Reviewerly/exHarmony huggingface dataset:
Tagging Items Automatically Based on Both Content Information and Browsing Behaviors

Tags have been adopted by many online services as a method to manage their online resources. Effective tagging benefits both users and firms. In real applications providing a user tagging mechanism, only a small portion of tags are usually provided by users. Therefore, an automatic tagging method, which can assign tags to different items automatically, is urgently needed. Previous works on automatic tagging focus on exploring the tagging behavior of users or the content information of items. In online service platforms, users frequently browse items related to their interests, which implies users’ judgment about the underlying features of items and is helpful for automatic tagging. Browsing-behavior records are much more plentiful compared with tagging behavior and easy to collect. However, existing studies about automatic tagging ignore this kind of information. To properly integrate both browsing behaviors and content information for automatic tagging, we propose a novel probabilistic graphical model and develop a new algorithm for the model parameter inference. We conduct thorough experiments on a real-world data set to evaluate and analyze the performance of our proposed method. The experimental results demonstrate that our approach achieves better performance than state-of-the-art automatic tagging methods. Summary of Contribution. In this paper, we study how to automatically assign tags to items in an e-commerce background. Our study is about how to perform item tagging for e-commerce and other online service providers so that consumers can easily find what they need and firms can manage their resources effectively. Specifically, we study if consumer browsing behavior can be utilized to perform the tagging task automatically, which can save efforts of both firms and consumers. Additionally, we transform the problem into how to find the most proper tags for items and propose a novel probabilistic graphical model to model the generation process of tags. Finally, we develop a variational inference algorithm to learn the model parameters, and the model shows superior performance over competing benchmark models. We believe this study contributes to machine learning techniques.
Optimizing Agent Training with Deep Q-Learning on a Self-Driving Reinforcement Learning Environment

Nowadays, there are many Deep Q-Learning (DQL) variants and Reinforcement Learning (RL) algorithms that have outperformed the original DQL algorithm using a simple Deep Q-Network (DQN) architecture on several RL applications. In this paper, we present an environment-oriented adapted DQN implementation on the OpenAI Gym CarRacing self-driving environment where a driver agent successfully learns difficult control policies from raw pixels using a deep convolutional neural network. By implementing multiple optimizations and environment-specific adaptations on the original DQL algorithm, we were able to improve on some of the limitations of the base DQN algorithm significantly on this platform. Our trained agent was therefore able to produce successful results and obtain higher convergence scores while being more time efficient and using less computational resources. Obtaining a promising average score over one hundred consecutive trials, the results achieved suggest that the proposed adapted DQN agent improves substantially on the base DQL algorithm. Our agent surpassed Double Deep Q-Network (DDQN) and multiple other approaches in the CarRacing RL environment.
Learned vs. Hand-Crafted Features for Deep Learning Based Aperiodic Laboratory Earthquake Time-Prediction

Earthquakes cause the deadliest and most costly disasters among all natural hazards. Geophysicists and data scientists have spent a lot of effort, trying to predict earthquakes time, location or magnitude to minimize these disastrous effects. However, earthquakes prediction remains a challenging problem. In this paper, the generated aperiodic earthquake failures data by Los Alamos National Laboratory (LANL) were utilized to implement three models: i) random forest (RF), ii) convolutional neural network (CNN) units followed by long short-term memory (LSTM) layers, and iii) hand-crafted features combined with CNN and LSTM layers. Among all, applying a network of CNN and LSTM layers to hand-crafted features is the most accurate and the fastest model to predict the time remaining for the next earthquake. This model achieved the prediction goal with a mean absolute error (MAE) of 1.51 in 44 seconds. These results are promising compared to other models. Thus, we anticipate that our model can be improved and applied to real seismic data that can save thousands of lives and billions of dollars in infrastructure.
Statistical exploratory analysis of mask-fill reproduction operators of Genetic Algorithms

To be successful, a search algorithm needs to balance exploration and exploitation. In Genetic Algorithms (GAs) this is achieved through proportionate selection of individuals and reproduction operators. GAs can suffer premature convergence, when the diversity of the population decreases over time and search gets trapped in a local optimum, returning a very poor quality solution. Mask-fill reproduction operators utilizes bit-masking oriented data structure (BMODS) that maintains a good ratio between exploration and exploitation. Mask-fill reproduction operators have been utilized in various applications, but the exploration and exploitation ability of mask-fill reproduction operators have not been compared against other reproduction operators. This paper describes a rigorous and practical statistical methodology for the exploratory analysis of the mask-fill reproduction operators. First, the issues of robust experimental design and setting the control parameters for implementing a GA is addressed. Second, the impact of various reproduction operator combinations are analysed. In this study, three crossover operators and five mutation operators are considered which creates fifteen crossover–mutation operator combinations. Third, the methodology is demonstrated by considering grammatical inference (GI) problem as domain of enquiry. A hybrid genetic algorithm integrated with Sequitur algorithm (GAWS) is proposed for GI. Numerical results are presented to describe the effect of crossover–mutation operator combinations. The performance of the proposed GAWS is compared against the state-of-the-art algorithms. Statistical test are conducted to determine the performance significance of both crossover–mutation operator combinations and the proposed GAWS algorithm.
Function Representations for Binary Similarity

The binary similarity problem consists in determining if two functions are similar considering only their compiled form. Advanced techniques for binary similarity recently gained momentum as they can be applied in several fields, such as copyright disputes, malware analysis, vulnerability detection, etc. In this article we describe SAFE, a novel architecture for function representation based on a self-attentive neural network. SAFE works directly on disassembled binary functions, does not require manual feature extraction, is computationally more efficient than existing solutions, and is more general as it works on stripped binaries and on multiple architectures. Results from our experimental evaluation show how SAFE provides a performance improvement with respect to previous solutions. Furthermore, we show how SAFE can be used in widely different use cases, thus providing a general solution for several application scenarios.
Auxiliary Tasks for Efficient Learning of Point-Goal Navigation

Top-performing approaches to embodied AI tasks like point-goal navigation often rely on training agents via reinforcement learning over tens of millions (or even billions) of experiential steps - learning neural agents that map directly from visual observations to actions. In this work, we question whether these extreme training durations are necessary or if they are simply due to the difficulty of learning visual representations purely from task reward. We examine the task of point-goal navigation in photorealistic environments and introduce three auxiliary tasks that encourage learned representations to capture key elements of the task - local scene geometry, transition dynamics of the environment, and progress towards the goal. Importantly, these can be evaluated independent of task performance and provide strong supervision for representation learning. Our auxiliary tasks are simple to implement and rely on supervision already present in simulators commonly used for point-goal navigation. Applying our auxiliary losses to agents from prior works, we observe a >4 × improvement in sample efficiency - in 17 million steps, our augmented agents outperforms state-of-the-art agents trained for 72 million steps.
Detecting Proper Mask Usage with Soft Attention

Proper mask usage in public areas has been shown to be critical in the efforts to reduce infection spread in circumstances such as the COVID-19 pandemic. In this paper, we propose mask usage detection approach based on deep learning: a Mask Regional-Convolutional Neural Network (Mask R-CNN) that provides segmentation of faces and masks, and another CNN using a novel Soft Attention unit to detect the correctness of the mask usage. We also provide a small instance segmented subset of the Masked Faces (MAFA) dataset for instance segmentation problems. We use the Mask R-CNN to provide instance segmentations of faces and face masks to the visual relationship detection CNN and predict improperly and properly worn face masks. Various CNN architectures such as ResNet50 were tested and compared to determine its effectiveness for the above task. We evaluate the CNN architectures on accuracy, precision, recall, and specificity of detecting properly worn masks. The best performing network was determined to be the ResNet50V2 architecture with 76.27% accuracy, 84.76% precision, 74.38% recall, and 79.20% specificity.
Can We Extract 3D Biometrics from 2D Images for Facial Beauty Analysis?

Geometric features are important traits in facial beauty analysis due to its clear definition and coherence to our intuition. Existing works often extract these features from 2D face images captured in constrained environment. With the recent development of monocular reconstruction algorithms, we wonder if it is possible to reconstruct 3D faces from 2D images and extract 3D biometrics instead of extracting features from the 2D images directly. If the features are robust, a large amount of in-the-wild face images will be available for facial beauty analysis. In this paper, we design experiments to evaluate the precision and robustness of 2D-3D geometric features. 3D faces in the BJUT-3D database were taken as the ground truth. Based on these 3D faces, we generated 2D images with different poses and fed them to a deep neural network to obtain the reconstructed 3D faces. Ratio and angle features were extracted from the ground truth 3D faces, the generated 2D faces, and the reconstructed 3D faces, respectively. The results show that the 2D-3D geometric features were more robust to pose variations compared with those of 2D images; ambiguities exist in monocular reconstruction, but the error is smaller than the pairwise individual differences.
Holistically Associated Transductive Zero-Shot Learning

With the explosive growth of visual media categories, zero-shot learning (ZSL) aims to transfer the knowledge obtained from the seen classes to the unseen classes for recognizing novel instances. However, there is a domain gap between the seen and the unseen classes, and simply matching the unseen instances using nearest neighbor searching in the embedding space cannot bridge this gap effectively. In this article, we propose a holistically associated model to overcome this obstacle. In particular, the proposed model is designed to combat two fundamental problems of ZSL: 1) the representation learning and 2) label assignment of the unseen classes. The first problem is addressed by proposing an affinity propagation network, which considers holistic pairwise connections of all classes for producing exemplar features of the unseen samples. We cope with the second issue by proposing a progressive clustering module. It iteratively refines unseen clusters so that holistic unseen instance features can be used for a reliable classwise label assignment. Thanks to the precise exemplar features and classwise label assignment, our model eliminates the domain gap effectively. We extensively evaluate the proposed model on five human action and image data sets, i.e., Olympics Sports, HMDB51, UCF101, Animals with Attributes 2, and SUN. The experimental results show that the proposed model outperforms state-of-the-art methods on these substantially different data sets.
Gamma-ray spectrometric survey for mineral exploration at Baljurashi area, Saudi Arabia

Mineral resources exploration in the Kingdom of Saudi Arabia became an important target for the Saudi government. Nowadays, the economic vision of the country is to move away from oil-based country to multisource energy-based country. In Baljurashi area there were ancient mines for Copper, Cobalt, Magnesium, Zircon, and some other minerals. In the current study, we aim to evaluate the Baljurashi area in terms of mineral resources using the advanced technique. Consequently, we carried out ground gamma ray spectrometry survey in an area of about 50 km2, elevation variation ranges from 380 m to 2280 m above the sea level. We used the RS-700 system to collect the gamma ray spectrometry data. In addition, we used the existing geochemical analysis results for this area and integrated the results to map the most important minerals. The integration work indicates that the area of Baljurashi is rich in mineral resources. The mineralization zones are located in the eastern part of the area where elevation is high (e.g., east of the escarpment). We also found that these mineralization zones are structurally controlled. Important mineral maps were produced (e.g. Cu, Cr, Mn, and Zr). We highly recommend more geophysical studies to evaluate the Baljurashi area for mineral identifications and quantifications.
WEmbSim: A Simple yet Effective Metric for Image Captioning

The area of automatic image caption evaluation is still undergoing intensive research to address the needs of generating captions which can meet adequacy and fluency requirements. Based on our past attempts at developing highly sophisticated learning-based metrics, we have discovered that a simple cosine similarity measure using the Mean of Word Embeddings (MOWE) of captions can actually achieve a surprisingly high performance on unsupervised caption evaluation. This inspires our proposed work on an effective metric WEmbSim, which beats complex measures such as SPICE, CIDEr and WMD at system-level correlation with human judgments. Moreover, it also achieves the best accuracy at matching human consensus scores for caption pairs, against commonly used unsupervised methods. Therefore, we believe that WEmbSim sets a new baseline for any complex metric to be justified.
A Gradient-Based Clustering for Multi-Database Mining

Multinational corporations have multiple databases distributed throughout their branches, which store millions of transactions per day. For business applications, identifying disjoint clusters of similar and relevant databases contributes to learning the common buying patterns among customers and also increases the profits by targeting potential clients in the future. This process is called clustering, which is an important unsupervised technique for big data mining. In this article, we present an effective approach to search for the optimal clustering of multiple transaction databases in a weighted undirected similarity graph. To assess the clustering quality, we use dual gradient descent to minimize a constrained quasi-convex loss function whose parameters will determine the edges needed to form the optimal database clusters in the graph. Therefore, finding the global minimum is guaranteed in a finite and short time compared with the existing non-convex objectives where all possible candidate clusterings are generated to find the ideal clustering. Moreover, our algorithm does not require specifying the number of clusters a priori and uses a disjoint-set forest data structure to maintain and keep track of the clusters as they are updated. Through a series of experiments on public data samples and precomputed similarity matrices, we show that our algorithm is more accurate and faster in practice than the existing clustering algorithms for multi-database mining.
Making good on a promise

Abstract Construction Grammar was founded on the promise of maximal empirical coverage without compromising on formal precision. Its main claim is that all linguistic knowledge can be represented as constructions, similar to the notion of constructions from traditional grammars. As such, Construction Grammar may finally reconcile the needs of descriptive and theoretical linguistics by establishing a common ground between them. Unfortunately, while the construction grammar community has developed a sophisticated understanding of what a construction is supposed to be, many critics still believe that a construction is simply a new jacket for traditional linguistic analyses and therefore inherits all of the problems of those analyses. The goal of this article is to refute such criticisms by showing how constructions can be formalized as open-ended and multidimensional linguistic representations that make no prior assumptions about the structure of a language. While this article’s proposal can be simply written down in a pen-and-paper style, it verifies the validity of its approach through a computational implementation of German field topology in Fluid Construction Grammar.
RETRACTED: Design and implementation of parallel algorithm for image matching based on Hausdorff Distance

Object matching two-dimensional images in computer vision has become a significant subject of article acknowledgment and picture investigation. Hausdorff Distance assumes a significant function in coordinating image. To proposed system parallel algorithm for image matching manage the instance of arbitrary commotion, image coordinating, new Hausdorff Distance is proposed in this. In contrast to coordinating two twofold pictures, different techniques, the proposed strategy might be coordinated with a few dark scale image pixel esteems. One case of article acknowledgment is utilized to show the productivity of the proposed strategy. The outcomes demonstrated that, contrasted and, the new Hausdorff Distance (HD) might be more alluring approach to discard image clamor coordinating, because of the extensive reflection to decide the dark scale data Hausdorff Distance of adjoining pixels in the shooting of his realities into account. Besides, the strategy can be acknowledged in a straightforward manner.
Default options: a powerful behavioral tool to increase COVID-19 contact tracing app acceptance in Latin America?

Abstract Given the rates of transmission of COVID-19, relying only on manual contact tracing might be infeasible to control the epidemic without sustained costly lockdowns or rapid vaccination efforts. In the first study of its kind in Latin America, we find through a phone survey of a nationally representative sample of ten countries that an opt-out regime (automatic installation) increases self-reported intention to accept a contact tracing app with exposure notification by 22 percentage points compared to an opt-in regime (voluntary installation). This effect is triple the size and of opposite sign of the effect found in Europe and the United States, potentially due to lower concerns regarding privacy and lower levels of interpersonal trust. We see that an opt-out regime is more effective in increasing willingness to accept for those who do not trust the government or do not use their smartphones for financial transactions. The local severity of the pandemic does not affect our results, but feeling personally at risk increases intent to accept such apps in general. These results can shed light on the use of default options not only for contact tracing apps but in public health overall in the context of a pandemic in Latin America.
Anomaly Detection, Active Learning, Precursor Identification, and Human Knowledge for Autonomous System Safety

The project Autonomy Teaming and TRajectories for Complex Trusted Operational Reliability (ATTRACTOR) researched and developed Artificial Intelligence with application to multi-Unmanned Aerial Systems (UAS) missions. Such missions, like other complex systems-of-systems, are likely to have previously-unknown, safety relevant anomalies occur due to many possible factors including system failures or degradations, emergent behavior, changes in the environment in which the systems operate, changes in the way the systems are operated. We discuss the application of anomaly detection, active learning, and precursor identification to identify such anomalies and the conditions under which they are more likely to appear. We demonstrate results on simulated multi-UAS missions that show promise to be applied to real missions.
Grammatical Error Correction: More Data with More Context

Grammatical Error Correction (GEC) seriously suffers from a scarcity of data, both annotated and unannotated, as humans do not intentionally make grammatical errors. To account for this, we make use of the plentiful unlabeled plain text available and augment a dataset with artificial noise to increase our effective training data and pre-train our model as a denoising autoencoder (DAE), which offers an intuitive data augmentation solution for GEC. In a novel approach, we enhance our DAE, a Transformer Model, with a cross-document context mechanism and use a parallel encoder to encode the cross-document context before fusing the two contexts of the encoders in the decoder. Supplied by the combination of document similarity metrics and any unlabeled plain text, this serves as a new method of equipping a GEC model with supplemental context and allowing it to glean grammatical information from a separate plain text corpus. We evaluate our model on the CoNLL-2014 GEC Shared Task and achieve results that approach state-of-the-art for single models and show great potential with ever available and plentiful plain text.
RETRACTED: Research on prepaid account financing model based on embedded system and Internet of Things

Internet of Things (IoT) network interconnection to create objects and things will play the Internet to play an active role in the global network in the future. For the Internet of Things, which is widely adopted through funding models, it must be trusted in the IoT security infrastructure. Efficiently and Securely IoT is very important to define how each other can communicate with remote servers and get Exchange account information. Prepayments for effective financial management and an important choice for financial IoT for service providers and customers. However, it must be supported by real-time credit checking and costing. Internet resources are consumed by these real-time action stuff providers and impose high costs on the old system. To solve this problem, to propose the K Means Algorithm scalable accounting solutions, where the user is hosted each occupies a prepaid account, constitute the components of embedded systems. Based on each of our prepaid billing components' supervision, it is at the same time consumed by the embedded system of all services, based on the calculation of the service packages consumed by the customer. Prepaid accounts are reassigned when the customer had sufficient credit to supplement their use and are allocated based on IoT services' consumption. This work aims to reduce the cost of pre-paid services and ensure that service delivery is not to interfere with the charging unit. Also, embedded systems' theoretical and experimental analysis shows that this work can store long-lived services on the Internet of Things to provide inexpensive accounting solutions.