Reviewerly/exHarmony huggingface dataset:
Collaborative learning-based unknown-class instance identification for open-set domain adaptation

For domain adaptation in open-set scenarios, target domain samples may be collected from unknown object categories, which are not associated with the original source domain. It is important to judge if a target instance is from one of the classes shared by the source and target domains. Toward this end, we propose a Collaborative learning-based unknown-Class Instance Identification (CoCII) model, in which a cross-domain network and a dedicated network are jointly optimized. The knowledge is learnt from the labeled source data, and then leveraged to predict the labels of target domain samples by the first network. The second network specializes in the target domain under the guidance of the first one. We further incorporate an augmented classification head together with semantic-based contrastive regularization into the dedicated network. This will enable the model in capturing information useful for identifying unknown-class instances, as well as identifying the shared classes. The dedicated network in turn guides the cross-domain network via consistency regularization. Empirical results on Office-31/Home, DIGIT and VisDA-2017 demonstrate that CoCII can outperform other existing state-of-the-art approaches in terms of the average of class-wise accuracies over both known and unknown classes.
Multi-operator Image Retargeting based on Saliency Object Ranking and Similarity Evaluation Metric

Image Retargeting (IR) technology is proposed to flexibly display images on various display devices while protecting the important content of the images undistorted. IR methods mainly use Salient Object Detection (SOD) to obtain important content, however, most existing SOD methods treat multiple salient objects with the same saliency degrees, which makes IR methods assign the same retargeting ratios for different objects and leads to producing information-loss retargeted results. Multi-operator IR demonstrates better generalization than single operator by using multiple operators to find the optimal sequence of operators. Meanwhile, the tremendous processing time limits its practical use. To address these problems, we propose a multi-operator IR method based on Salient Object Ranking (SOR) and Similarity Evaluation Metric (SORSEM-IR), which includes two stages: importance map generation and multi-operator IR. In the first stage, a SOR module with Context-aware Semantic Refinement (SORCSR) is proposed, which extracts the salient instances and infers their saliency ranks with a context-aware semantic refinement module, then the SOR map, face map, and gradient map are fused as the importance map. In the second stage, to speed up multiple operations, a similarity evaluation metric is proposed to measure the similarity between the original image and the seam-removal image by Seam Carving (SC) operation, and switch SC to uniform scaling to meet the aspect ratio when distortion caused by SC arrives at a certain extent. Experimental results show that the SORCSR network achieves state-of-the-art performance on the ASSR dataset subjectively and objectively, and the SORSEM-IR guided by SORCSR can not only protect the salient objects with minimum deformation but also meet human aesthetic perception.
Relational prompt-based single-module single-step model for relational triple extraction

The relational triple extraction is a fundamental and essential information extraction task. The existing approaches of relation triple extraction achieve considerable performance but still suffer from 1) treating the relation between entities as a meaningless label while ignoring the relational semantic information of the relation itself and 2) ignoring the interdependence and inseparability of three elements of the triple. To address these problems, this paper proposes a Relational Prompt approach, based on which constructs a Single-module Single-step relational triple extraction model (RPSS). In particular, the proposed relational prompt approach consist of a relational hard-prompt and a relational soft-prompt, while provide take into account different level of relational semantic information, covering both thg token-level and the feature-level relational prompt information. Then, we jointly encode entities and relational prompts to obtain a unified global representation. We mine deep correlations between different embeddings through attention mechanism and then construct a triple interaction matrix. Then, all triples could be directly extracted from a single module in a single step. Experiments demonstrate the effectiveness of the relational prompt approach, as well as relational semantics and triple integrity are essential for relation extraction. Experimental results on two benchmark datasets demonstrate our model outperforms current state-of-the-art models.
Improved deep neural network performance under dynamic programming mode

For Deep Neural Networks (DNN), the standard gradient-based algorithms may not be efficient because of the raised computational expense resulting from the increase in the number of layers. This paper offers an alternative to the classic training solutions: an in-depth study to find conditions under which the underlying Artificial Neural Networks ANN minimisation problem can be addressed from a Dynamic Programming (DP) perspective. Specifically, we prove that any ANN with monotonic activation is separable when regarded as a parametric function. Particularly, when the ANN is viewed as a network representation of a dynamical system (as a coupled cell network), we also prove that the transmission-of-signal law is separable provided the activation function is a monotone non-decreasing function. This strategy may have a positive impact on the performance of ANNs by improving their learning accuracy, particularly for DNNs. For our purposes, ANNs are also viewed as universal approximators of continuous functions and as abstract compositions of an even number of functions. This broader representation makes it easier to analyse them from many other perspectives (universal approximation issues, inverse problem solving) leading to a general improvement in knowledge on NNs and their performance.
Quantum algorithm for de novo DNA sequence assembly based on quantum walks on graphs

De novo DNA sequence assembly is based on finding paths in overlap graphs, which is a NP-hard problem. We developed a quantum algorithm for de novo assembly based on quantum walks in graphs. The overlap graph is partitioned repeatedly to smaller graphs that form a hierarchical structure. We use quantum walks to find paths in low rank graphs and a quantum algorithm that finds Hamiltonian paths in high hierarchical rank. We tested the partitioning quantum algorithm, as well as the quantum algorithm that finds Hamiltonian paths in high hierarchical rank and confirmed its correct operation using Qiskit. We developed a custom simulation for quantum walks to search for paths in low rank graphs. The approach described in this paper may serve as a basis for the development of efficient quantum algorithms that solve the de novo DNA assembly problem.
Concrete or abstract: How chatbot response styles influence customer satisfaction

Lack of empathy is the key reason why chatbots cannot effectively handle service problems. Empathy consists of affective and cognitive components. Existing studies primarily emphasize empathic concern—the affective component of empathy—to improve the problem-solving performance of chatbots but neglect the cognitive component of empathy, or empathic accuracy. Focusing on empathy's cognitive component, three experiments were conducted to explore how problem response styles of chatbots (concrete vs. abstract) influence customer satisfaction based on social response theory. When chatbots respond to service problems, their concrete responses can improve customer satisfaction than abstract responses. Empathic accuracy mediates the relationship between chatbot response styles and customer satisfaction. When responses are abstract, increasing chatbots' cuteness can alleviate the negative effect of lack of empathic accuracy on customer satisfaction. These findings provide a deeper understanding of chatbots' empathy and help enterprises improve the response performance of chatbots to deal with service problems.
The Present and the Future of Cognitive Robotics [TC Spotlight]

There is an increasing demand for robots capable of safely interacting with individuals in everyday scenarios. This necessitates their ability to comprehend human behavior, predict future actions, and respond accordingly. This is precisely the focus of the field known as <i>cognitive robotics</i> . Cognitive robots can learn from experience and others, retain and retrieve relevant knowledge and skills, and employ this knowledge to make informed decisions while considering the consequences. They can reason about their actions and modify their behavior to improve their effectiveness. In short, cognitive robots are capable of flexible, context-sensitive action, knowing what they are doing and why they are doing it <xref ref-type="bibr" rid="ref1">[1]</xref> .
Local projections on Einstein–Podolsky–Rosen states

The nonlocality of Einstein–Podolsky–Rosen (EPR) states is discussed in the framework of Weyl algebras. To include all possible limits of expectation values, the associated canonical cyclic representations of EPR states are considered. The momentum and position operators of individual particles do not exist in such representations, which explains the uncertainty principle in these cases. However, there are still one-to-one correspondences between the local Weyl operators associated with individual particles. The local observable algebras of individual particles are Type II1 von Neumann algebras and have numerous projection operators. The states obtained by applying local projection operations to the EPR states have perfect correlations between local subalgebras. Bell’s inequality is maximally violated by these states. The lack of atomic projections of Type II1 von Neumann algebras implies that the EPR states cannot be disentangled by local projection operations.
Talking Fingers – A Cost Effective Hand Gesture to Speech Device for Mute

This paper addresses the communication challenges faced by mute individuals who lack the ability to convey information through verbal means, which is the most common form of communication among people. The absence of acoustic sounds greatly hinders their ability to interact effectively. Furthermore, the general population often lacks understanding of sign language, further exacerbating the alienation and exclusion experienced by mute individuals. To bridge this communication gap, we propose the development of a cost-effective glove equipped with encoders, a gyroscope, an ESP-32 microcontroller, and a Text-To-Speech android application. This innovative solution aims to convert hand gestures, representing sign language, into audible speech. By leveraging their familiar sign language, mute individuals can communicate more efficiently and comfortably. Moreover, the proposed glove is notable for its affordability, making it one of the cheapest solutions in this domain.
Resource Management in Serverless Computing - Review, Research Challenges, and Prospects

Serverless Computing has become one of the major trends for running cloud applications. The main advantage of serverless computing is that the infrastructure is being completely taken care of by the providers. Users are spared the burden and the complexity of managing the infrastructure. In serverless computing, cloud applications become for the users a collection of multiple cloud functions. Even though the infrastructure is completely under the control of providers, the resource management is very complex in nature and it is essential to take both the users’ and the consumers’ service requirements into consideration to achieve quality of service (QoS). In this research paper, we conduct a literature review on serverless computing, on various opensource frameworks supporting serverless computing, and on complexities involved in the management of serverless computing platform. The paper ends with a proposal for a rankbased cloud function scheduler for the allocation of cloud functions in the serverless computing environment. Based on the existing works and the preliminary experimentation, we are evident that the proposed mechanism will impact the performance metrics of throughput and the success rate of cloud functions execution.
An Visual Question Answering System using Cross-Attention Network

In this paper, we present a novel approach for tackling the challenging task of Visual Question Answering (VQA). Our method combines sparse cross-attention and masked self-attention techniques to improve the accuracy of answer prediction by effectively integrating visual and textual information. By leveraging cross-attention mechanisms, we establish strong relationships between visual and textual features, enhancing the model’s ability to comprehend the question and the visual content. Additionally, we introduce masked self-attention to further refine the answer prediction process. This module selectively focuses on relevant textual features while disregarding the answer, enabling the model to concentrate on the most informative aspects of the question. To evaluate the effectiveness of our proposed approach, we conduct experiments on the COCO VQA dataset. We compare our method against state-of-the-art approaches and demonstrate its competitive performance in terms of accuracy. Overall, our approach offers a promising solution for improving the efficiency, interpretability, and accuracy of VQA systems.
Pixels to Phrases: Bridging the Gap with Computationally Effective Deep Learning models in Image Captioning

Image captioning combines computer vision and natural language processing using deep learning for generating descriptive image captions, transforming how we perceive and interact with visual data, enhancing comprehension, and enabling multimodal communication. This Research aims to use lightweight deep-learning models for image captioning, bridging the gap between visual understanding and caption generation. Models adopt an encoder-decoder architecture, using CNNs (VGG16, InceptionV3, DenseNet, EffcientNet, MobileNet) as encoders for extracting high-level visual features. Complementing the encoder, a recurrent neural network (RNN) such as Long-Short-Term-Memory (LSTM) serves as the decoder, generating captions word by word. The transfer learning technique is incorporated to enhance the image caption generator’s performance. This involves pretraining all the CNN models with the ImageNet dataset, followed by fine-tuning using the Flickr8k dataset specifically for caption generation. By leveraging this approach, the proposed system achieves improved captioning quality and accuracy, showcasing the significance of transfer learning in the domain of image caption generation. In addition to enhancing caption quality, this research work also focuses on reducing the caption generation time and computational complexity of the model. The proposed image generator with MobileNet and LSTM outperformed when compared with other models in all aspects with a better bleu Score of 55.15% and lesser computational complexity of 5.09 million trainable parameters.
Design of Voice Automated Floor Cleaning Robot

To reduce people’s workload and save time, an intelligent cleaning robot that can sweep, mop and sanitize floor is developed. The robot is controlled through an Android application, enabling it to clean an area with less physical labor. It is powered by a rechargeable battery and change direction when it encounters obstacles. The system employs a water pump, relay to clean the floor, and sensors provide information about approaching objects. The Android app will receive voice commands as input and execute them on the robot. The robot is integrated with Google home to accept voice commands. The robot comprises of a mop and sprinkler mechanism for wet cleaning. The user will be alerted when the battery or cleaning solution level is low. The work aims to create an independent cleaning robot that can efficiently clean floors, offering convenience and efficiency to people leading a busy life.
Energy-Efficient Machine Learning Acceleration: From Technologies to Circuits and Systems

Advanced computing systems have long been enablers for breakthroughs in Machine Learning (ML) algorithms either through sheer computational power or form-factor miniaturization. However, as ML algorithms become more complex and the size of datasets increase, existing computing platforms are no longer sufficient to bridge the gap between algorithmic innovation and hardware design. With the rising needs of advanced algorithms for large-scale data analysis and data-driven discovery, and significant growth in emerging applications from the edge to the cloud, we need energy-efficient, low-cost, high- performance, and reliable computing systems targeted for these applications. This paper presents the latest developments in oscillatory neural networks, optical computing, and memristive processing-in-memory (PIM) to address the various challenges in designing efficient computing systems specifically targeting ML applications.
Exploring the Multifaceted Roles of Non-Native English-Speaking Teachers (NNEST) in Enhancing participants' Speaking Proficiency

&lt;p class="abstrak"&gt;This study explores the roles of Non-Native English Speaking Teachers (NNESTs) in facilitating the improvement of participants' English speaking skills. The research was prompted by the challenge faced by a NNESTs from Indonesia, who relocated to Kyrgyzstan to teach English as a second language. The teacher lacked cultural knowledge to support his participants' language learning. To address this, the study employed a descriptive qualitative research design and utilized interviews as the primary data collection method to investigate how NNESTs support the development of their participants' English speaking ability. The analysis employed a Miles and Huberman data analysis to identify patterns and themes in the data. The findings reveal that NNESTs design instructional approaches that foster an environment conducive to speaking practice and enable participants to engage in conversation without fear of criticism. Additionally, the teachers provide personalized feedback to help participants identify their areas of weakness. Creating an engaging learning environment encourages participants to use English more frequently.&lt;/p&gt;
Evolutionary Interest Representation Network for Click-Through Rate Prediction

The rapid development of the Internet has revo-lutionized our lives, providing us with an array of convenient services. However, this revolution has also led to the problem of data overload. Personalized recommendation services have been widely recognized as an effective solution to this problem by both the industrial and academic communities. Accurate prediction of click-through rate (CTR) is a critical task in personalized recommendation systems since it can improve the user’s shopping experience, ultimately increasing revenue for the platform. To achieve accurate CTR prediction, it is crucial to capture user interests. While several methods for interest modeling exist, challenges related to user interest evolution and user behavior noise need further attention. This paper proposes a novel CTR prediction model named the Evolutionary Interest Representation Network (EIRN), which learns the evolving interest representation of users from their behaviors, profiles, and environmental attributes. In the model, we introduce an advertisement correlation graph decomposition layer and a noise filter layer to enhance and purify the user behavior representation. An interest extraction layer is designed to capture sequential correlations and common characteristics between user behaviors, representing their interests. Additionally, as user interests evolve over time and their importance changes, an attention-based interest evolution layer is designed to track the evolution of user interests. Our proposed method is validated using four public datasets and an industrial dataset, showing its superiority over existing CTR prediction methods.
High-Order Collaborative Filtering for Third-Party Library Recommendation

Developers of mobile applications (apps) can enhance their work efficiency by reusing suitable third-party libraries (TPLs). TPLs recommendation methods have been proposed to assist app developers in quickly finding useful TPLs, but the existing methods, such as those based on graph neural networks (GNN), have limitations in extracting high-order neighborhood information that can enhance the representation ability of nodes. To address this issue, we propose a novel hypergraph neural network method based on collaborative filtering, called High-order Collaborative Filtering (HCF). We first build two hypergraphs by fully exploiting the TPLs usage records in apps and then extracting the high-order neighborhood information from the hypergraphs. The neighborhood information extracted contains less noise compared to classic GNNs, thereby alleviating the problem of over-smoothing in GNN node representations. This advantage enables HCF to recommend more accurate and diverse TPLs for apps development. Extensive experiments on a real-world dataset demonstrate that HCF significantly outperforms the state-of-the-art methods in terms of recommendation accuracy and diversity.
Cross-chain Transfer of Snapshot Archives for Low-overhead Peer Management in Web 3.0

The concept of a decentralized web has been realized with the idea of Web 3.0 through interconnecting over multiple blockchain-based networks. However, blockchain incurs significant time and space overhead. This problem can be solved using snapshots that store the blockchain’s states compactly. But, the existing snapshot mechanism used in Hyperledger Fabric is limited to siloed operations on a single blockchain only. In this paper, we contribute towards overcoming this drawback by developing a novel mechanism for peer selection, snapshot archival, and cross-blockchain sharing of the snapshot. We extend the snapshot collection mechanism in Hyperledger Fabric to implement the above idea and test it over two blockchain networks emulating a decentralized web architecture.
MVGCL: Multi-View Graph Contrastive Learning for Service Recommendation

In service recommender system, graph neural networks (GNNs) perform message passing through diffusion mechanism based on user-service relationship graph. However, existing GNN-based service recommendation models suffer from two limitations: ❨1❩ message passing is only carried out at firstorder neighbors, as higher-order may cause over-smoothing phenomenon, confining feature propagation in GNNs; and ❨2❩ due to sparse and noisy interactions, the distribution of embedding vectors is nonuniform in the latent space, resulting in unsatisfactory performance for downstream applications. To this end, we propose a fixed global graph diffusion view that is independent of the original user-service observed local view to form a multi-view learning by building contrastive learning (CL) relationship, named as Multi-View Graph Contrastive Learning (MVGCL). Specifically, it enhances the capability of message passing through constructed local and global multi-view graphs, and alleviates the sparse and noisy influences by performing intra-CL within local/global view and inter-CL between multi-view to obtain a more uniform distribution of user and service node representations. Extensive experiments are conducted on three benchmark datasets within different scales, and the results demonstrate that our proposed MVGCL can remarkably outperforms state-of-the-art competing baselines on various evaluation metrics.