Reviewerly/exHarmony huggingface dataset:
Efficient Discovery of Partial Periodic-Frequent Patterns in Temporal Databases

Partial periodic-frequent pattern mining is an important knowledge discovery technique in data mining. It involves identifying all frequent patterns that have exhibited partial periodic behavior in a temporal database. The following two limitations have hindered the successful industrial application of this technique: (i) there exists no algorithm to find the desired patterns in columnar temporal databases, and (ii) existing algorithms are computationally expensive both in terms of runtime and memory consumption. This paper tackles these two challenging problems by proposing a novel algorithm known as partial periodic-frequent depth-first search (PPF-DFS). The proposed algorithm compresses a given row or columnar temporal database into a unified dictionary structure and mines this structure recursively to find all desired patterns. Experimental results demonstrate that PPF-DFS is 2 to 8.8 times faster and 5 to 31 times more memory efficient than the state-of-the-art algorithm.
Unified Autoregressive Modeling for Joint End-to-End Multi-Talker Overlapped Speech Recognition and Speaker Attribute Estimation

In this paper, we present a novel modeling method for single-channel multi-talker overlapped automatic speech recognition (ASR) systems. Fully neural network based end-to-end models have dramatically improved the performance of multi-taker overlapped ASR tasks. One promising approach for end-to-end modeling is autoregressive modeling with serialized output training in which transcriptions of multiple speakers are recursively generated one after another. This enables us to naturally capture relationships between speakers. However, the conventional modeling method cannot explicitly take into account the speaker attributes of individual utterances such as gender and age information. In fact, the performance deteriorates when each speaker is the same gender or is close in age. To address this problem, we propose unified autoregressive modeling for joint end-to-end multi-talker overlapped ASR and speaker attribute estimation. Our key idea is to handle gender and age estimation tasks within the unified autoregressive modeling. In the proposed method, transformer-based autoregressive model recursively generates not only textual tokens but also attribute tokens of each speaker. This enables us to effectively utilize speaker attributes for improving multi-talker overlapped ASR. Experiments on Japanese multi-talker overlapped ASR tasks demonstrate the effectiveness of the proposed method.
Research on Big Data Compression Algorithm Based on BIM

With the wide application of BIM, the mass BIM data generated has brought severe challenges to the storage, processing, and transmission on the limited bandwidth network of the model. In order to achieve smooth browsing and query of BIM data on the shared platform, this paper built a BIM big data storage framework based on the IFC standard, analyzed the inefficiency of the Huffman compression algorithm when constructing a binary tree, and proposed a construction process based on heap sorting. The proposed new algorithm was tested through experiments, and the results show that the efficiency of Huffman compression algorithm was improved when the amount of data was relatively large. In order to realize fast query based on compressed data, the idea of ElasticSearch+Hadoop server cluster is proposed.
TaScaaS: A Multi-Tenant Serverless Task Scheduler and Load Balancer as a Service

A combination of distributed multi-tenant infrastructures, such as public Clouds and onpremises installations belonging to different organisations, are frequently used for scientific research because of the high computational requirements involved. Although resource sharing maximises their usage, it typically causes undesirable effects such as the noisy neighbour, producing unpredictable variations of the infrastructure computing capabilities. These fluctuations affect execution efficiency, even of loosely coupled applications, such as many Monte Carlo based simulation programs. This highlights the need of a service capable to handle workload distribution across multiple infrastructures to mitigate these unpredictable performance fluctuations. With this aim, this work introduces TaScaaS, a highly scalable and completely serverless service deployed on AWS to distribute loosely coupled jobs among several computing infrastructures, and load balance them using a completely asynchronous approach to cope with the performance fluctuations with minimum impact in the execution time. We demonstrate how TaScaaS is not only capable of handling these fluctuations efficiently, achieving reduction in execution times up to 45% in our experiments, but also split the jobs to be computed to meet the user-defined execution time.
Path Recognition of the Regional Education Expansion Based on Improved Dragonfly Algorithm

To solve the problems of low recognition rate, high misrecognition rate, and long recognition time, the path recognition method of the regional education scale expansion based on the improved dragonfly algorithm is proposed. Through a variety of different behaviors utilized in the optimization process, the dragonfly algorithm model has been constructed. The step size and the position vector are introduced to update the dragonfly’s location. The dragonfly’s foraging behaviors are accurately simulated. Afterward, the dragonfly algorithm is combined with the flower authorization algorithm. The conversion probability is added, and the dragonfly’s global development ability is adjusted in real-time. Then, the dragonfly algorithm is improved. The improved dragonfly algorithm is employed to extract the features of the expansion path of the regional education scale. The improved support vector machine is utilized as a classifier to realize the recognition of the regional education scale expansion path. The experimental results denote that the proposed method has a high recognition rate of the regional education scale expansion path and can effectively reduce the misrecognition rate and shorten the recognition time.
FixMe: A GitHub Bot for Detecting and Monitoring On-Hold Self-Admitted Technical Debt

Self-Admitted Technical Debt (SATD) is a special form of technical debt in which developers intentionally record their hacks in the code by adding comments for attention. Here, we focus on issue-related "On-hold SATD", where developers suspend proper implementation due to issues reported inside or outside the project. When the referenced issues are resolved, the On-hold SATD also need to be addressed, but since monitoring these issue reports takes a lot of time and effort, developers may not be aware of the resolved issues and leave the On-hold SATD in the code. In this paper, we propose FixMe, a GitHub bot that helps developers detecting and monitoring On-hold SATD in their repositories and notify them whenever the On-hold SATDs are ready to be fixed (i.e. the referenced issues are resolved). The bot can automatically detect On-hold SATD comments from source code using machine learning techniques and discover referenced issues. When the referenced issues are resolved, developers will be notified by FixMe bot. The evaluation conducted with 11 participants shows that our FixMe bot can support them in dealing with On-hold SATD. FixMe is available at https://www.fixmebot.app/ and FixMe's VDO is at https://youtu.be/YSz9kFxN_YQ.
An efficient glomerular object locator for renal whole slide images using proposal-free network and dynamic scale evaluation method

In the diagnosis of chronic kidney disease, glomerulus as the blood filter provides important information for an accurate disease diagnosis. Thus automatic localization of the glomeruli is the necessary groundwork for future auxiliary kidney disease diagnosis, such as glomerular classification and area measurement. In this paper, we propose an efficient glomerular object locator in kidney whole slide image(WSI) based on proposal-free network and dynamic scale evaluation method. In the training phase, we construct an intensive proposal-free network which can learn efficiently the fine-grained features of the glomerulus. In the evaluation phase, a dynamic scale evaluation method is utilized to help the well-trained model find the most appropriate evaluation scale for each high-resolution WSI. We collect and digitalize 1204 renal biopsy microscope slides containing more than 41000 annotated glomeruli, which is the largest number of dataset to our best knowledge. We validate the each component of the proposed locator via the ablation study. Experimental results confirm that the proposed locator outperforms recently proposed approaches and pathologists by comparing F 1 and run time in localizing glomeruli from WSIs at a resolution of 0.25 μm/pixel and thus achieves state-of-the-art performance. Particularly, the proposed locator can be embedded into the renal intelligent auxiliary diagnosis system for renal clinical diagnosis by localizing glomeruli in high-resolution WSIs effectively.
End-to-End Rich Transcription-Style Automatic Speech Recognition with Semi-Supervised Learning

We propose a semi-supervised learning method for building end-to-end rich transcription-style automatic speech recognition (RT-ASR) systems from small-scale rich transcription-style and large-scale common transcription-style datasets. In spontaneous speech tasks, various speech phenomena such as fillers, word fragments, laughter and coughs, etc. are often included. While common transcriptions do not give special awareness to these phenomena, rich transcriptions explicitly convert them into special phenomenon tokens as well as textual tokens. In previous studies, the textual and phenomenon tokens were simultaneously estimated in an end-to-end manner. However, it is difficult to build accurate RT-ASR systems because large-scale rich transcription-style datasets are often unavailable. To solve this problem, our training method uses a limited rich transcription-style dataset and common transcription-style dataset simultaneously. The Key process in our semi-supervised learning is to convert the common transcription-style dataset into a pseudo-rich transcription-style dataset. To this end, we introduce style tokens which control phenomenon tokens are generated or not into transformer-based autoregressive modeling. We use this modeling for generating the pseudo-rich transcription-style datasets and for building RT-ASR system from the pseudo and original datasets. Our experiments on spontaneous ASR tasks showed the effectiveness of the proposed method.
Cluster Attack: Query-based Adversarial Attacks on Graph with Graph-Dependent Priors

While deep neural networks have achieved great success in graph analysis, recent work has shown that they are vulnerable to adversarial attacks. Compared with adversarial attacks on image classification, performing adversarial attacks on graphs is more challenging because of the discrete and non-differential nature of the adjacent matrix for a graph. In this work, we propose Cluster Attack -- a Graph Injection Attack (GIA) on node classification, which injects fake nodes into the original graph to degenerate the performance of graph neural networks (GNNs) on certain victim nodes while affecting the other nodes as little as possible. We demonstrate that a GIA problem can be equivalently formulated as a graph clustering problem; thus, the discrete optimization problem of the adjacency matrix can be solved in the context of graph clustering. In particular, we propose to measure the similarity between victim nodes by a metric of Adversarial Vulnerability, which is related to how the victim nodes will be affected by the injected fake node, and to cluster the victim nodes accordingly. Our attack is performed in a practical and unnoticeable query-based black-box manner with only a few nodes on the graphs that can be accessed. Theoretical analysis and extensive experiments demonstrate the effectiveness of our method by fooling the node classifiers with only a small number of queries.
Detecting Salient Image Objects Using Color Histogram Clustering for Region Granularity

Salient object detection represents a novel preprocessing stage of many practical image applications in the discipline of computer vision. Saliency detection is generally a complex process to copycat the human vision system in the processing of color images. It is a convoluted process because of the existence of countless properties inherent in color images that can hamper performance. Due to diversified color image properties, a method that is appropriate for one category of images may not necessarily be suitable for others. The selection of image abstraction is a decisive preprocessing step in saliency computation and region-based image abstraction has become popular because of its computational efficiency and robustness. However, the performances of the existing region-based salient object detection methods are extremely hooked on the selection of an optimal region granularity. The incorrect selection of region granularity is potentially prone to under- or over-segmentation of color images, which can lead to a non-uniform highlighting of salient objects. In this study, the method of color histogram clustering was utilized to automatically determine suitable homogenous regions in an image. Region saliency score was computed as a function of color contrast, contrast ratio, spatial feature, and center prior. Morphological operations were ultimately performed to eliminate the undesirable artifacts that may be present at the saliency detection stage. Thus, we have introduced a novel, simple, robust, and computationally efficient color histogram clustering method that agglutinates color contrast, contrast ratio, spatial feature, and center prior for detecting salient objects in color images. Experimental validation with different categories of images selected from eight benchmarked corpora has indicated that the proposed method outperforms 30 bottom-up non-deep learning and seven top-down deep learning salient object detection methods based on the standard performance metrics.
VRSketch: Investigating 2D Sketching in Virtual Reality with Different Levels of Hand and Pen Transparency

Sketching is a vital step in design processes. While analog sketching on pen and paper is the defacto standard, Virtual Reality (VR) seems promising for improving the sketching experience. It provides myriads of new opportunities to express creative ideas. In contrast to reality, possible drawbacks of pen and paper drawing can be tackled by altering the virtual environment. In this work, we investigate how hand and pen transparency impacts users’ 2D sketching abilities. We conducted a lab study ( $$N=20$$ ) investigating different combinations of hand and pen transparency. Our results show that a more transparent pen helps one sketch more quickly, while a transparent hand slows down. Further, we found that transparency improves sketching accuracy while drawing in the direction that is occupied by the user’s hand.
Global Scene Filtering, Exploration, and Pointing in Occluded Virtual Space

Target acquisition in an occluded environment is challenging given the omni-directional and first-person view in virtual reality (VR). We propose Solar-Casting, a global scene filtering technique to manage occlusion in VR. To improve target search, users control a reference sphere centered at their head through varied occlusion management modes: Hide, SemiT (Semi-Transparent), Rotate. In a preliminary study, we find SemiT to be better suited for understanding the context without sacrificing performance by applying semi-transparency to targets within the controlled sphere. We then compare Solar-Casting to highly efficient selection techniques to acquire targets in a dense and occluded VR environment. We find that Solar-Casting performs competitively to other techniques in known environments, where the target location information is revealed. However, in unknown environments, requiring target search, Solar-Casting outperforms existing approaches. We conclude with scenarios demonstrating how Solar-Casting can be applied to crowded and occluded environments in VR applications.
Learning to Robustly Aggregate Labeling Functions for Semi-supervised Data Programming

A critical bottleneck in supervised machine learning is the need for large amounts of labeled data which is expensive and time-consuming to obtain.Although a small amount of labeled data cannot be used to train a model, it can be used effectively for the generation of humaninterpretable labeling functions (LFs).These LFs, in turn, have been used to generate a large amount of additional noisy labeled data in a paradigm that is now commonly referred to as data programming.Previous methods of generating LFs do not attempt to use the given labeled data further to train a model, thus missing opportunities for improving performance.Additionally, since the LFs are generated automatically, they are likely to be noisy, and naively aggregating these LFs can lead to suboptimal results.In this work, we propose an LF-based bi-level optimization framework WISDOM to solve these two critical limitations.WISDOM learns a joint model on the (same) labeled dataset used for LF induction along with any unlabeled data in a semi-supervised manner, and more critically, reweighs each LF according to its goodness, influencing its contribution to the semi-supervised loss using a robust bi-level optimization algorithm.We show that WISDOM significantly outperforms prior approaches on several text classification datasets.The source code can be found at https://github.com/ayushbits/robust-aggregate-lfs.
The Use of Multimodal Text in Enhancing Students’ Reading Habit

Reading habit is a fundamental skill that is necessary for every student's life. However, Malaysia is far from a culture of reading. Therefore, to step forward to a world that enjoys reading, everyone needs to spread good reading habits as soon as possible. Thus, this conceptual paper provides a literature review that is relevant to students' reading habits by using multimodal text. Additionally, it explores reading habits, the importance of reading, multimodal text as instructional material, and the advantages of using multimodal text to improve students' reading habits. By identifying some advantages of using multimodal text in improving students' reading habits, such as making the learning environment more interesting and productive, encouraging more on reading habits and motivate students to read texts with passion. Thus, teachers should be encouraged to develop their own multimodal text to be used in the ESL classroom.
Optimal approximation to unitary quantum operators with linear optics

Abstract Linear optical systems acting on photon number states produce many interesting evolutions, but cannot give all the allowed quantum operations on the input state. Using Toponogov’s theorem from differential geometry, we propose an iterative method that, for any arbitrary quantum operator U acting on n photons in m modes, returns an operator $$\widetilde{U}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mover><mml:mi>U</mml:mi><mml:mo>~</mml:mo></mml:mover></mml:math> which can be implemented with linear optics. The approximation method is locally optimal and converges. The resulting operator $$\widetilde{U}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mover><mml:mi>U</mml:mi><mml:mo>~</mml:mo></mml:mover></mml:math> can be translated into an experimental optical setup using previous results.
Joint Estimation of Multiple RF Impairments Using Deep Multi-Task Learning

Radio-frequency (RF) front-end forms a critical part of any radio system, defining its cost as well as communication performance. However, these components frequently exhibit non-ideal behavior, referred to as impairments, due to the imperfections in the manufacturing/design process. Most of the designers rely on simplified closed-form models to estimate these impairments. On the other hand, these models do not holistically or accurately capture the effects of real-world RF front-end components. Recently, machine learning-based algorithms have been proposed to estimate these impairments. However, these algorithms are not capable of estimating multiple RF impairments jointly, which leads to limited estimation accuracy. In this paper, the joint estimation of multiple RF impairments by exploiting the relationship between them is proposed. To do this, a deep multi-task learning-based algorithm is designed. Extensive simulation results reveal that the performance of the proposed joint RF impairments estimation algorithm is superior to the conventional individual estimations in terms of mean-square error. Moreover, the proposed algorithm removes the need of training multiple models for estimating the different impairments.
Fourier transforms on finite group actions and bent functions

Highly nonlinear functions (bent functions, perfect nonlinear functions, etc.) on finite fields and finite (abelian or nonabelian) groups have been studied in numerous papers. As a generalization of bent functions on finite groups, bent functions on group actions have been studied in quite a few papers. In this paper, we study Fourier transforms and bent functions on finite nonabelian group actions. Let G be a finite (nonabelian) group acting on a finite set X. Our goal is to develop a Fourier analysis on X and study the bentness of complex valued functions on X via their Fourier transforms. We will first construct a G-dual set $$\widehat{X}$$ , which is a special basis of the G-space of all complex valued functions on X. This G-dual set $${\widehat{X}}$$ plays a role similar to that of $${\widehat{G}}$$ . Then, for a complex-valued function f on X, we define its Fourier transform $${\widehat{f}}$$ as a function on $${\widehat{X}}$$ and characterize the bentness of f by its Fourier transform $${\widehat{f}}$$ . Some known and new results about bent functions on finite groups will be obtained as direct consequences. We will also discuss the constructions of bent functions and construct examples of bent functions on group actions of the dihedral group of order 6.