Reviewerly/exHarmony huggingface dataset:
Vouw: Geometric Pattern Mining using the MDL Principle

We introduce geometric pattern mining, the problem of finding recurring local structure in discrete, geometric matrices. It differs from existing pattern mining problems by identifying complex spatial relations between elements, resulting in arbitrarily shaped patterns. After we formalise this new type of pattern mining, we propose an approach to selecting a set of patterns using the Minimum Description Length principle. We demonstrate the potential of our approach by introducing Vouw, a heuristic algorithm for mining exact geometric patterns. We show that Vouw delivers high-quality results with a synthetic benchmark.
Planning with Goal-Conditioned Policies

Planning methods can solve temporally extended sequential decision making problems by composing simple behaviors. However, planning requires suitable abstractions for the states and transitions, which typically need to be designed by hand. In contrast, model-free reinforcement learning (RL) can acquire behaviors from low-level inputs directly, but often struggles with temporally extended tasks. Can we utilize reinforcement learning to automatically form the abstractions needed for planning, thus obtaining the best of both approaches? We show that goal-conditioned policies learned with RL can be incorporated into planning, so that a planner can focus on which states to reach, rather than how those states are reached. However, with complex state observations such as images, not all inputs represent valid states. We therefore also propose using a latent variable model to compactly represent the set of valid states for the planner, so that the policies provide an abstraction of actions, and the latent variable model provides an abstraction of states. We compare our method with planning-based and model-free methods and find that our method significantly outperforms prior work when evaluated on image-based robot navigation and manipulation tasks that require non-greedy, multi-staged behavior.
Prediction Focused Topic Models for Electronic Health Records

Electronic Health Record (EHR) data can be represented as discrete counts over a high dimensional set of possible procedures, diagnoses, and medications. Supervised topic models present an attractive option for incorporating EHR data as features into a prediction problem: given a patient's record, we estimate a set of latent factors that are predictive of the response variable. However, existing methods for supervised topic modeling struggle to balance prediction quality and coherence of the latent factors. We introduce a novel approach, the prediction-focused topic model, that uses the supervisory signal to retain only features that improve, or do not hinder, prediction performance. By removing features with irrelevant signal, the topic model is able to learn task-relevant, interpretable topics. We demonstrate on a EHR dataset and a movie review dataset that compared to existing approaches, prediction-focused topic models are able to learn much more coherent topics while maintaining competitive predictions.
The Markup Language for Designing Gaze Controlled Applications

The Gaze Interaction Markup Language (GIML) is presented, which is new language for designing the gaze-controlled application and psychological experiments (also by non-programmers).
Long-range Prediction of Vital Signs Using Generative Boosting via LSTM  Networks

Vital signs including heart rate, respiratory rate, body temperature and blood pressure, are critical in the clinical decision making process. Effective early prediction of vital signs help to alert medical practitioner ahead of time and may prevent adverse health outcomes. In this paper, we suggest a new approach called generative boosting, in order to effectively perform early prediction of vital signs. Generative boosting consists of a generative model, to generate synthetic data for next few time steps, and several predictive models, to directly make long-range predictions based on observed and generated data. We explore generative boosting via long short-term memory (LSTM) for both the predictive and generative models, leading to a scheme called generative LSTM (GLSTM). Our experiments indicate that GLSTM outperforms a diverse range of strong benchmark models, with and without generative boosting. Finally, we use a mutual information based clustering algorithm to select a more representative dataset to train the generative model of GLSTM. This significantly improves the long-range predictive performance of high variation vital signs such as heart rate and systolic blood pressure.
OntoScene, A Logic-based Scene Interpreter: Implementation and Application in the Rock Art Domain

We present OntoScene, a framework aimed at understanding the semantics of visual scenes starting from the semantics of their elements and the spatial relations holding between them. OntoScene exploits ontologies for representing knowledge and Prolog for specifying the interpretation rules that domain experts may adopt, and for implementing the SceneInterpreter engine. Ontologies allow the designer to formalize the domain in a reusable way, and make the system modular and interoperable with existing multiagent systems, while Prolog provides a solid basis to define complex rules of interpretation in a way that can be affordable even for people with no background in Computational Logics. The domain selected for experimenting OntoScene is that of prehistoric rock art, which provides us with a fascinating and challenging testbed. Under consideration in Theory and Practice of Logic Programming (TPLP)
Principal Component Regression-Based Adaptive Multiple Extrema Seeking Cat Swarm Resource Optimized Task Scheduling In Cloud Computing

In this paper, task scheduling process is a challenging task in cloud computing to determine the best optimal virtual machine for each task. Many types of scheduling algorithms have been introduced for small or medium-scale cloud computing. However, dynamic scheduling is a major challenging problem for large-scale cloud computing environments. To address the issue, this paper proposes a novel technique called Principal Component Regression-based Adaptive Multiple Extrema Seeking Cat Swarm Resource Optimization (PCR-AMESCSRO) technique for efficient task scheduling with lesser makespan and higher efficiency. The PCR-AMESCSRO technique is designed with the contribution of Principal Component Regression (PCR) and Adaptive Multiple Extrema Seeking Cat Swarm Optimization Algorithm (AMESCSOA). First, the PCR is applied to analyze the user requested task and assign the priority level with lesser makespan. Second, the AMESCSOA is used to identify the optimal virtual machine by the cloud manager. Lastly, the experimental valuation is performed on factors such as task scheduling efficiency, false-positive rate, makespan, and memory consumption with respect to a number of user tasks. The observed results show the superior performance of our proposed PCR-AMESCSRO technique when compared to state-of-the-art methods.
A prototype Malayalam to Indian Sign Language (ISL) Automatic Translator

Sign Language is one of the major mediums of communication for deaf people. It uses gestures which makes use of hand, facial expression and other body parts to convey meaning. The use of parallel modification as opposed to the sequential nature of spoken language makes it difficult for efficient representation. This calls for the development of animation techniques for representing signs which can easily model human gestures. This paper presents a prototype Malayalam language text to Indian Sign Language (ISL) automatic translation system. The proposed system takes Malayalam text as input and generates corresponding Indian Sign Language (ISL) signs as output. Output animation is rendered using a computer-generated model. This system will help to disseminate information to deaf people in public utility places like railways, banks, hospitals etc. This will also act as an educational tool.
Improved Generalization Guarantees in Restricted Data Models

Differential privacy is known to protect against threats to validity incurred due to adaptive, or exploratory, data analysis -- even when the analyst adversarially searches for a statistical estimate that diverges from the true value of the quantity of interest on the underlying population. The cost of this protection is the accuracy loss incurred by differential privacy. In this work, inspired by standard models in the genomics literature, we consider data models in which individuals are represented by a sequence of attributes with the property that where distant attributes are only weakly correlated. We show that, under this assumption, it is possible to "re-use" privacy budget on different portions of the data, significantly improving accuracy without increasing the risk of overfitting.
Face Expressions Understanding by Geometrical Characterization of Deep Human Faces Representation

Face expressions understanding is a key to have a better understanding of the human nature. In this contribution we propose an end-to-end pipeline that takes color images as inputs and produces a semantic graph that encodes numerically what are facial emotions. This approach levrages low-level geometric details as face representation which are numerical representations of facial muscles activation patterns to build this emotional understanding. It shows that our method recovers social expectations of what characterize facial emotions.
Cauchy tensor and the classicality and separability condition of spin states

In this literature, we are concerned with the necessary condition for classicality and separability of the spin states by virtue of Cauthy tensors. The classicality and separability of the spin-j states are identified with the positive semidefiniteness of the 2j order associated tensor under tensor representation of quantum states. According to the properties of Bloch vector and trρ=1, we present necessary condition for classicality (separability) and entanglement of the spin-j states by virtue of Cauthy tensors with the positive generating vector c. This necessary condition not only perfectly maintains the physical properties of the quantum system, but also depends only on spin-j and the generating vector c. In addition, we show that Cauchy tensor method has a wider detection range and is easy to verify compared with the existing smallest tensor eigenvalue method.
Early childhood teachers’ experiences in the teacher learning community for the practice of play-based curriculum

&#x0D; This study aimed to explore the experiences of early childhood teachers participating in the teacher learning community for the practice of the play-based curriculum. For this, a teacher learning community was formed for 14 kindergarten teachers in Seoul and Gyeonggi regions to cover the subject ‘invigoration of play through the practice of a play-based curriculum’. And through participation observation, the operation process from March to July 2021 was collected, and qualitative analysis was performed. As a result, the experiences of early childhood teachers' participation in the teacher learning community for the practice of the play-based curriculum were found to be solving a maze, sharing the emotion of being together, changing perspectives through rediscovery, and endless voyages. This study shows that teachers experienced change and growth through participation in the teacher learning community, and has implications in that it recognized the importance of an equal cooperative structure among teachers and provided an opportunity to reconsider the role of teachers as collaborators and learners.&#x0D;
Proximal Support Matrix Machine

In this paper, we have proposed a novel model called proximal support matrix machine (PSMM), which is mainly based on the models of proximal support vector machine (PSVM) and low rank support matrix machine (LRSMM).In design, the PSMM model has comprehensively considered both the relationship between samples of the same class and the structure of rows or columns of matrix data.To a certain extent, our novel model can be regarded as a synthesis of the PSVM model and the LRSMM model.Since the PSMM model is an unconstrained convex problem in essence, we have established an alternating direction method of multipliers algorithm to deal with the proposed model.Finally, since a great deal of experiments on the minst digital database show that the PSMM classifier has a good ability to distinguish two digits with little difference, it encourages us to conduct more complex experiments on MIT face database, INRIA person database, the students face database and Japan female facial expression database.Meanwhile, the final experimental results show that PSMM performs better than PSVM, twin support vector machine, LRSMM and linear twin multiple rank support matrix machine in the demanding image classification tasks.
The Effect of Vocational School Leadership and Educational Innovations on the Culture of Innovation

The study aims to determine the effect of leadership implemented in vocational schools on the culture of innovation.The study expanded the empirical overview on vocational school principals' role in implementing educational innovation to improve the culture of innovation.The quantitative research method was used as the background of the study that can be developed for further research.Linear regression was implemented as the technique of data analysis.The data were collected through observation, interview, and questionnaire.The research uses primary data and simple secondary information from random sampling as the technic of research examples.The respondents as the source of the research are 50 vocational school principals in Bekasi.The result showed that, partially, leadership and educational innovation positively and significantly affect the culture of innovation.
Real-Time Implementation of CNN-Based Face Mask Detection: A Method to Diminish the Risk of COVID-19

The fast occurrence of COVID-19 has caused significant harm and infected trillions of people globally. The COVID-19 pandemic has urged communities to take careful measures for safety against the virus. One of the measures is wearing a face mask. Although the Indian government made wearing a mask mandatory in all public places. Some people violate the rule by not wearing a mask. In such cases, a huge risk encounters for the surrounded public also. If we have a mechanism wherein people can be monitored using cameras at public places and there is software that automatically detects whether the public wears mask or not, a huge boon. Our project will serve the above purpose. The application will take live image surveillance as input and will classify if public wearing or not the face masks. The model that proposed here will be built using deep learning approaches like convolutional neural networks (CNNs) and trained using a large pre-classified dataset. The model is adaptive, so the application can identify all types of masks with dependable accuracy.
Security Enhancement in Cloud Environment Using Modified Key-based Crypto Method

Rapid advancement of information and communication technology in amalgamation of web has led to evolution of cloud computing. There is no apprehension that cloud computing is the most prevailing technological advancement in the field of computation and has become a key driving factor to many ameliorations. In spite of continuous growth in popularity, concerns about increased security risks continue to limit its adoption and cloud services could be impeding due to security issues. In this paper, a security framework called as modified key-based crypto method (MKBCM) is proposed which is improvisation of key-based crypto method (KBCM). MKBCM ensures security to a greater extent. It was compared with existing works in literature and found to produce promising results.
Computational Models of Reading and Mathematical Difficulties

Computational modelling is a powerful tool in cognitive science to evaluate or compare existing theories and to make novel experimental predictions. In contrast to the vague formulation of traditional verbal theories (e.g., box-and-arrow models), computational models need to be formally explicit in any implementational detail and can produce accurate simulations of human performance. Computational modelling has many different flavours that reflect distinct theoretical approaches to understanding human cognition (see McClelland 2009, for a review).
In Search of the Good Life: Through the Eyes of Aristotle, Maimonides, and AquinasCoreyMiller. Eugene, OR: Pickwick, 2019. Pp. x, 205. $26.00/£19.00.

The Heythrop JournalVolume 63, Issue 4 p. 860-861 Book Review In Search of the Good Life: Through the Eyes of Aristotle, Maimonides, and Aquinas , Corey Miller. Eugene, OR: Pickwick, 2019. Pp. x, 205. $26.00/£19.00. First published: 27 July 2022 https://doi.org/10.1111/heyj.14120Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume63, Issue4July 2022Pages 860-861 RelatedInformation
A Study on Cyber Sexual Violence Crimes

Crimes occurring on the cyber world today are causing more fatal damage than crimes occurring in the real world. Among them, cyber sex crimes are even more serious. In the case of cyber sexual violence crimes, there is a continuous increase, and there are many cases where they are not caught with new methods despite the strictness of punishment, so active response seems necessary.&#x0D; The activities of criminal justice agencies alone cannot monitor vast cyber sexual violence crimes, and furthermore, it is difficult to grasp the actual state of damage due to the development of new methods. Therefore, above all, cyber sexual violence crimes should be suppressed and prevented through active reporting. Accordingly, this study analyzed the influence of the perception of punishment on the intention to report from the perspective of inhibition theory on cyber sexual violence that occurs through logistic regression analysis.&#x0D; As a result of the analysis, the certainty of punishment for cyber sexual violence crimes was statistically significant at the level of .05. Looking at this, it was found that the higher the intention to report was 1.8 times higher as the punishment for cyber sexual violence crimes was clearly carried out. Therefore, the speed, severity, and certainty of punishment for cyber sexual violence crimes should all be guaranteed, but more policy alternatives should be sought to recognize that punishment is certain in the case of cyber sexual violence.
Are Existing Tort Theories Ready for AI?

Tort law has a number of systems and structures by which the system will be used to address the challenges posed by AI technologies. It will not be necessary to significantly alter our understanding of tort law’s foundations to be ready for AI, and this may significantly and potentially affect AI innovation and utilization.
Efficiency through Auto-Sizing: Notre Dame NLP's Submission to the WNGT 2019 Efficiency Task

This paper describes the Notre Dame Natural Language Processing Group's (NDNLP) submission to the WNGT 2019 shared task (Hayashi et al., 2019). We investigated the impact of auto-sizing (Murray and Chiang, 2015; Murray et al., 2019) to the Transformer network (Vaswani et al., 2017) with the goal of substantially reducing the number of parameters in the model. Our method was able to eliminate more than 25% of the model's parameters while suffering a decrease of only 1.1 BLEU.
Learning Navigation by Visual Localization and Trajectory Prediction

When driving, people make decisions based on current traffic as well as their desired route. They have a mental map of known routes and are often able to navigate without needing directions. Current self-driving models improve their performances when using additional GPS information. Here we aim to push forward self-driving research and perform route planning even in the absence of GPS. Our system learns to predict in real-time vehicle's current location and future trajectory, as a function of time, on a known map, given only the raw video stream and the intended destination. The GPS signal is available only at training time, with training data annotation being fully automatic. Different from other published models, we predict the vehicle's trajectory for up to seven seconds ahead, from which complete steering, speed and acceleration information can be derived for the entire time span. Trajectories capture navigational information on multiple levels, from instant steering commands that depend on present traffic and obstacles ahead, to longer-term navigation decisions, towards a specific destination. We collect our dataset with a regular car and a smartphone that records video and GPS streams. The GPS data is used to derive ground-truth supervision labels and create an analytical representation of the traversed map. In tests, our system outperforms published methods on visual localization and steering and gives accurate navigation assistance between any two known locations.
Unsupervised Speaker Diarization that is Agnostic to Language, Overlap-Aware, and Tuning Free

Podcasts are conversational in nature and speaker changes are frequent -- requiring speaker diarization for content understanding. We propose an unsupervised technique for speaker diarization without relying on language-specific components. The algorithm is overlap-aware and does not require information about the number of speakers. Our approach shows 79% improvement on purity scores (34% on F-score) against the Google Cloud Platform solution on podcast data.
Aesthetic Image Captioning From Weakly-Labelled Photographs

Aesthetic image captioning (AIC) refers to the multi-modal task of generating critical textual feedbacks for photographs. While in natural image captioning (NIC), deep models are trained in an end-to-end manner using large curated datasets such as MS-COCO, no such large-scale, clean dataset exists for AIC. Towards this goal, we propose an automatic cleaning strategy to create a benchmarking AIC dataset, by exploiting the images and noisy comments easily available from photography websites. We propose a probabilistic caption-filtering method for cleaning the noisy web-data, and compile a large-scale, clean dataset "AVA-Captions", (230, 000 images with 5 captions per image). Additionally, by exploiting the latent associations between aesthetic attributes, we propose a strategy for training the convolutional neural network (CNN) based visual feature extractor, the first component of the AIC framework. The strategy is weakly supervised and can be effectively used to learn rich aesthetic representations, without requiring expensive ground-truth annotations. We finally show-case a thorough analysis of the proposed contributions using automatic metrics and subjective evaluations.
Towards Assessing the Impact of Bayesian Optimization's Own  Hyperparameters

Bayesian Optimization (BO) is a common approach for hyperparameter optimization (HPO) in automated machine learning. Although it is well-accepted that HPO is crucial to obtain well-performing machine learning models, tuning BO's own hyperparameters is often neglected. In this paper, we empirically study the impact of optimizing BO's own hyperparameters and the transferability of the found settings using a wide range of benchmarks, including artificial functions, HPO and HPO combined with neural architecture search. In particular, we show (i) that tuning can improve the any-time performance of different BO approaches, that optimized BO settings also perform well (ii) on similar problems and (iii) partially even on problems from other problem families, and (iv) which BO hyperparameters are most important.