OpenRaiser/Intern-Atlas huggingface dataset:
A Survey on Adaptive Random Testing
Venue: arXiv cs.SE 2020
Year: 2020

Random testing (RT) is a well-studied testing method that has been widely applied to the testing of many applications, including embedded software systems, SQL database systems, and Android applications. Adaptive random testing (ART) aims to enhance RT's failure-detection ability by more evenly spreading the test cases over the input domain. Since its introduction in 2001, there have been many contributions to the development of ART, including various approaches, implementations, assessment and evaluation methods, and applications. This paper provides a comprehensive survey on ART, classifying techniques, summarizing application areas, and analyzing experimental evaluations. This paper also addresses some misconceptions about ART, and identifies open research challenges to be further investigated in the future work.
Double Prioritized State Recycled Experience Replay
Venue: arXiv cs.LG 2020
Year: 2020

Experience replay enables online reinforcement learning agents to store and reuse the previous experiences of interacting with the environment. In the original method, the experiences are sampled and replayed uniformly at random. A prior work called prioritized experience replay was developed where experiences are prioritized, so as to replay experiences seeming to be more important more frequently. In this paper, we develop a method called double-prioritized state-recycled (DPSR) experience replay, prioritizing the experiences in both training stage and storing stage, as well as replacing the experiences in the memory with state recycling to make the best of experiences that seem to have low priorities temporarily. We used this method in Deep Q-Networks (DQN), and achieved a state-of-the-art result, outperforming the original method and prioritized experience replay on many Atari games.
A complete characterization of the APN property of a class of quadrinomials
Venue: arXiv cs.IT 2020
Year: 2020

In this paper, by the Hasse-Weil bound, we determine the necessary and sufficient condition on coefficients $a_1,a_2,a_3\in\mathbb{F}_{2^n}$ with $n=2m$ such that $f(x) = {x}^{3\cdot2^m} + a_1x^{2^{m+1}+1} + a_2 x^{2^m+2} + a_3x^3$ is an APN function over $\mathbb{F}_{2^n}$. Our result resolves the first half of an open problem by Carlet in International Workshop on the Arithmetic of Finite Fields, 83-107, 2014.
Learning Neural Textual Representations for Citation Recommendation
Venue: arXiv cs.CL 2020
Year: 2020

With the rapid growth of the scientific literature, manually selecting appropriate citations for a paper is becoming increasingly challenging and time-consuming. While several approaches for automated citation recommendation have been proposed in the recent years, effective document representations for citation recommendation are still elusive to a large extent. For this reason, in this paper we propose a novel approach to citation recommendation which leverages a deep sequential representation of the documents (Sentence-BERT) cascaded with Siamese and triplet networks in a submodular scoring function. To the best of our knowledge, this is the first approach to combine deep representations and submodular selection for a task of citation recommendation. Experiments have been carried out using a popular benchmark dataset - the ACL Anthology Network corpus - and evaluated against baselines and a state-of-the-art approach using metrics such as the MRR and F1-at-k score. The results show that the proposed approach has been able to outperform all the compared approaches in every measured metric.
Tracking-by-Trackers with a Distilled and Reinforced Model
Venue: arXiv cs.CV 2020
Year: 2020

Visual object tracking was generally tackled by reasoning independently on fast processing algorithms, accurate online adaptation methods, and fusion of trackers. In this paper, we unify such goals by proposing a novel tracking methodology that takes advantage of other visual trackers, offline and online. A compact student model is trained via the marriage of knowledge distillation and reinforcement learning. The first allows to transfer and compress tracking knowledge of other trackers. The second enables the learning of evaluation measures which are then exploited online. After learning, the student can be ultimately used to build (i) a very fast single-shot tracker, (ii) a tracker with a simple and effective online adaptation mechanism, (iii) a tracker that performs fusion of other trackers. Extensive validation shows that the proposed algorithms compete with real-time state-of-the-art trackers.
Mastering the working sequence in human-robot collaborative assembly based on reinforcement learning
Venue: arXiv cs.RO 2020
Year: 2020

A long-standing goal of the Human-Robot Collaboration (HRC) in manufacturing systems is to increase the collaborative working efficiency. In line with the trend of Industry 4.0 to build up the smart manufacturing system, the Co-robot in the HRC system deserves better designing to be more self-organized and to find the superhuman proficiency by self-learning. Inspired by the impressive machine learning algorithms developed by Google Deep Mind like Alphago Zero, in this paper, the human-robot collaborative assembly working process is formatted into a chessboard and the selection of moves in the chessboard is used to analogize the decision making by both human and robot in the HRC assembly working process. To obtain the optimal policy of working sequence to maximize the working efficiency, the robot is trained with a self-play algorithm based on reinforcement learning, without guidance or domain knowledge beyond game rules. A neural network is also trained to predict the distribution of the priority of move selections and whether a working sequence is the one resulting in the maximum of the HRC efficiency. An adjustable desk assembly is used to demonstrate the proposed HRC assembly algorithm and its efficiency.
On Optimal Placement of Hybrid Service Function Chains (SFCs) of Virtual Machines and Containers in a Generic Edge-Cloud Continuum
Venue: arXiv cs.NI 2020
Year: 2020

Traditionally, Network Function Virtualization (NFV) has been implemented to run on Virtual Machines (VMs) in form of Virtual Network Functions (VNFs). More recently, the so-called Serverless Computing has gained traction in cloud computing, offering Function-as-a-Service (FaaS) platforms that make use of containerization techniques to deploy services. In contrast to VM-based VNFs, where resources are usually reserved and continuously running, FaaS can just be subsets of code implementing small functions allowing for event-driven, on-demand instantiations. Thus, a hybrid VM-Container based Service Function Chains (SFCs) are a natural evolution of NFV architecture. We study a novel problem of optimal placement of hybrid SFCs from an Internet Service Provider (ISP) point of view, whereby VNFs can be instantiated either over VMs or containers in a generic edge and cloud continuum. To this end, we propose a Mixed-Integer Linear Programming model as well as a heuristic solution to solve this optimization problem that considers three objectives unique to the specific VM and container deployment in a carrier network: operational costs for maintaining servers in the edge, costs of placing VNFs in third-party cloud providers and penalty costs applied when SLA agreements are violated in terms of end-to-end delay. We also propose 2-phases optimization process to analyze the effect on performance as a result of replications and migrations of VNFs. The model can be used to highlight scenarios where a combination of VMs and containers can provide most benefits from the monetary costs point of view.
The Scattering Compositional Learner: Discovering Objects, Attributes, Relationships in Analogical Reasoning
Venue: arXiv cs.LG 2020
Year: 2020

In this work, we focus on an analogical reasoning task that contains rich compositional structures, Raven's Progressive Matrices (RPM). To discover compositional structures of the data, we propose the Scattering Compositional Learner (SCL), an architecture that composes neural networks in a sequence. Our SCL achieves state-of-the-art performance on two RPM datasets, with a 48.7% relative improvement on Balanced-RAVEN and 26.4% on PGM over the previous state-of-the-art. We additionally show that our model discovers compositional representations of objects' attributes (e.g., shape color, size), and their relationships (e.g., progression, union). We also find that the compositional representation makes the SCL significantly more robust to test-time domain shifts and greatly improves zero-shot generalization to previously unseen analogies.
Neural relation extraction: a survey
Venue: arXiv cs.CL 2020
Year: 2020

Neural relation extraction discovers semantic relations between entities from unstructured text using deep learning methods. In this study, we present a comprehensive review of methods on neural network based relation extraction. We discuss advantageous and incompetent sides of existing studies and investigate additional research directions and improvement ideas in this field.
A Multi-Level Approach to Waste Object Segmentation
Venue: arXiv cs.CV 2020
Year: 2020

We address the problem of localizing waste objects from a color image and an optional depth image, which is a key perception component for robotic interaction with such objects. Specifically, our method integrates the intensity and depth information at multiple levels of spatial granularity. Firstly, a scene-level deep network produces an initial coarse segmentation, based on which we select a few potential object regions to zoom in and perform fine segmentation. The results of the above steps are further integrated into a densely connected conditional random field that learns to respect the appearance, depth, and spatial affinities with pixel-level accuracy. In addition, we create a new RGBD waste object segmentation dataset, MJU-Waste, that is made public to facilitate future research in this area. The efficacy of our method is validated on both MJU-Waste and the Trash Annotation in Context (TACO) dataset.
HALCONE : A Hardware-Level Timestamp-based Cache Coherence Scheme for Multi-GPU systems
Venue: arXiv cs.AR 2020
Year: 2020

While multi-GPU (MGPU) systems are extremely popular for compute-intensive workloads, several inefficiencies in the memory hierarchy and data movement result in a waste of GPU resources and difficulties in programming MGPU systems. First, due to the lack of hardware-level coherence, the MGPU programming model requires the programmer to replicate and repeatedly transfer data between the GPUs' memory. This leads to inefficient use of precious GPU memory. Second, to maintain coherency across an MGPU system, transferring data using low-bandwidth and high-latency off-chip links leads to degradation in system performance. Third, since the programmer needs to manually maintain data coherence, the programming of an MGPU system to maximize its throughput is extremely challenging. To address the above issues, we propose a novel lightweight timestamp-based coherence protocol, HALCONE, for MGPU systems and modify the memory hierarchy of the GPUs to support physically shared memory. HALCONE replaces the Compute Unit (CU) level logical time counters with cache level logical time counters to reduce coherence traffic. Furthermore, HALCONE introduces a novel timestamp storage unit (TSU) with no additional performance overhead in the main memory to perform coherence actions. Our proposed HALCONE protocol maintains the data coherence in the memory hierarchy of the MGPU with minimal performance overhead (less than 1\%). Using a set of standard MGPU benchmarks, we observe that a 4-GPU MGPU system with shared memory and HALCONE performs, on average, 4.6$\times$ and 3$\times$ better than a 4-GPU MGPU system with existing RDMA and with the recently proposed HMG coherence protocol, respectively. We demonstrate the scalability of HALCONE using different GPU counts (2, 4, 8, and 16) and different CU counts (32, 48, and 64 CUs per GPU) for 11 standard benchmarks.
Open Domain Suggestion Mining Leveraging Fine-Grained Analysis
Venue: arXiv cs.CL 2020
Year: 2020

Suggestion mining tasks are often semantically complex and lack sophisticated methodologies that can be applied to real-world data. The presence of suggestions across a large diversity of domains and the absence of large labelled and balanced datasets render this task particularly challenging to deal with. In an attempt to overcome these challenges, we propose a two-tier pipeline that leverages Discourse Marker based oversampling and fine-grained suggestion mining techniques to retrieve suggestions from online forums. Through extensive comparison on a real-world open-domain suggestion dataset, we demonstrate how the oversampling technique combined with transformer based fine-grained analysis can beat the state of the art. Additionally, we perform extensive qualitative and qualitative analysis to give construct validity to our proposed pipeline. Finally, we discuss the practical, computational and reproducibility aspects of the deployment of our pipeline across the web.
Temporal aggregation of audio-visual modalities for emotion recognition
Venue: arXiv cs.CV 2020
Year: 2020

Emotion recognition has a pivotal role in affective computing and in human-computer interaction. The current technological developments lead to increased possibilities of collecting data about the emotional state of a person. In general, human perception regarding the emotion transmitted by a subject is based on vocal and visual information collected in the first seconds of interaction with the subject. As a consequence, the integration of verbal (i.e., speech) and non-verbal (i.e., image) information seems to be the preferred choice in most of the current approaches towards emotion recognition. In this paper, we propose a multimodal fusion technique for emotion recognition based on combining audio-visual modalities from a temporal window with different temporal offsets for each modality. We show that our proposed method outperforms other methods from the literature and human accuracy rating. The experiments are conducted over the open-access multimodal dataset CREMA-D.
Bio-Inspired Energy Distribution for Programmable Matter
Venue: arXiv cs.DC 2020
Year: 2020

In systems of active programmable matter, individual modules require a constant supply of energy to participate in the system's collective behavior. These systems are often powered by an external energy source accessible by at least one module and rely on module-to-module power transfer to distribute energy throughout the system. While much effort has gone into addressing challenging aspects of power management in programmable matter hardware, algorithmic theory for programmable matter has largely ignored the impact of energy usage and distribution on algorithm feasibility and efficiency. In this work, we present an algorithm for energy distribution in the amoebot model that is loosely inspired by the growth behavior of Bacillus subtilis bacterial biofilms. These bacteria use chemical signaling to communicate their metabolic states and regulate nutrient consumption throughout the biofilm, ensuring that all bacteria receive the nutrients they need. Our algorithm similarly uses communication to inhibit energy usage when there are starving modules, enabling all modules to receive sufficient energy to meet their demands. As a supporting but independent result, we extend the amoebot model's well-established spanning forest primitive so that it self-stabilizes in the presence of crash failures. We conclude by showing how this self-stabilizing primitive can be leveraged to compose our energy distribution algorithm with existing amoebot model algorithms, effectively generalizing previous work to also consider energy constraints.
Training design fostering the emergence of new meanings toward unprecedented and critical events
Venue: arXiv cs.HC 2020
Year: 2020

Our research is part of a technological research program in adult education conducted in reference to the "course of action" program. Using the activity-sign hypothesis of this programme, training situations are thought as opportunities to perturb/relaunch the participants'dynamics of meaning. Our contribution aims to (i) improve the conceptualization of training situations that are thought as aids to the understanding and transformation of the participants situations, and (ii) derive cross-cutting design principles that allow the enactment of these training situations in different contexts. We rely on the analysis of training programs aiming either at the management or at the overcoming of events experienced as unprecedented and critical by the individuals concerned. Depending on the case, these training programs have a "restorative" aim (resolving of impasse situations), or have a "preparatory" aim (prefiguration of crisis situations). The design principles of these training programs and their effects are analysed with the conceptual tools developed within the course-of-action, integrating two additional dimensions: fictional and event-driven. The conditions for the emergence of new meanings are described in terms of abduction, either for reparative purposes (resolution of deadlock situations) or for preparatory purposes (prefiguration of crisis situations). The contribution to course-of-action program and to research on adult education is discussed through the prism of the hypothesis of activity-sign.
A Critical Evaluation of Open-World Machine Learning
Venue: arXiv cs.LG 2020
Year: 2020

Open-world machine learning (ML) combines closed-world models trained on in-distribution data with out-of-distribution (OOD) detectors, which aim to detect and reject OOD inputs. Previous works on open-world ML systems usually fail to test their reliability under diverse, and possibly adversarial conditions. Therefore, in this paper, we seek to understand how resilient are state-of-the-art open-world ML systems to changes in system components? With our evaluation across 6 OOD detectors, we find that the choice of in-distribution data, model architecture and OOD data have a strong impact on OOD detection performance, inducing false positive rates in excess of $70\%$. We further show that OOD inputs with 22 unintentional corruptions or adversarial perturbations render open-world ML systems unusable with false positive rates of up to $100\%$. To increase the resilience of open-world ML, we combine robust classifiers with OOD detection techniques and uncover a new trade-off between OOD detection and robustness.
URSABench: Comprehensive Benchmarking of Approximate Bayesian Inference Methods for Deep Neural Networks
Venue: arXiv cs.LG 2020
Year: 2020

While deep learning methods continue to improve in predictive accuracy on a wide range of application domains, significant issues remain with other aspects of their performance including their ability to quantify uncertainty and their robustness. Recent advances in approximate Bayesian inference hold significant promise for addressing these concerns, but the computational scalability of these methods can be problematic when applied to large-scale models. In this paper, we describe initial work on the development ofURSABench(the Uncertainty, Robustness, Scalability, and Accu-racy Benchmark), an open-source suite of bench-marking tools for comprehensive assessment of approximate Bayesian inference methods with a focus on deep learning-based classification tasks
Artificial Intelligence and Machine Learning in 5G Network Security: Opportunities, advantages, and future research trends
Venue: arXiv cs.CR 2020
Year: 2020

Recent technological and architectural advancements in 5G networks have proven their worth as the deployment has started over the world. Key performance elevating factor from access to core network are softwareization, cloudification and virtualization of key enabling network functions. Along with the rapid evolution comes the risks, threats and vulnerabilities in the system for those who plan to exploit it. Therefore, ensuring fool proof end-to-end (E2E) security becomes a vital concern. Artificial intelligence (AI) and machine learning (ML) can play vital role in design, modelling and automation of efficient security protocols against diverse and wide range of threats. AI and ML has already proven their effectiveness in different fields for classification, identification and automation with higher accuracy. As 5G networks' primary selling point has been higher data rates and speed, it will be difficult to tackle wide range of threats from different points using typical/traditional protective measures. Therefore, AI and ML can play central role in protecting highly data-driven softwareized and virtualized network components. This article presents AI and ML driven applications for 5G network security, their implications and possible research directions. Also, an overview of key data collection points in 5G architecture for threat classification and anomaly detection are discussed.
Attention-based Residual Speech Portrait Model for Speech to Face Generation
Venue: arXiv cs.CV 2020
Year: 2020

Given a speaker's speech, it is interesting to see if it is possible to generate this speaker's face. One main challenge in this task is to alleviate the natural mismatch between face and speech. To this end, in this paper, we propose a novel Attention-based Residual Speech Portrait Model (AR-SPM) by introducing the ideal of the residual into a hybrid encoder-decoder architecture, where face prior features are merged with the output of speech encoder to form the final face feature. In particular, we innovatively establish a tri-item loss function, which is a weighted linear combination of the L2-norm, L1-norm and negative cosine loss, to train our model by comparing the final face feature and true face feature. Evaluation on AVSpeech dataset shows that our proposed model accelerates the convergence of training, outperforms the state-of-the-art in terms of quality of the generated face, and achieves superior recognition accuracy of gender and age compared with the ground truth.
Principal Word Vectors
Venue: arXiv cs.CL 2020
Year: 2020

We generalize principal component analysis for embedding words into a vector space. The generalization is made in two major levels. The first is to generalize the concept of the corpus as a counting process which is defined by three key elements vocabulary set, feature (annotation) set, and context. This generalization enables the principal word embedding method to generate word vectors with regard to different types of contexts and different types of annotations provided for a corpus. The second is to generalize the transformation step used in most of the word embedding methods. To this end, we define two levels of transformations. The first is a quadratic transformation, which accounts for different types of weighting over the vocabulary units and contextual features. Second is an adaptive non-linear transformation, which reshapes the data distribution to be meaningful to principal component analysis. The effect of these generalizations on the word vectors is intrinsically studied with regard to the spread and the discriminability of the word vectors. We also provide an extrinsic evaluation of the contribution of the principal word vectors on a word similarity benchmark and the task of dependency parsing. Our experiments are finalized by a comparison between the principal word vectors and other sets of word vectors generated with popular word embedding methods. The results obtained from our intrinsic evaluation metrics show that the spread and the discriminability of the principal word vectors are higher than that of other word embedding methods. The results obtained from the extrinsic evaluation metrics show that the principal word vectors are better than some of the word embedding methods and on par with popular methods of word embedding.