Reviewerly/exHarmony huggingface dataset:
Review on Applicability and Utilization of Blockchain Technology in Ubiquitous Computing

Abstract: In typical Internet of Things (IoT) networks, data is sent from sensors to fog devices and then onto a central cloud server. One single point of failure, a slowdown in the flow of data, identification, security, connection, privacy concerns caused by a third party managing cloud servers, and the difficulty of frequently updating the firmware on millions of smart devices from both a maintenance and a security point of view are just some of the problems that can occur. The evolution of ubiquitous computing and blockchain technology has inspired researchers worldwide in recent years. Key features of blockchain technology, such as the fact that it can't be changed and a decentralised and distributed approach to data security, have made it a popular choice for developing diverse applications. With the practically significant applicability of blockchain concepts (specifically consensus methods), modern-day applications in ubiquitous computing and other related areas have significantly benefited. In addition, we have taken advantage of the widely available blockchain platforms and looked into potential new study fields. As a result, this review paper elaborates the novel alternative privacy preservation options while simultaneously focusing on the universal domain as a starting point for blockchain technology applications. We also discuss obstacles, research gaps, and solutions. This review can assist early researchers who are beginning to investigate the applicability of blockchain technology in ubiquitous computing. It is also possible to use it as a reference in order to speed up the process of finding the appropriate markers for ongoing research subjects that are of interest.
Chemical Oxygen Demand (COD) and Total Suspended Solid (TSS) Removal from Rubber Wastewater Factory Using Electrocoagulation Technique

Rubber industrial wastewater is obtained during washing, shredding, grinding, crumbbing, drying, and pressing bokar. The wastewater produced can be an environmental pollutant because it contains relatively large amounts of organic matter. One alternative to treating rubber wastewater is to use the electrocoagulation process, which is a combination of the coagulation and electrolysis processes. This electrocoagulation process has several advantages in the form of simple equipment, short time, produces odorless wastewater, does not require large areas of land, and can remove various contaminants in water. In this study, rubber wastewater was treated by electrocoagulation using a batch system with variations in the contact time used, namely 20, 40, 60, 80, and 100 minutes. This type of research is experimental research, and descriptive analysis is carried out. Rubber wastewater is physically gray-black before treatment and chemically contains large organic matter. The results of this study showed that the content of chemical oxygen demand (COD) and total suspended solids (TSS) before processing had values of 691 mg/L and 317 mg/L. Optimum conditions for the elimination of COD and TSS contents were achieved within 80 minutes with the percentage of elimination being 89% and 85%.
Optimized Unconventional Geometric Gates in Superconducting Circuits

Nonadiabatic Abelian geometric quantum computation has been extensively studied, due to its fast manipulation and inherent noise resistance. However, to obtain the pure geometric phase, the quantum state is required to evolve along some special paths to eliminate the dynamical phase. This leads to increasing evolution time and weakened gate robustness. The unconventional geometric quantum computation is an effective way to solve the above problems. Here, we propose a general approach to realize the unconventional geometric computation. Then, we discuss the effect of the ratio of geometric phase to dynamic phase on the performance of quantum gates. The results show that the selection of ratio corresponds to different quantum gate robustness. Therefore, we can optimize the ratio to get higher-fidelity quantum gates. At last, we construct the ratio-optimized quantum gates in a superconducting circuit and test its robustness. The fidelities of the T-gate, Hadamard H-gate, and controlled phase gate can be obtained as 99.98%, 99.95%, and 99.85%, respectively. Therefore, our scheme provides a promising way to realize large-scale fault-tolerant quantum computation in superconducting circuits.
A Secure and Scalable IoT Data Management System based on Blockchain

Today’s market is home to an increasing number of Internet of Things devices. We often talk about gadgets like the Amazon Echo, Chromecast Mini, watches, and others. Since the majority of these systems employ a centralized architectural strategy, a single entity or organization often controls the majority of the online data. Although these firms have rigorous regulations against data abuse and illegal change, the fact that they have the authority to do so must be taken into account. Blockchains are decentralized, so if someone attempts to alter data on one blockchain, other blockchains will stay intact and the hashes of that particular blockchain will no not match, invalid that particular blockchain and protecting users’ data. Running a complete Chain server on all connected systems, especially low power or data storage devices, is challenging due to the extensive resource needs. In just this manuscript, we developed a modular approach blockchain based, as well as a centralized cloud architecture alternative that is a traditional user structure with a fundamental Blockchain at power or ability, as well as a solidity-written smart contract application on the Ethereum platform. By using Blockchain characteristics, we build this framework and show how it avoids data exploitation without requiring all IoT technologies to run a Chain node. From the standpoint of the user, it will show up to be the same as a typical web app, and from the standpoint of the developer, the reference implementation application will have equivalent software design to interactive web apps, enabling for an interesting experience for both centralized and decentralized methods while still upholding the trust of decentralization.
A lightweight CNN-based knowledge graph embedding model with channel attention for link prediction

&lt;abstract&gt;&lt;p&gt;Knowledge graph (KG) embedding is to embed the entities and relations of a KG into a low-dimensional continuous vector space while preserving the intrinsic semantic associations between entities and relations. One of the most important applications of knowledge graph embedding (KGE) is link prediction (LP), which aims to predict the missing fact triples in the KG. A promising approach to improving the performance of KGE for the task of LP is to increase the feature interactions between entities and relations so as to express richer semantics between them. Convolutional neural networks (CNNs) have thus become one of the most popular KGE models due to their strong expression and generalization abilities. To further enhance favorable features from increased feature interactions, we propose a lightweight CNN-based KGE model called IntSE in this paper. Specifically, IntSE not only increases the feature interactions between the components of entity and relationship embeddings with more efficient CNN components but also incorporates the channel attention mechanism that can adaptively recalibrate channel-wise feature responses by modeling the interdependencies between channels to enhance the useful features while suppressing the useless ones for improving its performance for LP. The experimental results on public datasets confirm that IntSE is superior to state-of-the-art CNN-based KGE models for link prediction in KGs.&lt;/p&gt;&lt;/abstract&gt;
GNN Link Prediction for Time-Triggered Systems

Research on graph neural networks (GNNs) has increasingly gained popularity recently. GNN is considered a powerful tool for solving machine learning tasks that require dealing with irregular topologies such as graph data. Meanwhile, solving the scheduling problems for time-triggered systems has been debated for a long time. Even though several algorithms were proposed to solve this problem, none considered exploiting GNN partially or wholly, solving time-triggered scheduling. In this work, we propose an approach for dynamic adaptation in time-triggered systems using GNN. We use GNNs to solve scheduling problems for time-triggered systems by transforming job allocation probelms to link prediction tasks. The preliminary results show that GNNs have a promising potential to perform job allocation problems in time-triggered systems.
Session 26 Overview: Display and User Interaction Technologies

Display and user interaction technologies continue to evolve and specialize for next-generation applications.The first paper describes an 8K display driver IC with a high-speed display interface receiver, followed by a virtual rotating gesture recognizable touch readout IC with haptic feedback on a circular bezel area.The final paper shows a self-capacitance touchscreen controller IC with high common-mode noise immunity.
Knowledge-enhanced Prompt-tuning for Stance Detection

Investigating public attitudes on social media is important in opinion mining systems. Stance detection aims to analyze the attitude of an opinionated text (e.g., favor, neutral, or against) toward a given target. Existing methods mainly address this problem from the perspective of fine-tuning. Recently, prompt-tuning has achieved success in natural language processing tasks. However, conducting prompt-tuning methods for stance detection in real-world remains a challenge for several reasons: (1) The text form of stance detection is usually short and informal, which makes it difficult to design label words for the verbalizer. (2) The tweet text may not explicitly give the attitude. Instead, users may use various hashtags or background knowledge to express stance-aware perspectives. In this article, we first propose a prompt-tuning-based framework that performs stance detection in a cloze question manner. Specifically, a knowledge-enhanced prompt-tuning framework (KEprompt) method is designed, which consists of an automatic verbalizer (AutoV) and background knowledge injection (BKI). Specifically, in AutoV, we introduce a semantic graph to build a better mapping from the predicted word of the pretrained language model and detection labels. In BKI, we first propose a topic model for learning hashtag representation and introduce ConceptGraph as the supplement of the target. At last, we present a challenging dataset for stance detection, where all stance categories are expressed in an implicit manner. Extensive experiments on a large real-world dataset demonstrate the superiority of KEprompt over state-of-the-art methods.
Literature Review on X-Ray Image Enhancement

In common, a raw X-ray image holds bad quality of the image if obtained directly coming out of a digital flat detector. It isn't satisfactory for treatment planning as well as diagnosis. For this image, enhancement is required. There are distinct ways through which image enhancement takes place. It is one of the preprocessing techniques helpful for moving further into treatment planning. Common methodologies like N-CLAHE which works with local enhancement and global enhancement can make an image look more extensive and more realistic. This technique consists of two main steps. Firstly, intensity correction of the raw image is encountered by the log-normalization function which adjusts the intensity contrast of the image dynamically. Secondly, the Contrast Limited Adaptive Histogram Equalization (CLAHE) method is used for enhancing small details,texture and local contrast of the images. For purpose of the survey, light is thrown on some of the wellknown developed models, the strategies used, and the shortcomings which were encountered in the development. This review will help researchers to further make advancements in this field
Local Binary Pattern Histogram Based Web Facial Authentication System

The objective of the project is to Detect and Recognize Human Facial Features via a Camera. The basic concept of this project is:- To convert the input image to HSV format to extract the binary image and additionally remove the noise from binary image using morphological operations and finally then contours are used to segment out the region of interest and further the region is analyzed to get the final result. While biometric data is generally considered one of the most reliable authentication methods, it also carries significant risk. That’s because if someone’s credit card details are hacked, that person has the option to freeze their credit and take steps to change the personal information that was breached. What do you do if you lose your digital ‘face’? Around the world, biometric information is being captured, stored, and analyzed in increasing quantities, often by organizations and governments, with a mixed record on cybersecurity. A question increasingly being asked is, how safe is the infrastructure that holds and processes all this data? As facial recognition software is still in its relative infancy, the laws governing this area are evolving (and sometimes non-existent).
Solving Subset Sum Problems using Quantum Inspired Optimization Algorithms with Applications in Auditing and Financial Data Analysis

Many applications in automated auditing and the analysis and consistency check of financial documents can be formulated in part as the subset sum problem: Given a set of numbers and a target sum, find the subset of numbers that sums up to the target. The problem is NP-hard and classical solving algorithms are therefore not practical to use in many real applications.We tackle the problem as a QUBO (quadratic unconstrained binary optimization) problem and show how gradient descent on Hopfield Networks reliably finds solutions for both artificial and real data. We outline how this algorithm can be applied by adiabatic quantum computers (quantum annealers) and specialized hardware (field programmable gate arrays) for digital annealing and run experiments on quantum annealing hardware.
Improving Fashion Attribute Classification Accuracy with Limited Labeled Data Using Transfer Learning

Classification of fashion item attributes, such as neckline types, graphic patterns, sleeve lengths, hem types/length, etc., plays an essential role in the fashion item recommender systems by means of providing precise categorization and recommendations. With recent advances in Artificial Intelligence, many online retailers have adopted deep learning models to detect and classify the attributes of their product images effectively. However, these deep learning models require large and diverse datasets with labels to achieve an acceptable accuracy, which takes time and effort to collect and annotate. Another challenge of deep learning is its limited generalization capability to a new type of sample data. We hypothesize that imbuing a neural classification model with a human’s cognitive capability, such as recognizing complex patterns by simple geometric shapes, can improve fashion attribute classification performance. This paper proposes a transfer learning-based image classification model that exploits synthetic examples of canonical shapes (e.g., circles, triangles, rectangles, etc.) from publicly available datasets and our examples created using simple graphic tools. We use these datasets of geometric shapes as a source domain to pre-train a model and fine-tune it with labeled images to solve the target problem (i.e., fashion attribute classification). Our proposed framework increases the accuracy of the neckline type and graphical pattern classifications of Resnet50 by 40.7% and 19.8%, from 49.4% and 54.8% to 90.1% and 74.6%, respectively.
Integrity Audit Method for Cloud Data Based on Blockchain

With the emergence of cloud storage providers (CSP), an increasing number of users tend to migrate their data to the cloud, but users lose direct control over the data. To ensure data security in the cloud, it is necessary to introduce a third-party auditor (TPA) to audit the integrity of cloud data, but the TPA is not fully trusted. Therefore, our approach proposes a blockchain-based integrity auditing method. First, we construct a new integrity audit tree, which can be used to quickly generate the verification tag. Second, we use blockchain to store the audit records of the TPA so that the audit process can be checked at any time. Finally, we design a verification algorithm for audit results that can identify dishonest behavior of the TPA and the CSP. Theoretical analysis shows that our scheme can detect the behavior of TPA delaying audits, falsifying audit results and colluding with the CSP. Moreover, simulation experiments show that our scheme outperforms existing blockchain-based auditing schemes in terms of time overhead.
System-level Implications of Serverless: Workload Characterizing and Performance Understanding

Serverless is a new online services paradigm of cloud computing for running extremely resilient and short-lived applications. Reducing the cost of running services is a critical goal of Serverless computing. However, most of the current state-of-the-art works optimize the Serverless performance via mitigating the cold-start overheads, the system-level resource overheads of typical real-world Serverless workloads is not fully explored. In this paper, we study the Serverless performance and overheads from the perspective of system-level resource analysis. We firstly present a Kmeans-based method to categorize Microsoft Azure workloads into four typical workloads and then introduce a Bayesian-based approach to fit these workloads into four time-series models respectively for online workload predicting. To investigate the system-level resource overheads, we replay the Azure workloads with the time series models in a Kubernetes cluster with 20 servers and profile application performance and system-level metrics. We highlight two observations via workload modeling and introduce three system-level implementations by analyzing the data profiled from the experiment cluster. In particular, we find that the task-balancing schedule could not guarantee fair resource usage among nodes, which leads to 3-4X resource utilization bias and interference among function instances.
Attention Mechanism indicating Item Novelty for Sequential Recommendation

Most sequential recommendation systems, including those that employ a variety of features and state-of-the-art network models, tend to favor items that are the most popular or of greatest relevance to the historic behavior of the user. Recommendations made under these conditions tend to be repetitive; i.e., many options that might be of interest to users are entirely disregarded. This paper presents a novel algorithm that assigns a novelty score to potential recommendation items. We also present an architecture by which to incorporate this functionality in existing recommendation systems. In experiments, the proposed NASM system outperformed state-of-the-art sequential recommender systems, thereby verifying that the inclusion of novelty score can indeed improve recommendation performance.
Classes versus Communities: Outlier Detection and Removal in Tabular Datasets via Social Network Analysis (ClaCO)

In this research, we introduce a model to detect inconsistent & anomalous samples in tabular labeled datasets which are used in machine learning classification tasks, frequently. Our model, abbreviated as the ClaCO (Classes vs. Communities: SNA for Outlier Detection), first converts tabular data with labels into an attributed and labeled undirected network graph. Following the enrichment of the graph, it analyses the edge structure of the individual egonets, in terms of the class and community belongings, by introducing a new SNA metric named as ‘the Consistency Score of a Node - CSoN’. Through an exhaustive analysis of the ego network of a node, CSoN tries to exhibit consistency of a node by examining the similarity of its immediate neighbors in terms of shared class and/or shared community belongings. To prove the efficiency of the proposed ClaCO, we employed it as a subsidiary method for detecting anomalous samples in the train part in the traditional ML classification task. With the help of this new consistency score, the least CSoN scored set of nodes flagged as outliers and removed from the training dataset, and remaining part fed into the ML model to see the effect on classification performance with the ‘whole’ dataset through competing outlier detection methods. We have shown this outlier detection model as an efficient method since it improves classification performance both on the whole dataset and reduced datasets with competing outlier detection methods, over several known both real-life and synthetic datasets.
Utilizing Unsupervised kNN on Customer Risk Assessment for Banking

Facing the rise of anti-money laundering and anticapital terrorism, financial institutions spend a lot of money and time every year to conduct Know Your Customer (KYC) verification for customers. Therefore, we devise an approach to carefully handle the tasks of data processing. Specifically, we choose to adopt instance-based learning in this work to consider each target customer one at a time. Also, we separate all the data features into profile and transaction ones. Based on the concept that customers of similar profiles tend to have similar transaction behaviors, we calculate the outlier score of each customer. Only when a suspicious customer having a high outlier score is determined, further actions of manual screening should be taken. Moreover, our approach is explainable as statistics of the corresponding profile and transaction features are reported. With these carefully designed means, our approach helps to significantly improve and speed up the whole KYC process.
Research and Application of Real-Time High-Resolution Video Matting Algorithm

Nowadays, many application tools provide the video matting function. The accuracy of the results of video matting is of great importance in practical applications. Existing video matting methods view video as multiple consecutive frames. The matting for video is also a continuous single-frame matting, and the synthesized video will have obvious flickering problems. We introduce a human video matting method that can address this problem well. We use the temporal information existing in the video to perform video matting. Our method uses a recurrent structure to exploit the temporal information in videos, resulting in improvements in both temporal coherence and matting quality. We train the segmentation and matting on the network at the same time, and take the results of semantic segmentation as input. The method does not require any auxiliary inputs, such as trimap or pre-captured background images, and can be widely applied to existing human matting applications. A large number of experimental results show that our model is superior to MODNet in terms of evaluation metrics, where the lift value is 2.73 on MAD(Mean Absolute Difference), 1.83 on MSE(Mean Squared Error), 0.46 on Grad(Spatial Gradient), 0.3 on Conn(Connectivity), and 0.49 on dtSSD. We also designed a simple, real-time, visual, user-friendly and understandable video matting system, which is convenient for users to achieve video matting.