Reviewerly/exHarmony huggingface dataset:
Adaptive Failure Search Using Critical States from Domain Experts

Uncovering potential failure cases is a crucial step in the validation of safety critical systems such as autonomous vehicles. Failure search may be done through logging substantial vehicle miles in either simulation or real world testing. Due to the sparsity of failure events, naive random search approaches require significant amounts of vehicle operation hours to find potential system weaknesses. As a result, adaptive searching techniques have been proposed to efficiently explore and uncover failure trajectories of an autonomous policy in simulation. Adaptive Stress Testing (AST) is one such method that poses the problem of failure search as a Markov decision process and uses reinforcement learning techniques to find high probability failures. However, this formulation requires a probability model for the actions of all agents in the environment. In systems where the environment actions are discrete and dependencies among agents exist, it may be infeasible to fully characterize the distribution or find a suitable proxy. This work proposes the use of a data driven approach to learn a suitable classifier that tries to model how humans identify critical states and use this to guide failure search in AST. We show that the incorporation of critical states into the AST framework generates failure scenarios with increased safety violations in an autonomous driving policy with a discrete action space.
Enhancing Utility In The Watchdog Privacy Mechanism

This paper is concerned with enhancing data utility in the privacy watchdog method for attaining information-theoretic privacy. For a specific privacy constraint, the watchdog method filters out the high-risk data symbols through applying a uniform data regulation scheme, e.g., merging all high-risk symbols together. While this method entirely trades the symbols resolution off for privacy, we show that the data utility can be greatly improved by partitioning the high-risk symbols set and individually privatizing each subset. We further propose an agglomerative merging algorithm that finds a suitable partition of high-risk symbols: it starts with a singleton high-risk symbol, which is iteratively fused with others until the resulting subsets are private. Numerical simulations demonstrate the efficacy of this algorithm in privately achieving higher utilities in the watchdog scheme.
Knowledge Distillation from Language Model to Acoustic Model: A Hierarchical Multi-Task Learning Approach

The remarkable performance of the pre-trained language model (LM) using self-supervised learning has led to a major paradigm shift in the study of natural language processing. In line with these changes, leveraging the performance of speech recognition systems with massive deep learning-based LMs is a major topic of speech recognition research. Among the various methods of applying LMs to speech recognition systems, in this paper, we focus on a cross-modal knowledge distillation method that transfers knowledge between two types of deep neural networks with different modalities. We propose an acoustic model structure with multiple auxiliary output layers for cross-modal distillation and demonstrate that the proposed method effectively compensates for the shortcomings of the existing label-interpolation-based distillation method. In addition, we extend the proposed method to a hierarchical distillation method using LMs trained in different units (senones, monophones, and subwords) and reveal the effectiveness of the hierarchical distillation method through an ablation study.
Meta-Adversarial Inverse Reinforcement Learning for Decision-making Tasks

Learning from demonstrations has made great progress over the past few years. However, it is generally data hungry and task specific. In other words, it requires a large amount of data to train a decent model on a particular task, and the model often fails to generalize to new tasks that have a different distribution. In practice, demonstrations from new tasks will be continuously observed and the data might be unlabeled or only partially labeled. Therefore, it is desirable for the trained model to adapt to new tasks that have limited data samples available. In this work, we build an adaptable imitation learning model based on the integration of Meta-learning and Adversarial Inverse Reinforcement Learning (Meta-AIRL). We exploit the adversarial learning and inverse reinforcement learning mechanisms to learn policies and reward functions simultaneously from available training tasks and then adapt them to new tasks with the meta-learning framework. Simulation results show that the adapted policy trained with Meta-AIRL can effectively learn from limited number of demonstrations, and quickly reach the performance comparable to that of the experts on unseen tasks.
Quantifying Nondeterminism and Inconsistency in Self-organizing Map Implementations

Self-organizing maps (SOMs) are a popular approach for neural network-based unsupervised learning. However the reliability of self-organizing map implementations has not been investigated. Using internal and external metrics, we define and check two basic SOM properties. First, determinism: a given SOM implementation should produce the same SOM when run repeatedly on the same training dataset. Second, consistency: two SOM implementations should produce similar SOMs when presented with the same training dataset. We check these properties in four popular SOM implementations. We ran our approach on 381 popular datasets used in health, medicine, and other critical domains. We found that implementations violate these basic properties. For example, 375 out of 381 datasets have nondeterministic outcomes; for 51–92% of datasets, toolkits yield significantly different SOM clusterings; and clustering accuracy might be so inconsistent as to vary by a factor of four between toolkits. This undermines SOM reliability, and the reliability of results obtained via SOMs. Our study shines a light on what to expect, in practice, when running actual SOM implementations. Our findings suggest that for critical applications, SOM users should not take reliability for granted; rather, multiple runs and different toolkits should be considered and compared.
A Multidimensional Analysis of Robotic Deployment in Thai Hotels

Several hotel businesses are interested in robotic deployment to improve customer service, enhance hotel productivity, and gain a competitive advantage. In this study, I deployed a robot in a real working environment to explore stakeholders' perceptions and investigate factors involved in robotic deployment in Thai hotels. A qualitative research method, interpretative phenomenological analysis, was utilized. As there was no robotic deployment in Thai hotels, Peanut, a navigation service robot, was temporarily assigned to work in the hotels to allow key informants-hotel executives, human resource managers, reception managers, hotel staff, and hotel guests-to understand the phenomenon and experience robotic deployment. The findings revealed that stakeholders in Thai hotels accepted the robot; however, this acceptance was contingent on various robot, human, and organizational dimensions. Greater user acceptance can promote more widespread robotic deployment. This study has important implications for human-robot interactions in the hotel industry, especially in Thailand where this technology has not yet been applied.
A Gradient Balancing Approach for Robust Logo Detection

This paper presents the 1st place solution to the Grand Challenge of ACM MM2021 Robust Logo Detection. We build our end-to-end solution on top of Cascade RCNN (using Res2Net101 as backbone). Through careful observation during training, we find that the model performance is limited by imbalanced gradients from different classes of the long-tailed dataset. We adopt a gradient balancing approach to tackle this problem. Our approach reweighs the gradients of each class to guide the training process towards a balance between all classes. Moreover, we design a series of data augmentation policies and propose a progressive data augmentation strategy to train our model to deal with adversarial samples. We demonstrate the accuracy and robustness of our method by achieving 70.2448 mAP on leaderboard A, and 63.8793 mAP on leaderboard B, which contains adversarial images.
Application of Multi-Objective Optimization Problem Based on Genetic Algorithm

With the rise and rapid development of mobile communications, smart terminals and data mining technologies, we are entering the mobile Internet era. When dealing with the multi-objective problem encountered in the network, it cannot control the operation strategy of various specific applications by itself. Therefore, a fault-tolerant mechanism is needed to provide a high degree of flexibility at the application level. When the mobile database changes, only the error handling strategy needs to be modified without considering the specific code of the application. This paper mainly introduces the application research of multi-objective optimization problem based on genetic algorithm. In this paper, the application research of multi-objective optimization problems based on genetic algorithm is used, and the multi-objective evolutionary algorithm still needs to be improved in terms of convergence and distribution. Some algorithms with better convergence performance often have slightly insufficient distribution performance, but the distribution performance is relatively low. The best algorithm often needs to improve its convergence performance. Therefore, it is of great significance to study the multi-objective co-evolution genetic algorithm. The experimental results of this paper show that the application research for the multi-objective optimization problem based on genetic algorithm increases the optimization rate of multi-objective problem by 14%, and the limitation of the multi-objective optimization problem of genetic algorithm provides good indoor path planning for the application of genetic algorithm. Analysis, discussion and summary of the methods and approaches to enrich the academic research results.
Topic-Controlled Text Generation

Today, the text generation subject in the field of Natural Language Processing (NLP) has gained a lot of importance. In particular, the quality of the text generated with the emergence of new transformer-based models has reached high levels. In this way, controllable text generation has become an important research area. There are various methods applied for controllable text generation, but since these methods are mostly applied on Recurrent Neural Network (RNN) based encoder decoder models, which were used frequently, studies using transformer-based models are few. Transformer-based models are very successful in long sequences thanks to their parallel working ability. This study aimed to generate Turkish reviews on the desired topics by using a transformer-based language model. We used the method of adding the topic information to the sequential input. We concatenated input token embedding and topic embedding (control) at each time step during the training. As a result, we were able to create Turkish reviews on the specified topics.
Mixed Differential Evolution and Genetic Algorithm Hybridization for Solving Global Optimization Problems

Evolutionary Algorithms (EAs) have progressively been proving their appropriacy in solving diverse optimization problems. Nonetheless, scrutiny of new variants of classical EAs is an ongoing active research area. Hybridization of EAs—mixing them with other EAs or with other optimization algorithms—is one of the avenues for this research direction. This paper proposes to appraise hybridization strategies of two EAs—Differential Evolution (DE) and Genetic Algorithm (GA). The chosen algorithms are hybridized by embedding variation operators (mutation and crossover (recombination)), of other algorithms, into their structures. Adopting this strategy, this study designed 7 versions of DE and GA hybrid algorithms. The hybrid versions and the classical versions of DE and GA were implemented to solve a standard benchmarking problem. Their performances were compared, empirically and statistically, based on the value of the best objective function (bOFV) and the execution time (eTime). The results reported that the hybrid versions of the DE-GA algorithms still need to be tuned, to achieve superior performance, or, at least, comparable as to their classical counterparts. The premature convergence and stagnating nature of the hybrid versions of these algorithms were identified as the reasons for their lower performance. Apt remedial measures are suggested along with the results and discussions.
A Morphologically Annotated Corpus and a Morphological Analyzer for Egyptian Arabic

This paper sheds light on an in-progress work for building a morphological analyzer for Egyptian Arabic (EGY). To build such a tool, a corpus of 527,000 EGY words is built covering different sources and genres, a tag-set is developed and about 239,000 words are morphologically annotated according to their contexts. Each annotated word is associated with its suitable Proclitic(s), Original Word Form, Tag, Enclitic(s), Glossary, Number, Gender, Definiteness, Conventional Lemma and Word Form. The conventional orthography is assigned for each word to be close to the EGY pronunciation as much as possible regardless the way a word is typically written.
On Using Decision Tree Coverage Criteria forTesting Machine Learning Models

Over the past decade, there has been a growing interest in applying machine learning (ML) to address a myriad of tasks. Owing to this interest, the adoption of ML-based systems has gone mainstream. However, this widespread adoption of ML-based systems poses new challenges for software testers that must improve the quality and reliability of these ML-based solutions. To cope with the challenges of testing ML-based systems, we propose novel test adequacy criteria based on decision tree models. Differently from the traditional approach to testing ML models, which relies on manual collection and labelling of data, our criteria leverage the internal structure of decision tree models to guide the selection of test inputs. Thus, we introduce decision tree coverage (DTC) and boundary value analysis (BVA) as approaches to systematically guide the creation of effective test data that exercises key structural elements of a given decision tree model. To evaluate these criteria, we carried out an experiment using 12 datasets. We measured the effectiveness of test inputs in terms of the difference in model’s behavior between the test input and the training data. The experiment results indicate that our testing criteria can be used to guide the generation of effective test data.
Selectable Set Randomized Kaczmarz

Abstract The Randomized Kaczmarz method (RK) is a stochastic iterative method for solving linear systems that has recently grown in popularity due to its speed and low memory requirement. Selectable Set Randomized Kaczmarz is a variant of RK that leverages existing information about the Kaczmarz iterate to identify an adaptive “selectable set” and thus yields an improved convergence guarantee. In this article, we propose a general perspective for selectable set approaches and prove a convergence result for that framework. In addition, we define two specific selectable set sampling strategies that have competitive convergence guarantees to those of other variants of RK. One selectable set sampling strategy leverages information about the previous iterate, while the other leverages the orthogonality structure of the problem via the Gramian matrix. We complement our theoretical results with numerical experiments that compare our proposed rules with those existing in the literature.
Improving Kinodynamic Planners for Vehicular Navigation with Learned Goal-Reaching Controllers

This paper aims to improve the path quality and computational efficiency of sampling-based kinodynamic planners for vehicular navigation. It proposes a learning framework for identifying promising controls during the expansion process of sampling-based planners. Given a dynamics model, a reinforcement learning process is trained offline to return a low-cost control that reaches a local goal state (i.e., a waypoint) in the absence of obstacles. By focusing on the system’s dynamics and not knowing the environment, this process is data-efficient and takes place once for a robotic system. In this way, it can be reused in different environments. The planner generates online local goal states for the learned controller in an informed manner to bias towards the goal and consecutively in an exploratory, random manner. For the informed expansion, local goal states are generated either via (a) medial axis information in environments with obstacles, or (b) wavefront information for setups with traversability costs. The learning process and the resulting planning framework are evaluated for a first and second-order differential drive system, as well as a physically simulated Segway robot. The results show that the proposed integration of learning and planning can produce higher quality paths than sampling-based kinodynamic planning with random controls in fewer iterations and computation time.
Prediction of Hot Topics of Agricultural Public Opinion Based on Attention Mechanism LSTM Model

In order to timely and accurately analyze the focus and appeal of public opinion on the Internet, A LSTM-ATTN model was proposed to extract the hot topics and predict their changing trend based on tens of thousands of news and commentary messages. First, an improved LDA model was used to extract hot words and classify the hot topics. Aimed to more accurately describe the detailed characteristics and long-term trend of topic popularity, a prediction model is proposed based on attention mechanism Long Short-Term Memory (LSTM) network, which named LSTM-ATTN model. A large number of numerical experiments were carried out using the public opinion information of "African classical swine fever" event in China. According to results of evaluation indexes, the relative superiority of LSTM-ATTN model was demonstrated. It can capture and reflect the inherent characteristics and periodic fluctuations of the agricultural public opinion information. Also, it has higher convergence efficiency and prediction accuracy.
Highly Efficient OT-Based Multiplication Protocols

We present a new $${\text {OT}}$$ -based two-party multiplication protocol that is almost as efficient as Gilboa’s semi-honest protocol (Crypto ’99), but has a high-level of security against malicious adversaries without further compilation. The achieved security suffices for many applications, and, assuming DDH, can be cheaply compiled into full security.
Vibrational spectroscopic study of synthetic analogs of schultenite PbHAsO4–“phosphoschultenite” PbHPO4 solid solution series

Lead hydrogen arsenate PbHAsO4 and lead hydrogen phosphate PbHPO4, which can form a continuous solid solution series, have been extensively studied owing to their ferroelectric properties. PbHAsO4 has also been found to occur in weathered As- and Pb-rich outcrops and historically contaminated soils as a schultenite mineral. Although most ferroelectric studies were of substantial interest more than 30 years ago, the contamination of the Earth’s surface with toxic Pb and As remains a major concern for environmentalists. In this study, six compounds of PbHAsO4–PbHPO4 solid solution series were synthesized from aqueous solutions at ambient conditions. They were subsequently analyzed using Raman and Fourier transform infrared spectroscopy to describe their vibrational characteristics, which should help identify the series members in natural or synthetic samples in further environmental and mineralogical studies. Additionally, powder X-ray diffraction analyses were conducted to investigate the variations in the unit cell parameters along the series. The results showed linear variations in the band positions in both Raman and infrared spectra, which depend on the extent of the isomorphic substitution of (PO4)3− for (AsO4)3−. However, the bands become broader or split as a result of phosphate substitution, making it difficult to correlate the band positions with the molecular composition of an unknown phase. The X-ray diffraction data revealed linear variations of all lattice constants as a function of P. Moreover, a relevant finding of this study is the systematic deviation in the chemical compositions of the synthetic phases from the composition of aqueous solutions from which they precipitated, caused by the preferential incorporation of (AsO4)3− with respect to (PO4)3− into the structure.
Vehicle Detection Using Deep Learning with Deformable Convolution

Aiming at accurately detect vehicles in high-resolution remote sensing images, this paper proposes a target detection framework combining region-based fully convolutional networks (R-FCN) and deformable convolution (DCN). The difficulty of vehicle detection is that its pixel range is small and difficult to detect, R-FCN calculates confidence scores pixel by pixel, and uses a confidence scoring map related to the number of categories and local parts of the target as the output of the network, which can make full use of the limited feature information of vehicles. As to the precision reduction caused by geometric deformation of vehicle images, the fixed structure of the convolution kernel is improved, and the convolution kernel of part of the convolution layers and region of interest (RoI) pooling layers in the network are deformable to make it adapt to the deformation of targets. Experiments show that the R-FCN equipped with deformable convolution and deformable RoI pooling has advantages in detection precision and detection time.
PSENet-based efficient scene text detection

Abstract Text detection is a key technique and plays an important role in computer vision applications, but efficient and precise text detection is still challenging. In this paper, an efficient scene text detection scheme is proposed based on the Progressive Scale Expansion Network (PSENet). A Mixed Pooling Module (MPM) is designed to effectively capture the dependence of text information at different distances, where different pooling operations are employed to better extract information of text shape. The backbone network is optimized by combining two extensions of the Residual Network (ResNet), i.e., ResNeXt and Res2Net, to enhance feature extraction effectiveness. Experimental results show that the precision of our scheme is improved more than by 5% compared with the original PSENet.
Structure verification of deep neural networks at compilation time

This paper presents TensorSafe, a Haskell library that makes possible the definition and structural validation of deep neural network architectures. Nowadays, the development process of deep learning models has been notably simplified due to the availability of sophisticated tools in the industry. However, most of these tools do not provide any security controls at compilation time, making the developers deal with unexpected run-time errors and uncertainties. In particular, validating the structure of deep neural networks at compilation time is a complex subject that involves the mathematical validation of all operations that a deep learning model will perform. Moreover, this structural checking requires an advanced usage of types systems theories to manipulate abstract type definitions capable of modeling neural network constructions. Many different programming techniques were involved in the specification of TensorSafe. Primarily, the application of the functional programming paradigm and the use of type-level programming were of great importance for the development process and to probe the correctness of the neural network models. The experimental evaluation showed that by using TensorSafe it is possible to correctly create well-known deep neural network architectures, such as AlexNet or ResNet50.