Reviewerly/exHarmony huggingface dataset:
GaussianMask: Uncertainty-aware Instance Segmentation based on Gaussian Modeling

Instance segmentation, which has been required in various applications in recent years, is aimed at reliable bounding box (bbox) detection (i.e., localization) and stable mask prediction (i.e., segmentation). However, the mask uncertainty problem is still unresolved, which hinders the ability to achieve an accurate instance segmentation. In this paper, we propose GaussianMask, an uncertainty-aware instance segmentation technique based on Gaussian modeling. We can determine the uncertainty of the network through the variance of masks extracted by redesigning the loss function based on Gaussian modeling, and the mask accuracy can be improved by constructing a robust model that adaptively applies this uncertainty to the network. In particular, the additional computations caused during this process are insignificant, and thus negligible for the processing speed of the network. Moreover, GaussianMask has the advantage of being applicable to any network due to its high compatibility. Experimental results show that the mask average precision (AP) of the representative instance segmentation models, YOLACT and Mask R-CNN, increases from 29.7% to 31.8% and from 35.7% to 37.5%, respectively, on the MS-COCO dataset.
ATFE-Net: Axial Transformer and Feature Enhancement-based CNN for ultrasound breast mass segmentation

Breast mass is one of the main clinical symptoms of breast cancer. Recently, many CNN-based methods for breast mass segmentation have been proposed. However, these methods have difficulties in capturing long-range dependencies, causing poor segmentation of large-scale breast masses. In this paper, we propose an axial Transformer and feature enhancement-based CNN (ATFE-Net) for ultrasound breast mass segmentation. Specially, an axial Transformer (Axial-Trans) module and a Transformer-based feature enhancement (Trans-FE) module are proposed to capture long-range dependencies. Axial-Trans module only calculates self-attention in width and height directions of input feature maps, which reduces the complexity of self-attention significantly from O(n2) to O(n). In addition, Trans-FE module can enhance feature representation by capturing dependencies between different feature layers, since deeper feature layers have richer semantic information and shallower feature layers have more detailed information. The experimental results show that our ATFE-Net achieved better performance than several state-of-the-art methods on two publicly available breast ultrasound datasets, with Dice coefficient of 82.46% for BUSI and 86.78% for UDIAT, respectively.
Spectral Feature Augmentation for Graph Contrastive Learning and Beyond

Although augmentations (e.g., perturbation of graph edges, image crops) boost the efficiency of Contrastive Learning (CL), feature level augmentation is another plausible, complementary yet not well researched strategy. Thus, we present a novel spectral feature argumentation for contrastive learning on graphs (and images). To this end, for each data view, we estimate a low-rank approximation per feature map and subtract that approximation from the map to obtain its complement. This is achieved by the proposed herein incomplete power iteration, a non-standard power iteration regime which enjoys two valuable byproducts (under mere one or two iterations): (i) it partially balances spectrum of the feature map, and (ii) it injects the noise into rebalanced singular values of the feature map (spectral augmentation). For two views, we align these rebalanced feature maps as such an improved alignment step can focus more on less dominant singular values of matrices of both views, whereas the spectral augmentation does not affect the spectral angle alignment (singular vectors are not perturbed). We derive the analytical form for: (i) the incomplete power iteration to capture its spectrum-balancing effect, and (ii) the variance of singular values augmented implicitly by the noise. We also show that the spectral augmentation improves the generalization bound. Experiments on graph/image datasets show that our spectral feature augmentation outperforms baselines, and is complementary with other augmentation strategies and compatible with various contrastive losses.
The use of ChatGPT as a learning tool to improve foreign language writing in a multilingual and multicultural classroom

The association of Artificial Intelligence (AI) and education has attracted researchers’ attention, as it is very beneficial for learners affecting the whole teaching procedure. ChatGPT, an AI language model developed by OpenAI and based on the Generative Pre-trained Transformers (GPT)-3.5 architecture, though newly introduced in November 2022, became very popular even within the first two months. Researchers have already explored and acknowledged the affordances of ChatGPT for language learning and teaching. In this context, the study aimed to examine the effectiveness of ChatGPT as a feedback tool on foreign language writing, especially for socially vulnerable populations, such as refugees/migrants, who face extra language difficulties. The study was conducted in a Junior High School (Gymnasium) classroom in an urban city in south Greece, which consisted of 8 students with a migrant/refugee background aged 15 years old who were attending German as a foreign language learning. The focus of the study was to analyze how the use of ChatGPT could improve foreign language writing in terms of vocabulary and grammar. After the students' first writing attempt, we asked ChatGPT to enhance the vocabulary and grammar of the texts. In contrast, the students received an improved version in terms of language and grammar, and then, they were asked to produce another writing piece of work on a similar topic to their first attempt. The study results indicated an increase in the total number of words, the unique words and the average word number per sentence in the improved version of their work. Overall, the study results were promising, indicating that ChatGPT could act as a language learning tool and support the teaching procedure, especially for students with a refugee/migrant background. However, further research is needed to investigate its use and effectiveness in diverse contexts.
User Engagement in the Era of Hybrid Agile Methodology

Contemporary software development and implementation projects are increasingly adopting agile methods by tailoring and blending agile techniques into a traditional project framework. Common tailoring methods employed by project teams emphasize flexibility to embrace local project context.
An Improved B-hill Climbing Optimization Technique for Solving the Text Documents Clustering Problem

Background: Considering the increasing volume of text document information on Internet pages, dealing with such a tremendous amount of knowledge becomes totally complex due to its large size. Text clustering is a common optimization problem used to manage a large amount of text information into a subset of comparable and coherent clusters. Aims: This paper presents a novel local clustering technique, namely, β-hill climbing, to solve the problem of the text document clustering through modeling the β-hill climbing technique for partitioning the similar documents into the same cluster. Methods: The β parameter is the primary innovation in β-hill climbing technique. It has been introduced in order to perform a balance between local and global search. Local search methods are successfully applied to solve the problem of the text document clustering such as; k-medoid and kmean techniques. Results: Experiments were conducted on eight benchmark standard text datasets with different characteristics taken from the Laboratory of Computational Intelligence (LABIC). The results proved that the proposed β-hill climbing achieved better results in comparison with the original hill climbing technique in solving the text clustering problem. Conclusion: The performance of the text clustering is useful by adding the β operator to the hill climbing.
SDN-based and multitenant-aware resource provisioning mechanism for cloud-based big data streaming

Cloud computing provides elastic on-demand resource allocation, enabling big data systems to process large amounts of streaming data in real time. However, a shared cloud infrastructure (multitenant at the hypervisor level) may reduce system performance or even resource availability, particularly when big data processing demands significantly increase through concurrent task allocations on the same physical hardware. Such situations are not easily detectable from the tenant's perspective, because the tenant may suffer from poor performance without knowing why, as the infrastructure is not under the tenant's control. Moreover, as task processing demand changes over time, the available infrastructure may be insufficient owing to increased processing load or multitenant interference. This paper presents a multitenant-aware resource provisioning mechanism that is independent of any hypervisor and can perform task scheduling and dynamic ongoing task rescheduling for big data streaming while considering the state of each virtual machine (VM). Moreover, the proposed mechanism ensures load balancing through several cloud-based clusters of VMs using a software-defined network (SDN). The prototype was implemented using Apache Storm (big data), Helion Eucalyptus (cloud computing), and Floodlight (SDN). The evaluation shows that when the resources are under multitenant interference, our proposal results in an improvement of 50.1% for CPU-bound tasks, 62.3% for disk-bound tasks, and 43.8% for network-bound tasks. In addition, the load balancer forwarded 72.04% of the load to a fully available cluster, meaning that our mechanism can realize a 22.04% improvement in effectiveness over traditional approaches.
Quantum properties of a strongly driven Josephson junction

A Josephson junction embedded in a dissipative circuit can be externally driven to induce nonlinear dynamics of its phase. Classically, under sufficiently strong driving and weak damping, dynamic multistability emerges, associated with dynamical bifurcations so that the often-used modeling as a Duffing oscillator, which can exhibit bistability at most, is insufficient. The present work analyzes in this regime corresponding quantum properties by mapping the problem onto a highly nonlinear quasienergy operator in a rotating frame. This allows us to identify in detail parameter regions where simplifications such as the Duffing approximation are valid, to explore classical-quantum correspondences, and to study how quantum fluctuations impact the effective junction parameters as well as the dynamics around higher-amplitude classical fixed points.
Assessing the Impact of the Distributed Software Development Course on the Careers of Young Software Engineers

Various software engineering (SE) curricula in higher education have started including courses on global software engineering (GSE), carried out as internationally distributed project-based courses. These courses, known for their closeness to “real-world” work experience, emphasize the importance of involving industry partners as customers and focus on soft skills essential for employment, an aspect often neglected in engineering education. However, not many such courses are long-lived or consistent in form throughout the years, making their impact and relevance hard to assess. The Distributed Software Development course (DSD), currently run among three universities in Croatia, Italy, and Sweden, has now been carried out for 15 years consecutively, providing a rich source of in-course and post-graduation data. To evaluate the students’ experiences of the course after they graduate and start working, a study has been carried out among former DSD students from the University of Zagreb, Croatia. Its goal is to understand how useful this course was in students’ early careers, both in first and current employment, as well as related factors at the workplace (magnitude of distributed collaboration, company size). The study results show the relevance of such distributed course experiences for future employment, as well as the importance of building upon soft skills as part of the software engineering curricula. Higher education institutions are invited to consider including such courses in the software engineering curriculum, for the benefit of their students and, indirectly, students’ future employers.
Forecasting mesoscale distribution of surface solar irradiation using a proposed hybrid approach combining satellite remote sensing and time series models

A new hybrid forecasting tool is developed in this study which makes use of satellite remote sensing data of surface solar irradiation coupled to a Double Exponential Smoothing time series model. The prediction capabilities of the Double Exponential Smoothing model are reported to be higher than the ARMA and NAR-Neural Network. The mean absolute percentage error of this hybrid system is revealed to be the lowest (4.89%) on average for 5 consecutive days-ahead forecasts over the years 2013–2015, with the smallest standard deviation reported throughout the year, characteristic of a highly stable and robust model (3.83 W/m2). Exploring the performance of the model for the best and worst case scenarios reveal that high prediction accuracies on both spatial and temporal scales are achievable with strong positive linear correlations of the orders of 0.928 and 0.894 respectively, averaged over the 5 days forecasts. The performance of the hybrid system is found to be higher as compared with benchmark accuracy reached by several other models employed in literature. Finally, the use of the hybrid forecasting tool developed in providing energy and grid management facilities for the island of Mauritius is also presented.
Development of Performance System With Musical Dynamics Expression on Humanoid Saxophonist Robot

Talented musicians can deliver a powerful emotional experience to the audience by skillfully modifying several musical parameters, such as dynamics, articulation, and tempo. Musical robots are expected to control those musical parameters in the same way to give the audience an experience comparable to listening to a professional human musician. But practical control of those parameters depends on the type of musical instrument being played. In this study, we describe our newly developed music dynamics control system for the Waseda Anthropomorphic Saxophonist robot. We first built a physical model for the saxophone reed motion and verified the dynamics-related parameters of the overall robot-saxophone system. We found that the magnitude of air flow is related to the sound pressure level, as expected, but also that the lower lip is critical to the sound stability. Accordingly, we then implemented a music dynamics control system for the robot and succeeded in enabling the robot to perform a music piece with different sound pressure levels.
The Value Function Polytope in Reinforcement Learning

We establish geometric and topological properties of the space of value functions in finite state-action Markov decision processes. Our main contribution is the characterization of the nature of its shape: a general polytope (Aigner et al., 2010). To demonstrate this result, we exhibit several properties of the structural relationship between policies and value functions including the line theorem, which shows that the value functions of policies constrained on all but one state describe a line segment. Finally, we use this novel perspective to introduce visualizations to enhance the understanding of the dynamics of reinforcement learning algorithms.
Exploring Automatic Face Recognition on Match Performance and Gender Bias for Children

In this work we update the body of knowledge on the performance of child face recognition against a set of commercial-off-the-shelf (COTS) algorithms as well as a set of government sponsored algorithms. In particular, this work examines performance of multiple deep learning face recognition systems (8 distinct solutions) establishing a performance base line for a publicly available child dataset. Furthermore, we examine the phenomenon of gender bias as a function of match performance across the eight (8) systems. This work highlights the continued challenge that exists for child face recognition as a function of aging. Rank-1 accuracy ranges from 0.44 to 0.78 with an average accuracy of 0.63 on a dataset of 745 unique subjects (7,990 total images). Furthermore, when we introduce a distractor set of approximately 10; 000 child faces the rank-1 accuracy decreases across all systems on an average of 10 points. Additionally, the phenomenon of gender bias is exhibited across all systems, although the developers of the face recognition systems claim a near balance of genders was used in the development. The question of gender disparity is elusive, and although co-factors such as makeup, expression, and hair were not explicitly controlled, the dataset does not contain substantial differences across the genders. This work contributes to the body of knowledge in multiple categories, 1. child face recognition, 2. gender bias for face recognition and the notion that females as a sub-population may exhibit Lamb characteristics according to Doddington's Biometric Zoo, and 3. a dataset for child face recognition.
Entity Recognition at First Sight: Improving NER with Eye Movement Information

Previous research shows that eye-tracking data contains information about the lexical and syntactic properties of text, which can be used to improve natural language processing models. In this work, we leverage eye movement features from three corpora with recorded gaze information to augment a state-of-the-art neural model for named entity recognition (NER) with gaze embeddings. These corpora were manually annotated with named entity labels. Moreover, we show how gaze features, generalized on word type level, eliminate the need for recorded eye-tracking data at test time. The gaze-augmented models for NER using token-level and type-level features outperform the baselines. We present the benefits of eye-tracking features by evaluating the NER models on both individual datasets as well as in cross-domain settings.
Target Detection Method Based on Improved Particle Search and Convolution Neural Network

The border regression is a key technique of the regional convolution neural network (CNN) to locate the target. However, it relies on the border label information of a large number of sample data. Therefore, it is inefficient to generate the training sample set, and the location of the target is also inaccurate. For this, a novel target detection method based on the CNN and the particle search is proposed. A small number of probe particles are generated to roughly locate the target. The CNN is used to extract the image features, determine the target probability, and recognize the pattern of the target. A large number of searching particles are placed near the region where the target features are detected by the probe particles. The nearest neighbor clustering algorithm is used to classify the particles, which are recognized as the same category into different target sets. The positions of the targets can be determined by the bounding rectangle of the searching particles in the same target set. The method can be used to recognize and locate various kinds of targets. Furthermore, the method need not label the borders of the targets in the training samples, which enhance the generation efficiency of the samples. The simulation results show that the correctness of the recognition can be slightly improved, and the accuracy of the location can be significantly improved.
HOList: An Environment for Machine Learning of Higher-Order Theorem Proving (extended version)

We present an environment, benchmark, and deep learning driven automated theorem prover for higher-order logic. Higher-order interactive theorem provers enable the formalization of arbitrary mathematical theories and thereby present an interesting, open-ended challenge for deep learning. We provide an open-source framework based on the HOL Light theorem prover that can be used as a reinforcement learning environment. HOL Light comes with a broad coverage of basic mathematical theorems on calculus and the formal proof of the Kepler conjecture, from which we derive a challenging benchmark for automated reasoning. We also present a deep reinforcement learning driven automated theorem prover, DeepHOL, with strong initial results on this benchmark.
The open science infrastructure in Russia and the world

The article gives an overview of open access projects that form the new infrastructure for scientific communication. It provides information on initiatives and programs contributing to the development of open science in Russia and in the world. The paper also describes innovative scientific projects and platforms that significantly accelerate exchange of information and communication between scientists and create new opportunities for their cooperation and for society. The scientific novelty of the article consists in the distinction between the notions of ‘publication’ and ‘article’ or ‘edition’ in the context of scientific communication and the work of libraries, the description of the challenges that the new media era puts behind the system of scientific communications and libraries, and Russian projects, which aim to solve the described problems. The system of scientific communication has transformed significantly over the past two decades, alternative channels of information exchange are beginning to play a leading role and become the competitors of the traditional media (scientific journals), which have converted into electronic form, but still remain rooted in the past, supported by the inertia of administration of science. In Russia the achievements of open science are still much too modest compared to Europe, but the necessary background has already been created by libraries and non-for-profit organizations. Further development of such projects will depend on whether government, libraries and universities could use these instruments and the capabilities of the new media, reorienting to a new paradigm of access to scientific information.
Using Multi-Sense Vector Embeddings for Reverse Dictionaries

Popular word embedding methods such as word2vec and GloVe assign a single vector representation to each word, even if a word has multiple distinct meanings.Multi-sense embeddings instead provide different vectors for each sense of a word.However, they typically cannot serve as a drop-in replacement for conventional single-sense embeddings, because the correct sense vector needs to be selected for each word.In this work, we study the effect of multi-sense embeddings on the task of reverse dictionaries.We propose a technique to easily integrate them into an existing neural network architecture using an attention mechanism.Our experiments demonstrate that large improvements can be obtained when employing multi-sense embeddings both in the input sequence as well as for the target representation.An analysis of the sense distributions and of the learned attention is provided as well.