Reviewerly/exHarmony huggingface dataset:
Promotion of Skills Competition on Construction of Teaching Staff in Higher Vocational Colleges

Skills competition is an important part of the vocational education system and plays an important guiding role in the reform of vocational education.Skills competition is not only an arena for students in vocational colleges, but also a stage for teachers to exercise and improve their abilities.This paper analyzes the problems existing in the construction of teaching staff in higher vocational colleges from the perspective of the preparation for skills competition.On this basis, it explains the role of promotion of skills competition on the construction of teaching staff.Various skills competitions are important means to test the achievements of vocational education teaching reform, education quality and school running level.Teachers in higher vocational colleges should actively participate in skills competitions and improve their abilities in the process of guiding students.
Clustering with density based initialization and Bhattacharyya based merging

Centroid based clustering approaches, such as k-means, are relatively fast but inaccurate for arbitrary shape clusters.Fuzzy c-means with Mahalanobis distance can accurately identify clusters if data set can be modelled by a mixture of Gaussian distributions.However, they require number of clusters apriori and a bad initialization can cause poor results.Density based clustering methods, such as DBSCAN, overcome these disadvantages.However, they may perform poorly when the dataset is imbalanced.This paper proposes a clustering method, named clustering with density initialization and Bhattacharyya based merging based on the fuzzy clustering.The initialization is carried out by density estimation with adaptive bandwidth using k-Nearest Orthant-Neighbor algorithm to avoid the effects of imbalanced clusters.The local peaks of the point clouds constructed by the k-Nearest Orthant-Neighbor algorithm are used as initial cluster centers for the fuzzy clustering.We use Bhattacharyya measure and Jensen inequality to find overlapped Gaussians and merge them to form a single cluster.We carried out experiments on a variety of datasets and show that the proposed algorithm has remarkable advantages especially for imbalanced and arbitrarily shaped data sets.
Self-supervised Semantic-driven Phoneme Discovery for Zero-resource Speech Recognition

Phonemes are defined by their relationship to words: changing a phoneme changes the word.Learning a phoneme inventory with little supervision has been a longstanding challenge with important applications to underresourced speech technology.In this paper, we bridge the gap between the linguistic and statistical definition of phonemes and propose a novel neural discrete representation learning model for self-supervised learning of phoneme inventory with raw speech and word labels.Given the availability of phoneme segmentation and some mild conditions, we prove that the phoneme inventory learned by our approach converges to the true one with an exponentially low error rate.Moreover, in experiments on TIMIT and Mboshi benchmarks, our approach consistently learns a better phonemelevel representation and achieves a lower error rate in a zero-resource phoneme recognition task than previous state-of-the-art selfsupervised representation learning algorithms.
Approximating Subset Sum Ratio via Subset Sum Computations

We present a new FPTAS for the Subset Sum Ratio problem, which, given a set of integers, asks for two disjoint subsets such that the ratio of their sums is as close to 1 as possible. Our scheme makes use of exact and approximate algorithms for the closely related Subset Sum problem, hence any progress over those—such as the recent improvement due to Bringmann and Nakos [SODA 2021]—carries over to our FPTAS. Depending on the relationship between the size of the input set n and the error margin $$\varepsilon $$ , we improve upon the best currently known algorithm of Melissinos and Pagourtzis [COCOON 2018] of complexity $$\mathcal {O} (n^4 / \varepsilon )$$ . In particular, the exponent of n in our proposed scheme may decrease down to 2, depending on the Subset Sum algorithm used. Furthermore, while the aforementioned state of the art complexity, expressed in the form $$\mathcal {O} ((n + 1 / \varepsilon )^c)$$ , has constant $$c = 5$$ , our results establish that $$c < 5$$ .
Steganography and Cryptography Techniques Based Secure Data Transferring Through Public Network Channel

Attacking a transferred data over a network is frequently happened millions time a day. To address this problem, a secure scheme is proposed which is securing a transferred data over a network. The proposed scheme uses two techniques to guarantee a secure transferring for a message. The message is encrypted as a first step, and then it is hided in a video cover. The proposed encrypting technique is RC4 stream cipher algorithm in order to increase the message's confidentiality, as well as improving the least significant bit embedding algorithm (LSB) by adding an additional layer of security. The improvement of the LSB method comes by replacing the adopted sequential selection by a random selection manner of the frames and the pixels with two secret random keys. Therefore, the hidden message remains protected even if the stego-object is hacked because the attacker is unable to know the correct frames and pixels that hold each bit of the secret message in addition to difficulty to successfully rebuild the message. The results refer to that the proposed scheme provides a good performance for evaluation metric that is used in this purpose when compared to a large number of related previous methods.
Multitask Multigranularity Aggregation With Global-Guided Attention for Video Person Re-Identification

The goal of video-based person re-identification (Re-ID) is to identify the same person across multiple non-overlapping cameras. The key to accomplishing this challenging task is to sufficiently exploit both spatial and temporal cues in video sequences. However, most current methods are incapable of accurately locating semantic regions or efficiently filtering discriminative spatio-temporal features; so it is difficult to handle issues such as spatial misalignment and occlusion. Thus, we propose a novel feature aggregation framework, multi-task and multi-granularity aggregation with global-guided attention (MMA-GGA), which aims to adaptively generate more representative spatio-temporal aggregation features. Specifically, we develop a multi-task multi-granularity aggregation (MMA) module to extract features at different locations and scales to identify key semantic-aware regions that are robust to spatial misalignment. Then, to determine the importance of the multi-granular semantic information, we propose a global-guided attention (GGA) mechanism to learn weights based on the global features of the video sequence, allowing our framework to identify stable local features while ignoring occlusions. Therefore, the MMA-GGA framework can efficiently and effectively capture more robust and representative features. Extensive experiments on four benchmark datasets demonstrate that our MMA-GGA framework outperforms current state-of-the-art methods. In particular, our method achieves a rank-1 accuracy of 91.0% on the MARS dataset, the most widely used database, significantly outperforming existing methods.
Automatic Detection of Entity-Manipulated Text using Factual Knowledge

In this work, we focus on the problem of distinguishing a human written news article from a news article that is created by manipulating entities in a human written news article (e.g., replacing entities with factually incorrect entities).Such manipulated articles can mislead the reader by posing as a human written news article.We propose a neural network based detector that detects manipulated news articles by reasoning about the facts mentioned in the article.Our proposed detector exploits factual knowledge via graph convolutional neural network along with the textual information in the news article.We also create challenging datasets for this task by considering various strategies to generate the new replacement entity (e.g., entity generation from GPT-2).In all the settings, our proposed model either matches or outperforms the state-of-the-art detector in terms of accuracy.Our code and data are available at https://github.com/UBC-NLP/manipulated_entity_detection.Human written text PubNub, a startup that develops the infrastructure to power key features in real-time applications (...) has raised $23 million in a series D round of funding from Hewlett Packard Enterprise (HPE), Relay Ventures, Sapphire Ventures, Scale Venture Partners, Cisco Investments, Bosch, and Ericsson.Manipulated text using GPT-2 PubNub, a startup that develops the infrastructure to power key features in real-time applications (...)
Using Virtual Reality to Demonstrate and Promote Products: The Effect of Gender, Product Contextualization and Presence on Purchase Intention and User Satisfaction

Virtual Reality (VR) and its capability to replace real stimuli for synthesized ones as if they were real opened several research lines over the years.Many of those consist of trying to validate whether or not VR replicates the same user behaviours seen in reality.In this study, we investigated whether or not product contextualization and gender could influence users' intention to purchase as well as their satisfaction with the application and how presence levels correlate with purchase intention and user satisfaction.The product tested was a double door refrigerator with a touchscreen.We considered two independent variables: Contextualization (Context -The refrigerator was displayed in a kitchen and filled with food products and Neutral -The refrigerator was empty and displayed in an empty room) and gender (male and female).The results indicated that contextualization and gender had no effective impact on purchase intention, user satisfaction with the VR experience nor the sense of presence.A positive correlation was found between presence and user satisfaction.Evidence indicates that it is not necessary to represent products in their context, saving computational power and human resources.
S4-Tuning: A Simple Cross-lingual Sub-network Tuning Method-Tuning: A Simple Cross-lingual Sub-network Tuning Method

The emergence of multilingual pre-trained language models makes it possible to adapt to target languages with only few labeled examples.However, vanilla fine-tuning tends to achieve degenerated and unstable results, owing to the Language Interference among different languages, and Parameter Overload under the few-sample transfer learning scenarios.To address two problems elegantly, we propose S 4 -Tuning, a Simple CroSS-lingual Sub-network Tuning method.S 4 -Tuning first detects the most essential sub-network for each target language, and only updates it during fine-tuning.In this way, the language sub-networks lower the scale of trainable parameters, and hence better suit the low-resource scenarios.Meanwhile, the commonality and characteristics across languages are modeled by the overlapping and non-overlapping parts to ease the interference among languages.Simple but effective, S 4 -Tuning gains consistent improvements over vanilla fine-tuning on three multilingual tasks involving 37 different languages in total (XNLI, PAWS-X, and Tatoeba).
Mitigating the Inconsistency Between Word Saliency and Model Confidence with Pathological Contrastive Training

Neural networks are widely used in various NLP tasks for their remarkable performance.However, the complexity makes them difficult to interpret, i.e., they are not guaranteed right for the right reason.Besides the complexity, we reveal that the model pathology -the inconsistency between word saliency and model confidence, further hurts the interpretability.We show that the pathological inconsistency is caused by the representation collapse issue, which means that the representation of the sentences with tokens in different saliency reduced is somehow collapsed, and thus the important words cannot be distinguished from unimportant words in terms of model confidence changing.In this paper, to mitigate the pathology and obtain more interpretable models, we propose Pathological Contrastive Training (PCT) framework, which adopts contrastive learning and saliency-based samples augmentation to calibrate the sentences representation.Combined with qualitative analysis, we also conduct extensive quantitative experiments and measure the interpretability with eight reasonable metrics.Experiments show that our method can mitigate the model pathology and generate more interpretable models while keeping the model performance.Ablation study also shows the effectiveness.
Current Status of Whole Slide Image (WSI) Standardization in Japan

A whole slide image (WSI) is a digitized microscopic image that is particularly useful in histochemistry and cytochemistry. Several WSI scanners have been introduced in Japan and all use their own native format. Thus, there is basically no interchangeability. However, the Digital Imaging and Communications in Medicine (DICOM) standard format for WSI has been available since 2010. In this review, the configuration and differences among the native WSI and DICOM formats are examined, and the advantages and issues of DICOM standardization are discussed.
A Survey of Multi View Stereo

3D reconstruction is the process of obtaining the contour, color, depth, and other information of the real object through the sensor, and then transforming the 3D object in the real world into a 3D model that can be processed and displayed by the computer. According to the different sensors, there are a variety of ways to obtain data, among which the vision-based 3D reconstruction technology has been the focus of research in this field because of its wider scope of application and greater development prospects. At present, monocular, stereoscopic, and other reconstruction methods have been developed, and MVS (Multi View Stereo) is naturally developed in this process. This paper will start from sensor types, summarize the development process of 3d reconstruction methods, and focus on the introduction of multi-perspective stereo methods, including feature point matching in SFM (Structure from Motion) and SFM reconstruction method, traditional MVS method implementation and deep learning method implementation. Finally, the overall development of this field is summarized, and the future development is prospected.
Multi-task Learning with Modular Reinforcement Learning

The ability to learn compositional strategies in multi-task learning and to exert them appropriately is crucial to the development of artificial intelligence. However, there exist several challenges: (i) how to maintain the independence of modules in learning their own sub-tasks; (ii) how to avoid performance degradation in situations where modules’ reward scales are incompatible; (iii) how to find the optimal composite policy for the entire set of tasks. In this paper, we introduce a Modular Reinforcement Learning (MRL) framework that coordinates the competition and the cooperation between separate modules. Furthermore, a selective update mechanism enables the learning system to align incomparable reward scales in different modules. Moreover, the learning system follows a “joint policy” to calculate actions’ preferences combined with their responsibility for the current task. We evaluate the effectiveness of our approach on a classic food-gathering and predator-avoidance task. Results show that our approach has better performance than previous MRL methods in learning separate strategies for sub-tasks, is robust to modules with incomparable reward scales, and maintains the independence of the learning in each module.
Unsupervised Deep Learning: Taxonomy and algorithms

Clustering is a fundamental challenge in many data-driven application fields and machine learning techniques. The data distribution determines the quality of the outcomes, which has a significant impact on clustering performance. As a result, deep neural networks can be used to learn more accurate data representations for clustering. Many recent studies have focused on employing deep neural networks to develop a clustering-friendly representation, which has resulted in a significant improvement in clustering performance. We present a systematic survey of clustering with deep learning in this study. Then, a taxonomy of deep clustering is proposed, as well as some sample algorithms for our overview. Finally, we discuss some exciting future possibilities for clustering using deep learning and offer some remarks.
Ludic Pedagogy: Taking a serious look at fun in the COVID-19 classroom and beyond

The COVID-19 pandemic has affected deep reflection in higher education classrooms: how do we attract and retain students to (temporary but nevertheless increasing) online learning experiences, how do we keep them at our universities and colleges, and how do we give students a learning experience from which they will remember meaningful information?In this paper, we introduce a new pedagogical framework that we call Ludic Pedagogy.We address the four elements of this model: fun, positivity, play, and playfulness.Each of the elements is described in turn, together with literature outlining how each contributes to a positive classroom environment that helps students engage with and learn course content.Examples of how the authors have used this pedagogical model are included and described.We suggest that instructors consider using the Ludic Pedagogy model so as to improve engagement, learning outcomes, and retention in their classes and broader university/college contexts.
Methodology for the Risk Monitoring of Geological Hazards for Buildings and Structures

In this article, technology for monitoring geological hazards is proposed to reduce the risk of the collapse of buildings and structures.The example considered in the article shows that the reduction of the landslide hazard of the soil massif on the slope can be achieved by: equipping drainage and storm systems that ensure the effective removal of groundwater; monitoring the state of the soil massif for the timely determination of dynamic and geophysical parameters and the exclusion of the occurrence of resonant phenomena from dynamic impacts; complex monitoring of the "soil-construction" system.Thus, the proposed technology of integrated soil-structure monitoring can provide timely warning of the geological hazard risk and thereby reduce the likelihood of destruction of buildings and structures by taking appropriate measures.
Session Coalgebras: A Coalgebraic View on Regular and Context-free Session Types

Compositional methods are central to the verification of software systems. For concurrent and communicating systems, compositional techniques based on behavioural type systems have received much attention. By abstracting communication protocols as types, these type systems can statically check that channels in a program interact following a certain protocol—whether messages are exchanged in the intended order. In this article, we put on our coalgebraic spectacles to investigate session types , a widely studied class of behavioural type systems. We provide a syntax-free description of session-based concurrency as states of coalgebras. As a result, we rediscover type equivalence, duality, and subtyping relations in terms of canonical coinductive presentations. In turn, this coinductive presentation enables us to derive a decidable type system with subtyping for the π-calculus, in which the states of a coalgebra will serve as channel protocols. Going full circle, we exhibit a coalgebra structure on an existing session type system, and show that the relations and type system resulting from our coalgebraic perspective coincide with existing ones. We further apply to session coalgebras the coalgebraic approach to regular languages via the so-called rational fixed point, inspired by the trinity of automata, regular languages, and regular expressions with session coalgebras, rational fixed point, and session types, respectively. We establish a suitable restriction on session coalgebras that determines a similar trinity, and reveals the mismatch between usual session types and our syntax-free coalgebraic approach. Furthermore, we extend our coalgebraic approach to account for context-free session types, by equipping session coalgebras with a stack.
Factors affecting adoption of digital contact tracing during the COVID-19 pandemic: a literature review

Background and Objective: Digital tools have been extensively used in handling the COVID-19 pandemic, but governments struggle with the low uptake of digital contact tracing (DCT). Since this limits its effectiveness in infectious disease control, this topic is important to epidemiology and public health. The objectives of this review are: (I) to discuss the factors affecting DCT adoption in different communities; (II) to compile suggestions to improve DCT uptake; (III) to discuss implications of DCT utilisation in the pandemic on further digital and mobile health development.
Residual Contrastive Learning for Image Reconstruction: Learning Transferable Representations from Noisy Images

This paper is concerned with contrastive learning (CL) for low-level image restoration and enhancement tasks. We propose a new label-efficient learning paradigm based on residuals, residual contrastive learning (RCL), and derive an unsupervised visual representation learning framework, suitable for low-level vision tasks with noisy inputs. While supervised image reconstruction aims to minimize residual terms directly, RCL alternatively builds a connection between residuals and CL by defining a novel instance discrimination pretext task, using residuals as the discriminative feature. Our formulation mitigates the severe task misalignment between instance discrimination pretext tasks and downstream image reconstruction tasks, present in existing CL frameworks. Experimentally, we find that RCL can learn robust and transferable representations that improve the performance of various downstream tasks, such as denoising and super resolution, in comparison with recent self-supervised methods designed specifically for noisy inputs. Additionally, our unsupervised pre-training can significantly reduce annotation costs whilst maintaining performance competitive with fully-supervised image reconstruction.
DivSwapper: Towards Diversified Patch-based Arbitrary Style Transfer

Gram-based and patch-based approaches are two important research lines of style transfer. Recent diversified Gram-based methods have been able to produce multiple and diverse stylized outputs for the same content and style images. However, as another widespread research interest, the diversity of patch-based methods remains challenging due to the stereotyped style swapping process based on nearest patch matching. To resolve this dilemma, in this paper, we dive into the crux of existing patch-based methods and propose a universal and efficient module, termed DivSwapper, for diversified patch-based arbitrary style transfer. The key insight is to use an essential intuition that neural patches with higher activation values could contribute more to diversity. Our DivSwapper is plug-and-play and can be easily integrated into existing patch-based and Gram-based methods to generate diverse results for arbitrary styles. We conduct theoretical analyses and extensive experiments to demonstrate the effectiveness of our method, and compared with state-of-the-art algorithms, it shows superiority in diversity, quality, and efficiency.