on obstacles and targets position which is further smoothened by the Bezier curve. Simulations are performed which is further validated by real-time experiments in WEBOT and NAO robot respectively. They show good effectiveness with each other with a deviation of under 5%. Ultimately, the superiority of the developed approach is examined with existing techniques for navigation, and findings are substantially improved.
A Novel Soft Margin Loss Function for Deep Discriminative Embedding Learning

Deep embedding learning aims to learn discriminative feature representations through a deep convolutional neural network model.Commonly, such a model contains a network architecture and a loss function.The architecture is responsible for hierarchical feature extraction, while the loss function supervises the training procedure with the purpose of maximizing inter-class separability and intra-class compactness.By considering that loss function is crucial for the feature performance, in this article we propose a new loss function called soft margin loss (SML) based on a classification framework for deep embedding learning.Specifically, we first normalize the learned features and the classification weights to map them into the hypersphere.After that, we construct our loss with the difference between the maximum intra-class distance and minimum inter-class distance.By constraining the distance difference with a soft margin that is inherent in the proposed loss, both the inter-class discrepancy and intra-class compactness of learned features can be effectively improved.Finally, under the joint training with an improved softmax loss, the model can learn features with strong discriminability.Toy experiments on MNIST dataset are conducted to show the effectiveness of the proposed method.Additionally, experiments on re-identification tasks are also provided to demonstrate the superior performance of embedding learning.Specifically, 65.48% / 62.68% mAP on CUHK03 labeled / detected dataset (person re-id) and 74.36% mAP on VeRi-776 dataset (vehicle re-id) are achieved respectively.
Embodied Functional Relations: A Formal Account Combining Abstract Logical Theory with Grounding in Simulation

Functional relations such as containment or support have proven difficult to formalize. Although previous efforts have attempted this using hybrids of several theories, from mereology to temporal logic, we find that such purely symbolic approaches do not account for the embodied nature of functional relations, i.e. that they are used by embodied agents to describe fragments of a physical world. We propose a formalism that combines descriptions of a high level of abstraction with generative models that can be used to instantiate or recognize arrangements of objects and trajectories conforming to qualitative descriptions. The formalism gives an account of how a qualitative description of a scene or arrangement of objects can be converted into a quantitative description amenable to simulation, and how simulation results can be qualitatively interpreted. We use this to describe functional relations between objects in terms of spatial arrangements, expectations on behavior, and counterfactual expectations for when one of the participants is absent. Our method is able to tackle important questions facing an agent operating in the world, such as what would happen if an arrangement of objects is created and why. This gives the agent a deeper understanding of functional relations, including what role background objects, not explicitly asserted to participate in a functional relation such as containment, play in enabling or hindering the relation from holding.
Estimating Power Consumption of Containers and Virtual Machines in Data Centers

Virtualization technologies provide solutions of cloud computing. Virtual resource scheduling is a crucial task in data centers, and the power consumption of virtual resources is a critical foundation of virtualization scheduling. Containers are the smallest unit of virtual resource scheduling and migration. Although many effective models for estimating power consumption of virtual machines (VM) have been proposed, few power estimation models of containers have been put forth. In this paper, we offer a fast-training piecewise regression model based on decision tree to build a VM power estimation model and estimate the containers' power by treating the container as a group of processes on the VM. In our model, we characterize the nonlinear relationship between power and features and realize the effective estimation of the containers on the VM. We evaluate the proposed model on 13 workloads in PARSEC and compare it with several models. The experimental results prove the effectiveness of our proposed model on most workloads. Moreover, the estimated power of the containers is in line with expectations.
A Fast Algorithm to Find Best Matching Units in Self-Organizing Maps

Self-Organizing Maps (SOM) are well-known unsupervised neural networks able to perform vector quantization while mapping an underlying regular neighbourhood structure onto the codebook. They are used in a wide range of applications. As with most properly trained neural networks models, increasing the number of neurons in a SOM leads to better results or new emerging properties. Therefore highly efficient algorithms for learning and evaluation are key to improve the performance of such models. In this paper, we propose a faster alternative to compute the Winner Takes All component of SOM that scales better with a large number of neurons. We present our algorithm to find the so-called best matching unit (BMU) in a SOM, and we theoretically analyze its computational complexity. Statistical results on various synthetic and real-world datasets confirm this analysis and show an even more significant improvement in computing time with a minimal degradation of performance. With our method, we explore a new approach for optimizing SOM that can be combined with other optimization methods commonly used in these models for an even faster computation in both learning and recall phases.
Integrating Deepfake Detection into Cybersecurity Curriculum

Deep Learning (DL) has been widely adopted in the domain of cybersecurity to address a variety of security and privacy concerns. Moreover, in recent years attackers are also increasingly adopting deep learning to either develop new sophisticated DL-based security attacks, such as Deepfakes. Recently Deepfake technology is used to spread misinformation on social networking. Currently, the most popular algorithm for deepfake image generation is GANs. The goal of this paper is to adopt DL-based smart detection techniques to defend against smart misinformation. We develop a set of hands-on labs to integrate them in our cybersecurity curriculum so that our students, future cybersecurity professionals, can be educated to use detect software and identify Deepfakes. Finally, we will investigate the fundamental capabilities, challenges, and limitations of deep learning for detecting smart attacks.
A Pointer Network Architecture for Joint Morphological Segmentation and Tagging

Morphologically Rich Languages (MRLs) such as Arabic, Hebrew and Turkish often require Morphological Disambiguation (MD), i.e., the prediction of the correct morphological decomposition of tokens into morphemes, early in the pipeline.Neural MD may be addressed as a simple pipeline, where segmentation is followed by sequence tagging, or as an end-to-end model, predicting morphemes from raw tokens.Both approaches are suboptimal; the former is heavily prone to error propagation, and the latter does not enjoy explicit access to the basic processing units called morphemes.This paper offers an MD architecture that combines the symbolic knowledge of morphemes with the learning capacity of neural end-to-end modeling.We propose a new, general and easy-to-implement Pointer Network model where the input is a morphological lattice and the output is a sequence of indices pointing at a single disambiguated path of morphemes.We demonstrate the efficacy of the model on segmentation and tagging, for Hebrew and Turkish texts, based on their respective Universal Dependencies (UD) treebanks.Our experiments show that with complete lattices, our model outperforms all shared-task results on segmenting and tagging these languages.On the SPMRL treebank, our model outperforms all previously reported results for Hebrew MD in realistic scenarios.
Weakly- and Semi-supervised Evidence Extraction

For many prediction tasks, stakeholders desire not only predictions but also supporting evidence that a human can use to verify its correctness.However, in practice, evidence annotations may only be available for a minority of training examples (if available at all).In this paper, we propose new methods to combine few evidence annotations (strong semisupervision) with abundant document-level labels (weak supervision) for the task of evidence extraction.Evaluating on two classification tasks that feature evidence annotations, we find that our methods outperform baselines adapted from the interpretability literature to our task.Our approach yields gains with as few as hundred evidence annotations. 1
Joint Estimation and Analysis of Risk Behavior Ratings in Movie Scripts

Exposure to violent, sexual, or substanceabuse content in media increases the willingness of children and adolescents to imitate similar behaviors.Computational methods that identify portrayals of risk behaviors from audio-visual cues are limited in their applicability to films in post-production, where modifications might be prohibitively expensive.To address this limitation, we propose a model that estimates content ratings based on the language use in movie scripts, making our solution available at the earlier stages of creative production.Our model significantly improves the state-of-the-art by adapting novel techniques to learn better movie representations from the semantic and sentiment aspects of a character's language use, and by leveraging the co-occurrence of risk behaviors, following a multi-task approach.Additionally, we show how this approach can be useful to learn novel insights on the joint portrayal of these behaviors, and on the subtleties that filmmakers may otherwise not pick up on.
Hierarchical Gaussian Process Priors for Bayesian Neural Network Weights

Probabilistic neural networks are typically modeled with independent weight priors, which do not capture weight correlations in the prior and do not provide a parsimonious interface to express properties in function space. A desirable class of priors would represent weights compactly, capture correlations between weights, facilitate calibrated reasoning about uncertainty, and allow inclusion of prior knowledge about the function space such as periodicity or dependence on contexts such as inputs. To this end, this paper introduces two innovations: (i) a Gaussian process-based hierarchical model for network weights based on unit embeddings that can flexibly encode correlated weight structures, and (ii) input-dependent versions of these weight priors that can provide convenient ways to regularize the function space through the use of kernels defined on contextual inputs. We show these models provide desirable test-time uncertainty estimates on out-of-distribution data, demonstrate cases of modeling inductive biases for neural networks with kernels which help both interpolation and extrapolation from training data, and demonstrate competitive predictive performance on an active learning benchmark.
Active Learning Approaches to Enhancing Neural Machine Translation

Active learning is an efficient approach for mitigating data dependency when training neural machine translation (NMT) models.In this paper we explore new training frameworks by incorporating active learning into various techniques such as transfer learning and iterative back-translation (IBT) under a limited human translation budget.We design a word frequency based acquisition function and combine it with a strong uncertainty based method.The combined method steadily outperforms all other acquisition functions in various scenarios.As far as we know, we are the first to do a large-scale study on actively training Transformer (Vaswani et al., 2017) for NMT.Specifically, with a human translation budget of only 20% of the original parallel corpus, we manage to surpass Transformer trained on the entire parallel corpus in three language pairs.
The Box is in the Pen: Evaluating Commonsense Reasoning in Neural Machine Translation

Does neural machine translation yield translations that are congenial with common sense?In this paper, we present a test suite to evaluate the commonsense reasoning capability of neural machine translation.The test suite consists of three test sets, covering lexical and contextless/contextual syntactic ambiguity that requires commonsense knowledge to resolve.We manually create 1,200 triples, each of which contain a source sentence and two contrastive translations, involving 7 different common sense types.Language models pretrained on large-scale corpora, such as BERT, GPT-2, achieve a commonsense reasoning accuracy of lower than 72% on target translations of this test suite.We conduct extensive experiments on the test suite to evaluate commonsense reasoning in neural machine translation and investigate factors that have impact on this capability.Our experiments and analyses demonstrate that neural machine translation performs poorly on commonsense reasoning of the three ambiguity types in terms of both reasoning accuracy ( 60.1%) and reasoning consistency ( 31%).We will release our test suite as a machine translation commonsense reasoning testbed to promote future work in this direction.
Symmetries in Teleportation Assisted by N-Channels under Indefinite Causal Order and Post-Measurement

Quantum teleportation has had notorious advances in the last decade, being successfully deployed in the experimental domain. In other terrains, the understanding of indefinite causal order has demonstrated a valuable enhancement in quantum communication to correct channel imperfections. In this work, we address the symmetries underlying imperfect teleportation when it is assisted by indefinite causal order to correct the use of noisy entangled resources. In the strategy being presented, indefinite causal order introduces a control state to address the causal ordering. Then, by using post-selection, it fulfills the teleportation enhancement to recover the teleported state by constructive interference. By analysing primarily sequential teleportation under definite causal order, we perform a comparison basis for notable outcomes derived from indefinite causal order. After, the analysis is conducted by increasing the number of teleportation processes, thus suggesting additional alternatives to exploit the most valuable outcomes in the process by adding weak measurement as a complementary strategy. Finally, we discuss the current affordability for an experimental implementation.
A Hybrid Spatio-Temporal Prediction Model for Solar Photovoltaic Generation Using Numerical Weather Data and Satellite Images

Precise and accurate prediction of solar photovoltaic (PV) generation plays a major role in developing plans for the supply and demand of power grid systems. Most previous studies on the prediction of solar PV generation employed only weather data composed of numerical text data. The numerical text weather data can reflect temporal factors, however, they cannot consider the movement features related to the wind direction of the spatial characteristics, which include the amount of both clouds and particulate matter (PM) among other weather features. This study aims developing a hybrid spatio-temporal prediction model by combining general weather data and data extracted from satellite images having spatial characteristics. A model for hourly prediction of solar PV generation is proposed using data collected from a solar PV power plant in Incheon, South Korea. To evaluate the performance of the prediction model, we compared and performed ARIMAX analysis, which is a traditional statistical time-series analysis method, and SVR, ANN, and DNN, which are based on machine learning algorithms. The models that reflect the temporal and spatial characteristics exhibited better performance than those using only the general weather numerical data or the satellite image data.
Quantum Period Finding against Symmetric Primitives in Practice

We present the first complete descriptions of quantum circuits for the offline Simon’s algorithm, and estimate their cost to attack the MAC Chaskey, the block cipher PRINCE and the NIST lightweight finalist AEAD scheme Elephant. These attacks require a reasonable amount of qubits, comparable to the number of qubits required to break RSA-2048. They are faster than other collision algorithms, and the attacks against PRINCE and Chaskey are the most efficient known to date. As Elephant has a key smaller than its state size, the algorithm is less efficient and its cost ends up very close to or above the cost of exhaustive search.We also propose an optimized quantum circuit for boolean linear algebra as well as complete reversible implementations of PRINCE, Chaskey, spongent and Keccak which are of independent interest for quantum cryptanalysis. We stress that our attacks could be applied in the future against today’s communications, and recommend caution when choosing symmetric constructions for cases where long-term security is expected.