Reviewerly/exHarmony huggingface dataset:
On-line evolutionary identification technology for milling chatter of thin walled parts based on the incremental-sparse K-means and the online sequential extreme learning machine

In the milling process of thin-walled parts, chatter is very easy to occur, which has a very adverse impact on the surface quality and machining efficiency of the workpiece. In order to solve the problem of low accuracy of milling state identification caused by few initial samples and dynamic changes in the milling process, a hybrid online evolutionary chatter identification model combining unsupervised learning and supervised learning was proposed. First of all, aiming at the problem that traditional K-means algorithm is difficult to adapt to online dynamic clustering of milling chatter, an online incremental-sparse K-means algorithm (ISK-Means) was proposed, and the dynamic incremental-sparse strategy of K-means was designed. Second, aiming at the problem that the online sequential extreme learning machine (OS-ELM) algorithm directly adds its predicted samples to the training sample set during the incremental learning process, and the pseudo-samples in the training sample set would lead to the degradation of the OS-ELM model, a hybrid online evolutionary chatter identification model combining the ISK-means and the OS-ELM was proposed, and the online identification and evolution strategy was designed. Finally, the experimental results show that the ISK-means algorithm can greatly improve the clustering efficiency and is suitable for milling chatter online dynamic clustering. Meanwhile, compared with the existing model, the recognition accuracy of the hybrid online evolutionary chatter recognition model combined with ISK-means algorithm and OS-ELM algorithm is improved by 1.31%. This is of great significance for the online control of subsequent chatter.
Analysis and Improvement of PUF-based Secure Anonymous User Authentication Scheme in Smart Home Environment

With the rapid development of IoT technology, smart home is attracting much attention due to its convenience and comfort. In 2022, CHO et al. proposed an anonymous user authentication scheme using PUFs in smart home environment. However, this paper conducts a security analysis and finds that CHO et al.'s scheme cannot resist tracking attacks, replay attacks and cannot reach session keys. In order to overcome the shortcomings of CHO et al.'s scheme, this paper proposes an improved PUF-based secure anonymous user authentication scheme. After security analysis and comparison with related authentication schemes in terms of security and computational cost, it is demonstrated that the improved scheme is resistant to a variety of attacks and can achieve secure and efficient authentication.
The Syntactic Structure of Noun Phrases (1)

본고는 Cheng and Sybesma(2014)의 전반부를 번역한 것이다. 본고는 중국어 명사구의 구조를 다룬다. 1절에서는 명사구의 형식, 2절에서는 명사구의 다양한 해석적 속성을 살펴보고, 3절에서는 수식과 관련된 쟁점들을 조사하고, 4절에서는 분류사의 성질을 고찰한다. 특히 주목할 것은 ClP 즉 분류사 층위가 한정성의 원인이 된다는 주장이다. The paper is a translation of the first half part of Cheng and Sybesma (2014). This article deals with the structure of Chinese noun phrase. Section 1 examines the form of noun phrase, Section 2 investigates the various interpretational properties of noun phrase, Section 3 discusses issues related to modification, and Section 4 examines the nature of classifier. Of particular note is the claim that ClP is the cause of definiteness.
Towards Graph-based Cloud Cost Modelling and Optimisation

Cloud computing has become an increasingly popular choice for businesses and individuals due to its flexibility, scalability, and convenience; however, the rising cost of cloud resources has become a significant concern for many. The pay-per-use model used in cloud computing means that costs can accumulate quickly, and the lack of visibility and control can result in unexpected expenses. The cost structure becomes even more complicated when dealing with hybrid or multi-cloud environments. For businesses, the cost of cloud computing can be a significant portion of their IT budget, and any savings can lead to better financial stability and competitiveness. In this respect, it is essential to manage cloud costs effectively. This requires a deep understanding of current resource utilization, forecasting future needs, and optimising resource utilization to control costs. To address this challenge, new tools and techniques are being developed to provide more visibility and control over cloud computing costs. In this respect, this paper explores a graph-based solution for modelling cost elements and cloud resources and potential ways to solve the resulting constraint problem of cost optimisation. We primarily consider utilization, cost, performance, and availability in this context. Such an approach will eventually help organizations make informed decisions about cloud resource placement and manage the costs of software applications and data workflows deployed in single, hybrid, or multi-cloud environments.
Sergey Viktorovich Nagaev (On His 90th Birthday)

This article looks back on the life of world-renowned probabilist Sergey Viktorovich Nagaev, who celebrated his 90th birthday on December 11, 2022. At present, he continues to be actively involved in various fields of probability.
Robot Motion Planning with Optimization-based Algorithm and Neural Network: Unsupervised Path Regression

Planning collision-free motions for robots is challenging in environments with complex obstacle geometries. In this study, we combine the optimization-based algorithm and neural network to obtain a short and collision-free path through unsupervised path regression. The network can help to predict an educated initial path for an optimization-based planner, which will converge to a better solution. We compare different path representations, world representations, and also their combinations to improve the result. The motion planning problem is extended from a sphere robot in a single 2D world to a more complex static arm robot in multiple worlds. Through our experiments, we can find that in multiple worlds, the relative path with the signed distance field is the best combination for both robots. This is not only proved by metrics of feasible rate and loss but also by the visualization in the map.
Semi-supervised Short Text Classification Based On Dual-channel Data Augmentation

In practical applications, high-quality labeled data is critical for short text classification. But in many cases, it is expensive and time-consuming to obtain labeled information. Semi-supervised short text classification is hence attracting more attention. However, due to the sparsity of short texts, the performance of existing short text classification models always needs to be improved. Therefore, in this paper, we propose a semi-supervised Short text classification method based on Dual-Channel data Augmentation called SDCA. More specifically, in order to solve the sparsity of short texts, this model first adopts the multi-stage word-level TCN (Temporal Convolutional Network)-based attention to enhanced semantic features and an one-dimensional convolution-based attention mechanism to augment the relevance of surrounding short texts. Secondly, the unlabeled data are augmented by word embedding weighted augmentation and word replacement augmentation, so that the model can make full use of the unlabeled short texts and further enhance the network training. Finally, extensive experiments conducted on four benchmark datasets demonstrate the effectiveness of the proposed model on semi-supervised short text classification.
On the Use of Aggregation Functions for Semi-Supervised Network Embedding

Network embedding methods map nodes into vector representations, aiming to preserve important properties of relationships between nodes through similarities in a latent vector-space model. Graph Neural Networks (GNNs) based on aggregate functions have received significant attention among different network embedding methods. In general, the embeddings of a node are recursively generated by aggregating embeddings from neighboring nodes. Aggregation is a crucial step in these methods, and different aggregation functions have been proposed, from simple averaging and max pooling operations to complex functions based on attention mechanisms. However, we note that there is a lack of studies comparing aggregate functions, especially in more practical real-world scenarios involving semi-supervised tasks. This paper introduces a methodology to evaluate different aggregation functions for semi-supervised learning through a model selection strategy guided by a statistical significance analysis framework. We show that Transformers-based aggregation functions are competitive for semi-supervised scenarios and obtain relevant results in different domains. Furthermore, we also discuss scenarios where “less is more”, mainly when there are constraints on the availability of computational resources.
Symbol Detection for Polarization Shift Keying Based on Quaternion Neural Networks

This paper proposes quaternion-neural-network (QNN) based robust symbol detection in wireless polarization-shift-keying (PolSK) communications. In these years, many ma-chine learning approaches based on real-valued neural-networks (RVNNs) outperform conventional methods for symbol detection in wireless fading channels. However, existing studies mainly focuses on digital modulations having constellation diagrams in complex planes, such as phase shift keying and amplitude phase shift keying. In PolSK, information symbols are represented as the state of polarization (SOP) of the propagating wave. Since a SOP can be described by Stokes parameters and mapped on the Poincare sphere, its symbols have a three-dimensional (3-D) data structure. Quaternion-algebra expressions offer a unified representation to process 3-D data with rotational invariance and thus it keeps the internal relationship among three components of a PolSK symbol. Hence, QNNs learn the distribution of received symbols on the Poincare sphere with higher consistency and detect the transmitted symbols more efficiently than RVNNs. We compare PolSK symbol detection accuracy of two types of QNNs, RVNN and two conventional channel estimation methods, namely least square and minimum mean square error. Simulation results such as symbol error rates show that the proposed QNNs outperform other adaptive symbol detection methods robustly. We find that the QNN processing brings practical use of PolSK communications.
Multi-agent Knowledge Transfer in a Society of Interpretable Neural Network Minds for Dynamic Context Formation in Swarm Shepherding

Shepherding is a nature-inspired swarm guidance approach, where one or more sheepdogs act as actuators to guide a swarm towards a goal area. In the real-world, swarm guidance occurs in unknown environments. Context unfolds as the controller agent, the sheepdog, continues to discover new states, causing the state space to unfold during a mission due to the partial observability of the state space by each sheepdog. These individualised experiences could get shared among the shepherds to improve situation awareness. Our prior work introduced an approach to share interpretable knowledge between two agents. In this paper, we extend the two-agent interpretable knowledge fusion algorithm to multi-agent settings; allowing multiple sheepdogs to share their knowledge in an interpretable manner. When an agent receives knowledge from another agent, it decides on whether to integrate this new knowledge with what it already knows, leading to an increase in the size and space complexity of an agent's knowledge base. We propose a modular neural network society of mind architecture to store, update and manipulate the knowledge base of an agent. The architecture stores sub-networks and associate them with situations. When an agent is faced with a state, a gate controller decides based on the situation facing the agent which sub-networks are best suited to make decisions. The contribution is validated on a general classification task utilising the full-state-space for a swarm guidance shepherding problem. When compared to baseline methods, the proposed knowledge transfer algorithm improves generalisation, reduces catastrophic forgetting, and produces smaller models with faster adaptation.
Multi-Task Meta Learning: learn how to adapt to unseen tasks

This work proposes Multi-task Meta Learning (MTML), integrating two learning paradigms Multi-Task Learning (MTL) and meta learning, to bring together the best of both worlds. In particular, it focuses simultaneous learning of multiple tasks, an element of MTL and promptly adapting to new tasks, a quality of meta learning. It is important to highlight that we focus on heterogeneous tasks, which are of distinct kind, in contrast to typically considered homogeneous tasks (e.g., if all tasks are classification or if all tasks are regression tasks). The fundamental idea is to train a multi-task model, such that when an unseen task is introduced, it can learn in fewer steps whilst offering a performance at least as good as conventional single task learning on the new task or inclusion within the MTL. By conducting various experiments, we demonstrate this paradigm on two datasets and four tasks: NYU-v2 and the taskonomy dataset for which we perform semantic segmentation, depth estimation, surface normal estimation, and edge detection. MTML achieves state-of-the-art results for three out of four tasks for the NYU-v2 dataset and two out of four for the taskonomy dataset. In the taskonomy dataset, it was discovered that many pseudo-labeled segmentation masks lacked classes that were expected to be present in the ground truth; however, our MTML approach was found to be effective in detecting these missing classes, delivering good qualitative results. While, quantitatively its performance was affected due to the presence of incorrect ground truth labels. The the source code for reproducibility can be found at https://github.com/ricupa/MTML-learn-how-to-adapt-to-unseen-tasks.
Kodlayıcı Hurwitz Tamsayıları: Küçük Kalanlı Bölme Özelliğine Sahip Hurwitz Tamsayıları

Considering error-correcting codes over Hurwitz integers, prime Hurwitz integers are considered. On the other hand, considering transmission over Gaussian channel, Hurwitz integers, whose the norm is either a prime integer or not a prime integer, are considered. In this study, we consider Hurwitz integers, the greatest common divisor of components of which is one, i.e., primitive Hurwitz integers. We show, with the help of a proposition, that some primitive Hurwitz integers accompanied by a related modulo function are not suitable for constructing Hurwitz signal constellations. To solve this problem, we show, with the help of a proposition, the existence of primitive Hurwitz integers that have the "division with small remainder" property used to construct the Hurwitz constellations. We also call the set of these integers named as "Encoder Hurwitz Integers" set. Moreover, we examine some properties of the mentioned set. In addition, we investigate the performances of Hurwitz signal constellations, which are constructed accompanied by a related modulo function using Hurwitz integers, each component of which is in half-integers, for transmission over the additive white Gaussian noise (AWGN) channel by means of the constellation figure of merit (CFM), average energy, and signal-to-noise ratio (SNR).
Three questions with&amp;#8230; Marc-André!

Our lab is growing! In our Three Questions series, we’re profiling each of our members and the amazing work they’re doing. This week’s post highlights Marc-André Simard, a doctoral student in Information Science at the University of Montreal and member of the ScholCommLab. In this post, he shares his thoughts on work in the lab, the global use of open access literature, and stepping out of his comfort zones.
<i>Luminous skins</i>: Costume as a Central Element of a Swarm-Based Scenography

Abstract Luminous skins are interactive, light-costume hybrids that reimagine the boundaries of the performers’ physical bodies, broadcast their feelings, and alter their environment. The costumes are the central element of a performance that requires no stage space, no fixed lighting, no wall outlets, and no lighting technicians. The performers manage to dance through the darkness with the light they wear. The costumes are equipped with sensors and actuators that communicate wirelessly in real time with other scenographic elements. Together, they form a swarm-based scenography that involves close coordination between the performers and responsive technology to create visually dynamic and immersive atmospheres.
Multi-Turn Dialogue Agent as Sales' Assistant in Telemarketing

The traditional telemarketing call centers are being hunched by significant human resources costs related to high workers' workload, as well as even higher turnover-rate, which are also proven to be impossible to be solved by Artificial Intelligence (AI) chatbot alone. In view of this dilemma, applying AI techniques to assist human salespeople in call centers by instantly analyzing the phone call between salesperson and customer, thus recommending a response for the salesperson, becomes a challenging yet applicable means. In this paper, we propose a multiturn dialogue agent based on Deep Attention Matching Network (DAM) with SimBERT, used as a data augmentation tool to efficiently improve the response selection accuracy. Experiments on a real-world telemarketing scenario dataset have confirmed its validity.
Common and Unique Features Learning in Multi-view Network Embedding

Network embedding is a powerful representation learning method for graph data, using the learned low-dimensional compact vectors as node features, which are widely used in various tasks, such as link prediction, node clustering, and classification. Compared with traditional network analysis methods, network embedding reduces computational complexity and improves analysis efficiency. Although previous work has achieved outstanding performance, it faces challenges in multi-view network embedding containing multi-type node relations. Since multi-view networks share a node set but different edges, different networks not only have common information but also have their unique information. To simultaneously capture multi-view networks' common and unique information, we propose a new framework, Common and Unique Features Learning for Multi-view Network Embedding (CU-MNE), to integrate multi-type node relations. In this paper, we propose an inter-view contrastive objective to ensure the consistency of the common features of the same node in a different view and an inter-feature contrastive objective to capture the association between the common and unique features of each network node that can learn high-quality node embeddings. Extensive experiments on three real datasets show that CU-MNE outperforms the state-of-the-art methods.
Maximum Entropy Baseline for Integrated Gradients

Integrated Gradients (IG), one of the most popular explainability methods available, still remains ambiguous in the selection of baseline, which may seriously impair the credibility of the explanations. This study proposes a new uniform baseline, i.e., the Maximum Entropy Baseline, which is consistent with the “uninformative” property of baselines defined in IG. In addition, we propose an improved ablating evaluation approach incorporating the new baseline, where the information conservativeness is maintained. Furthermore, we explain the linear transformation invariance of IG baselines from an information perspective. Finally, extensive experiments indicate that IG with Maximum Entropy Baseline performs superiorly and the ablation tests derived from it are more plausible.
MHCP-RCNN: Multi-Human Color Parsing Segmentation using Multi-Task Network

Instance and semantic level segmentation are the two most widely used research topics used with assorted and diverse applications. Even though recent advancements in object detection and segmentation have grown by leaps and bounds, using it to better understand the environment and the people in it with more detail, instance-level segmentation and human parsing techniques are used which is still an essential provision. The parsing-by-detection multistage pipeline is used by other comparable research which objectively relies on separately trained detection and models for segmenting data to identify occurrences, before sequentially performing human parsing for each instance. Hence in this paper we propose a novel multi-task multi-stage network that incorporates human detection with the segmentation of body parts along with mapping its semantic color features for multiple person in a single pass. To benchmark, we have used a recently launched CCIHP dataset that consists of semantic body parts with its legion colour maps, size and patterns where color map extraction is important for fine-grained segmentation of a person's clothes which is the novel and cardinal aspect in the proposed architecture. The model is evaluated on standard datasets and have observed to achieve an increase of 12% in parsing and 3% in semantic color map mIOU scores which is an exceptionally unrivaled metric value comparing to the existing state of the art (SOTA) models.
DEPHN: Different Expression Parallel Heterogeneous Network using virtual gradient optimization for Multi-task Learning

Recommendation system algorithm based on multi-task learning (MTL) is the major method for Internet operators to understand users and predict their behaviors in the multi-behavior scenario of platform. Task correlation is an important consideration of MTL goals, traditional models use shared-bottom models and gating experts to realize shared representation learning and information differentiation. However, The relationship between real-world tasks is often more complex than existing methods do not handle properly sharing information. In this paper, we propose an Different Expression Parallel Heterogeneous Network (DEPHN) to model multiple tasks simultaneously. DEPHN constructs the experts at the bottom of the model by using different feature interaction methods to improve the generalization ability of the shared information flow. In view of the model's differentiating ability for different task information flows, DEPHN uses feature explicit mapping and virtual gradient coefficient for expert gating during the training process, and adaptively adjusts the learning intensity of the gated unit by considering the difference of gating values and task correlation. Extensive experiments on artificial and real-world datasets demonstrate that our proposed method can capture task correlation in complex situations and achieve better performance than baseline models.