Reviewerly/exHarmony huggingface dataset:
Better Classifier Calibration for Small Datasets

Classifier calibration does not always go hand in hand with the classifier's ability to separate the classes. There are applications where good classifier calibration, i.e. the ability to produce accurate probability estimates, is more important than class separation. When the amount of data for training is limited, the traditional approach to improve calibration starts to crumble. In this article we show how generating more data for calibration is able to improve calibration algorithm performance in many cases where a classifier is not naturally producing well-calibrated outputs and the traditional approach fails. The proposed approach adds computational cost but considering that the main use case is with small data sets this extra computational cost stays insignificant and is comparable to other methods in prediction time. From the tested classifiers the largest improvement was detected with the random forest and naive Bayes classifiers. Therefore, the proposed approach can be recommended at least for those classifiers when the amount of data available for training is limited and good calibration is essential.
What Information is Required for Explainable AI? : A Provenance-based Research Agenda and Future Challenges

Deriving explanations of an Artificial Intelligence-based system's decision making is becoming increasingly essential to address requirements that meet quality standards and operate in a transparent, comprehensive, understandable, and explainable manner. Furthermore, more security issues as well as concerns from human perspectives emerge in describing the explainability properties of AI. A full system view is required to enable humans to properly estimate risks when dealing with such systems. This paper introduces open issues in this research area to present the overall picture of explainability and the required information needed for the explanation to make a decision-oriented AI system transparent to humans. It illustrates the potential contribution of proper provenance data to AI-based systems by describing a provenance graph-based design. This paper proposes a six-Ws framework to demonstrate how a security-aware provenance graph-based design can build the basis for providing end-users with sufficient meta-information on AI-based decision systems. An example scenario is then presented that highlights the required information for better explainability both from human and security-aware aspects. Finally, associated challenges are discussed to provoke further research and commentary.
Adaptive Batchsize Selection and Gradient Compression for Wireless Federated Learning

In wireless federated learning system, wireless communication and local computation have a significant impact on the learning latency due to the limited bandwidth and computing power of mobile devices. To reduce the learning latency, local stochastic gradient methods and gradient compression can be applied, which however would decrease the convergence rate. To tackle such issues, in this paper, the trade-off between the convergence rate and the learning latency is taken into account. We first formulate an optimization problem to maximize the convergence rate under the given training latency constraint via jointly optimizing the batchsize, compression ratio, and spectrum allocation. Then, by decomposing the problem into two subproblems, an adaptive algorithm is proposed to obtain the optimal solution. The results show that batchsize and compression ratio should be selected according to the computing power and channel state information of the devices to improve the convergence rate. Finally, experimental results are presented to verify the effectiveness of the proposed algorithm.
Augmented Reality and Older Adults: A Comparison of Prompting Types

Older adults can benefit from technologies that help them to complete everyday tasks. However, they are an often-under-represented population in augmented reality (AR) research. We present the results of a study in which people aged 50 years or older were asked to perform actions by interpreting visual AR prompts in a lab setting. Our results show that users were less successful at completing actions when using ARROW and HIGHLIGHT augmentations than when using ghosted OBJECT or GHOSTHAND augmentations. We found that user confidence in performing actions varied according to action and augmentation type. Users preferred combined AUDIO+TEXT prompts (our control condition) overall, but the GHOSTHAND was the most preferred visual prompt. We discuss reasons for these differences and provide insight for developers of AR content for older adults. Our work provides the first comparative study of AR with older adults in a non-industrial context.
Adaptive Non-Linear Joint Probabilistic Data Association for Vehicle Target Tracking

This paper investigated the problem of multi-target tracking (MTT) over a vehicle sensor network. A novel adaptive square root cubature joint probabilistic data association (ASRCJPDA) was proposed. Motivated by enhancing the stability of joint probabilistic data association (JPDA) in practical application, the proposed methodology implemented a numerically stabled cubature Kalman filter for JPDA state estimate process. It improved numerical stability and acquired more accurate estimated results. Additionally, enlightened by enhancing the real time efficiency of JPDA, an adaptive tracking gate designed for the JPDA measurement associate process was proposed. It combined with the kinematics of vehicle to reduce the computational complexity of data association, which improved the robustness of MTT in complex scenarios. The virtual vehicle target tracking scenarios were built in PreScan software in order to better simulate the real traffic condition. Simulations of target tracking examples are presented to show great effectiveness and superiority of the proposed method.
Discovering UI display issues with visual understanding

GUI complexity posts a great challenge to the GUI implementation. According to our pilot study of crowdtesting bug reports, display issues such as text overlap, blurred screen, missing image always occur during GUI rendering on difference devices due to the software or hardware compatibility. They negatively influence the app usability, resulting in poor user experience. To detect these issues, we propose a novel approach, OwlEye, based on deep learning for modelling visual information of the GUI screenshot. Therefore, OwlEye can detect GUIs with display issues and also locate the detailed region of the issue in the given GUI for guiding developers to fix the bug. We manually construct a large-scale labelled dataset with 4,470 GUI screenshots with UI display issues. We develop a heuristics-based data augmentation method and a GAN-based data augmentation method for boosting the performance of our OwlEye. At present, the evaluation demonstrates that our OwlEye can achieve 85% precision and 84% recall in detecting UI display issues, and 90% accuracy in localizing these issues.
Multi-Cloud: A Comprehensive Review

In the span of a decade, innovations in cloud computing have led to a new understanding of computing to be used as a utility. Majority of cloud service providers are making the service better and competitive for end-user. Aside from the number of services introduced by these providers, users are feeling uneasy and are unaware of consequences while switching from one service to another. Internal architecture of the cloud makes it difficult for end-users to understand. To overcome this issue a new concept of multi-cloud has been introduced. In multi-cloud technology, we can use multiple clouds from different vendors without platform complexity. Hence summarized, Multi-cloud is the usage of autonomous cloud platforms with one interface which may clue to different administrative and implementation domains. This paper reviews the literature of recently presented solutions and architectures for multi-cloud platforms.
Dual aggregated feature pyramid network for multi label classification

While many deep convolutional neural networks show promising performance in various classification tasks, multiple objects appearing in very different sizes, shapes, and appearances cause difficulty in multi-label classification using conventional neural networks. In this paper, we introduce a dual aggregated network on pyramidal convolutional features for multi-label classification. The proposed method includes both feature- and classifier-level aggregation to learn discriminant multi-scale information of various target objects in the image. First, the feature-level aggregation collects the convolutional activation maps from the multi-scale pyramid network, and then it densely pools them to take localized features of each object. We elaborately design the feature aggregation method so that the responses from the objects with different sizes, aspect ratios, and shapes are properly reflected the aggregated activation map. Unlike conventional methods, this process does not require the region proposal step, which reduces the computational burden significantly. Second, we introduce the classifier level aggregation algorithm for integrating the multi-object classifier modules. To maximize the discrimination power of each class, we train one-vs-all classifiers for individual classes using the class-wise loss function. For each test image, the scores from the class-wise classifiers are aggregated to get the final multi-label classification result. By combining the above feature- and classifier-level aggregation methods, our network can be trained in an end-to-end fashion, which is not possible for the conventional multi-label classification algorithms using region proposals. Extensive evaluations on PASCAL VOC 2007 and PASCAL VOC 2012 demonstrate that the proposed algorithm outperforms the state-of-the-art methods.
Closing-the-Loop: A Data-Driven Framework for Effective Video Summarization

Today, videos are the primary way in which information is shared over the Internet. Given the huge popularity of video sharing platforms, it is imperative to make videos engaging for the end-users. Content creators rely on their own experience to create engaging short videos starting from the raw content. Several approaches have been proposed in the past to assist creators in the summarization process. However, it is hard to quantify the effect of these edits on the end-user engagement. Moreover, the availability of video consumption data has opened the possibility to predict the effectiveness of a video before it is published. In this paper, we propose a novel framework to close the feedback loop between automatic video summarization and its data-driven evaluation. Our Closing-The-Loop framework is composed of two main steps that are repeated iteratively. Given an input video, we first generate a set of initial video summaries. Second, we predict the effectiveness of the generated variants based on a data-driven model trained on users' video consumption data. We employ a genetic algorithm to search the space of possible summaries (i.e., adding/removing shots to the video) in an efficient way, where only those variants with the highest predicted performance are allowed to survive and generate new variants in their place. Our results show that the proposed framework can improve the effectiveness of the generated summaries with minimal computation overhead compared to a baseline solution - 28.3% more video summaries are in the highest effectiveness class than those in the baseline.
Column-based Decoder of Internal Prediction Representation in Cortical Learning Algorithms

This work proposes a decoder of internal prediction representation in the cortical learning algorithm (CLA). CLA is a time-series data prediction algorithm. CLA receives an input bit-string at each time-step and encodes it to an internal data representation based on the relationship between the input data bits and columns, which are components of the CLA predictor. To decode the internal predictive presentation to the same format of the input bit-string, the conventional CLA employs the sparse distributed representations classifier (SDRC), which uses the relationship between the input data bits and cells, which are other components of the CLA predictor. To build the relationship between the input data bits and the cells, the conventional SDRC additionally needs a time-consuming learning process and a learning parameter, and the prediction accuracy cannot be expected during its learning process. In order to overcome these problems of the SDRC decoder in the conventional CLA and improve the prediction accuracy, the proposed method decodes the prediction representation inside CLA by utilizing the relationship between the input data bits and the columns built during the encoding process. The proposed method avoids the additional learning process to build the relationship between the input data bits and the cells. Also, The proposed method avoids the setting of any learning parameter needed in the conventional SDRC decoder. Experimental results using artificial benchmark and real-world electricity load time-series data show the proposed column-based decoder contributes to rapidly decreasing the prediction error and achieving smaller prediction error than the conventional SDRC decoder.
Results from a 2020 field experiment encouraging voting by mail

The ability to cast a mail ballot can safeguard the franchise. However, because there are often additional procedural protections to ensure that a ballot cast in person counts, voting by mail can also jeopardize people's ability to cast a recorded vote. An experiment carried out during the COVID-19 pandemic illustrates both forces. Philadelphia officials randomly sent 46,960 Philadelphia registrants postcards encouraging them to apply to vote by mail in the lead-up to the June 2020 primary election. While the intervention increased the likelihood a registrant cast a mail ballot by 0.4 percentage points (P = 0.017)-or 3%-many of these additional mail ballots counted only because a last-minute policy intervention allowed most mail ballots postmarked by Election Day to count.
Solving the Rubik's cube with stepwise deep learning

Abstract This paper explores a novel technique for learning the fitness function for search algorithms such as evolutionary strategies and hillclimbing. The aim of the new technique is to learn a fitness function (called a Learned Guidance Function ) from a set of sample solutions to the problem. These functions are learned using a supervised learning approach based on deep neural network learning, that is, neural networks with a number of hidden layers. This is applied to a test problem: unscrambling the Rubik's Cube using evolutionary and hillclimbing algorithms. Comparisons are made with a previous LGF approach based on random forests, with a baseline approach based on traditional error‐based fitness, and with other approaches in the literature. This demonstrates how a fitness function can be learned from existing solutions, rather than being provided by the user, increasing the autonomy of AI search processes.
This is not an apple! Benefits and challenges of applying computer vision to museum collections

The application of computer vision on museum collection data is at an experimental stage with predictions that it will grow in significance and use in the coming years. This research, based on the analysis of five case studies and semi-structured interviews with museum professionals, examined the opportunities and challenges of these technologies, the resources and funding required, and the ethical implications that arise during these initiatives. The case studies examined in this paper are drawn from: The Metropolitan Museum of Art (USA), Princeton University Art Museum (USA), Museum of Modern Art (USA), Harvard Art Museums (USA), Science Museum Group (UK). The research findings highlight the possibilities of computer vision to offer new ways to analyze, describe and present museum collections. However, their actual implementation on digital products is currently very limited due to the lack of resources and the inaccuracies created by algorithms. This research adds to the rapidly evolving field of computer vision within the museum sector and provides recommendations to operationalize the usage of these technologies, increase the transparency on their application, create ethics playbooks to manage potential bias and collaborate across the museum sector.
Explaining BDI agent behaviour through dialogue

Abstract BDI agents act in response to external inputs and their internal plan library. Understanding the root cause of BDI agent action is often difficult, and in this paper we present a dialogue based approach for explaining the behaviour of a BDI agent. We consider two dialogue participants who may have different views regarding the beliefs, plans and external events which drove agent action (encoded via traces). These participants make utterances which incrementally reveal their traces to each other, allowing them to identify divergences in the traces, or to conclude that their traces agree. In practice, we envision a human taking on the role of a dialogue participant, with the BDI agent itself acting as the other participant. The dialogue then facilitates explanation, understanding and debugging of BDI agent behaviour. After presenting our formalism and its properties, we describe our implementation of the system and provide an example of its use in a simple scenario.
Machine Learning Approach for Hairstyle Recommendation

According to aesthetic evaluations, hair is the most unique feature which can enhance the facial features of a person. Beauty experts have identified that 70% of overall face appearance completely depends on the haircut or hairstyle. The physical attributes such as the haircut is a major determinant of women's psychology. This is the essence of why a haircut which is matching a woman's face is necessary articulation. But selecting the right haircut or hairstyle is one of the most difficult decisions to take in a woman's life. This paper presents a novel framework to select the most suitable hairstyle or haircut by classifying the face shape. The author considers the shape of the face, beauty experts knowledge related to hair cuts and hairstyles and the length of the hair to develop a model to recommend the most suitable hairstyle or haircut. The author focused to recommend the haircuts and hairstyles for women which is a subsection of this large research area. According to beauty experts identifying the shape of the face is the most important step before selecting the right hairstyle or haircut. The proposed model has the ability to classify the face shape when a user uploaded a portrait of herself. Machine Learning libraries were used to identify the landmarks of the face image and classify the face in the correct shape. Naïve Bayes classification algorithm has used to recommend the most suitable hairstyle or haircut according to the detected face shape., hair length and information collected from the hair experts. User has given an option to share the recommended hair style or haircut with the beautician via “The Beauty Quest” Salon network platform. Five thousand images were trained, and python language has used as the programming language. The accuracy of the face shape classification model is 91% and the accuracy of the hair recommendation is also 83%.
Real-Time Human Detection with Thermal Camera Feed using YOLOv3

Human detection is needed for various applications such as advanced driver assistance systems and autonomous driving, security and surveillance etc. Thermal imaging is often adopted for night time because of its capability of capturing the energy emitted from human body where visible light camera fails. In this paper, we employ YOLOv3 for an accurate real-time human detection using thermal images. We modified the network parameters according to the characteristics of the human, making this method more suitable for detecting human. Subset of Korea Advanced Institute of Science and Technology (KAIST) multispectral dataset consisting of 47650 thermal images is used for training and testing of YOLOv3. During experimentation, it is observed that humans are detected at 17 millisecond which is much faster than a local machine detection. Our test result also shows improved performance of the detector with thermal image with average precision of 95.5% and miss rate of 4.7%.
Exploration and Practice of Dance Teaching Mode under the Internet Background

Abstract In early 2020, the new crown pneumonia epidemic swept in, in order to block the spread of the epidemic to the campus, the Ministry of Education issued the “spring semester delayed school opening” request, “stop classes do not stop” the voice immediately rang. At one time, how to combine modern information technology with the teaching content of various subjects to maximize the efficiency of online teaching has become the primary problem for every teacher to think about, and in this environment, the dance educators who are mainly based on the traditional teaching form of “oral teaching” also ushered in a great test. By analyzing the advantages and disadvantages of traditional dance teaching methods and based on the author’s online teaching practice activities for one semester, this paper makes a comprehensive analysis of the application value and significance of modern information technology in Chinese dance teaching, in order to further enrich the Chinese dance teaching mode, optimize the dance teaching resources, make the dance teaching methods and contents more in line with the requirements of China’s modern technology, and provide theoretical basis and practical operation guidance for the future modern dance teaching mode.
Classification from positive and unlabeled data based on likelihood invariance for measurement

We propose novel approaches for classification from positive and unlabeled data (PUC) based on maximum likelihood principle. These are particularly suited to measurement tasks in which the class prior of the target object in each measurement is unknown and significantly different from the class prior used for training, while the likelihood function representing the observation process is invariant over the training and measurement stages. Our PUCs effectively work without estimating the class priors of the unlabeled objects. First, we present a PUC approach called Naive Likelihood PUC (NL-PUC) using the maximum likelihood principle in a nontrivial but rather straightforward manner. The extended version called Enhanced Likelihood PUC (EL-PUC) employs an algorithm iteratively improving the likelihood estimation of the positive class. This is advantageous when the availability of the labeled positive data is limited. These characteristics are demonstrated both theoretically and experimentally. Moreover, the practicality of our PUCs is demonstrated in a real application to single molecule measurement.
New concepts for cluster construction and similarity measurement

In this study we introduce two new concepts: (1) a new approach to construct clusters and (2) a methodology to compute similarities between numerical vectors based on clusters. The new approach to construct clusters is based on a variable-distance threshold. There are several important domains where using commonly utilized fixed distance threshold clustering method might create clusters contradicting human expert reasoning. The domains where variable-distance threshold clustering is more suitable are discussed and explained. In addition, we introduce a new concept for computing similarities between two numerical vectors, based on a membership in corresponding clusters. Such a concept constitutes an appropriate tool under greater degree of uncertainty where model structure is vague, and data are unreliable. First, we describe the procedure for construction of variable-distance threshold clusters. Then we provide several models for computing the similarity between the two numerical vectors based on clusters. Several examples are included to illustrate the practical application of the models.