Reviewerly/exHarmony huggingface dataset:
Arranging test tubes in racks using combined task and motion planning

The paper develops a robotic manipulation system to meet the pressing needs for handling a large number of test tubes in clinical examination and replace or reduce human labor. It presents the technical details of the system, which separates and arranges test tubes in racks with the help of 3D vision and artificial intelligence (AI) planning. The developed system only requires a person to put a rack with mixed and non-arranged tubes in front of a robot. The robot autonomously performs recognition, reasoning, planning, manipulation, etc., and returns a rack with separated and arranged tubes. The system is simple-to-use, and there are no requests for expert knowledge in robotics. We expect such a system to play an important role in helping managing bulky examination samples. We also hope similar systems could be extended to other clinical manipulation like handling mixers and pipettes in the future.
Novel classification method of plastic wastes with optimal hyperparameter tuning of Inception_ResnetV2

Plastics have been used extensively over the past few decades. Prior to that, the use of plastics wasn't a major issue, but now we are polluting the ocean with approximately 12.7 million tons of plastic a year, and the damage we are doing to marine life and the ecosystem in general may soon be irreversible. This can be accredited largely to disposable plastics which have regrettably become ubiquitous. Therefore, the objective of this project is to properly classify waste through the use of deep-learning models with fine tuning. By doing this, they are complying with their legal duty of care. Overall, after the classification process, the Inception_resnet_V2 was separated and classified into three different classes: plastic, cardboard, and garbage. VGG19, VGG16, Inception_v3, Xception, and MobileNet were also used for the classification. Principal findings of our research concluded that there were only nominal differences in accuracy compared to the related works, which conducted binary classification. Also, for the optimizer, Nadam had the highest degree of accuracy, and was the highest when the learning rate was 0.01. As the CNN+Autoencoder model and the VGG16 model had a 35% difference in accuracy, we could determine the importance of pretrained models.
GPU-Based Supervoxel Generation With a Novel Anisotropic Metric

Video over-segmentation into supervoxels is an important pre-processing technique for many computer vision tasks. Videos are an order of magnitude larger than images. Most existing methods for generating supervovels are either memory- or time-inefficient, which limits their application in subsequent video processing tasks. In this paper, we present an anisotropic supervoxel method, which is memory-efficient and can be executed on the graphics processing unit (GPU). Therefore, our algorithm achieves good balance among segmentation quality, memory usage and processing time. In order to provide accurate segmentation for moving objects in video, we use the optical flow information to design a brand new non-Euclidean metric to calculate the anisotropic distances between seeds and voxels. To efficiently compute the anisotropic metric, we adjust the classic jump flooding algorithm (which is designed for parallel execution on the GPU) to generate anisotropic Voronoi tessellation in the combined color and spatio-temporal space. We evaluate our method and the representative supervoxel algorithms for their capability on segmentation performance, computation speed and memory efficiency. We also apply supervoxel results to the application of foreground propagation in videos to test the performance on solving practical problems. Experiments show that our algorithm is much faster than the existing methods, and achieves good balance on segmentation quality and efficiency.
LiCaNext: Incorporating Sequential Range Residuals for Additional Advancement in Joint Perception and Motion Prediction

Autonomous driving can obtain accurate perception and reliable motion prediction with the support of multi-modal fusion. Recently, there has been growing interest in leveraging features from various onboard sensors to enhance the primary stages of autonomous driving. This paper proposes LiCaNext to capture additional accuracy advancements in joint perception and motion prediction while maintaining real-time requirements. LiCaNext is the next version of LiCaNet, which fuses LIDAR data in both bird’s-eye view (BEV) and range view (RV) representations with a camera image. In contrast to LiCaNet, we introduce sequential range residual images into the multi-modal fusion network to further improve performance, with minimal increase in inference time. Employing sequential range residual images has a substantial direct impact on motion prediction and positively influences perception. We provide an extensive evaluation on the public nuScenes dataset. Our experiments show that incorporating sequential range residuals secures significant performance gain, with monotonic progress for a larger number of exploited residuals.
Automatic Image Colorization using U-Net

Automatic Image Colorization is the process of converting a gray-scale image to a corresponding colored image as output, without any human interference. The main objective of the research is to develop an automated technique that colorizes a given gray-scale image and generates a colorized version of the image as the output. We have presented our custom U-Net architecture model. We have treated this problem as an image segmentation + multinomial classification problem. The results contain handpicked images from our outputs. We have included the black and white image, our model's colored output and the colored ground truth of the images.
SearchEHR

Finding patients with specific clinical conditions, such as having a familial disease history of diabetes, is an important task for clinical decision support. Clinical notes in Electronic Health Records (EHR), which document the patient medical history and familial disease history, are valuable resources for patient cohort selection. However, such information is difficult to discover in clinical text, and full-text search techniques often fail due to the unique characteristics of clinical language. We describe a system---SearchEHR---that combines Natural Language Processing (NLP) and Information Retrieval (IR) techniques to facilitate utilising clinical notes to find cohorts of patients, with a special focus on family disease history.
DML: Dynamic Multi-Granularity Learning for BERT-Based Document Reranking

Recently, pre-trained language models have been successfully applied to the task of text retrieval and ranking. However, in real scenes, users' click behavior is usually affected by selection, position, or exposure bias, which may lead to insufficient positive annotations and introduce additional noise. And for different candidate documents of the same query, the previous optimization objectives usually use a single granularity and static loss weights. It makes the performance of ranking models more susceptible to the bias issue mentioned above. Thus, in this paper, we focus on BERT-based document reranking and propose Dynamic Multi-Granularity Learning (DML). By introducing Gaussian distribution into traditional loss functions, the weights of different documents can change dynamically according to the prediction probability to avoid the impact of unlabeled positive documents. Besides, both document-granularity and instance-granularity are considered to balance relative relations and absolute scores of candidate documents. Extensive experiments show that DML significantly outperforms previous state-of-the-art models on the MS MARCO document ranking dataset.
Capacities of Gaussian Quantum Channels With Passive Environment Assistance

Passive environment-assisted communication takes place via a quantum channel modeled as a unitary interaction between the information carrying system and an environment, where the latter is controlled by a passive helper, who can set its initial state such as to assist sender and receiver, but not help actively by adjusting her behaviour depending on the message. Here we investigate the information transmission capabilities in this framework by considering Gaussian unitaries acting on Bosonic systems. We consider both quantum communication and classical communication with helper, as well as classical communication with free classical coordination between sender and helper (conferencing encoders). Concerning quantum communication, we prove general coding theorems with and without energy constraints, yielding multi-letter (regularized) expressions. In the search for cases where the capacity formula is computable, we look for Gaussian unitaries that are universally degradable or anti-degradable. However, we show that no Gaussian unitary yields either a degradable or anti-degradable channel for all environment states. On the other hand, restricting to Gaussian environment states, results in universally degradable unitaries, for which we thus can give single-letter quantum capacity formulas. Concerning classical communication, we prove a general coding theorem for the classical capacity under an energy constraint, given by a multi-letter expression. Furthermore, we derive an uncertainty-type relation between the classical capacities of the sender and the helper, helped respectively by the other party, showing a lower bound on the sum of the two capacities. Then, this is used to lower bound the classical information transmission rate in the scenario of classical communication between sender and helper.
Pedagogical Creativity vs Academic Dishonesty in Teaching University Mathematics

The authors address the problem of academic dishonesty, which is a concern of universities around the world. An analysis of the literature shows that there are two main types of counteraction to academic dishonesty. The first is punishment; the second is the cultivation of value of academic integrity. Each of the types has well-reasoned advantages and disadvantages. The authors propose to counteract cheating manifestations when solving tasks in mathematics by introducing non-traditional tasks based on mathematical creativity. Mathematical creativity is one of the main means of motivating students to study mathematics. The authors assess the possibilities of mathematical creativity to counter academic dishonesty. Based on an analysis of the literature and communication with colleagues, the authors identified two main types of non-traditional tasks aimed at cheating manifestation. The first ones are tasks with graphic content. The second ones are constructive tasks for which students build an object based on its description. Both types of tasks do not take a long time to receive an answer and to make cheating impossible, even with the help of modern information and communication technologies. The authors conducted research among academic groups of 1 year of study in economic areas at Peter the Great St. Petersburg Polytechnic University. The research involved 171 Russian students and 61 Chinese students. The results showed that Russian students adapt to introduction of non-traditional assignments in about 1 semester, Chinese students take a little longer. The results indicate that use of tasks those exclude academic dishonesty does not reduce average results of students’ academic success.
CDI Maps: Dynamic Estimation of the Radio Environment for Predictive Resource Allocation

The number of always-online vehicles continuously increases, and these vehicles will form an immense mobile sensor network. For example, cars can upload live temperature and precipitation information to enhance weather forecasting, and also transmit live cellular network measurements to the cloud. We leverage this vast amount of data, particularly the reference signal received power, to estimate the channel distribution information (CDI) for the vehicular environment. In particular, the proposed CDI maps depict the small-scale fading statistics for spatially separated regions, in contrast to the large-scale fading averages of classical radio maps (path loss and shadow fading). Our map generation framework includes a heuristic for clustering and predicts the fast-fading density per cluster via the Dirichlet process mixture model. This Bayesian nonparametric approach allows for modeling any fast-fading distribution (e.g., Rayleigh, Rice) without prior knowledge. We justify the choice of this approach by benchmarking it against other density estimation methods. Moreover, we support our assumption of local medium-term channel stationarity by measurements and show the framework’s effectiveness for anticipatory networking.
Machine Learning-based Estimation of Story Points in Agile Development: Industrial Experience and Lessons Learned

Estimating story points is an important activity in agile software engineering. Story-point estimation enables software development teams to, among other things, better scope products, prioritize requirements, allocate resources and measure progress. Several machine learning techniques have been proposed for automated story-point estimation. However, most of these techniques use open-source projects for evaluation. There are important differences between open-source and commercial projects with respect to story authoring. The goal of this paper is to evaluate a state-of-the-art machine learning technique, known as Deep-SE [3], for estimating story points in a commercial project. Our dataset is comprised of 4,727 stories for a data anonymization product developed by a 27-member agile team at a healthcare data science company, IQVIA. Over this dataset, Deep-SE achieved a mean absolute error of 1.46, significantly better than three different baselines. Model performance nonetheless varied across stories, with the estimation error being larger for stories that had higher points. Our results further indicate that model performance is correlated with certain story characteristics such as the level of detail and the frequency of vague terms in the stories. An important take-away from our study is that, before organizations attempt to introduce machine learning-based estimation into agile development, they need to better embrace agile best practices, particularly in relation to story authoring and expert-based estimation.
Classification of News Articles for Learning Using the K-Nearest Neighbor Algorithm

Tween Tribune is an online website that provides daily news articles for children, teenagers, and teachers. The purpose of this study is to classify articles based on the level of article popularity based on comments and the number of viewers available on Tween Tribune. The clustering of these categories is carried out using the k-Means Clustering method. The data classification process uses the k Nearest Neighbor algorithm. The classification results show that articles in the popular category contain the most commented and assigned data. Furthermore, it is shown by the reasonable result of the classification accuracy of 87.58%.
MOSafely: Building an Open-Source HCAI Community to Make the Internet a Safer Place for Youth

The goal of this one-day workshop is to build an active community of researchers, practitioners, and policy-makers who are jointly committed to leveraging human-centered artificial intelligence (HCAI) to make the internet a safer place for youth. This community will be founded on the principles of open innovation and human dignity to address some of the most salient safety issues of modern-day internet, including online harassment, sexual solicitation, and the mental health of vulnerable internet users, particularly adolescents and young adults. We will partner with Mozilla Research Foundation to launch a new open project named "MOSafely.org," which will serve as a platform for code library, research, and data contributions that support the mission of internet safety. During the workshop, we will discuss: 1) the types of contributions and technical standards needed to advance the state-of-the art in online risk detection, 2) the practical, legal, and ethical challenges that we will face, and 3) ways in which we can overcome these challenges through the use of HCAI to create a sustainable community. An end goal of creating the MOSafely community is to offer evidence-based, customizable, robust, and low-cost technologies that are accessible to the public for youth protection.
Exploring Efficiency of GAN-based Generated URLs for Phishing URL Detection

The URL (Uniform Resource Locator) is used to refer to the resources on the Internet by giving hyperlinks to the websites. Different resources are referenced by different network addresses or different URLs. As a result, embedding malware on websites by using malicious URLs is one of the most dangerous types of cyberattacks today and poses a serious threats to the safety of systems. In order to detect the phishing URLs, the most commonly used approach recently is using deep learning networks with a large number of URL samples, including both malign and benign ones for training the deep networks. However, the available URL databases have a modest number of samples. In addition, the disadvantage of these databases is the imbalance distribution of malicious and non-malicious URL strings. In fact, it is difficult to collect or update malicious URLs because these URLs only exist for a short time, after being detected they are changed again and again. In order to solve this challenge, in this work, we propose to train a GAN network named WGAN-GP for generating malicious URLs from the available phishing URL data. We then integrate the generated phishing URL data into the existing URL database and perform two URL classifiers of LSTM and GRU to give the comparative results. The experiments on different quantities of URL samples show the improvement for URL classification by using WGAN-GP and LSTM classifier.
Structure Aware Experience Replay for Incremental Learning in Graph-based Recommender Systems

Large-scale recommender systems are integral parts of many services. With the recent rapid growth of accessible data, the need for efficient training methods has arisen. Given the high computational cost of training state-of-the-art graph neural network (GNN) based models, it is infeasible to train them from scratch with every new set of interactions. In this work, we present a novel framework for incrementally training GNN-based models. Our framework takes advantage of an experience reply technique built on top of a structurally aware reservoir sampling method tailored for this setting. This framework addresses catastrophic forgetting, allowing the model to preserve its understanding of users' long-term behavioral patterns while adapting to new trends. Our experiments demonstrate the superior performance of our framework on numerous datasets when combined with state-of-the-art GNN-based models.
Senior DT - A Design Thinking Method to Improve Requirements Engineering for Elderly Citizens

Requirement engineers face challenges in assessing the needs of elderly citizens, who typically differ a lot in terms of experiences, perspectives, and cognition. Design Thinking (DT) is a promising methodology for overcoming these barriers because it offers a structured approach for assessing users’ needs and developing new ideas. However, DT needs to be adapted to the specifics of older participants to fully grasp these users’ insights. For instance, the endurance of older participants is limited, and collaboration with other stakeholders is hampered due to decreasing physical, cognitive, and psychological capacity. This research utilizes a design science approach for developing the SeniorDT methodology. The core objectives of the SeniorDT method are the support of the older participants’ engagement, the gathering of deep user insights, and the facilitation of the elderlies’ collaboration with other workshop participants. The method is tested in a real-life workshop setting and evaluated by observation, group discussions and interviews.
The Performance Impact of New Ventures in Working Environment and Innovation Behavior From the Perspective of Personality Psychology

A new venture barely makes a profit in its initial stage, and its success depends on innovation. Innovation is related to the work environment, and the innovation behavior of employees is of great significance to the performance improvement of new venture. Based on the previous research, in this study, hypotheses on the correlation between work environment, employee innovation behavior, and corporate performance are put forward first. Then, with team cooperation, organizational incentive, leadership support, sufficient resources, and work pressure as the factors of the work environment, the bosses, middle and senior managers involved in entrepreneurship, and the main members of the entrepreneurial team of 202 newly established enterprises in developed regions are surveyed online or in scene. Multivariate hierarchical regression analysis is performed to analyze the data collected from the questionnaire. The results show that the effective recovery rate of the questionnaire is 86.4%; the number of traditional enterprises is 108 (53.47%), and that of R&D enterprises is 68 (33.66%); teamwork, leadership support, and work pressure are all correlated with employees' innovative behavior (P < 0.05), while organizational motivation and sufficient resources are not correlated with employees' innovative behavior (P > 0.05); employee innovation behavior is positively correlated with enterprise performance (β = 0.375, P ≤ 0.01); the working environment and employee innovation behavior promote enterprise performance (β = 0.433, P ≤ 0.01); and the working environment affects the relationship between employee innovation behavior and enterprise performance (β = 0.399, P ≤ 0.05). The study theoretically enriches the research on the relationship between innovation behavior, work environment, and enterprise performance of new ventures. In practice, it is suggested that start-up enterprises provide good working environment for employees and attach importance to innovation activities at the individual level of employees, which provides useful guidance and reference for the development of Chinese start-up enterprises.