Reviewerly/exHarmony huggingface dataset:
Gradient Consistency Based Multi-Scale Optical Flow

Estimating optical flow and/or depth from multiple views or frames, all with multiple scales can be considered a data selection problem. We propose a gradient consistency based multi-scale optical flow technique which aims to address this data selection problem. We propose a method to assess gradient consistency and show how to use it to appropriately downweight data with poor consistency. We also introduce a spatial regularisation term that exploits optical flow acceleration between consecutive frame pairs as part of a global variational framework, coupling data between consecutive frame pairs. The proposed gradient consistency based multi-scale approach produces improved results on both real and synthetic scenes compared to a coarse to fine framework especially with limited numbers of warps.
Knowledge Graph Induction Enabling Recommending and Trend Analysis: A Corporate Research Community Use Case

A research division plays an important role of driving innovation in an organization. Drawing insights, following trends, keeping abreast of new research, and formulating strategies are increasingly becoming more challenging for both researchers and executives as the amount of information grows in both velocity and volume. In this paper we present a use case of how a corporate research community, IBM Research, utilizes Semantic Web technologies to induce a unified Knowledge Graph from both structured and textual data obtained by integrating various applications used by the community related to research projects, academic papers, datasets, achievements and recognition. In order to make the Knowledge Graph more accessible to application developers, we identified a set of common patterns for exploiting the induced knowledge and exposed them as APIs. Those patterns were born out of user research which identified the most valuable use cases or user pain points to be alleviated. We outline two distinct scenarios: recommendation and analytics for business use. We will discuss these scenarios in detail and provide an empirical evaluation on entity recommendation specifically. The methodology used and the lessons learned from this work can be applied to other organizations facing similar challenges.
SecWasm: Information Flow Control for WebAssembly

We introduce SecWasm, the first general purpose information-flow control system for WebAssembly (Wasm), thus extending the safety guarantees offered by Wasm with guarantees that applications manipulate sensitive data in a secure way. SecWasm is a hybrid system enforcing termination-insensitive noninterference which overcomes the challenges posed by the uncommon characteristics for machine languages of Wasm in an elegant and thorough way.
Proposal of Teaching Method for Middle School Chinese Textbook on the Metaverse Platform: Focusing on the Use of Gathertown

This study proposes a Teaching Method for Middle School Chinese Textbook using Gathertown, which corresponds to the virtual reality type among metaverse platforms. A school main hall, a classroom and a rooftop bar were built as the learning spaces on the Gathertown. The main hall is a social activity space and resting area for students. Most teaching and learning activities are held in the classroom. The rooftop bar was constructed as a space where realistic speech application activities are available. In terms of pedagogical factors, Gathertown is suitable for language education such as Chinese because learners can train the language in a virtual space. In terms of contextual factors, classes using Gathertown can be more easily participated in classes if learners have experience using online education platforms such as Zoom and Webex. In terms of logistical factors, in order to conduct classes in Gathertown, learners must have the ability to use smart devices by themselves. Gathertown is available in PC environments, not mobile or tablet. Various platforms are emerging in the educational field. Therefore, research on Teaching-Learning methods is continuously needed so that instructors can understand the platform and utilize its strengths to educate effectively.
Integrating Computer Science and Physical Education in Elementary Schools with Data Science Learning Modules Using Wearable Microcontrollers

With limited time in the school day to meet required state-level objectives across subjects, it is challenging to meet the need to address declining physical activity levels and to incorporate additional computer science instruction in elementary schools. Project moveSMART uses a web-based platform to integrate opportunities for physical education with computer science and computational thinking (CS/CT) learning activities. Fourth grade students who underwent a single-day intervention experienced a significant improvement in their interest in coding and their perceptions of coders. This paper presents a series of three new lessons in the fifth grade moveSMART curriculum that allow students to create and improve a physical activity monitor. We employ a project-based learning approach to expose students to data analysis and machine learning in the framework of classification and pattern recognition.
Handling Imbalanced Dataset on Hate Speech Detection in Indonesian Online News Comments

In the current technological era, people can get information quickly, up to date, and in large quantities by accessing online news. A comment column is usually provided as a feature in the online news to express criticism, suggestions, and opinions on certain news. This convenience is a form of freedom of expression for everyone. However, it increases the opportunity for users to express their hate in the comment column. The rise of posts containing hate speech is a problem that needs to be addressed by automatic hate speech detection. Naturally, there is an imbalance between the number of comments that include hate speech and those that do not. Therefore, in this study, the detection of Indonesian hate speech posts in the comments was carried out under several imbalanced conditions. Models used in this study are the SVM model, SVM with various resampling methods, fine-tuned IndoBERT, and fine-tuned mBERT. Based on the results, an imbalanced state can reduce the performance of SVM and the fine-tuned models. Even under an extreme imbalanced condition, the fine-tuned IndoBERT can capture the sentence contexts better than SVM. Then, applying oversampling strategies to SVM can improve the performance of SVM under imbalanced settings. The combination of SVM and SMOTE models perform the best in handling imbalanced problem.
The Use of Recount Text by Implementing Pre-Questioning Technique to Improve the Students Reading Comprehension at SMA Muhammadiyah Limbung

The purpose of this study was to determine whether the pre-questioning technique was effective in increasing students' understanding in reading recount text or not, and to find out how students' perceptions of the pre-questioning and answering technique applied by the teacher to their understanding of reading recount text.This study uses quantitative methods. This study used a pre-experimental design in which the pre-test, treatment, and post-test were carried out in class. The pre-test was conducted to determine the students' prior knowledge, while in the treatment process the students were taught recount text using the pre-question technique to improve their reading comprehension. Then a post-test was distributed to determine the progress or reading ability of students, this test was given after students were given treatment for four meetings through the pre-question technique.The findings showed that the application of pre-questions was effective in improving students' reading achievement, especially in reading recount texts, this was indicated by the students' post-test scores which were better than the pre-test, both on multiple choice questions and essay questions. In addition, student learning achievement has been shown to increase significantly. This is indicated by the pre-test and post-test interval scores. Students can also find the main idea and supporting details in the text.
Environment-aware Interactive Movement Primitives for Object Reaching in Clutter

The majority of motion planning strategies developed over the literature for reaching an object in clutter are applied to two dimensional (2-d) space where the state space of the environment is constrained in one direction. Fewer works have been investigated to reach a target in 3-d cluttered space, and when so, they have limited performance when applied to complex cases. In this work, we propose a constrained multi-objective optimization framework (OptI-ProMP) to approach the problem of reaching a target in a compact clutter with a case study on soft fruits grown in clusters, leveraging the local optimisation-based planner CHOMP. OptI-ProMP features costs related to both static, dynamic and pushable objects in the target neighborhood, and it relies on probabilistic primitives for problem initialisation. We tested, in a simulated poly-tunnel, both ProMP-based planners from literature and the OptI-ProMP, on low (3-dofs) and high (7-dofs) dexterity robot body, respectively. Results show collision and pushing costs minimisation with 7-dofs robot kinematics, in addition to successful static obstacles avoidance and systematic drifting from the pushable objects center of mass.
Rethinking Clustering-Based Pseudo-Labeling for Unsupervised Meta-Learning

The pioneering method for unsupervised meta-learning, CACTUs, is a clustering-based approach with pseudo-labeling. This approach is model-agnostic and can be combined with supervised algorithms to learn from unlabeled data. However, it often suffers from label inconsistency or limited diversity, which leads to poor performance. In this work, we prove that the core reason for this is lack of a clustering-friendly property in the embedding space. We address this by minimizing the inter- to intra-class similarity ratio to provide clustering-friendly embedding features, and validate our approach through comprehensive experiments. Note that, despite only utilizing a simple clustering algorithm (k-means) in our embedding space to obtain the pseudo-labels, we achieve significant improvement. Moreover, we adopt a progressive evaluation mechanism to obtain more diverse samples in order to further alleviate the limited diversity problem. Finally, our approach is also model-agnostic and can easily be integrated into existing supervised methods. To demonstrate its generalization ability, we integrate it into two representative algorithms: MAML and EP. The results on three main few-shot benchmarks clearly show that the proposed method achieves significant improvement compared to state-of-the-art models. Notably, our approach also outperforms the corresponding supervised method in two tasks. The code and models are available at https://github.com/xingpingdong/PL-CFE .
An enhanced secure data aggregation routing protocol for sensor networks

For the past decade, the utilisation of sensor devices in real world applications has increased rapidly. To meet the demand of applications, the sensor nodes are deployed in remote areas where the operation is very complex. The security of the sensor nodes may be compromised at any time. Therefore, a secure data aggregation mechanism is needed to overcome their limitations. In this paper, a secure data aggregation mechanism is proposed for securing the data from unauthorised access. The proposed method concentrated on three modules such as data encryption, data aggregation and data decryption. Additionally, the data aggregation module concentrated on removing the redundant data for minimising the energy consumption of the sensor nodes. The proposed method is evaluated under different conditions. The proposed method showed improvement in performance in terms of reducing the communication overhead, minimising the difference in energy consumption and increasing the data aggregation accuracy.
Structure-Aware Editable Morphable Model for 3D Facial Detail Animation and Manipulation

Morphable models are essential for the statistical modeling of 3D faces. Previous works on morphable models mostly focus on large-scale facial geometry but ignore facial details. This paper augments morphable models in representing facial details by learning a Structure-aware Editable Morphable Model (SEMM). SEMM introduces a detail structure representation based on the distance field of wrinkle lines, jointly modeled with detail displacements to establish better correspondences and enable intuitive manipulation of wrinkle structure. Besides, SEMM introduces two transformation modules to translate expression blendshape weights and age values into changes in latent space, allowing effective semantic detail editing while maintaining identity. Extensive experiments demonstrate that the proposed model compactly represents facial details, outperforms previous methods in expression animation qualitatively and quantitatively, and achieves effective age editing and wrinkle line editing of facial details. Code and model are available at https://github.com/gerwang/facial-detail-manipulation .
A Comparative Analysis on the Summarization of Legal Texts Using Transformer Models

Transformer models have evolved natural language processing tasks in machine learning and set a new standard for the state of the art. Thanks to the self-attention component, these models have achieved significant improvements in text generation tasks (such as extractive and abstractive text summarization). However, research works involving text summarization and the legal domain are still in their infancy, and as such, benchmarks and a comparative analysis of these state of the art models is important for the future of text summarization of this highly specialized task. In order to contribute to these research works, the researchers propose a comparative analysis of different, fine-tuned Transformer models and datasets in order to provide a better understanding of the task at hand and the challenges ahead. The results show that Transformer models have improved upon the text summarization task, however, consistent and generalized learning is a challenge that still exists when training the models with large text dimensions. Finally, after analyzing the correlation between objective results and human opinion, the team concludes that the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) [13] metrics used in the current state of the art are limited and do not reflect the precise quality of a generated summary.
Modulation Recognition of Overlapping Radar Signals Under Low SNR Based on Se-Incepatnet

In the field of modern electronic reconnaissance, due to the complex electromagnetic environment and the denser pulse stream, multiple radar signals will be received simultaneously. These radar signals overlap in the time domain and frequency domain, which makes modulation recognition difficult, especially under low signal-to-noise ratio (SNR). This paper proposes a Squeeze-and-Excitation InceptionNet with an adaptive threshold (SE-IncepAtNet) to deal with the above problem. The network includes an Inception block to extract features of different receptive fields and reduces the influence of noise through a Squeeze-and-Excitation (SE) block. An adaptive threshold block is used to provide adaptive thresholds, avoiding the difficulty of threshold selection in multi-classification tasks. The simulation of five typical radar signals shows that the proposed method is robust and effective.
Tenant placement in over-subscribed database-as-a-service clusters

Relational cloud Database-as-a-Service offerings run on multi-tenant infrastructure consisting of clusters of nodes, with each node hosting multiple tenant databases. Such clusters may be over-subscribed to increase resource utilization and improve operational efficiency. When resources are over-subscribed, it is possible that anode has insufficient resources to satisfy the resource demands of all databases on it, making it necessary to move databases to other nodes. Such moves can significantly impact database performance and availability. Therefore, it is important to reduce the likelihood of such resource shortages through judicious placement of databases in the cluster. We propose a novel tenant placement approach that leverages historical traces of tenant resource demands to estimate the probability of resource shortages and leverages these estimates in placement. We have prototyped our techniques in the Service Fabric cluster manager. Experiments using production resource traces from Azure SQL DB and an evaluation on a real cluster deployment show significant improvements over the state-of-the-art.
Pay Attention to the “Tails”: A Novel Aspect-Fusion Model for Long-Tailed Aspect Category Detection

Aspect Category Detection (ACD), which belongs to the research of fine-grained sentiment analysis, aims to identify the aspect categories mentioned in given sentences. However, the distribution of data from the real world is imbalanced or even long-tailed. This fact poses significant challenges for ACD because it is hard to fully extract the features of tail classes. Since a sentence usually contains one or more aspect categories, we model ACD as a multi-label text classification task. Under the long-tailed setting, this paper proposes a novel Aspect-Fusion model for Long-Tailed Aspect Category Detection (AFLoT-ACD). AFLoT-ACD first extracts the fine-grained aspect features from sentence vectors by the mechanism of Interactive Attention Network with characteristics of Long-Tailed distribution (IAN-LoT). A long-tailed distribution-based attention mechanism is also incorporated, which integrates contextual aspect-level semantic information. Additionally, an Advanced Distribution-Balanced loss (A-DB) is introduced to overcome the problems of label co-occurrence and the dominance of negative classes in training a long-tailed multi-label text classifier. We conduct experiments on three datasets and compare AFLoT-ACD with eight baselines. AFLoT-ACD outperforms the SOTA with over 7% improvements in Macro F1 score for tail classes and also achieves higher detection performance in general.
Distance learning in higher education: problems and benefits

The basis of socio-economic development of society is not material production, but the production of information and knowledge. Acquiring new knowledge and skills that are practically useful and applied in the work of the information society significantly expands opportunities for self-realization and promotes career growth. Information resources play a particularly important role in the organization of distance learning. Under such conditions, there is a constant search for more advanced forms of organization of the educational process. It is possible to implement distance learning technologies by developing a model of using virtual learning environments and distance learning platforms: Zoom, Microsoft Teams, Webex, Google Classroom, ClassDojo, Padlet, etc. Due to this, non-traditional sources of information became available to students, the efficiency of independent work increased. They were able to study at a time convenient to them, in a familiar environment and at a relatively autonomous pace. Students have opportunities for creative self-expression, finding and consolidating various professional skills. Teachers have moved to a different format of teaching material, trying to create more interactive and interesting activities for students, implement new forms and methods of teaching, have the opportunity to give grades in ezines and leave comments on each submitted work. However, the distance learning system also has shortcomings. First, it is important to have direct contact with the applicant for successful correction of training and adequate assessment. In addition, it is impossible to accurately verify whether that person is working, performing a task, or whether someone else is doing it. In addition, not all localities have uninterrupted access to the Internet. And most importantly, in distance learning, direct contact between teacher and student is lost. At the same time, the distance education system is currently a promising form of higher education for Ukraine. It is this system that can most adequately and flexibly respond to the needs of society for the training of highly professional professionals, as it provides a high-tech approach to the process of knowledge transfer.
CTI-GAN: Cross-Text-Image Generative Adversarial Network for Bidirectional Cross-modal Generation

Cross-modal tasks between text and images are increasingly a research hotspot. This paper proposed a cross-text-image generative adversarial network(CTI-GAN). This model can complete the cross-modal bidirectional generation task between image and text. The method effectively connects text and image modeling to realize bidirectional generation between image and text. The extraction effect of text features is improved by hierarchical LSTM encoding. Through feature pyramid fusion, the features of each layer are fully utilized to improve the image feature representation. In this paper, experiments are conducted to verify the effectiveness of the above improvements for image text generation. The improved algorithm can efficiently complete the task of cross-modal image text generation and improve the accuracy of the generated samples. In the text description generation image task, the inception score of CTI-GAN is improved by about 2% compared with StackGAN++, HDGAN, GAN-INT-CLS, and other models under the same conditions of the same dataset.
A New 3-D Memristive Hyperchaotic Map With Multi-Parameter-Relied Dynamics

The construction of chaotic maps by discrete memristor has become a hot research work. This brief has proposed a discrete memristor model and verified its memory characteristics by numerical analysis. The model is coupled with a trigonometric map to obtain the 3D memristive hyperchaotic map. The most remarkable feature of this map is that it can exhibit multi-parameter-relied dynamics. It has a great range of hyperchaos under different parameter variations, and two positive Lyapunov exponents have an excellent numerical value. Notably, the parameters affect all the chaotic signals it outputs, which can control amplitude magnitude. A hardware platform based on a microcontroller was designed to implement the proposed discrete memristor and the new map, and its amplitude control signal was verified.
Semi-Jordan curve theorem on the Marcus-Wyse topological plane

&lt;abstract&gt;&lt;p&gt;The paper initially develops the semi-Jordan curve theorem on the digital plane with the Marcus-Wyse topology, i.e., $ MW $-topological plane or $ ({\mathbb Z}^2, \gamma) $ for brevity. We first prove that while every simple closed $ MW $-curve is semi-open in $ ({\mathbb Z}^2, \gamma) $, it may not be semi-closed. Given a simple closed $ MW $-curve with $ l $ elements, denoted by $ SC_{\gamma}^l $, after establishing a continuous analog of $ SC_{\gamma}^l $ denoted by $ \mathcal{A}(SC_{\gamma}^l) $, we initially show that $ \mathcal{A}(SC_{\gamma}^l) $ is both semi-open and semi-closed in $ ({\mathbb R}^2, \mathcal{U}) $, where $ ({\mathbb R}^2, \mathcal{U}) $ is the $ 2 $-dimensional real plane $ {\mathbb R}^2 $ with the usual topology $ \mathcal{U} $. Furthermore, we find a condition for $ \mathcal{A}(SC_{\gamma}^l) $ to separate $ ({\mathbb R}^2, \mathcal{U}) $ into exactly two non-empty components, compared to a typical Jordan curve theorem on $ ({\mathbb R}^2, \mathcal{U}) $. Since not every $ SC_{\gamma}^l $ always separates ($ {\mathbb Z}^2, \gamma) $ into two nonempty components, we find a condition for $ SC_{\gamma}^l, l\neq 4, $ to separate $ ({\mathbb Z}^2, \gamma) $ into exactly two components. The semi-Jordan curve theorem on the $ MW $-topological plane plays an important role in applied topology such as digital topology, mathematical morphology as well as computer science.&lt;/p&gt;&lt;/abstract&gt;