Reviewerly/exHarmony huggingface dataset:
Deep classification of breast cancer in ultrasound images: more classes, better results with multi-task learning

Ultrasound (US) is a low-cost, portable, and safe tool for breast cancer screening. However, automatic classification of invasive ductal carcinoma (IDC) in US is a difficult classification task due to their similar appearance to fibroadenoma (FA) (a type of benign tumor). Another challenge is the limited availability of US data with ground truth labels, further complicating the adoption of deep learning techniques for IDC detection. It has been shown that deep classification networks perform better when they simultaneously learn multiple correlated tasks. However, most previous studies on breast US classifications focused on the binary classification of benign versus malignant tumors. To this end, we propose a multi-class classification deep learning-based strategy mainly focusing on the classification of IDC. Inspired by multi-task learning (MTL), we adopt a novel scheme in adding the background tissue as an additional class and show substantial improvements in IDC detection.
A self‐adjusting task granularity mechanism for the Java lifeline‐based global load balancer library on many‐core clusters

Summary Global load balancer libraries should be easy to use and allow users to easily obtain good performance for their applications on a variety of distributed systems. In this article, we introduce a new tuning mechanism to our Java implementation of the lifeline‐based global load balancer which automatically adjusts the task granularity to reach good performance based on some selected runtime metrics. We evaluate our system against four backtrack‐search problems on both a many‐core supercomputer environment and on a beowulf server, achieving ideal performance with our tuning mechanism on the supercomputer. We also identify the limits of our mechanism in handling situations with reduced imbalance.
Psychological Pedagogy for Extended Reality in STEMx Education

This work-in-progress paper describes a project that aims to explore the quality assurance of learning outcomes in extended reality focused on STEMx education and the affordability of Metasystems Learning Design Theory in STEMx from the perspective of psychological pedagogy. Authors contend that extended reality technologies for learning should be contingent on psychological pedagogy requirements to improve metacognitive knowledge, competence, and experience of the learner. To this end, it is proposed a “roadmap” for future research that encompasses studies based on metasystems learning design norms, which integrates psychological pedagogy with actualized research in philosophy, cybernetics, psychology, pedagogy, and management.
A User-Centered Agile Approach to the Development of a Real-World Social Robot Application for Reception Areas

As social robots are increasingly entering the real world, developing a viable robot application has become highly important. While a growing body of research has acknowledged that the integration of an agile development methodology with user-centered design (UCD) provides advantages for both organizations and end users, integrating UCD in an agile methodology has been a challenging endeavor. The present paper illustrates a user-centered agile approach that integrates user perspectives through formative usability testing during an agile development process of a robot application and thus differentiates from most robot application evaluations, which conduct summative usability testing (i.e., they quantitatively test goal achievement after technological developments). Through an active involvement of organization and end users, the intermediate results of our ongoing project show that the developed social robot application is both useful and usable.
Storyline Visualizations with Ubiquitous Actors

Storyline visualizations depict the temporal dynamics of social interactions, as they describe how groups of actors (individuals or organizations) change over time. A common constraint in storyline visualizations is that an actor cannot belong to two different groups at the same time instant. However, this constraint may be too severe in some application scenarios, thus we generalize the model by allowing an actor to simultaneously belong to distinct groups at any point in time. We call this model Storyline with Ubiquitous Actors (SUA). Essential to our model is that an actor is represented as a tree rather than a single line. We describe an algorithmic pipeline to compute storyline visualizations in the SUA model and discuss case studies on publication data.
Quantum image interest point extraction

With the development of quantum computing, the application of it to image processing has lots of advantages compared to classical image processing. In this paper, we propose a scheme to extract the interest point in quantum images. Interest point is a kind of feature point which can help to identify the target object in the image. Our scheme is based on the idea of Luminance Contrast (LC) algorithm. The scheme computes the absolute value of gray level differences between a pixel and the others, and then adds all these differences together. The sum is defined as a saliency. After computing the saliency of every pixel, we label the pixels with the maximal saliency as the interest points. The algorithm has pretty good performance and its time complexity is much better than the classical algorithm in same conditions, which provides a new idea for the extraction of image interest point.
Preceding Vehicle Detection Based on Optimized Faster R-CNN Algorithm

Abstract Preceding vehicle detection is still a challenge for unmanned driving technology. Deep learning has achieved great success in target detection. Among them, the Faster R-CNN algorithm is a classic representative. However, the algorithm still has some room for improvement in detection accuracy. By analyzing the problems of Faster R-CNN in the detection of occluded vehicles, taking the target detection post-processing algorithm Soft-NMS as the research object, two new penalty coefficients, inverse proportional penalty coefficient and exponential penalty coefficient, were proposed. It further improves the algorithm’s detection accuracy of the blocked vehicle in front.
Pyramidal Edge-Maps and Attention Based Guided Thermal Super-Resolution

Guided super-resolution (GSR) of thermal images using visible range images is challenging because of the difference in the spectral-range between the images. This in turn means that there is significant texture-mismatch between the images, which manifests as blur and ghosting artifacts in the super-resolved thermal image. To tackle this, we propose a novel algorithm for GSR based on pyramidal edge-maps extracted from the visible image. Our proposed network has two sub-networks. The first sub-network super-resolves the low-resolution thermal image while the second obtains edge-maps from the visible image at a growing perceptual scale and integrates them into the super-resolution sub-network with the help of attention-based fusion. Extraction and integration of multi-level edges allows the super-resolution network to process texture-to-object level information progressively, enabling more straightforward identification of overlapping edges between the input images. Extensive experiments show that our model outperforms the state-of-the-art GSR methods, both quantitatively and qualitatively.
Image classification using delay-based optoelectronic reservoir computing

Reservoir computing has emerged as a lightweight, high-speed machine learning paradigm. We introduce a new optoelectronic reservoir computer for image recognition, in which input data is first pre-processed offline using two convolutional neural network layers with randomly initialized weights, generating a series of random feature maps. These random feature maps are then multiplied by a random mask matrix to generate input nodes, which are then passed to the reservoir computer. Using the MNIST dataset in simulation, we achieve performance in line with state-of-the-art convolutional neural networks (1% error), while potentially offering order-of-magnitude improvement in training speeds.
Weapons of War, Tools of Justice

Abstract Just as the internal combustion engine revolutionized warfare in the early twentieth century, artificial intelligence is shaping warfare in the Digital Age. Fuelled by data rather than gasoline, artificial intelligence (AI) derivative technologies are driven by innovation in both the military and civilian sectors. Today’s defence scientists and engineers, like their predecessors, develop physical equipment and weapons, but instead of simply making them more powerful and lethal, they are also making them more intelligent and connected. As AI is increasingly integrated into military tools, the digital footprints of modern battlefields will grow exponentially, generating new sources and types of data that will fundamentally alter war crimes investigations. This article examines emerging military applications of AI in order to identify what opportunities and challenges these tools might offer international criminal investigators. Will more sensors and smart devices on the battlefield benefit or burden the investigation of war crimes? Moreover, will intelligent machines add to the fog of war or help us see through it?
HDRS: Hindi Dialogue Restaurant Search Corpus for Dialogue State Tracking in Task-Oriented Environment

Due to the rapid increase in the development of Task-oriented dialogue systems, the need for labelled dialogue corpus has become inevitable. For the Hindi language, there is no such dialogue corpus yet available. As a first attempt, we release a Hindi Dialogue Restaurant Search (HDRS) corpus and compare various state-of-the-art dialogue state tracking (DST) models on it. The corpus consists of 1.4 k human-to-human typed dialogues collected using Wizard-of-Oz paradigm. The paper starts with a brief description of the corpus by providing the details of features, corpus collection process and statistical analysis, then the performance of baseline NLU and DST models are investigated. Further, we experimented two categories of state-of-the-art belief state trackers: (1) Non-contextual pre-trained word embedding based DST models; (2) Contextual pre-trained BERT based DST models. All belief trackers follow a three-layered generic architecture. The category-1 models use the static domain ontology, while category-2 models have the capability to handle the dynamic ontology. The DST models are compared on joint-goal and turn-request accuracy. Global encoder and Slot-ATtentive decoders (GSAT) outperforms all the models with 83.25% joint-goal accuracy, followed by SUMBT.
Convolutional Neural Network Equipped with Attention Mechanism and Transfer Learning for Enhancing Performance of Sentiment Analysis

With the rapid development of textual information on the web, sentiment analysis is changing to an essential analytic tool rather than an academic endeavor and numerous studies have been carried out in recent years to address this issue. By the emergence of deep learning, deep neural networks have attracted a lot of attention and become mainstream in this field. Despite the remarkable success of deep learning models for sentiment analysis of text, they are in the early steps of development and their potential is yet to be fully explored. Convolutional neural network is one of the deep learning methods that has been surpassed for sentiment analysis but is confronted with some limitations. Firstly, convolutional neural network requires a large number of training data. Secondly, it assumes that all words in a sentence have an equal contribution to the polarity of a sentence. To fill these lacunas, a convolutional neural network equipped with the attention mechanism is proposed in this paper which not only takes advantage of the attention mechanism but also utilizes transfer learning to boost the performance of sentiment analysis. According to the empirical results, our proposed model achieved comparable or even better classification accuracy than the state-of-the-art methods.
Discussion on Practice and Promotion Strategy of College Music Education Informatization Based on Artificial Intelligence

The application of artificial intelligence (AI) in music education (ME) has broken the traditional ME model and greatly improved the quality of music teaching. The purpose of this article is to promote the in-depth integration of college music and information education by discussing the informationization of college ME under AI. In this paper, we conducted a questionnaire survey on students from several major universities in City A to understand the current status of the informationization of ME in universities, and randomly selected some students for interviews. It is concluded that 88.1% of the students surveyed believe that the combination of informatization and ME has enriched music resources; 73.81% of the students surveyed believed that education informatization promoted communication between teachers and students and broadened their academic horizons. The research in this article is helpful to the application of informatization of ME in colleges and universities (CAU), and promotes the equity of ME resources in CAU, thereby promoting the innovative development of ME.
Solving Cram Using Combinatorial Game Theory

In this paper we investigate the board game Cram, which is an impartial combinatorial game, using an $$\alpha \beta $$ solver. Since Cram is a notoriously hard game in the sense that it is difficult to obtain reliable and useful domain knowledge to use in the search process, we decided to rely on knowledge obtained from Combinatorial Game Theory (CGT). The first and most effective addition to our solver is to incorporate endgame databases pre-filled with CGT values (nimbers) for all positions fitting on boards with at most 30 squares. This together with two efficient move-ordering heuristics aiming at early splitting positions into fragments fitting in the available databases gives a large improvement of solving power. Next we define five more heuristics based on CGT that can be used to further reduce the sizes of the search trees considerably. In the final version of our program we were able to solve all odd $$\times $$ odd Cram boards for which results were available from the literature (even $$\times $$ even and odd $$\times $$ even boards are trivially solved). Investigating new boards led to solving two boards not solved before, namely the $$3 \times 21$$ board, a first-player win, and the $$5 \times 11$$ board, a second-player win.
Comparing Print and eBook Usage to Meet Patron Needs

Over the past two decades the availability of eBooks to library users has increased exponentially despite continued financial challenges. In this study, four librarians examined eBook and print book usage to determine the best way to allocate collection funds. After determining which subjects were being used, the researchers surveyed students across all campuses and sites to discover the users’ preferences for, and knowledge of, the library’s collections. The study resulted in an improved distribution of funds and insight into what could be better emphasized in bibliographic instruction sessions.
Iteration-based parameter identification and its applications about distributed parameter systems

This paper addresses a general problem to parameter identification in distributed parameter systems (DPSs), as well as handles a secure communication issue by the extended identification method. First, an iterative approach is put forward based on the approximator and historical information. Meanwhile, an execution scheme for the iterative approach is provided by an optimization policy. Then, combined with the reference model and measurement data, an extended identification method is applied in secure communication. Subsequently, a quantum-leading-following-based optimization with mutation strategy (MQLFBO) algorithm is proposed as the optimization policy. Next, the global convergence and computational complexity are respectively discussed for MQLFBO algorithm in theory. Finally, simulation experiments are performed on parameter identification for DPSs and its application in secure communication, which verify the fast convergence and high precision of the developed method.
Unsupervised Anomaly Detection with Multi-scale Interpolated Gaussian Descriptors

Current unsupervised anomaly detection and pixel-wise anomaly localisation systems are commonly formulated as one-class classifiers that depend on an effective estimation of the distribution of normal images and robust criteria to identify anomalies. However, the distribution of normal images estimated by current systems tends to be unstable for classes of normal images that are under-represented in the training set, and the anomaly identification criteria commonly explored in the field does not work well for multi-scale structural and non-structural anomalies. In this paper, we introduce a new unsupervised anomaly detection and localisation method designed to address these two issues. More specifically, we introduce a normal image distribution estimation method that is robust to under-represented classes of normal images -- this method is based on adversarially interpolated descriptors from training images and a Gaussian classifier. We also propose a new anomaly identification criterion that can accurately detect and localise multi-scale structural and non-structural anomalies. In extensive experiments on MNIST, Fashion MNIST, CIFAR10, MVTec AD and two medical datasets, our approach shows better results than the current state of the art in the standard experimental setup for unsupervised anomaly detection and localisation. Code is available at this https URL.
Optimization of automotive color filter arrays for traffic light color separation

Traffic light (TL) classification is an important feature for automated driving, and it requires correct color separation of the TL signals captured using cameras. A key camera component for the color separation performance is the color filter array (CFA). For common automotive-specific CFAs, we have observed unsatisfactory performance for TL color separation, which indicates the need for an optimization. Based on typical scenarios for TL classification and a set of recorded TL signals, we evaluate the performance of common automotive CFAs. For a quantitative evaluation, we propose a suitable color distance metric. We also propose a method for optimization of the CFA and show that using this method, reference color separation performance can be achieved, trading in only a small amount of sensitivity.
Organization of astronomy hometasks with the use of informational and communicative technologies for cognitive activity increase

Abstract The method of astronomy homework organization in order to increase students’ cognitive activity is described in the article, as well as the possibility of individualisation and differentiation of homework is shown. Modern methods offer a lot of techniques and innovative forms of teaching methods in order to improve students’ knowledge. These innovations are aimed at applying activity-based and person-centered approaches to learning that will help to intensify students’ training activities therefore it will be engaging and productive. Examples of independent practical tasks of different types and use of information and communication technologies are given. The article gives examples of such kinds of work on astronomy classes as: preparation of a textbook, preparation of supporting notes, practical tasks, solution of astronomical problems and creative tasks.
Single Quantum Deletion Error-Correcting Codes

In this paper, we discuss a construction method of quantum deletion error-correcting codes. First of all, we define deletion errors for quantum states, an encoder, a decoder, and three conditions that are expressed by only the combinatorial language. Then, we prove that quantum deletion error-correcting codes can be constructed by two sets that satisfy the conditions. In other words, problems that correct the deletion errors for quantum states are reduced to problems that find the sets satisfying the condition by this paper. Also, we experimented with the codes over IBM Quantum Experience.
Autoregressive Asymmetric Linear Gaussian Hidden Markov Models

In a real life process evolving over time, the relationship between its relevant variables may change. Therefore, it is advantageous to have different inference models for each state of the process. Asymmetric hidden Markov models fulfil this dynamical requirement and provide a framework where the trend of the process can be expressed as a latent variable. In this paper, we modify these recent asymmetric hidden Markov models to have an asymmetric autoregressive component in the case of continuous variables, allowing the model to choose the order of autoregression that maximizes its penalized likelihood for a given training set. Additionally, we show how inference, hidden states decoding and parameter learning must be adapted to fit the proposed model. Finally, we run experiments with synthetic and real data to show the capabilities of this new model.
Semantic similarity–based descriptive answer evaluation

The sole purpose of providing education, whether primary or advanced, is to impart knowledge that can be applied to real-life issues. This process inherently requires finding the degree of knowledge acquired by the learner, which can be achieved by conducting examinations of various types. This is then followed by a uniform assessment via examination. In an evaluation by descriptive examination, students are assessed at a higher level of Bloom’s Taxonomy as compared to an objective examination. Unfortunately, evaluation in a descriptive examination is a cumbersome process and is done manually due to the complexity and ambiguity present in natural language. This work is an effort to deal with the problem of automated computer assessment in descriptive examination and presents a step-by-step algorithm for the programmed evaluation of descriptive answers by considering both syntactic and semantic similarities. Conceptual similarity and word sense disambiguation are also considered for better results in the evaluation process. The proposed model produces convincing results and is very comparable to human evaluation.
Multicenter Privacy-Preserving Cox Analysis Based on Homomorphic Encryption

The Cox proportional hazards model is one of the most widely used methods for analyzing survival data. Data from multiple data providers are required to improve the generalizability and confidence of the results of Cox analysis; however, such data sharing may result in leakage of sensitive information, leading to financial fraud, social discrimination or unauthorized data abuse. Some privacy-preserving Cox regression protocols have been proposed in past years, but they lack either security or functionality. In this paper, we propose a privacy-preserving Cox regression protocol for multiple data providers and researchers. The proposed protocol allows researchers to train models on horizontally or vertically partitioned datasets while providing privacy protection for both the sensitive data and the trained models. Our protocol utilizes threshold homomorphic encryption to guarantee security. Experimental results demonstrate that with the proposed protocol, Cox regression model training over 9 variables in a dataset of 113,035 samples takes approximately 44 min, and the trained model is almost the same as that obtained with the original nonsecure Cox regression protocol; therefore, our protocol is a potential candidate for practical real-world applications in multicenter medical research.