Reviewerly/exHarmony huggingface dataset:
Image Captioning on Fine Art Paintings via Virtual Paintings

Machine learning in fine art paintings is attracting increasing attention recently. Image captioning of paintings is of great importance for painting analysis, but it is rarely studied. The paintings have abstract expressions and lack annotated datasets, leading to the data-hungry problem in painting captioning. Thus, painting captioning has more significant challenges than photographic image captioning. This paper makes a novel attempt at generating content descriptions of paintings. We generate virtual paintings using the style transfer technique to deal with the data-hungry problem, then train the painting captioning model via a two-step manner. We evaluate our method on an annotated small-scale painting captioning dataset and demonstrate our improvements.
Improving Transformer Based End-to-End Code-Switching Speech Recognition Using Language Identification

A Recurrent Neural Networks (RNN) based attention model has been used in code-switching speech recognition (CSSR). However, due to the sequential computation constraint of RNN, there are stronger short-range dependencies and weaker long-range dependencies, which makes it hard to immediately switch languages in CSSR. Firstly, to deal with this problem, we introduce the CTC-Transformer, relying entirely on a self-attention mechanism to draw global dependencies and adopting connectionist temporal classification (CTC) as an auxiliary task for better convergence. Secondly, we proposed two multi-task learning recipes, where a language identification (LID) auxiliary task is learned in addition to the CTC-Transformer automatic speech recognition (ASR) task. Thirdly, we study a decoding strategy to combine the LID into an ASR task. Experiments on the SEAME corpus demonstrate the effects of the proposed methods, achieving a mixed error rate (MER) of 30.95%. It obtains up to 19.35% relative MER reduction compared to the baseline RNN-based CTC-Attention system, and 8.86% relative MER reduction compared to the baseline CTC-Transformer system.
Fine-Tuned Pre-Trained Model for Script Recognition

Script recognition is the first necessary preliminary step for text recognition. In the deep learning era, for this task two essential requirements are the availability of a large labeled dataset for training and computational resources to train models. But if we have limitations on these requirements then we need to think of alternative methods. This provides an impetus to explore the field of transfer learning, in which the previously trained model knowledge established in the benchmark dataset can be reused in another smaller dataset for another task, thus saving computational power as it requires to train only less number of parameters from the total parameters in the model. Here we study two pre-trained models and fine-tune them for script classification tasks. Firstly, the VGG-16 pre-trained model is fine-tuned for publically available CVSI-15 and MLe2e datasets for script recognition. Secondly, a well-performed model on Devanagari handwritten characters dataset has been adopted and fine-tuned for the Kaggle Devanagari numeral dataset for numeral recognition. The performance of proposed fine-tune models is related to the nature of the target dataset as similar or dissimilar from the original dataset and it has been analyzed with widely used optimizers.
GRF: Learning a General Radiance Field for 3D Scene Representation and Rendering

We present a simple yet powerful implicit neural function that can represent and render arbitrarily complex 3D scenes in a single network only from 2D observations. The function models 3D scenes as a general radiance field, which takes a set of 2D images with camera poses and intrinsics as input, constructs an internal representation for each 3D point of the scene, and renders the corresponding appearance and geometry of any 3D point viewing from an arbitrary angle. The key to our approach is to explicitly integrate the principle of multi-view geometry to obtain the internal representations from observed 2D views, such that the learned implicit representations empirically remain multi-view consistent. In addition, we introduce an effective neural module to learn general features for each pixel in 2D images, allowing the constructed internal 3D representations to be general as well. Extensive experiments demonstrate the superiority of our approach.
Order-sorted equational generalization algorithm revisited

Generalization, also called anti-unification, is the dual of unification. A generalizer of two terms t and $t^{\prime }$ is a term $t^{\prime \prime }$ of which t and $t^{\prime }$ are substitution instances. The dual of most general equational unifiers is that of least general equational generalizers, i.e., most specific anti-instances modulo equations. In a previous work, we extended the classical untyped generalization algorithm to: (1) an order-sorted typed setting with sorts, subsorts, and subtype polymorphism; (2) work modulo equational theories, where function symbols can obey any combination of associativity, commutativity, and identity axioms (including the empty set of such axioms); and (3) the combination of both, which results in a modular, order-sorted equational generalization algorithm. However, Cerna and Kutsia showed that our algorithm is generally incomplete for the case of identity axioms and a counterexample was given. Furthermore, they proved that, in theories with two identity elements or more, generalization with identity axioms is generally nullary, yet it is finitary for both the linear and one-unital fragments, i.e., either solutions with repeated variables are disregarded or the considered theories are restricted to having just one function symbol with an identity or unit element. In this work, we show how we can easily extend our original inference system to cope with the non-linear fragment and identify a more general class than one–unit theories where generalization with identity axioms is finitary.
Detect and Perturb: Neutral Rewriting of Biased and Sensitive Text via Gradient-based Decoding

Written language carries explicit and implicit biases that can distract from meaningful signals.For example, letters of reference may describe male and female candidates differently, or their writing style may indirectly reveal demographic characteristics.At best, such biases distract from the meaningful content of the text; at worst they can lead to unfair outcomes.We investigate the challenge of re-generating input sentences to 'neutralize' sensitive attributes while maintaining the semantic meaning of the original text (e.g. is the candidate qualified?).We propose a gradient-based rewriting framework, Detect and Perturb to Neutralize (DEPEN), that first detects sensitive components and masks them for regeneration, then perturbs the generation model at decoding time under a neutralizing constraint that pushes the (predicted) distribution of sensitive attributes towards a uniform distribution.Our experiments in two different scenarios show that DEPEN can regenerate fluent alternatives that are neutral in the sensitive attribute while maintaining the semantics of other attributes.
Statistical uncertainty quantification to augment clinical decision support: a first implementation in sleep medicine

Machine learning has the potential to change the practice of medicine, particularly in areas that require pattern recognition (e.g. radiology). Although automated classification is unlikely to be perfect, few modern machine learning tools have the ability to assess their own classification confidence to recognize uncertainty that might need human review. Using automated single-channel sleep staging as a first implementation, we demonstrated that uncertainty information (as quantified using Shannon entropy) can be utilized in a "human in the loop" methodology to promote targeted review of uncertain sleep stage classifications on an epoch-by-epoch basis. Across 20 sleep studies, this feedback methodology proved capable of improving scoring agreement with the gold standard over automated scoring alone (average improvement in Cohen's Kappa of 0.28), in a fraction of the scoring time compared to full manual review (60% reduction). In summary, our uncertainty-based clinician-in-the-loop framework promotes the improvement of medical classification accuracy/confidence in a cost-effective and economically resourceful manner.
Halos and undecidability of tensor stable positive maps

A map $\mathcal{P}$ is tensor stable positive (tsp) if $\mathcal{P}^{\otimes n}$ is positive for all $n$, and essential tsp if it is not completely positive or completely co-positive. Are there essential tsp maps? Here we prove that there exist essential tsp maps on the hypercomplex numbers. It follows that there exist bound entangled states with a negative partial transpose (NPT) on the hypercomplex, that is, there exists NPT bound entanglement in the halo of quantum states. We also prove that tensor stable positivity on the matrix multiplication tensor is undecidable, and conjecture that tensor stable positivity is undecidable. Proving this conjecture would imply existence of essential tsp maps, and hence of NPT bound entangled states.
Phoebe

Easy-to-use programming interfaces paired with cloud-scale processing engines have enabled big data system users to author arbitrarily complex analytical jobs over massive volumes of data. However, as the complexity and scale of analytical jobs increase, they encounter a number of unforeseen problems, hotspots with large intermediate data on temporary storage, longer job recovery time after failures, and worse query optimizer estimates being examples of issues that we are facing at Microsoft. To address these issues, we propose Phoebe, an efficient learning-based checkpoint optimizer. Given a set of constraints and an objective function at compile-time, Phoebe is able to determine the decomposition of job plans, and the optimal set of checkpoints to preserve their outputs to durable global storage. Phoebe consists of three machine learning predictors and one optimization module. For each stage of a job, Phoebe makes accurate predictions for: (1) the execution time, (2) the output size, and (3) the start/end time taking into account the inter-stage dependencies. Using these predictions, we formulate checkpoint optimization as an integer programming problem and propose a scalable heuristic algorithm that meets the latency requirement of the production environment. We demonstrate the effectiveness of Phoebe in production workloads, and show that we can free the temporary storage on hotspots by more than 70% and restart failed jobs 68% faster on average with minimum performance impact. Phoebe also illustrates that adding multiple sets of checkpoints is not cost-efficient, which dramatically reduces the complexity of the optimization.
sbp-env: A Python Package for Sampling-based Motion Planner and Samplers

Sampling-based motion planners' testing environment (sbp-env) is a full feature framework to quickly test different sampling-based algorithms for motion planning. sbp-env focuses on the flexibility of tinkering with different aspects of the framework, and had divided the main planning components into two categories (i) samplers and (ii) planners. The focus of motion planning research had been mainly on (i) improving the sampling efficiency (with methods such as heuristic or learned distribution) and (ii) the algorithmic aspect of the planner using different routines to build a connected graph. Therefore, by separating the two components one can quickly swap out different components to test novel ideas.
Gauss: program synthesis by reasoning over graphs

While input-output examples are a natural form of specification for program synthesis engines, they can be imprecise for domains such as table transformations. In this paper, we investigate how extracting readily-available information about the user intent behind these input-output examples helps speed up synthesis and reduce overfitting. We present Gauss, a synthesis algorithm for table transformations that accepts partial input-output examples, along with user intent graphs. Gauss includes a novel conflict-resolution reasoning algorithm over graphs that enables it to learn from mistakes made during the search and use that knowledge to explore the space of programs even faster. It also ensures the final program is consistent with the user intent specification, reducing overfitting. We implement Gauss for the domain of table transformations (supporting Pandas and R), and compare it to three state-of-the-art synthesizers accepting only input-output examples. We find that it is able to reduce the search space by 56×, 73× and 664× on average, resulting in 7×, 26× and 7× speedups in synthesis times on average, respectively.
Agile Business Engineering: From Transformation Towards ContinuousInnovation

We discuss how to overcome the often fatal impact of violating integral quality constraints: seemingly successful (software) development projects turn into failures because of a mismatch with the business context. We investigate the similarities and differences between the today popular DevOps scenarios for aligning development and operations and the more general alignment problem concerning software and business engineering based on 33 structured expert interviews. It appears that both scenarios are driven by creativity in a continuous collaboration process relying on continuous goal validation. On the other hand, differences appear when considering Thorngate’s trade-off between accuracy, generality and simplicity: the different level of accuracy is the main hurdle for transferring the automation-driven DevOps technology. The paper closes with the hypothesis that this hurdle may be overcome by increasing the accuracy within the business context using domain-specific languages, a hypothesis supported by the interviews that now needs further confirmation via case studies.
Data-Free Ensemble Knowledge Distillation for Privacy-conscious Multimedia Model Compression

Recent advances in deep learning bring impressive performance for multimedia applications. Hence, compressing and deploying these applications on resource-limited edge devices via model compression becomes attractive. Knowledge distillation (KD) is one of the most popular model compression techniques. However, most well-behaved KD approaches require the original dataset, which is usually unavailable due to privacy issues, while existing data-free KD methods perform much worse than data-required counterparts. In this paper, we analyze previous data-free KD methods from the data perspective and point out that using a single pre-trained model limits the performance of these approaches. We then propose a Data-Free Ensemble knowledge Distillation (DFED) framework, which contains a student network, a generator network, and multiple pre-trained teacher networks. During training, the student mimics behaviors of the ensemble of teachers using samples synthesized by a generator, which aims to enlarge the prediction discrepancy between the student and teachers. A moment matching loss term assists the generator training by minimizing the distance between activations of synthesized samples and real samples. We evaluate DFED on three popular image classification datasets. Results demonstrate that our method achieves significant performance improvements compared with previous works. We also design an ablation study to verify the effectiveness of each component of the proposed framework.
La Covid-19, motor de cambio de la transformación educativa más grande de los últimos siglos

The changes in the time of COVID have transformed educational communication to unsuspected limits. Technology has taken over the educational literature or literary discourse of education to the point of disfiguring it, upsetting the traditional vision of schools and metamorphosing even the development of teachers and their habits, so that, some habits have disappeared and others have been transformed by adopting 180 degree turns. The objective of this study is to demonstrate how one of the greatest educational transformations in recent centuries is experienced. The methodology to prepare this work has a mixed character, but much more qualitative than quantitative, and the tools are articles published almost all throughout 2020, as well as direct observations of one's own experience in the field of education, which is where recent data could be extracted. The rest is part of the research experience and knowledge as a university educator. Thus, the end of the classroom-based education system may be near. Before that, however, we will likely see a purely hybrid model of education, as can be deduced from the comparison and exemplification of education systems from various countries in different parts of the world.
Intelligent retrieval method of mobile learning resources in the intelligent higher education system

Mobile learning has become more important for the new generation. It helps students think better, pushes them to study more deeply, and leads them to significant knowledge production. Mobile learning (mobile learning) is a learning paradigm that enables students to get resources from mobile technology and the Internet everywhere and anytime. The mobile learning components should be appropriately arranged. The interactions between the different components should be combined effectively and optimally for m-learning to be successful and effective. It is important to arrange the features of mobile learning and how they are applied to mobile learning activities, the application procedures, and the duration of the application time well in advance. In this paper, Human-interaction machine-based intelligent retrieved (HIM-IR) method has been suggested to improve student performance using mobile education. In mobile learning, students would find information through the network. Thus, the retrieval of quality information in support services is quite crucial. Mobile intelligent recovery would help the intelligence engine for mobile learning. The existing web server has technology on the server that doesn't have great precision and intelligence. The input string format is necessary for the retrieval process. The proposed methods aim to define the fundamental aspects and features of mobile learning in new trends in technological development. HIM-IR can be beneficial for anyone engaged in mobile learning design, preparation, and implementation.
Avalanche effect and bit independence criterion of perfectly secure Shannon cipher based on matrix power

In 2020 E. Sakalauskas with coauthors published a paper defining perfectly secure Shannon cipher based on matrix power function, proposing effective parallelization, and ensuring no need for multiple rounds encrypting one data block [1]. In this paper we present computational results with the avalanche effect and bit independence criterion (BIC). These criteria are important when describing the rate of confusion of bits in the ciphertext. It was observed that increasing matrix order and group size enhance BIC and avalanche effect results converging to the desired values. Based on the outputs it is possible to pick appropriate parameters satisfying security needs and available memory in a device where appropriate keys are going to be stored.
Insulator Contamination Grade Recognition Using the Deep Learning of Color Information of Images

To implement the non-contact detection of contamination on insulators, a contamination severity assessment methodology using the deep learning of the colored image information of insulators can be used. For the insulator images taken at the substation site, a mathematical morphology-improved optimal entropic threshold (OET) method is utilized to extract the insulator from the background. By performing feature calculations of insulator images in RGB and HSI color spaces, sixty-six color features are obtained. By fusing the features of the two color spaces using kernel principal component analysis (KPCA), fused features are obtained. The recognition of contamination grades is then accomplished with a deep belief network (DBN) that consists of a three-layered restricted Boltzmann machine. The experimental results of the images taken on-site show that the fused features obtained by the KPCA can fully reflect the contamination state of the insulators. Compared with the identification obtained using RGB or HSI color-space features alone, accuracy is significantly improved, and insulator contamination grades can be effectively identified. The research provides a new method for the accurate, efficient, and non-contact detection of insulator contamination grades.
Dryadic: Flexible and Fast Graph Pattern Matching at Scale

Graph pattern matching searches a data graph for all instances of one or more query patterns. Since it is one of the most fundamental problems in graph analytics, many graph pattern matching systems have been proposed with distinct features to provide a mix of flexibility and performance, and it is generally accepted that distinct use cases may necessitate the use of different systems. In this paper, we propose Dryadic, a system which integrates comprehensive flexibility features, yet can still outperform four state-of-the-art graph pattern matching systems on the primary use cases they target. Unlike existing systems that employ a case-by-case design strategy, all functionalities of Dryadic are centered around a powerful intermediate representation, the computation tree structure, which encodes the matching algorithms for arbitrary patterns. Dryadic implements novel techniques to optimize the computation tree and maps it to different backends to perform compiled, interpreted, or distributed graph pattern matching. Extensive experiments on nine real-world graphs of different scales show that Dryadic, despite its all-in-one nature, is often one to three orders of magnitude faster than other systems in three common usage scenarios.