Reviewerly/exHarmony huggingface dataset:
Dynamically Shifting Multimodal Representations via Hybrid-Modal Attention for Multimodal Sentiment Analysis

In the field of multimodal machine learning, multimodal sentiment analysis task has been an active area of research. The predominant approaches focus on learning efficient multimodal representations containing intra- and inter-modality information. However, the heterogeneous nature of different modalities brings great challenges to multimodal representation learning. In this paper, we propose a multi-stage fusion framework to dynamically fine-tune multimodal representations via a hybrid-modal attention mechanism. Previous methods mostly only fine-tune the textual representation due to the success of large corpus pre-trained models and neglect the inconsistency problem of different modality spaces. Thus, we design a module called the Multimodal Shifting Gate (MSG) to fine-tune the three modalities by modeling inter-modality dynamics and shifting representations. We also adopt a module named Masked Bimodal Adjustment (MBA) on the textual modality to improve the inconsistency of parameter spaces and reduce the modality gap. In addition, we utilize syntactic-level and semantic-level textual features output from different layers of the Transformer model to sufficiently capture the intra-modality dynamics. Moreover, we construct a Shifting HuberLoss to robustly introduce the variation of the shifting value into the training process. Extensive experiments on the public datasets, including CMU-MOSI and CMU-MOSEI, demonstrate the efficacy of our approach.
Secure Outsourced SIFT: Accurate and Efficient Privacy-Preserving Image SIFT Feature Extraction

Cloud computing has become an important IT infrastructure in the big data era; more and more users are motivated to outsource the storage and computation tasks to the cloud server for convenient services. However, privacy has become the biggest concern, and tasks are expected to be processed in a privacy-preserving manner. This paper proposes a secure SIFT feature extraction scheme with better integrity, accuracy and efficiency than the existing methods. SIFT includes lots of complex steps, including the construction of DoG scale space, extremum detection, extremum location adjustment, rejecting of extremum point with low contrast, eliminating of the edge response, orientation assignment, and descriptor generation. These complex steps need to be disassembled into elementary operations such as addition, multiplication, comparison for secure implementation. We adopt a serial of secret-sharing protocols for better accuracy and efficiency. In addition, we design a secure absolute value comparison protocol to support absolute value comparison operations in the secure SIFT feature extraction. The SIFT feature extraction steps are completely implemented in the ciphertext domain. And the communications between the clouds are appropriately packed to reduce the communication rounds. We carefully analyzed the accuracy and efficiency of our scheme. The experimental results show that our scheme outperforms the existing state-of-the-art.
Knowledge-Aware Collaborative Filtering With Pre-Trained Language Model for Personalized Review-Based Rating Prediction

Personalized review-based rating prediction aims at leveraging existing reviews to model user interests and item characteristics for rating prediction. Most of the existing studies mainly encounter two issues. First, the rich knowledge contained in the fine-grained aspects of each review and the knowledge graph is rarely considered to complement the pure text for better modeling user-item interactions. Second, the power of pre-trained language models is not carefully studied for personalized review-based rating prediction. To address these issues, we propose an approach named Knowledge-aware Collaborative Filtering with Pre-trained Language Model (KCF-PLM). For the first issue, to utilize rich knowledge, KCF-PLM develops a transformer network to model the interactions of the extracted aspects w.r.t. a user-item pair. For the second issue, to better represent users and items, KCF-PLM takes all the historical reviews of a user or an item as input to pre-trained language models. Moreover, KCF-PLM integrates the transformer network and the pre-trained language models through representation propagation on the knowledge graph and user-item guided attention of the aspect representations. Thus KCF-PLM combines review text, aspect, knowledge graph, and pre-trained language models together for review-based rating prediction. We conduct comprehensive experiments on several public datasets, demonstrating the effectiveness of KCF-PLM.
Binary Search

Have you ever played this game? Your friend has an integer between, say, 1 and 15, and asks you to guess it. If your guess is correct, your friend shouts “Got it!”, and the game stops. If not, your friend tells you whether the hidden number is larger or smaller than your guess, and you guess again.
Efficient Classification by Removing Bayesian Confusing Samples

Improving the generalization performance of classifiers from data pre-processing perspective has recently received considerable attention in the machine learning community. Although many methods have been proposed in the past decades, most of them lack theoretical foundations and cannot guarantee better generalization performance of classifiers on processed datasets. To overcome this flaw, in this paper, we propose a method, which is supported by Bayesian decision theory and percolation theory, to improve generalization performance by removing Bayesian confusing samples (abbr. BCS). Specifically, for a training set, we define the samples that misclassified by the Bayesian optimal classifier as BCS and prove that a classifier trained on the training set after removing BCS can obtain better generalization performance. To find out BCS, we indicate that BCS can be identified according to the size of global homogeneous cluster, a set of samples with the same labels, based on percolation theory. Based on these analysis, we propose a method to construct global homogeneous clusters and remove BCS from the training set. Extensive experiments show that the proposed method is effective for a number of classical and state-of-the-art classifiers.
About Smart Systems in Libraries

The purpose of this article is to discuss the actual problem of using smart systems, metaphorically called “artificial intelligence technology”, “virtual reality”, etc. In addition to the terminological problem, special attention is paid at such important aspect as “digital transformation”, which is the fourth stage of automation of activity, including that of libraries. In digital transformation, the main thing is the transformation of the regulatory framework, in particular, putting it in order. Without terminologization, algorithmization and creation of an ontology of the subject area of information and library activities, it is impossible to create a professional smart system. The study bases on the categorical tensor approach. It shows that the main trend in the development of digital innovations in library and information activities is smartization, mediatization and virtualization. The basic properties of smart systems that allow analyzing the authenticity of scientific and technical texts and identifying authentic texts in them are revealed. It is noted that compression and visualization of analyzed texts can enable librarians to carry out information analysis of publicly available documents, for example, abstracts of dissertations in order to identify detonation data located in the peripheral zone. The emergence of smart systems in the form of a GPT chatbot (Generative Pretrained Transformer) will reflect in the use of professional smart systems, which, unlike anti-plagiarism systems, will identify scientific media products at the semiotic level. The presented results can intensify the discussion on the topic under consideration.
Can GAN-Induced Attribute Manipulations Impact Face Recognition?

Impact due to demographic factors such as age, sex, race, etc., has been studied extensively in automated face recognition systems. However, the impact of digitally modified demographic and facial attributes on face recognition is relatively under-explored. In this work, we study the effect of attribute manipulations induced via generative adversarial networks (GANs) on face recognition performance. We conduct experiments on the CelebA dataset by intentionally modifying thirteen attributes using AttGAN and STGAN and evaluating their impact on two deep learning-based face verification methods, ArcFace and VGGFace. Our findings indicate that some attribute manipulations involving eyeglasses and digital alteration of sex cues can significantly impair face recognition by up to 73% and need further analysis.
Geolocation of Cultural Heritage Using Multi-view Knowledge Graph Embedding

Knowledge Graphs (KGs) have proven to be a reliable way of structuring data. They can provide a rich source of contextual information about cultural heritage collections. However, cultural heritage KGs are far from being complete. They are often missing important attributes such as geographical location, especially for sculptures and mobile or indoor entities such as paintings. In this paper, we first present a framework for ingesting knowledge about tangible cultural heritage entities from various data sources and their connected multi-hop knowledge into a geolocalized KG. Secondly, we propose a multi-view learning model for estimating the relative distance between a given pair of cultural heritage entities, based on the geographical as well as the knowledge connections of the entities.
On the Hardness of Scheme-Switching Between SIMD FHE Schemes

Fully homomorphic encryption (FHE) schemes are either lightweight and can evaluate boolean circuits or are relatively heavy and can evaluate arithmetic circuits on encrypted vectors, i.e., they perform single instruction multiple data operations (SIMD). SIMD FHE schemes can either perform exact modular arithmetic in the case of the Brakerski-Gentry-Vaikuntanathan (BGV) and Brakerski-Fan-Vercauteren (BFV) schemes or approximate arithmetic in the case of the Cheon-Kim-Kim-Song (CKKS) scheme. While one can homomorphically switch between BGV/BFV and CKKS using the computationally expensive bootstrapping procedure, it is unknown how to switch between these schemes without bootstrapping. Finding more efficient methods than bootstrapping of converting between these schemes was stated as an open problem by Halevi and Shoup, Eurocrypt 2015 [33, 34]. In this work, we provide strong evidence that homomorphic switching between BGV/BFV and CKKS is as hard as bootstrapping. In more detail, if one could efficiently switch between these SIMD schemes, then one could bootstrap these SIMD FHE schemes using a single call to a homomorphic scheme-switching algorithm without applying homomorphic linear transformations. Thus, one cannot hope to obtain significant improvements to homomorphic scheme-switching without also significantly improving the state-of-the-art for bootstrapping. We also explore the relative hardness of computing homomorphic comparison in these same SIMD FHE schemes as a secondary contribution. We show that given a comparison algorithm, one can bootstrap these schemes using a few calls to the comparison algorithm for typical parameter settings. While we focus on the comparison function in this work, the overall approach to demonstrate relative hardness of computing specific functions homomorphically extends beyond comparison to other useful functions such as min/max or ReLU.
Restaurant menus and COVID-19: implications for technology adoption in the post-pandemic era

Purpose This paper aims to systematically explore the experiences of restaurant patrons with quick response (QR) code menus during the pandemic to extract insights pertaining to the acceptance of technology adoption in service settings for the post-COVID era. Design/methodology/approach COVID-19 has enormously impacted consumer perceptions toward technology adoption in restaurants. Identifying these impacts requires qualitative inquiries because qualitative techniques, unlike quantitative methods, enable researchers to seek insights into the phenomenon without a predetermined stance. This study applied a qualitative research method. The qualitative data was obtained through in-depth interviews and a focus group. Thematic analysis was conducted. Findings This study identified perceived attributes of QR code technology by patrons: positive attributes such as easy to learn, quick and contactless; and negative attributes such as effortful and reliance on a smartphone. Also, the results distinguished perceived advantages of utilization of QR codes as restaurants’ menu: advantages such as hygienic, environmentally friendly due to no printing and contactless; and disadvantages such as poor menu design and lack of interaction. Practical implications This research presents patrons’ views on QR code menu utilization at restaurants under the pandemic conditions. Contradictory responses on the same features of technology reveal a lack of menu design in the utilization of QR codes. Restaurant practitioners can extract insights about how to better use technology like QR codes, cost-effective and environmentally friendly, in their operations in the postpandemic era. Originality/value This study included both deductive and inductive approaches. An inductive approach that progressed from theory to data was applied while forming interview questions. A deductive approach was used to inform theory with the findings of the data. The value of research derives from differentiating technology attributes (QR codes) and utilization domain (restaurant menu) in the realm of acceptance of technology in the consumer context.
Secure task scheduling in cloud computing with enhanced ordinal optimisation and integrated DNA, Morse code techniques

This paper presents two innovative approaches to address the challenges of task scheduling, efficient resource utilisation, and security in cloud computing environments. The first approach proposes an optimal task-scheduling strategy for virtual machines in dynamic cloud scenarios, utilising an enhanced ordinal optimisation technique and linear regression to predict future dynamic workload. The second approach utilises Morse code encoding of DNA sequences to optimise scheduling outcomes while ensuring secure scheduling in the cloud environment, integrating ordinal optimisation techniques and the unique properties of DNA sequences. The effectiveness of both proposed approaches is evaluated by comparing them to conventional task-scheduling techniques. The results demonstrate that the proposed approaches achieve better performance in terms of scheduling efficiency and security. These approaches offer a comprehensive solution to address the critical aspects of task scheduling, efficient resource utilisation, and security in cloud computing environments.
Object Visual Styles

From this point onward in this book, you will be delving into the deepest recesses of JSON theme files. This has the potential to be overwhelming, so I intend to proceed progressively to show you how JSON formatting is largely a process of building up the required knowledge step by step. Moreover, as Power BI themes contain many repetitive pieces of code, you will learn to discern the core elements that are used over and over again in many (if not most) visuals from the code that is specific to each visual. The art is to learn to appreciate not only how JSON is used to define visual formatting but also to build on acquired knowledge and principles to use as a springboard to discover new areas where themes can be applied.
How blockchain solves the supply chain problems using RFID techniques

Blockchain technology has revolutionised the software industry and is proving to be a powerful tool to solve unresolved issues in supply chain management. This paper discusses how blockchain can address challenges in transparency, accountability, and trust, especially when dealing with valuable goods like diamonds, gold, liquor, or medical drugs. By utilising its peer-to-peer architecture, blockchain can eliminate intermediaries and reduce costs. When combined with RFID technology, it creates a highly secure and transparent system that can predict and prevent problems before they occur. We propose a supply chain model that incorporates machine learning with RFID technology, which can automate processes and improve system speed while meeting customer demands during peak periods. This paper shows how blockchain is a future-proof solution that can give a significant boost to the current supply chain system. The proposed blockchain-based supply chain model with RFID technology is a promising avenue for supply chain management that provides benefits for years to come.
Test-Driven Development Essentials

In this chapter, we will learn how to apply TDD in your daily development routine through a step-by-step guide. Along with this demo, you will get an idea of how to split a big task into relatively smaller ones and complete each one with a set of passing tests while learning some refactoring techniques. Before we dive into the code, let’s get a fundamental understanding of how to write a proper test.
Hospital Inpatient Information System at Siti Suaibah Clinic

This study aims to improve and expedite healthcare services for inpatients at Siti Suaibah Clinic in Krian. By implementing an effective Inpatient Information System, the study sought to provide accurate, efficient, and effective patient care. The research methodology involved data processing and analysis, resulting in several findings. The system facilitated patient data management, including medical histories, treatment schedules, and procedures. However, challenges such as development and maintenance costs, training for medical staff, and data security concerns were identified. Overall, the study highlights the benefits and implications of implementing the Inpatient Information System, emphasizing the need for careful planning and management. The outcome underscores the significance of optimizing healthcare services through innovative technology.&#x0D; Highlights:&#x0D; &#x0D; Improved patient care efficiency and effectiveness through the implementation of an Inpatient Information System.&#x0D; Challenges in implementing the system: development and maintenance costs, training for medical staff, and data security concerns.&#x0D; The significance of careful planning and management to optimize healthcare services and ensure the successful implementation of the Inpatient Information System.&#x0D; &#x0D; Keywords: Inpatient care, Information system, Healthcare services, Clinic, Implementation
East Nusa Tenggara (NTT) Student Communication Accommodation Strategy in Sidoarjo

This qualitative study aims to explore the accommodation strategies employed by students from East Nusa Tenggara (NTT) studying in Sidoarjo, Indonesia, in their interactions with local students and the surrounding community. Drawing on the communication accommodation theory, data was collected through purposive sampling using interviews, observations, and documentation. The findings revealed three forms of communication accommodation: convergence (using both Indonesian and Javanese languages during interactions), divergence (using their native language from NTT), and overaccommodation (expressed through loud laughter and speech patterns). The study highlights the significance of the accommodation process between NTT students and local students and community members in achieving effective cross-cultural communication. The implications of these findings shed light on the importance of cultural sensitivity and understanding in fostering harmonious interactions among diverse populations.&#x0D; Highlights:&#x0D; &#x0D; The study explores cross-cultural accommodation strategies of NTT students in Sidoarjo.&#x0D; Findings reveal convergence, divergence, and overaccommodation as forms of communication accommodation.&#x0D; The study emphasizes the importance of cultural sensitivity for effective cross-cultural communication.&#x0D; &#x0D; Keywords: cross-cultural accommodation, NTT students, communication accommodation, cultural sensitivity, effective communication
Discussion on Computer Development Course and New Computer

Computer, as an important infrastructure of modern science and technology and information society, has had a profound impact on human society.Its development process is full of technological breakthroughs and application innovation, from the early stone tools, abacus to the first electronic computer ENIAC, and then to the invention of integrated circuit and microprocessor, the advent of the personal computer era, the computer has made great development in the continuous evolution and improvement.With the continuous progress of science and technology, new computer technology is constantly emerging, which has produced a huge space for imagination in the future.In the new computer technology, quantum computer is considered a major breakthrough in the computer field.Quantum computer uses the principle of quantum mechanics, can calculate in a parallel way, has super computing power, and has a wide range of application prospects in cryptography, drug research and development, climate simulation and other fields.In addition, biological computer is a new type of computer using biomolecules for information storage and processing, with a high degree of biological compatibility and information density, with great application potential in bioinformatics, gene editing and other fields.Photonic computer is a new type of computer based on photon information transmission and processing.It has the advantages of high speed and low energy consumption, and has a broad application space in the high-speed communication, data center and other fields.This paper analyzes the history of computer development, and explores various types of new computers, hoping to provide reference for the development of computer.
Enhancing Communication Skills for Studentpreneurs: A Training Program on Public Speaking

&#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; This research aimed to enhance the communication skills of studentpreneurs in the WEBS unit at Universitas Airlangga. Through a community engagement activity aligned with SDGs 4 and 8, a training program focused on public speaking skills was conducted. The two-day training in May 2023 resulted in notable improvements in participants' knowledge and skills. They displayed increased confidence, effective body language, eye contact, and improved delivery, all crucial for their entrepreneurial pursuits. The training received positive feedback, with high engagement and enthusiasm from participants and speakers. Pre- and post-test assessments revealed enhanced understanding of communication elements, cultural factors, and psychological aspects related to public speaking. Participants also demonstrated growth in public speaking abilities, showcasing greater boldness, facial expressions, vocal modulation, and originality in presenting ideas. The findings underscore the significance of practical application and continuous development of communication skills. This study offers valuable insights to researchers and professionals, providing an effective approach for enhancing communication competencies among studentpreneurs.&#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; Highlight:&#x0D; &#x0D; Community engagement activity targeting studentpreneurs within the WEBS unit at Universitas Airlangga, addressing communication weaknesses.&#x0D; Training program on public speaking resulting in increased knowledge, skills, confidence, and improved communication techniques.&#x0D; Positive outcomes demonstrated through high participant engagement, knowledge enhancement, and noticeable improvements in public speaking abilities.&#x0D; &#x0D; Keyword:&#x0D; WEBS, Communication Skills, Public Speaking, Studentpreneurs, Training Program&#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D;