Reviewerly/exHarmony huggingface dataset:
Estimating the drivable area of a mobile robot using a 3D point cloud

Mobile robots, such as self-driving wheelchairs, need to be able to recognize bumps in door sill and gutters in order to move safely. If the robot does not recognize the bumps and moves forward at a certain speed, it may not be able to get over the bumps or may fall down. In this research, we propose a method for recognizing obstacles such as bumps and reflecting them on a local map to ensure safe autonomous movement.
RACA: Relation-Aware Credit Assignment for Ad-Hoc Cooperation in Multi-Agent Deep Reinforcement Learning

In recent years, reinforcement learning has faced several challenges in the multi-agent domain, such as the credit assignment issue. Value function factorization emerges as a promising way to handle the credit assignment issue under the centralized training with decentralized execution (CTDE) paradigm. However, existing value function factorization methods cannot deal with ad-hoc cooperation, that is, adapting to new configurations of teammates at test time. Specifically, these methods do not explicitly utilize the relationship between agents and cannot adapt to different sizes of inputs. To address these limitations, we propose a novel method, called Relation-Aware Credit Assignment (RACA), which achieves zero-shot generalization in ad-hoc cooperation scenarios. RACA takes advantage of a graph-based relation encoder to encode the topological structure between agents. Furthermore, RACA utilizes an attention-based observation abstraction mechanism that can generalize to an arbitrary number of teammates with a fixed number of parameters. Experiments demonstrate that our method outperforms baseline methods on the StarCraftII micromanagement benchmark and ad-hoc cooperation scenarios.
Curse of co-Dimensionality: Explaining Adversarial Examples by Embedding Geometry of Data Manifold

It is widely known that adversarial examples cause misclassification in classifiers trained by deep learning. In spite of numerous previous researches, the phenomenon has not been well understood and clearly explained, hence there is no effective countermeasures available now. In this research, instead of exploring on neural networks and their training algorithms, we focus on the training data of the classifier, as submanifolds embedded in Euclidean space. We show that occurrence of adversarial examples is due to the embedding structure of the data manifold in the data space, specifically existence of so-called co-dimension of the data manifold. In particular, they lie in the orthogonal complementary subspaces of the tangent spaces of the data manifold. The theory is then verified on adversarial examples by visualization in 3D space and analysis in high dimensional space.
Integrated Power System multi-node model, taking into account the nondispatchable of renewable energy sources

Mathematical model with integer variables of a multi-node integrated power system is proposed. It takes into account the specific features of the Ukrainian and neighboring power systems included in ENTSO-E. The model using allows to study the possibilities of optimized dispatching of components, the limits of permissible values of the generating and reserve parameters, at which stable balanced operation of the integrated power system in synchronous operation modes is possible.
Student Vocabulary Advancement in a Seamless Chinese Vocabulary Learning Environment

This paper reports a pilot study on the utilization of seamless learning in improving Chinese vocabulary learning and advancement of vocabulary growth in a seamless Chinese vocabulary learning environment. In particular, a prominent technology-enhanced learning platform named DingTalk was exhibited to facilitate Chinese vocabulary learning as reflected in the students' social media learning outcomes. To validate the practicality of seamless learning in improving vocabulary learning and achieving vocabulary growth, the design-based research (DBR) approach was adopted. One-way repeated measures ANOVA was conducted for in-depth data analysis. Specifically, the data encompasses diverse social media items, which include student artefacts comprising sentences made using target vocabulary online. Notably, they were meticulously analyzed to scrutinize the extent of seamless learning in enhancing vocabulary growth. Subsequently, the results unanimously demonstrated their significance and positive results regarding the use of seamless learning on students' vocabulary growth. Furthermore, the result of this pilot study validates the authenticity and practicality in the utilization of seamless learning and contributes to the advancement of Chinese vocabulary learning. Predominantly, Chinese educators and the entirety of language education would immensely benefit from the active usage of seamless learning to induce positive learning outcomes concerning vocabulary growth in writing.
Evaluation of time-shifted emotion through shared emoji reactions in a video watching experience

Using 2-minute animated films with non-verbal expressions, we conducted an online experiment to evaluate the effects of proposed online video watching system to confirm "time-shifted shared emotion" with and without the emoji ("stimulus emoji") that had been pre-recorded to appear at particular times during viewing. The proposed system revealed enthusiastic users who send the same emoji requests more than four times in less than a second by repeated tapping. The stimulus emoji did not affect the timing or frequency of viewers’ tapping, but had a positive effect on presence and retention, and can induce viewers’ emoji choices for particular video scenes, i.e. evoking time-shifted shared emotion.
On the Construction Structures of $$3 \times 3$$ Involutory MDS Matrices over $$\mathbb {F}_{2^{m}}$$

In this paper, we propose new construction structures, in other words, transposition-permutation path patterns for $$3 \times 3$$ involutory and MDS permutation-equivalent matrices over $$\mathbb {F}_{2^{3}}$$ and $$\mathbb {F}_{2^{4}}$$ . We generate $$3 \times 3$$ involutory and MDS matrices over $$\mathbb {F}_{2^{3}}$$ and $$\mathbb {F}_{2^{4}}$$ by using the matrix form given in [1], and then all these matrices are analyzed by finding all their permutation-equivalent matrices. After that, we extract whether there are any special permutation patterns, especially for this size of the matrix. As a result, we find new 28,088 different transposition-permutation path patterns to directly construct $$3 \times 3$$ involutory and MDS matrices from any $$3 \times 3$$ involutory and MDS representative matrix over $$\mathbb {F}_{2^{3}}$$ and $$\mathbb {F}_{2^{4}}$$ . The 35 patterns are in common with these finite fields. By using these new transposition-permutation path patterns, new $$3 \times 3$$ involutory and MDS matrices can be generated especially for different finite fields such as $$\mathbb {F}_{2^{8}}$$ (is still an open problem because of the large search space). Additionally, the idea of finding the transposition-permutation path patterns can be applicable to larger dimensions such as $$8 \times 8$$ , $$16 \times 16$$ , and $$32 \times 32$$ . To the best of our knowledge, the idea given in this paper to find the common and unique transposition-permutation path patterns over different finite fields is the first work in the literature.
Assuring Safety-Critical Machine Learning Enabled Systems: Challenges and Promise

Machine learning is increasingly being used in safety-critical systems, where the public safety requires a rigorous assurance process. We shall outline how assurance processes work for conventional systems and identify the primary difficulty in applying them to machine learning enabled systems. We will then outline a path forward including identifying where considerable basic research remains.
DFS: A Diverse Feature Synthesis Model for Generalized Zero-Shot Learning

Generative based strategy has shown great potential in the Generalized Zero-Shot Learning task. However, it suffers severe generalization problem due to lacking of feature diversity for unseen classes to train a good classifier. In this paper, we propose to enhance the generalizability of GZSL models via improving feature diversity of unseen classes. For this purpose, we present a novel Diverse Feature Synthesis (DFS) model. Different from prior works that solely utilize semantic knowledge in the generation process, DFS leverages visual knowledge with semantic one in a unified way, thus deriving class-specific diverse feature samples and leading to robust classifier for recognizing both seen and unseen classes in the testing phase. To simplify the learning, DFS represents visual and semantic knowledge in the aligned space, making it able to produce good feature samples with a low-complexity implementation. Accordingly, DFS is composed of two consecutive generators: an aligned feature generator, transferring semantic and visual representations into aligned features; a synthesized feature generator, producing diverse feature samples of unseen classes in the aligned space. We conduct comprehensive experiments to verify the efficacy of DFS. Results demonstrate its effectiveness to generate diverse features for unseen classes, leading to superior performance on multiple benchmarks. Code will be released upon acceptance.
Linking Scholarly Contents: The Design and Construction of an Argumentation Graph

In this study, we propose a way to link the scholarly contents of scientific papers by constructing a knowledge graph based on the semantic organization of argumentation units and relations in scientific papers. We carried out an argumentation graph data model aimed at linking multiple discourses, and also developed a semantic annotation platform for scientific papers and an argumentation graph visualization system. A construction experiment was performed using 12 articles. The final argumentation graph has 1,262 nodes and 1,628 edges, including 1,628 intra-article relations and 190 inter-article relations. Knowledge evolution representation, strategic reading, and automatic abstracting use cases are presented to demonstrate the application of the argumentation graph. In contrast to existing knowledge graphs used in academic fields, the argumentation graph better supports the organization and representation of scientific paper content and can be used as data infrastructure in scientific knowledge retrieval, reorganization, reasoning, and evolution. Moreover, it supports automatic abstract and strategic reading.
An approach for forecasting workload in data center for cloud computing using ANN based PSO

Maintaining resilience of resources and their ability to expand in data centers has become a prediction of future workloads which is very important and indispensable. Resource requests are in a state of variation as a result of high and low workloads, and there may be another effect that hinders forecasting workloads such as noise and redundancy requests and this makes it difficult to predict future workloads. In our search, we present a Neural Network trained using a particle swarming optimization (PSO) algorithm for workload prediction. The method is based on inputting past loads for periods of time and the network predicts the future value based on past data. We used a historical NASA dataset and we took a timestamp and calculated the loads at certain time intervals to be an earlier entry and predicted the next value. When applying the proposed method to training the neural network using the (PSO) algorithm, the results were obtained in the training condition and the accuracy was 0.97%, the average MSE square error was 0.001, the RMSE square error was 0.03 and the results were in the test phase, the accuracy was 0.95 and MSE=0.003 and RMSE=0.05. Results of the prediction periods used were better compared to ANN, differential adaptive development, posterior propagation, mean, and maximum. The role of the Particle Swarm Optimization PSO algorithm in improving the results is through changing the position of each individual in the swarm, and this means changing the scale in order to reduce the error rate of one session. In each cycle, the members of the squadron change their locations, update the speed, and then move to the new location, which will represent a new measure that depends on the evaluation of the fitness function. This process is repeated for a number of cycles. The value of is the best is the value and it was explained in the third and second chapters to obtain the ideal weights for the neural network, and then the best is determined according to the number of weights and this change in locations and updating the speed gives better results for the weights that we use in the neural network and through these weights We get high resolution in the neural network.
Measuring Drift Severity by Tree Structure Classifiers

Streaming data has become more common as our ability to collect data in real-time increases. A primary concern in dealing with data streams is concept drift, which describes changes in the underlying distribution of streaming data. Measuring drift severity is crucial for model adaptation. Drift severity can be a proxy in choosing concept drift adaptation strategies. Current methods measure drift severity by monitoring the changes in the learner performance or measuring the difference between data distributions. However, these methods cannot measure the drift severity if the ground truth labels are unavailable. Specifically, performance-based methods cannot measure marginal drift, and distribution-based methods cannot measure conditional drift. We propose a novel framework named Tree-based Drift Measurement (TDM) that measures both marginal and conditional drift without revisiting historical data. TDM measures the difference between tree classifiers by transforming them into sets of binary vectors. An experiment shows that TDM achieves similar performance to the state-of-the-art methods and provides the best trade-off between runtime and memory usage. A case study shows that the online learner performance can be improved by adapting different drift adaptation strategies based on the drift severity.
Hierarchical Cache Transformer: Dynamic Early Exit for Language Translation

The transformer model significantly improves the performance of natural language processing tasks. However, the downside of employing the transformer-based model is its heavy inference cost, which raises the concern of putting transformer-based models into industrial operations. Thus, studies for im-proving inference performance were reported. However, the major studies mainly consider classification tasks but not for natural language generation (NLG). In this paper, we propose Hierarchical Cache (HC) Transformer model tailored to NLG tasks. Our experiments show the feasible results on German-English translation dataset. The experiment result demonstrates that HC-Transformer can speed up inference by 32% with a 3% loss in performance.
A Channel-Spatial Hybrid Attention Mechanism using Channel Weight Transfer Strategy

Attention is one of the most valuable breakthroughs in the deep learning community, and how to effectively utilize the attention information of channel and spatial is still one of the hot research topics. In this work, we integrate the advantages of channel and spatial mechanism to propose a Channel-Spatial hybrid Attention Module (CSHAM). Specifically, max-average fusion Channel Attention Module and Spatial Attention Neighbor Enhancement Module are firstly proposed, respectively. Then the connection between the two modules is analyzed and designed, and an alternate connection strategy with the transformation of channel weights is proposed. The key idea is to repeatedly use the channel weight information generated by the channel attention module, and to reduce the negative impact of the network complexity caused by the addition of the attention mechanism. Finally, a series of comparison experiments are conducted on CIFAR100 and Caltech-101 based on various backbone models. The results show that the proposed methods can obtain the best Top-1 performance among the existing popular methods, and can rise by nearly 1% in accuracy while basically maintaining the parameters and FLOPs. The code is publicly available at https://github.com/HuHaigen/A-Channel-Spatial-Hybrid-Attention-Mechanism-using-Channel-Weight-Transfer-Strategy. The package includes the proposed CSHAM for reproducibility purposes.
A Study on the Relative Importance of Emotional Intelligence Industry Revitalization Factors Using AHP Analysis: Focused on the Use of Artificial Emotional Intelligence

The purpose of this study is to derive important factors for the revitalization of the emotional intelligence industry by using AHP analysis techniques. Three top factors and 12 bottom factors were selected for the revitalization of the emotional intelligence industry, and a survey was conducted on experts in various industries. As a result of the analysis, the importance of the top factors was in the order of ‘emotional intelligence technology’, ‘artificial intelligence technology, and ’environmental factors’. The importance of sub-factors was in the order of ‘emotional measurement and evaluation technology’, ‘emotional inference and cognitive technology’, ‘data analysis technology’, ‘emotional content production technology’, ‘government support and regulation’, ‘ethics problem’, ‘human training and technology support infrastructure construction’, and ‘citizenship expansion’. This study has meaning in examining the emotional intelligence industry, finding important factors for revitalizing the emotional intelligence industry, and presenting important factors for revitalizing the emotional intelligence industry in the future.
RECLAIM: Reverse Engineering Classification Metrics

Being able to compare machine learning models in terms of performance is a fundamental part of improving the state of the art in a field. However, there is a risk of getting locked into only using a few - possibly not ideal - performance metrics, only for comparability with earlier works. In this work, we explore the possibility of reconstructing new classification metrics starting from what little information may be available in existing works. We propose three approaches to reconstruct confusion matrices and, as a consequence, other classification metrics. We empirically verify the quality of the reconstructions, drawing conclusions on the usefulness that various classification metrics have for the reconstruction task.
Complex Question Answering over Incomplete Knowledge Graph as N-ary Link Prediction

The Question Answering over Knowledge Graph (KGQA) task seeks entities (answers) from the Knowledge Graph (KG) in order to answer natural language questions. In practice, KG is often incomplete, with numerous missing links and nodes. With such an incomplete KG, it is tricky to use the semantics inside the KG to get the golden answers, particularly for complex questions. Some current efforts concentrate on using external corpora to overcome KG sparsity; however, identifying and obtaining the corpora is challenging. Other types of work aim to leverage the pre-trained embeddings to resolve the issue but perform slightly worse on complex questions involving numerous triple facts in KG. To address the aforementioned problems, we present a framework CAPKGQA, which transforms Complex KGQA into an n-Ary link Prediction task capable of explicitly modeling complex questions. Furthermore, previous methods also suffer from incomplete KG throughout the candidate answer generation phase. Therefore, we devise an embedding-based retrieval strategy to extract more reliable candidate answers from incomplete KG. Extensive experiments reveal that our approach beats the state-of-the-art models on incomplete and complex KGQA tasks by a significant margin.
Image Attribute Modification Based on Text Guidance

The goal of this paper is to manipulate image attributes using text description. Although many methods can synthesize images with new properties from text, they cannot fully preserve the text-independent content of the original image. There are two major limitations: (1) Some important details in image sub-region will be lost in the process of image modification; (2) Compared with the original image, the shape and edge of the object in the modified image will be more blurred. Therefore, we propose a novel framework edge aware generative adversarial network (EA-GAN) that uses edge information to guide image modification, which ensures the network’s ability to identify local regions and realizes accurate modification of image sub-regions. At the same time, an edge reconstruction loss (ERLoss) is added to the generator to constrain the generation of edges, generate sharper edges, and improve the clarity of the image. The experimental data on the CUB and Oxford-102 datasets show that the algorithm used in this paper can well distinguish the corresponding image features in the conditional text, and modify the image attribute of specific regions in the image.
On Some Properties of Solution Sets of Discontinuous Quantum Stochastic Differential Inclusions

We establish properties of solution sets for upper semicontinuous quantum stochastic differential inclusions whose existence of solution satisfy a noncommutative generalisation of Kakutani fixed point theorem. The result here complement earlier results.
Sherlock Holmes Zero-Knowledge Protocols

We present two simple zero knowledge interactive proofs that can be instantiated with many of the standard decisional or computational hardness assumptions. Compared with traditional zero knowledge proofs, in our protocols the verifiers starts first, by emitting a challenge, and then the prover answers the challenge.
FFFN: Frame-By-Frame Feedback Fusion Network for Video Super-Resolution

Video super-resolution (VSR) is a fundamental and challenging task in computer vision. Many of the existing VSR works focus on how to effectively align neighboring frames to better incorporate temporal information, while little work is devoted to the important subsequent step of inter-frame information fusion, and the existing methods on frame fusion have shortcomings such as not being able to make full use of spatio-temporal information. In this work, we propose a Frame-by-frame Feedback Fusion Network (FFFN) for VSR tasks. By applying the feedback learning mechanism commonly existing in the human cognitive system to the frame fusion stage, FFFN can refine low-level representation of the fused frames with high-level information in a coarse-to-fine manner. Specifically, after the neighboring frames are aligned, we first rearrange them from near to far according to the distance from the reference frame in the temporal space, and then feed them one-by-one into a proposed recurrent structure called Feedback Fusion Module (FFM), which is then able to iteratively generate high-level representation of the fused frames with several Feature Refinement Groups (FRGs) and feedback connections. Finally, we design a Dual-path Residual Reconstruction Module (DRRM) to reconstruct the final high-resolution image. The proposed FFFN comes with a strong frame fusion and reconstruction ability, and extensive experiments on several benchmark data sets show that it achieves favorable performance against state-of-the-art methods.