Reviewerly/exHarmony huggingface dataset:
FIRST: Exploiting the Multi-Dimensional Attributes of Functions for Power-Aware Serverless Computing

Emerging cloud-native development models raise new challenges for managing server performance and power at microsecond scale. Compared with traditional cloud workloads, serverless functions exhibit unprecedented heterogeneity, variability, and dynamicity. Designing cloud-native power management schemes for serverless functions requires significant engineering effort. Current solutions remain sub-optimal since their orchestration process is often one-sided, lacking a systematic view. A key obstacle to truly efficient function deployment is the fundamental wide abstraction gap between the upper-layer request scheduling and the low-level hardware execution.In this work, we show that the optimal operating point (OOP) for energy efficiency cannot be attained without synthesizing the multi-dimensional attributes of functions. We present FIRST, a novel mechanism that enables servers to better orchestrate serverless functions. The key feature of FIRST is that it leverages a lightweight Internal Representation and meta-Scheduling (IRS) layer for collecting the maximum potential revenue from the servers. Specifically, FIRST follows a pipeline-style workflow. Its frontend components aim to analyze functions from different angles and expose their key features to the system. Meanwhile, its backend components are able to make informed function assignment decisions to avoid OOP divergence. We further demonstrate the way to create extensions based on FIRST to enable versatile cloud-native power management. In total, our design constitutes a flexible management layer that supports power-aware function deployment. We show that FIRST could allow 94% functions to be processed under the OOP, which brings up to 24% energy efficiency improvements.
Convolutional Neural Networks for Image Emotion Recognition by fusing Differential and Supplementary Information

Emotions arise out of complex phenomena, which are believed to have a biological basis. Neuroscience research has demonstrated that emotions are related to distinct patterns of brain activity and the release of certain chemicals, such as hormones and neurotransmitters, into the bloodstream. Emotion recognition is used in many applications, such as advertising, social networking, and cinema. In this paper, we propose a deep convolutional neural network (CNN) fusion technique made up of two parts: a differential-CNN system to extract emotional features from an image and combine them using convex combination and a supplementary CNN to extract central object details of an image and combine it with differential-CNN features. We amplify the difference between minute latent representations of an image via differential-CNN features. The type of emotion an image elicits is highly influenced by its central object. Hence supplementary CNN helps supplement the differential-CNN features to improve emotion recognition. In comparison to the contemporary state-of-the-art methods, our proposed method showed an absolute gain of 6% on the primary dataset and outperformed them on the different secondary datasets social media web scraping process.
Facial Expression Recognition using Convolutional Neural Network

Facial expression is a way of non-verbal communication by using eyes, lips, nose and facial muscles. Smiling and rolling eyes are some examples. Facial expression recognition is the process of extracting facial features from a person. Facial expressions include anger, happy, disgust, sad, neutral, fear and surprise. By the use of machine learning, an expression recognition model is built using Convolutional Neural Network. The input data is fed to the system in order to give the expected results. The model is trained using Facial Expression Recognition (FER) dataset. The Convolutional Neural Network (CNN) gives good and accurate results. The Haar cascade classifier classifies the face and non-face regions in the input image which helps the convolutional network to classify the images. Good classification of images can be desirable by the use of classifiers. These classifiers can be implemented by using the OpenCV library.
Resampling-free fast particle filtering with application to tracking rhythmic biomedical signals

The particle filter is known to be a powerful tool for the estimation of time varying latent states guided by nonlinear dynamics and sensor measurements.Particle filter’s traditional resampling step is essential because it avoids degeneracy of particles by stochastically eliminating the small weight particles that do not contribute to the posterior probability density function and replacing them by copies of those having large weights. Nevertheless, resampling is computationally costly since it requires extensive and sequential communication among the particles. This work proposes a novel method of particle filtering that eliminates the need for resampling and prevents degeneracy by substituting low-weight particles with a simple cutoff decision strategy based on the cumulative sum of weights. The proposed scheme limits replacement over only a few important particles and hence substantially accelerates the filtering process. We show the merits of the proposed method via simulations using a nonlinear example and also apply the method for tracking harmonics of real biomedical signals.
A Novel Approach in Web Based 3D Virtualization For Healthcare

3D Virtualisation refers to the process of creation of graphical contents using 3D softwares. These include images and animations that improve the communication and provide the users with a more realistic online experience. It is also used to demonstrate either a prototype or a finished product to the stakeholders. In the present, the customers and learners prefer viewing and learning the virtual model of the product in order to efficiently understand the concept, theory and principle behind it. In this case, 3D virtualization provides an excellent opportunity for customers and learners to see the layout of the product design, texture and working model as animation.
The Relationship of The Implementation of Warta DNAS O/153 with The Performance of Operations Officers

The Babaranjang train accident at Penanggiran Station due to the negligence of operating officers playing cellphones caused the Operations Directors of PT.KAI will issue a ban on using cell phones during working hours to operating officers in Warta Dinas O/153. In the implementation of the O/153 official bulletin at Madiun Station, there are several problems, namely (1) limited coordination between the train travel control officer and the track inspector when there is a disturbance on the road because the walkie-talkie cannot reach, (2) the phenomenon of operational staff who is trying to break things, and (3) there are symptoms of stress, anxiety, or anxiety due to someone being away from a mobile phone, namely nomophobia (No Mobile Phone). Therefore, the author wants to examine the relationship between implementing official bulletins O/153 and the performance of operations officers at Madiun Station. Methods of data collection using questionnaires and data analysis using Spearman rank correlation. The research results obtained a correlation coefficient value of 0.432 and a significance value (2-tailed) of 0.024, less than 0.05. This means there is a relationship between the implementation of news service O/153 and the performance of operations officers at Madiun Station, with a moderately strong relationship and a positive direction.
Forget Me Now: Fast and Exact Unlearning in Neighborhood-based Recommendation

Modern search and recommendation systems are optimized using logged interaction data. There is increasing societal pressure to enable users of such systems to have some of their data deleted from those systems. This paper focuses on "unlearning" such user data from neighborhood-based recommendation models on sparse, high-dimensional datasets. We present caboose, a custom top-k index for such models, which enables fast and exact deletion of user interactions. We experimentally find that caboose provides competitive index building times, makes sub-second unlearning possible (even for a large index built from one million users and 256 million interactions), and, when integrated into three state-of-the-art next-basket recommendation models, allows users to effectively adjust their predictions to remove sensitive items.
Neural Architectures for Searching Subgraph Structures

With the development of new neural network architectures for graph learning in recent years, the use of graphs to store, represent and process data become more trendy. Nowadays, graphs as a rich structured form of data representation are used in many real-world projects. In these projects, objects are often defined in terms of their connections to other things. There are many practical applications in areas such as antibacterial discovery, physics simulations, fake news detection, traffic prediction and recommendation systems.
MaxSimE: Explaining Transformer-based Semantic Similarity via Contextualized Best Matching Token Pairs

Current semantic search approaches rely on black-box language models, such as BERT, which limit their interpretability and transparency. In this work, we propose MaxSimE, an explanation method for language models applied to measure semantic similarity. Our approach is inspired by the explainable-by-design ColBERT architecture and generates explanations by matching contextualized query tokens to the most similar tokens from the retrieved document according to the cosine similarity of their embeddings. Unlike existing post-hoc explanation methods, which may lack fidelity to the model and thus fail to provide trustworthy explanations in critical settings, we demonstrate that MaxSimE can generate faithful explanations under certain conditions and how it improves the interpretability of semantic search results on ranked documents from the LoTTe benchmark, showing its potential for trustworthy information retrieval.
Limitations of Open-Domain Question Answering Benchmarks for Document-level Reasoning

Many recent QA models retrieve answers from passages, rather than whole documents, due to the limitations of deep learning models with limited context size. However, this approach ignores important document-level cues that can be crucial in answering questions. This paper reviews three open-domain QA benchmarks from a document-level perspective and finds that they are biased towards passage-level information. Out of 17,000 assessed questions, 82 were identified as requiring document-level reasoning and could not be answered by passage-based models. Document-level retrieval (BM25) outperformed both dense and sparse passage-level retrieval on these questions, highlighting the need for more evaluation of models' ability to understand documents, an often-overlooked challenge in open-domain QA.
Learning Query-aware Embedding Index for Improving E-commerce Dense Retrieval

The embedding index has become an essential part of the dense retrieval (DR) system, which enables a fast search for billion of items in online E-commerce applications. To accelerate the retrieval process in industrial scenarios, most of the previous studies only utilize item embeddings. However, the product quantization process without query embeddings will lead to inconsistency between queries and items. A straightforward solution is to put query embedding into the product quantization process. But we found that the distance of the positive query and item embedding pairs is too large, which means the query and item embeddings learned by the two-tower are not fully aligned. This problem would lead to performance decay when directly putting query embeddings into the product quantization.
Prediction then Correction: An Abductive Prediction Correction Method for Sequential Recommendation

Sequential recommender models typically generate predictions in a single step during testing, without considering additional prediction correction to enhance performance as humans would. To improve the accuracy of these models, some researchers have attempted to simulate human analogical reasoning to correct predictions for testing data by drawing analogies with the prediction errors of similar training data. However, there are inherent gaps between testing and training data, which can make this approach unreliable. To address this issue, we propose an Abductive Prediction Correction (APC) framework for sequential recommendation. Our approach simulates abductive reasoning to correct predictions. Specifically, we design an abductive reasoning task that infers the most probable historical interactions from the future interactions predicted by a recommender, and minimizes the discrepancy between the inferred and true historical interactions to adjust the predictions. We perform the abductive inference and adjustment using a reversed sequential model in the forward and backward propagation manner of neural networks. Our APC framework is applicable to various differentiable sequential recommender models. We implement it on three backbone models and demonstrate its effectiveness. We release the code at https://github.com/zyang1580/APC.
Gated Attention with Asymmetric Regularization for Transformer-based Continual Graph Learning

Continual graph learning (CGL) aims to mitigate the topological-feature-induced catastrophic forgetting problem (TCF) in graph neural networks, which plays an essential role in the field of information retrieval. The TCF is mainly caused by the forgetting of node features of old tasks and the forgetting of topological features shared by old and new tasks. Existing CGL methods do not pay enough attention to the forgetting of topological features shared between different tasks. In this paper, we propose a transformer-based CGL method (Trans-CGL), thereby taking full advantage of the transformer's properties to mitigate the TCF problem. Specifically, to alleviate forgetting of node features, we introduce a gated attention mechanism for Trans-CGL based on parameter isolation that allows the model to be independent of each other when learning old and new tasks. Furthermore, to address the forgetting of shared parameters that store topological information between different tasks, we propose an asymmetric mask attention regularization module to constrain the shared attention parameters ensuring that the shared topological information is preserved. Comparative experiments show that the method achieves competitive performance on four real-world datasets.
SLAM-DRLnav: A SLAM-Enhanced Deep Reinforcement Learning Navigation Framework for Indoor Self-driving

This work proposes a new framework for mobile robot navigation that combines two existing methods - SLAM and DRL - to enhance performance. Instead of relying solely on SLAM for mapping and location, or solely on DRL for path planning, the proposed framework utilizes both techniques in conjunction. Specifically, it utilizes SLAM for creating maps and determining the robot’s location, while using an ACO algorithm to generate a static path plan. In dynamic environments with moving obstacles, the framework employs DRL-based local path planning. Additionally, the proposed framework compares and evaluates the performance of three different DRL-based navigation algorithms - DGN, TD3, and PPO. Overall, the goal of this framework is to improve the localization and path planning capabilities of mobile robots in dynamic environments.
CGScholar and How It Interfaces With Mobile Devices

CGScholar is a learning platform that is cutting-edge in how it allows users to access it and how users can interact within it. Mobile devices are becoming the device of choice when it comes to accessing content on the internet due to their ubiquitous nature and compact size. How the user sees the content in CGScholar depends on which device they choose to access it with, as screen size of the mobile device and how that content is rendered on the screen depends on the device choice. Another major consideration is security which must be taken into account at the mobile device level, CGScholar, web browser, wireless networks, and computer networks. Security is of high concern in a mobile learning environment such as CGScholar because the nature of the data being stored and exchanged can possibly be protected under FERPA and/or PII regulations.
Application of Genetic Algorithm in Global Optimization Design of Mechanical Engineering

Compared with traditional mechanical design, global optimization design for mechanical engineering is a completely new concept. Its purpose is to improve the efficiency and quality of mechanical design. Therefore, this paper proposes a new kind of intelligent optimization method based on the basic theory of genetic algorithm. Then the Matlab software genetic algorithm toolbox is used to solve it. Then it is combined with a mechanical engineering global optimization design example to compare and analyze the sequential quadratic programming method and genetic algorithm. The results show that the volumes of the genetic algorithm and the traditional optimization method are 64.21% and 76.19%, respectively, suggesting that the process of genetic algorithm is efficient and can obtain good results. This paper presents an effective and innovative approach to global optimization design for mechanical engineering. The proposed genetic algorithm method is a promising method for enhancing the efficiency and quality of mechanical design. The findings of this study can be used as a reference for future research and development of global optimization design in mechanical engineering.
Fault-Prone Software Requirements Specification Detection Using Ensemble Learning for Edge/Cloud Applications

Ambiguous software requirements are a significant contributor to software project failure. Ambiguity in software requirements is characterized by the presence of multiple possible interpretations. As requirements documents often rely on natural language, ambiguity is a frequent challenge in industrial software construction, with the potential to result in software that fails to meet customer needs and generates issues for developers. Ambiguities arise from grammatical errors, inappropriate language use, multiple meanings, or a lack of detail. Previous studies have suggested the use of supervised machine learning for ambiguity detection, but limitations in addressing all ambiguity types and a lack of accuracy remain. In this paper, we introduce the fault-prone software requirements specification detection model (FPDM), which involves the ambiguity classification model (ACM). The ACM model identifies and selects the optimal algorithm to classify ambiguity in software requirements by employing the deep learning technique, while the FPDM model utilizes Boosting ensemble learning algorithms to detect fault-prone software requirements specifications. The ACM model achieved an accuracy of 0.9907, while the FPDM model achieved an accuracy of 0.9750. To validate the results, a case study was conducted to detect fault-prone software requirements specifications for 30 edge/cloud applications, as edge/cloud-based applications are becoming crucial and significant in the current digital world.
Development of forked divergence particle swarm optimization

There are more and more issues, requirements and complexities that engineering design problems must take into account in industrial applications. Therefore, the number of behavior and design variables tends to increase, making optimization problem harder. PSO was proposed and has been widely used for its simplicity and capability to solve problems, even though their convexity is not guaranteed. However, its effectiveness is limited to a relatively small number of design variables. In this study, we propose the following four ideas to extend its efficacy for the number of design variables - adjustments for PSO parameters, a large explosion, a small explosion, and a genetic estimation technique. The explosion is kind of mutation in GA. The genetic estimation uses a chromosome called Hoxgene to identify variable dependencies, enabling PSO to solve the problem with a smaller number of variables. The results of experiments on benchmark problems showed the highly accurate solutions were stably obtained even for problems with a large number of variables using the proposed method.
The Influence of Motion Factors on Perception of Motion in VR Spaces

Providers sometimes create moving arrows, 3D objects, effects, and other items to guide the users of VR spaces. It should be possible to provide better visual guidance and increase its effectiveness by investigating how users feel about how these guiding items move in VR spaces. In this study, with the aim of creating more effective interactions for visual guidance, we provided subjects with the task of visually registering the trajectory of a moving body in a VR space using simple solid shapes. We investigated how the user’s perception of motion was affected by the speed of movement and range of movement of the object and by the distance between the user and the object. We found that the optimal distance for perception of motion in a VR space was 12 meters, and the wider the range of movement, the higher the correct answer rate. It was found that the object’s speed of movement had a small effect on the perception of motion if the speed was within the range in which its motion could be registered. It was also revealed that the combination of the distance between the user and the moving body and the speed at which it moves significantly affects the user’s perception of its motion. A subjective evaluation showed that users felt that ‘speed’ greatly affects their perception of motion.
A Study of Emotional Optimization Strategies for Young Teachers in Higher Education

Teaching is a special profession, and the multiplicity of its professional roles, as well as the tediousness and diversity of its work, makes teachers under great pressure in their work, especially young teachers who have been teaching for a short time and are not yet experienced enough, and are more likely to This is especially true for young teachers who have been teaching for a short time and are not yet experienced enough.Young teachers are the backbone of the teaching force and are the backbone of the future teaching force.This paper examines the relationship between negative and positive emotions, personal teaching efficacy and each voice type, and the interrelationship between personal teaching efficacy and personality type in young teachers.It provides a favorable grounded theory for exploring the influencing factors and prevention strategies of negative emotion generation, so as to promote teachers' healthy physical and mental development and smooth and effective teaching.