Reviewerly/exHarmony huggingface dataset:
Personalized Knowledge Distillation for Recommender System

Nowadays, Knowledge Distillation (KD) has been widely studied for recommender system. KD is a model-independent strategy that generates a small but powerful student model by transferring knowledge from a pre-trained large teacher model. Recent work has shown that the knowledge from the teacher’s representation space significantly improves the student model. The state-of-the-art method, named Distillation Experts (DE), adopts cluster-wise distillation that transfers the knowledge of each representation cluster separately to distill the various preference knowledge in a balanced manner. However, it is challenging to apply DE to a new environment since its performance is highly dependent on several key assumptions and hyperparameters that need to be tuned for each dataset and each base model. In this work, we propose a novel method, dubbed Personalized Hint Regression (PHR), distilling the preference knowledge in a balanced way without relying on any assumption on the representation space nor any method-specific hyperparameters. To circumvent the clustering, PHR employs personalization network that enables a personalized distillation to the student space for each user/item representation, which can be viewed as a generalization of DE. Extensive experiments conducted on real-world datasets show that PHR achieves comparable or even better performance to DE tuned by a grid search for all of its hyperparameters.
Comparison of Semi-Quantitative Scoring and Artificial Intelligence Aided Digital Image Analysis of Chromogenic Immunohistochemistry

Semi-quantitative scoring is a method that is widely used to estimate the quantity of proteins on chromogen-labelled immunohistochemical (IHC) tissue sections. However, it suffers from several disadvantages, including its lack of objectivity and the fact that it is a time-consuming process. Our aim was to test a recently established artificial intelligence (AI)-aided digital image analysis platform, Pathronus, and to compare it to conventional scoring by five observers on chromogenic IHC-stained slides belonging to three experimental groups. Because Pathronus operates on grayscale 0-255 values, we transformed the data to a seven-point scale for use by pathologists and scientists. The accuracy of these methods was evaluated by comparing statistical significance among groups with quantitative fluorescent IHC reference data on subsequent tissue sections. The pairwise inter-rater reliability of the scoring and converted Pathronus data varied from poor to moderate with Cohen’s kappa, and overall agreement was poor within every experimental group using Fleiss’ kappa. Only the original and converted that were obtained from Pathronus original were able to reproduce the statistical significance among the groups that were determined by the reference method. In this study, we present an AI-aided software that can identify cells of interest, differentiate among organelles, protein specific chromogenic labelling, and nuclear counterstaining after an initial training period, providing a feasible and more accurate alternative to semi-quantitative scoring.
Bangla News Classification Using GloVe Vectorization, LSTM, and CNN

TextChowdhury, P. Eumi, E.M. Sarkar, O. Ahamed, M.F. mining has gained considerable popularity over the last few years. Since the awareness of mobile media, streaming media, print media, and many other outlets are now accessible to consumers. Due to the large availability of text in many respects, research experts registered many unstructured data and found various ways in the literature of converting this dispersed text into a given, organized volume. Compared to the short text, the emphasis on complete classification (complete news, big records, long text, etc.) is prevalent. We addressed in this paper the process of text classification, grading, and various methodologies for feature extraction in short texts, i.e., news classification based on their headlines. Existing classification is compared and their operating methodologies presented efficiently. This work serves the purpose of classifying different types of Bangla Newspaper articles into 10 specific categories. The classification task is performed on Bengali text from three renowned newspapers of Kolkata. We have used advanced data tokenization techniques and unsupervised ‘GloVe’ vectorization for better classification performance. We applied LSTM and CNN as our main feature extractors. Comparing with other models like binary SVM classifier, standard LSTM, BiLSTM, CNN, or ANN, this proposed work gives better accuracy of 87%.
GrpAvoid: Multigroup Collision-Avoidance Control and Optimization for UAV Swarm

Collision-avoidance control for UAV swarm has recently drawn great attention due to its significant implications in many industrial and commercial applications. However, traditional collision-avoidance models for UAV swarm tend to focus on avoidance at individual UAV level, and no explicit strategy is designed for avoidance among multiple UAV groups. When directly applying these models for multigroup UAV scenarios, the deadlock situation may happen. A group of UAVs may be temporally blocked by other groups in a narrow space and cannot progress toward achieving its goal. To this end, this article proposes a modeling and optimization approach to multigroup UAV collision avoidance. Specifically, group level collision detection and adaption mechanism are introduced, efficiently detecting potential collisions among different UAV groups and restructuring a group into subgroups for better collision and deadlock avoidance. A two-level control model is then designed for realizing collision avoidance among UAV groups and of UAVs within each group. Finally, an evolutionary multitask optimization method is introduced to effectively calibrate the parameters that exist in different levels of our control model, and an adaptive fitness evaluation strategy is proposed to reduce computation overhead in simulation-based optimization. The simulation results show that our model has superior performances in deadlock resolution, motion stability, and distance maintenance in multigroup UAV scenarios compared to the state-of-the-art collision-avoidance models. The model optimization results also show that our model optimization method can largely reduce execution time for computationally-intensive optimization process that involves UAV swarm simulation.
Efficient stereo matching on embedded GPUs with zero-means cross correlation

Mobile stereo-matching systems have become an important part of many applications, such as automated-driving vehicles and autonomous robots. Accurate stereo-matching methods usually lead to high computational complexity; however, mobile platforms have only limited hardware resources to keep their power consumption low; this makes it difficult to maintain both an acceptable processing speed and accuracy on mobile platforms. To resolve this trade-off, we herein propose a novel acceleration approach for the well-known zero-means normalized cross correlation (ZNCC) matching cost calculation algorithm on a Jetson Tx2 embedded GPU. In our method for accelerating ZNCC, target images are scanned in a zigzag fashion to efficiently reuse one pixel’s computation for its neighboring pixels; this reduces the amount of data transmission and increases the utilization of on-chip registers, thus increasing the processing speed. As a result, our method is 2X faster than the traditional image scanning method, and 26% faster than the latest NCC method. By combining this technique with the domain transformation (DT) algorithm, our system show real-time processing speed of 32 fps, on a Jetson Tx2 GPU for 1280x384 pixel images with a maximum disparity of 128. Additionally, the evaluation results on the KITTI 2015 benchmark show that our combined system is more accurate than the same algorithm combined with census by 7.26%, while maintaining almost the same processing speed.
Advancing primary care with Artificial Intelligence and Machine Learning

Primary care is the largest healthcare delivery platform in the US. Facing the Artificial Intelligence and Machine Learning technology (AI/ML) revolution, the primary care community would benefit from a roadmap revealing priority areas and opportunities for developing and integrating AI/ML-driven clinical tools. This article presents a framework that identifies five domains for AI/ML integration in primary care to support care delivery transformation and achieve the Quintuple Aims of the healthcare system. We concluded that primary care plays a critical role in developing, introducing, implementing, and monitoring AI/ML tools in healthcare and must not be overlooked as AI/ML transforms healthcare.
Lumping and splitting: Toward a classification of mineral natural kinds

Abstract How does one best subdivide nature into kinds? All classification systems require rules for lumping similar objects into the same category, while splitting differing objects into separate categories. Mineralogical classification systems are no exception. Our work in placing mineral species within their evolutionary contexts necessitates this lumping and splitting because we classify “mineral natural kinds” based on unique combinations of formational environments and continuous temperature-pressure-composition phase space. Consequently, we lump two minerals into a single natural kind only if they: (1) are part of a continuous solid solution; (2) are isostructural or members of a homologous series; and (3) form by the same process. A systematic survey based on these criteria suggests that 2310 (~41%) of 5659 IMA-approved mineral species can be lumped with one or more other mineral species, corresponding to 667 “root mineral kinds,” of which 353 lump pairs of mineral species, while 129 lump three species. Eight mineral groups, including cancrinite, eudialyte, hornblende, jahnsite, labuntsovite, satorite, tetradymite, and tourmaline, are represented by 20 or more lumped IMA-approved mineral species. A list of 5659 IMA-approved mineral species corresponds to 4016 root mineral kinds according to these lumping criteria. The evolutionary system of mineral classification assigns an IMA-approved mineral species to two or more mineral natural kinds under either of two splitting criteria: (1) if it forms in two or more distinct paragenetic environments, or (2) if cluster analysis of the attributes of numerous specimens reveals more than one discrete combination of chemical and physical attributes. A total of 2310 IMA-approved species are known to form by two or more paragenetic processes and thus correspond to multiple mineral natural kinds; however, adequate data resources are not yet in hand to perform cluster analysis on more than a handful of mineral species. We find that 1623 IMA-approved species (~29%) correspond exactly to mineral natural kinds; i.e., they are known from only one paragenetic environment and are not lumped with another species in our evolutionary classification. Greater complexity is associated with 587 IMA-approved species that are both lumped with one or more other species and occur in two or more paragenetic environments. In these instances, identification of mineral natural kinds may involve both lumping and splitting of the corresponding IMA-approved species on the basis of multiple criteria. Based on the numbers of root mineral kinds, their known varied modes of formation, and predictions of minerals that occur on Earth but are as yet undiscovered and described, we estimate that Earth holds more than 10 000 mineral natural kinds.
Developing a deep learning-based layer-3 solution for thermal infrared large-scale photovoltaic module inspection from orthorectified big UAV imagery data

The increasing adoption of photovoltaic (PV) technology highlights the need for efficient and large-scale deployment-ready inspection solutions. In the thermal infrared imagery-based inspection framework, we develop a robust and versatile deep learning model for the classification of defect-related patterns on PV modules. The model is developed from big UAV imagery data, and designed as a layer-3 building block that can be implemented on top of any two-stage PV inspection workflow comprising: (1) An aerial Structure from Motion – MultiView Stereo (SfM-MVS) photogrammetric acquisition/processing stage, at which a georeferenced thermal orthomosaic of an inspected PV site is generated, and which enables to locate precisely defective modules on field; then (2) an instance segmentation stage that extracts the images of modules. Orthomosaics from 28 different PV sites were produced, comprising 93 220 modules with various types, layouts and thermal patterns. Modules were extracted through a developed semi-automatic workflow, then labeled into six classes. Data augmentation and balancing techniques were used to prepare a highly representative and balanced deep learning-ready dataset. The dataset was used to train, cross-validate and test the developed classifier, as well as benchmarking with the VGG16 architecture. The developed model achieves the state-of-art performance and versatility on the addressed classification problem, with a mean F1-score of 94.52%. The proposed three-layer solution resolves the issues of conventional imagery-based workflows. It ensures highly accurate and versatile defect detection, and can be efficiently deployed to real-world large-scale applications.
Study of the Subjective and Objective Quality of High Motion Live Streaming Videos

Video livestreaming is gaining prevalence among video streaming service s, especially for the delivery of live, high motion content such as sport ing events. The quality of the se livestreaming videos can be adversely affected by any of a wide variety of events, including capture artifacts, and distortions incurred during coding and transmission. High motion content can cause or exacerbate many kinds of distortion, such as motion blur and stutter. Because of this, the development of objective Video Quality Assessment (VQA) algorithms that can predict the perceptual quality of high motion, live streamed videos is greatly desired. Important resources for developing these algorithms are appropriate databases that exemplify the kinds of live streaming video distortions encountered in practice. Towards making progress in this direction, we built a video quality database specifically designed for live streaming VQA research. The new video database is called the Laboratory for Image and Video Engineering (LIVE) Livestream Database. The LIVE Livestream Database includes 315 videos of 45 source sequences from 33 original contents impaired by 6 types of distortions. We also performed a subjective quality study using the new database, whereby more than 12,000 human opinions were gathered from 40 subjects. We demonstrate the usefulness of the new resource by performing a holistic evaluation of the performance of current state-of-the-art (SOTA) VQA models. We envision that researchers will find the dataset to be useful for the development, testing, and comparison of future VQA models. The LIVE Livestream database is being made publicly available for these purposes at https://live.ece. utexas.edu/research/LIVE_APV_Study/apv_index.html.
Technological Solutions for Sustainable Development: Effects of a Visual Prompt Scaffolding-Based Virtual Reality Approach on EFL Learners’ Reading Comprehension, Learning Attitude, Motivation, and Anxiety

As is indicated by the United Nation’s Sustainable Development Goal 4, it is crucial to have access to inclusive and quality education for all. For English as a Foreign Language (EFL) learners, reading English is a basic skill for learners to acquire and exchange information and to have lifelong learning experiences. To provide a vivid EFL learning environment, a visual prompt scaffolding-based VR (VPS-VR) approach was proposed to enhance students’ reading comprehension skills. To investigate the effectiveness of the proposed approach, an experiment was conducted in an English reading course at a Chinese university. Students from experimental group A (N = 31) learned with the VPS-VR approach, experimental group B (N = 32) learned with the virtual reality (VR) approach, and the control group learned with the traditional instruction (TI) approach. The results revealed the positive effects of the VPS-VR approach on students’ EFL reading comprehension, learning motivation, and English learning anxiety. Furthermore, it was also found that experimental students’ lower-level skills of reading comprehension, such as information location and text comprehension, were significantly improved, rather than the higher-level skills of reflection and evaluation. Fifteen students participated in interviews, and their learning experience and technology acceptance are also discussed.
On the documentation of refactoring types

Commit messages are the atomic level of software documentation. They provide a natural language description of the code change and its purpose. Messages are critical for software maintenance and program comprehension. Unlike documenting feature updates and bug fixes, little is known about how developers document their refactoring activities. Specifically, developers can perform multiple refactoring operations, including moving methods, extracting classes, renaming attributes, for various reasons, such as improving software quality, managing technical debt, and removing defects. Yet, there is no systematic study that analyzes the extent to which the documentation of refactoring accurately describes the refactoring operations performed at the source code level. Therefore, this paper challenges the ability of refactoring documentation, written in commit messages, to adequately predict the refactoring types, performed at the commit level. Our analysis relies on the text mining of commit messages to extract the corresponding features (i.e., keywords) that better represent each class (i.e., refactoring type). The extraction of text patterns, specific to each refactoring type (e.g., rename, extract, move, inline, etc.) allows the design of a model that verifies the consistency of these patterns with their corresponding refactoring. Such verification process can be achieved via automatically predicting, for a given commit, the method-level type of refactoring being applied, namely Extract Method, Inline Method, Move Method, Pull-up Method, Push-down Method, and Rename Method. We compared various classifiers, and a baseline keyword-based approach, in terms of their prediction performance, using a dataset of 5004 commits. Our main findings show that the complexity of refactoring type prediction varies from one type to another. Rename Method and Extract Method were found to be the best documented refactoring activities, while Pull-up Method, and Push-down Method were the hardest to be identified via textual descriptions. Such findings bring the attention of developers to the necessity of paying more attention to the documentation of these types.
The application of augmented reality–based navigation for accurate target acquisition of deep brain sites: advances in neurosurgical guidance

OBJECTIVE The objective of this study is to quantify the navigational accuracy of an advanced augmented reality (AR)–based guidance system for neurological surgery, biopsy, and/or other minimally invasive neurological surgical procedures. METHODS Five burr holes were drilled through a plastic cranium, and 5 optical fiducials (AprilTags) printed with CT-visible ink were placed on the frontal, temporal, and parietal bones of a human skull model. Three 0.5-mm-diameter targets were mounted in the interior of the skull on nylon posts near the level of the tentorium cerebelli and the pituitary fossa. The skull was filled with ballistic gelatin to simulate brain tissue. A CT scan was taken and virtual needle tracts were annotated on the preoperative 3D workstation for the combination of 3 targets and 5 access holes (15 target tracts). The resulting annotated study was uploaded to and launched by VisAR software operating on the HoloLens 2 holographic visor by viewing an encrypted, printed QR code assigned to the study by the preoperative workstation. The DICOM images were converted to 3D holograms and registered to the skull by alignment of the holographic fiducials with the AprilTags attached to the skull. Five volunteers, familiar with the VisAR, used the software/visor combination to navigate an 18-gauge needle/trocar through the series of burr holes to the target, resulting in 70 data points (15 for 4 users and 10 for 1 user). After each attempt the needle was left in the skull, supported by the ballistic gelatin, and a high-resolution CT was taken. Radial error and angle of error were determined using vector coordinates. Summary statistics were calculated individually and collectively. RESULTS The combined angle of error of was 2.30° ± 1.28°. The mean radial error for users was 3.62 ± 1.71 mm. The mean target depth was 85.41 mm. CONCLUSIONS The mean radial error and angle of error with the associated variance measures demonstrates that VisAR navigation may have utility for guiding a small needle to neural lesions, or targets within an accuracy of 3.62 mm. These values are sufficiently accurate for the navigation of many neurological procedures such as ventriculostomy.
Cross-Modal 3D Object Detection and Tracking for Auto-Driving

Detecting and tracking objects in 3D scenes play crucial roles in autonomous driving. Successfully recognizing objects through space and time hinges on a strong detector and a reliable association scheme. Recent 3D detection and tracking approaches widely represent objects as points when associating detection results with trajectories. Despite the demonstrated success, these approaches do not fully exploit the rich appearance information of objects. In this paper, we present a conceptually simple yet effective algorithm, named AlphaTrack, which considers both the location and appearance changes to perform joint 3D object detection and tracking. To achieve this, we propose a cross-modal fusion scheme that fuses camera appearance feature with LiDAR feature to facilitate 3D detection and tracking. We further attach an additional branch to the 3D detector to output instance-aware appearance embedding, which significantly improves tracking performance with our designed association mechanisms. Extensive validations on large-scale autonomous driving dataset demonstrate the effectiveness of the proposed algorithm in comparison with state-of-the-art approaches. Notably, the proposed algorithm ranks first on the nuScenes tracking leaderboard to date.