Reviewerly/exHarmony huggingface dataset:
Comparing Active Learning with Random Selection when Building Predictive Models

In order to study whether active machine learning would provide more efficient construction of a predictive model than random selection of experiments, this paper describes an experiment about comparing the accuracy of the models built by these two algorithms. The experiment is based on a data set which describes the diagnosis of different cancer cells. In the experiment, the author established an active learning algorithm and a random selection algorithm, and applied them to a variety of common predictive models. The evaluation process, which includes cross-validation and learning curve, compares these two algorithms&#x2019; accuracy under different amounts of training data. It is found that in most cases, the active learning algorithm can obtain higher accuracy with less data compared with the random selection algorithm.
A Statistical Approach to Cost-Sensitive AdaBoost for Imbalanced Data Classification

To address the imbalanced data problem in classification, the studies of the combination of AdaBoost, short for “Adaptive Boosting,” and cost-sensitive learning have shown convincing results in the literature. The cost-sensitive AdaBoost algorithms are practical since the “boosting” property in AdaBoost can iteratively enhance the small class of the cost-sensitive learning to solve the imbalanced data issue. However, the most available cost-sensitive AdaBoost algorithms are heuristic approaches, which are improved from the standard AdaBoost algorithm by cost-sensitively adjusting the voting weight parameters of weak classifiers or the sample updating weight parameters without strict theoretic proof. The algorithms are appended the cost-sensitive factors to focus on the high-cost and small-class samples, but they have no procedures to show the best place to add the cost factors and the cost factor value set. To complete the cost-sensitive AdaBoost algorithms’ framework, the present article has two main contributions. First, we summarize the popular cost-sensitive boosting algorithms in the literature and propose a generally comprehensive form. We name our specific one, the “AdaImC algorithm,” which is typically appliable to solve the imbalanced data classification problem with theoretic proof. Second, a statistical approach to prove the AdaImC algorithm is proposed to verify the inner relationship between the cost parameters. We show that our proposed algorithm in the machine learning field is identical to the Product of Experts (PoE) model in the statistics field. Besides, a way to determine the cost parameter value by the statistical analysis is introduced. Several numeric studies are listed finally to support our proposed algorithm.
Recreating a TransMedia Architectural Location In-Game via Modular Environment Assets

Existing architectural locations are often recreated in games using unique “hero” meshes instead of modular assets, which in these cases are commonly perceived as too limited or inaccurate. This applies to real-world locations or, as in this case study, transmedia locations. This study proposes that hero meshes are not always necessary and that modular assets have the potential to recreate even complex architecture. The paper presents a set of development steps for modular assets for game environment art according to a game design lifecycle, and proceeds to demonstrate its potential via a case study. The case study focuses on planning and designing steps; these preliminary results indicate that, when well-designed, modular assets have the potential to recreate complex architectural locations without requiring extensive use of hero meshes. Adopting modular assets instead of hero meshes could potentially reduce the cost and development time of environment art for transmedia games and games featuring real-world architectural locations, as well as increase the reusability of such assets.
A Deep Cascaded Multi-task Face Recognition Framework

In an unhindered environment, the detection of face and alignment are more challenging due to a variety of poses, illuminations, and occlusions. To better understand and boost, a deep cascaded multi-task framework is proposed in this paper, which exploits alignment and detection inherent correlation. In a coarse and fine prediction of landmark location of the face, the proposed framework leverages Multi-task Cascaded Convolution Neural network [1] (MTCNN) followed by FaceNet [2] to recognize the face identities efficiently. The work has been extended in the form of a real-time attendance marking system. The proposed technique achieves better recognition performance compared to the other state-of-the-arts. Three publicly available datasets: ORL, AR, LFW, datasets are used for experimentation.
Self-Supervised Embedding for Subspace Clustering

Subspace clustering based on data self-expressive model aims to represent each data point as a linear combination of other data points on the dataset. Most existing methods focus on developing the regularization of self-expressive coefficients to solve the transductive unsupervised problem. In this paper, we propose a novel Self-Supervised Embedding for Subspace Clustering (S2ESC), which exploits a low-dimensional feature space where the data points belonging to the same category can be well-connected. Specifically, each data point is encouraged to be close to its transformation which is termed as self-supervised embedding learning. Therefore, the self-expressive coefficients can be learned by representing the data points over the corresponding transformations in the feature space. We introduce the low-rank and sparse regularization to illustrate the performance of self-supervised embedding. Extensive experiments on benchmark datasets demonstrate that our method outperforms the compared traditional subspace clustering methods.
Few shot learning for cross-lingual isolated word recognition

We address the problem of low resource machine learning in the form of few-shot learning (FSL) applied to word recognition in both mono-lingual and cross-lingual settings. Recently, we proposed an adaptation of a FSL framework, matching networks (MN) to a suite of speech recognition tasks such as multi-speaker small-to-medium vocabulary word recognition and frame-wise phoneme recognition tasks under mel-spectrogram and single-frame feature representations. In this paper, we extend this FSL adaptation of MN to multi-speaker isolated word recognition (IWR), in a framework termed MN-IWR. The IWR task is specifically set in a ‘command-and-control’ (C&C) scenario with the requirement of needing only very few-shot examples (e.g. up to 20) for a target IWR classification task with vocabularies defined dynamically. Moreover, our proposed MN-IWR framework addresses a cross-domain and cross-lingual setting defined as below: a model is trained on a possibly large set of words in a source-language and used for inference on a cross-domain task (vocabulary of words different from the training vocabulary) or a cross-lingual task (vocabulary of words from a target-language different from the source-language). In this work, we present the main formulation of the MN-IWR framework, its adaptation from source-to-target tasks and results on TIMIT vocabulary of words in a mono-lingual setting and on English, Kannada and Tamil words in cross-lingual settings and report very high performances of the proposed MN-IWR FSL paradigm over conventional IWR classification without the FSL advantage of the MN formulation.
Supporting Iterative Virtual Reality Analytics Design and Evaluation by Systematic Generation of Surrogate Clustered Datasets

Virtual Reality (VR) is a promising technology platform for immersive visual analytics. However, the design space of VR analytics interface design is vast and difficult to explore using traditional A/B comparisons in formal or informal controlled experiments— a fundamental part of an iterative design process. A key factor that complicates such comparisons is the dataset. Exposing participants to the same dataset in all conditions introduces an unavoidable learning effect. On the other hand, using different datasets for all experimental conditions introduces the dataset itself as an uncontrolled variable, which reduces internal validity to an unacceptable degree. In this paper, we propose to rectify this problem by introducing a generative process for synthesizing clustered datasets for VR analytics experiments. This process generates datasets that are distinct while simultaneously allowing systematic comparisons in experiments. A key advantage is that these datasets can then be used in iterative design processes. In a two-part experiment, we show the validity of the generative process and demonstrate how new insights in VR-based visual analytics can be gained using synthetic datasets.
Weibo-MEL, Wikidata-MEL and Richpedia-MEL: Multimodal Entity Linking Benchmark Datasets

Multimodal entity linking (MEL) aims to utilize multimodal information to map mentions to corresponding entities defined in knowledge bases. In this paper, we release three MEL datasets: Weibo-MEL, Wikidata-MEL and Richpedia-MEL, containing 25,602, 18,880 and 17,806 samples from social media, encyclopedia and multimodal knowledge graphs respectively. A MEL dataset construction approach is proposed, including five stages: multimodal information extraction, mention extraction, entity extraction, triple construction and dataset construction. Experiment results demonstrate the usability of the datasets and the distinguishability between baseline models. All resources are available at https://github.com/seukgcode/MELBench.
Detecting the Fake Candidate Instances

Ambiguous Label Learning (ALL), as an emerging paradigm of weakly supervised learning, aims to induce the prediction model from training datasets with ambiguous supervision, where, specifically, each training instance is annotated with a set of candidate labels but only one is valid. To handle this task, the existing shallow methods mainly disambiguate the candidate labels by leveraging various regularization techniques. Inspired by the great success of deep generative adversarial networks, we apply it to perform effective candidate label disambiguation from a new instance-pivoted perspective. Specifically, for each ALL instance, we recombine its feature representation with each of candidate labels to generate a set of candidate instances, where only one is real and all others are fake. We formulate a unified adversarial objective with respect to three players, i.e., a discriminator, a generator, and a classifier. The discriminator is used to detect the fake candidate instances, so that the classifier can be trained without them. With this insight, we develop a novel ALL method, namely Adversarial Ambiguous Label Learning with Candidate Instance Detection (A2L2CID). Theoretically, we analyze that there is a global equilibrium point between the three players. Empirically, extensive experimental results indicate that A2L2CID outperforms the state-of-the-art ALL methods.
The use of the <i>t</i>-test in Shakespeare scholarship

Abstract In a recent article, the New Oxford Shakespeare scholar Hugh Craig used the t-test in statistics to compare the uses of one hundred function words across plays, authors, genres, characters, and periods in Shakespeare’s era. Craig drew some strong conclusions; for example, that authorship is a greater differentiator between plays than genre. Using both theoretical arguments and the results of further experiments, this note challenges Craig’s analysis, showing that he applied the t-test even when it is not clear that its premises are satisfied, failed to apply necessary controls, and, moreover, that his reasoning contains a fundamental error which invalidates his interpretation of the results.
Video-Based Reconstruction of Smooth 3D Human Body Motion

We describe a method to directly recover a 3D human mesh from videos. The existing methods often show that the movement is not smooth and the motion jitter in a certain frame. The contribution of our method is to judge whether the difference between two frames exceeds the threshold range by adding constraint loss, and then optimize it. It effectively limits the changes of pose and shape parameters in the video sequence, and solves the jitter and mutation issues of the human model. Using an adversarial learning framework, the generator outputs the predicted human body parameters, and the discriminator to distinguish real human actions from those produced by our generator. Such adversarial training can produce kinematically plausible motion results. We use GRU network to effectively learn the temporal information which are hidden in the sequence. This is conducive to the continuity and smoothness of the human movement. We conduct some experiments to analyze the importance of constraint loss and demonstrate the effectiveness of our method on the challenging 3D pose estimation datasets.
What Emotion Is Hate? Incorporating Emotion Information into the Hate Speech Detection Task

Finding ethical, platform-independent, computationally efficient methods of adding contextual information to the hate speech detection task is difficult. Methods that rely only on the text for successful classification are of extreme importance. Emotion information extracted from text has been shown to be effective for sentiment analysis and thus we hypothesize that it could have a potential for hate speech. In this study, we propose several methods of introducing emotions into the task of hate speech detection. Using an emotion lexicon, we counter-fitted pre-trained word embeddings (Word2Vec, GloVe, FastText) and also generated a binary and a weighted emotional embedding vector. These were used as features for classification on four publicly available hate speech datasets. Our results and analysis demonstrate that the inclusion of emotion information especially anger, sadness, disgust, fear are helpful for hate speech detection.
Understanding and Evaluating Commonsense Reasoning in Transformer-based Architectures

Ascertaining the reason for and identifying the differences between sensical and nonsensical statements is a task that humans are naturally adept at. NLP methods, however, might struggle with such a task. This paper aims to evaluate various transformer-based architectures on common sense validation and explanation task, methods to improve their performance, as well as interpreting how fine-tuned language models perform such tasks, using the attention distribution of sentences at inference time. The tasks entail identifying the nonsensical statement from a given pair of similar statements (validation), followed by selecting the correct reason for its nonsensical nature(explanation). An accuracy of83.90 and 88.42 is achieved on the respective tasks, using RoBERTa (large) language model fine-tuned on the data sets using the ULMFiT training approach.
Quantum non-Markovian dynamics controlled by a local nanoprobe in nanosystems coupled via optical near fields

Abstract The optical near field originates from the non-Markovianity of quantum coherent dynamics due to the light–matter interaction. To observe the optical near field localized near the surface of a nanomaterial, a local nanoprobe must be in close proximity, and the effect of the local nanoprobe cannot be ignored. Therefore, we elucidate the effect of the local nanoprobe on the non-Markovianity of the optical-near-field interaction, estimating the trace distance between the density matrices in the non-Markovian and Markovian cases and its integration as a quantitative measure of the non-Markovianity. The effect of the local nanoprobe plays an important role in the quantum coherent and non-Markovian dynamics via the optical near field, and furthermore, this result suggests that the local nanoprobe can control the non-Markovianity in the quantum coherent dynamics.
Blockchain –booster of the Russian forest information systems

Digitalization of the Russian forest sector is successfully developing. The first Unified State Automated Information System for timber accounting and transactions (LesEGAIS) has been operating since December 30, 2015 and continues to improve. In 2023, it will be qualitatively transformed into the Federal State Information System of the Forestry Sector FGIS LK. However, these systems continue to be built on centralized databases and rapidly outdated information concepts of the last century. They do not consider the fundamentally new opportunities that the new breakthrough information technology of the distributed ledger (the blockchain) provides to the forest.
Say What You Are Looking At: An Attention-Based Interactive System for Autistic Children

Gaze-following is an effective way for intention understanding in human–robot interaction, which aims to follow the gaze of humans to estimate what object is being observed. Most of the existing methods require people and objects to appear in the same image. Due to the limitation in the view of the camera, these methods are not applicable in practice. To address this problem, we propose a method of gaze following that utilizes a geometric map for better estimation. With the help of the map, this method is competitive for cross-frame estimation. On the basis of this method, we propose a novel gaze-based image caption system, which has been studied for the first time. Our experiments demonstrate that the system follows the gaze and describes objects accurately. We believe that this system is competent for autistic children’s rehabilitation training, pension service robots, and other applications.
View selection over knowledge graphs in triple stores

Knowledge Graphs (KGs) are collections of interconnected and annotated entities that have become powerful assets for data integration, search enhancement, and other industrial applications. Knowledge Graphs such as DBPEDIA may contain billion of triple relations and are intensively queried with millions of queries per day. A prominent approach to enhance query answering on Knowledge Graph databases is View Materialization, ie., the materialization of an appropriate set of computations that will improve query performance. We study the problem of view materialization and propose a view selection methodology for processing query workloads with more than a million queries. Our approach heavily relies on subgraph pattern mining techniques that allow to create efficient summarizations of massive query workloads while also identifying the candidate views for materialization. In the core of our work is the correspondence between the view selection problem to that of Maximizing a Nondecreasing Submodular Set Function Subject to a Knapsack Constraint . The latter leads to a tractable view-selection process for native triple stores that allows a (1 - e ---1 )-approximation of the optimal selection of views. Our experimental evaluation shows that all the steps of the view-selection process are completed in a few minutes, while the corresponding rewritings accelerate 67.68% of the queries in the DBPEDIA query workload. Those queries are executed in 2.19% of their initial time on average.
Efficient Online Transfer Learning for 3D Object Classification in Autonomous Driving

Autonomous driving has achieved rapid development over the last few decades, including the machine perception as an important issue of it. Although object detection based on conventional cameras has achieved remarkable results in 2D/3D, non-visual sensors such as 3D LiDAR still have incomparable advantages in the accuracy of object position detection. However, the challenge also exists with the difficulty in properly interpreting point cloud generated by LiDAR. This paper presents a multi-modal-based online learning system for 3D LiDAR-based object classification in urban environments, including cars, cyclists and pedestrians. The proposed system aims to effectively transfer the mature detection capabilities based on visual sensors to the new model learning based on non-visual sensors through a multi-target tracker (i.e. using one sensor to train another). In particular, it integrates the Online Random Forests (ORF) [1] method, which inherently has the abilities of fast and multi-class learning. Through experiments, we show that our system is capable of learning a high-performance model for LiDAR-based 3D object classification on-the-fly, which is especially suitable for robotics in-situ deployment while responding to the widespread challenge of insufficient detector generalization capabilities.
Generalized Birnbaum–Saunders mixture cure frailty model: inferential method and an application to bone marrow transplant data

Cluster time data are commonly encountered in survival analysis due to unobservable factors such as shared environmental conditions and genetic similarity. In such cases, careful attention needs to be paid to model the possible correlation between the subjects within the same cluster. Moreover, some diseases are curable due to great advances on modern medical techniques and treatments. For tracking these issues, we consider here a mixture cure frailty model, with generalized Birnbaum–Saunders frailty distribution, and propose a marginal likelihood approach for the estimation of model parameter. We approximate the intractable integrals in the likelihood function by the use of Monte-Carlo method. Thereafter, the maximum likelihood estimates are numerically determined. A simulation study and model discrimination are then carried out for evaluating the performance of the proposed model. It is observed from this study that the proposed model provides more flexibility and the method of inference is quite robust. Finally, we conduct an analysis of the effects of allogeneic and autologous bone marrow transplant treatments on acute lymphoblastic leukemia patients to demonstrate the usefulness of the proposed model and the method of inference.
Memory Leak Detection Tools: A Comparative Analysis

Memory leak is a situation when the memory taken up by dynamically allocated objects is not deallocated after its use. Memory leak is a serious problem in embedded systems as they are memory constrained devices. This can also be a serious problem in servers as continuous leaking of memory space results in denial of client requests in due time. The programmer has to take care while writing the code of the application for such main memory related issues. Apart from manual inspection of the code for memory leak, finding illegal memory issues can be tedious sometimes. Various tools are available in order to detect main memory related issues in the software which can ease the testing time of application. These tools are categorized as static and dynamic analysis tools. These tools help programmers to write memory safe and clean code without going into memory leak and related issues. This paper discusses different memory leakage detection tools. The paper also provides the comparison of different tools on the basis of different parameters and concludes with the evaluation of the memory detection tools on the basis of time taken. The results show that Mtrace tool takes maximum time for analysis and Electric fence tools takes minimum time for analysis.