Reviewerly/exHarmony huggingface dataset:
LABDRIVE, a Petabyte scalable, OAIS/ISO 16363 conformant, for scientific research organisations to preserve documents, processed data, and software

Vast amounts of scientific, cultural, social, business and government, and other, information is being created every day. There are billions of objects, in a multitude of formats, semantics and associated software. Much of this information is transitory but there is still an immense amount which should be preserved for the medium and long term, even indefinitely.Preservation requires that the information continues to be usable, not simply to be printed or displayed. Of course, the digital objects (the bits) must be preserved, as must the “metadata” which enables the bits to the understood which includes the software.Before LABDRIVE no system could adequately preserve such information, especially in such gigantic volume and variety.In this paper we describe the development of LABDRIVE and its ability to preserve and to scale up to tens or hundreds of Petabytes in a way which is conformant to the OAIS Reference Model and capable of being ISO 16363 certified.
Multi-criteria Rating and Review based Recommendation Model

These days, due to the advancement of information technology, recommendation system has become one of the key tools for e-commerce business. E-commerce platforms allow users to provide feedback in both written comments and numerical ratings. Recommendation systems are utilized to recommend users to new or unseen items based on these previously collected comments or ratings. In recent years, multi-aspect or multi-criteria based recommendation systems have been studied a lot by the recommendation research community. However, these research works are conducted either with reviews or ratings, not with both. In this project, we argue that integrating both textual reviews (with multiple aspects) and numerical multi-criteria ratings can further enhance the overall rating prediction accuracy. We propose a Multi-criteria Rating and Review based Recommendation model (MRRRec). We show that incorporating multi-criteria ratings into multi-aspect ratings from reviews has a great impact on performance. Our proposed model outperforms several state-of-the-art models such as ANR, DeepCoNN, and Deep Multi-criteria Recommendation System in terms of MSE, MAE, precision, recall, and F1. We show that our proposed model achieves an average of 19% and 23.0% lower MSE and MAE respectively and 7.0%, 1.0% and 3.8% higher precision, recall, and F1 score respectively. We further show that our model performs significantly better with Word2Vec word embedding than the GloVe word embedding method.
Adaptive Differential Privacy Mechanism in Collaborative Training of Internet of Vehicles

During the last few years, with the development of the Internet-of- Vehicles (IOV), the application of federated learning to the IOV has become possible. Federated learning modifies the manner in which data is transferred, replacing sensitive data with neural network models. However, federated learning still has the potential to leak personal privacy. In this paper, we propose an adaptive differential privacy mechanism appropriate to federated learning in the IOV. The mechanism considers the privacy of the data set participating in each batch of training, the privacy of the local data for the global data, and the subjective requirements of the user for privacy protection. Finally, we calculate an appropriate local privacy budget. The experimental results show that the method proposed in this paper can efficiently improve the performance of the network within the limit of differential privacy.
Fair Collective Classification in Networked Data

Collective classification utilizes network structure information via label propagation to improve prediction accuracy for node classification tasks. Because these models use information from previously labeled nodes which often contain historical bias, they may result in predictions that are biased w.r.t. the sensitive attributes of nodes such as race and gender. Throughout inference, this bias may even be amplified due to propagation especially for networks characterized by homophily. Despite past and ongoing research on fair classification, research to ensure fair collective classification s till remains unexplored. In this paper, we present a fair collective classification framework (denoted as FairCC) and formulate various heuristic methodologies, including node reweighting, threshold adjustment, and postprocessing, to achieve fair prediction. We also implement and test several naive methodologies for fair collective classification. Experiments on semi-synthetic datasets highlight the insufficiency of the naive methodologies and demonstrate the effectiveness of the proposed heuristics in significantly reducing prediction bias.
Is Unimodal Bias Always Bad for Visual Question Answering? A Medical Domain Study with Dynamic Attention

Medical visual question answering (Med-VQA) is to answer medical questions based on clinical images provided. This field is still in its infancy due to the complexity of the trio formed of questions, multimodal features and expert knowledge. In this paper, we tackle, a ’myth’ in the Natural Language Processing area - that unimodal bias is always considered undesirable in learning models. Additionally, we study the effect of integrating a novel dynamic attention mechanism into such models, inspired by a recent graph deep learning study.Unlike traditional attention, dynamic attention scores are conditioned on different query words in a question and thus enhance the representation learning ability of texts. We propose that some questions are answered more accurately with a reinforcement of question embedding after fusing multimodal features. Extensive experiments have been implemented on the VQA-RAD datasets and demonstrate that our proposed model, reinforCe unimOdal dynamiC Attention (COCA), outperforms the state-of-the-art methods overall and performs competitively at open-ended question answering.
Question-Attentive Review-Level Recommendation Explanation

Recommendation explanations help to improve their acceptance by end users. The form of explanation of interest here is presenting an existing review of the recommended item. The challenge is in selecting a suitable review, which is customarily addressed by assessing the relative importance of each review to the recommendation objective. Our focus is on improving review-level explanation by leveraging additional information in the form of questions and answers (QA). The proposed framework employs QA in an attention mechanism that aligns reviews to various QAs of an item and assesses their contribution jointly to the recommendation objective. The benefits are two-fold. For one, QA aids in selecting more useful reviews. For another, QA itself could accompany a well-aligned review in an expanded form of explanation. Experiments showcase the efficacies of our method as compared to baselines in identifying useful reviews and QAs, while maintaining parity in recommendation performance.
Semi-supervised Linear Local Tangent Space Alignment and Application to Pattern Recognition

Aiming at the fact that linear local tangent space alignment (LLTSA) is an unsupervised dimension reduction algorithm, semi-supervised linear local tangent space alignment (SS-LLTSA) dimension reduction algorithm is proposed in this paper. In SS-LLTSA, the distance between different points is adjusted by utilizing part class label information, thereby a new distance matrix is formed and the neighborhood is construct through this new distance matrix. The improved method realized the combination of data intrinsic manifold structure and class label information, and more discriminative low-dimension features can been obtained. The effectiveness of the proposed method was verified through several benchmark datasets.
Evaluating Hybrid Approximate Nearest Neighbor Indexing and Search (HANNIS) for High-dimensional Image Feature Search

In this paper, we evaluate the performance of a novel method for efficient and effective retrieval of similar high-dimensional image features. The proposed solution —- hybrid approximate nearest neighbor indexing and search (HANNIS) —-retrieves truly similar items in the database, even if the retrieval set is large. This approach enables us to load items that are truly close to the incoming query at retrieval time. HANNIS outperforms all state-of-the-art methods in terms of recall, precision, and F1 score at depths of up to 100 and offers the fastest index loading and consistent retrieval performance.
Design of Intelligent Management System for Manhole Cover

In recent years, there have been more and more traffic accidents caused by the lack or damage of manhole covers on urban roads. How to improve the management of manhole covers has become a hot topic in the society. As it is difficult to manage manhole covers by manual patrol and the cost is large, considering that the RFID technology is very mature, this paper takes advantage of RFID to resolve the problem of manhole covers' management. Specifically, each RFID electronic tag contains the manhole cover information for each cover, and transmit the cover information to the intelligent manhole cover management platform through the Internet of things. This method helps the related departments to carry out the inspection, maintenance of the manhole cover and underground pipeline.
Uncertainty Visualization for Graph Coarsening

The complexity of large real-world graphs makes their analyses prohibitively costly and their visualizations uninformative. The idea behind graph reduction is to reduce the size of a graph while preserving its properties of interest. To improve computational efficiency and to provide provable guarantees, many graph reduction techniques employ randomization. However, the uncertainty associated with randomized graph reduction and its subsequent interpretation has remained largely unexplored. In this paper, we present a framework to quantify and visualize the uncertainty associated with randomized graph reduction techniques. We focus on spectral clustering introduced by Ng, Jordan, and Weiss, a popular graph reduction technique that reduces the number of nodes by clustering the nodes of a graph into super-nodes. We introduce two uncertainty measures – local adjusted Rand indices and co-occurrences – to quantify and visualize uncertainty associated with an ensemble of reduced graphs. We demonstrate via experiments, that these measures complement each other in visualizing uncertainty and guiding the selection of optimal numbers of clusters.
Event Time Extraction from Japanese News Archives

This paper proposes an integrated method for extracting the time information of events from Japanese news archives. We first utilize a new pattern-based method named TRE/ERT combined with a neural-based model to extract all temporal expressions possibly related with an event. Then, we apply a simple but efficient clustering and narrowing process to summarize these temporal expressions into a small time frame for events lasting shorter than a day, or time frames for the beginning and the end days of the events for events spanning multiple days. We conducted two experiments where the results show that when working with one-day events, our system has a precision high up to 57% and the rate that the actual date of the event falls in our extracted time frame reaches 100% as long as the event name is found in the archive. The results also show that our system works with multiple-day events, but needs further improvements to get better results.
Matching Attacks on Non-deterministic Algorithms for Cell Suppression Problem for Tabular Data

The objective of the cell suppression problem (CSP) is to protect sensitive cell values in tabular data under the presence of linear relations concerning marginal sums. Previous algorithms for solving CSPs ensure that every sensitive cell has enough uncertainty on its values based on the interval width of all possible values. However, every deterministic CSP algorithm is vulnerable to an attack scheme that narrows down the width of sensitive cell values by matching the suppression pattern of an original table with that of each candidate table with the same CSP algorithm. Although to make a CSP algorithm non-deterministic is a promising approach against the matching attack, we find that there still exists an expanded matching attack to the algorithm.
Federated Learning for Autoencoder-based Condition Monitoring in the Industrial Internet of Things

Enabled by the increasing availability of sensor data monitored from production machinery, condition monitoring and predictive maintenance methods are key pillars for an efficient and robust manufacturing production cycle in the Industrial Internet of Things. The employment of machine learning models to detect and predict deteriorating behavior by analyzing a variety of data collected across several industrial environments shows promising results in recent works, yet also often requires transferring the sensor data to centralized servers located in the cloud. Moreover, although collaborating and sharing knowledge between industry sites yields large benefits, especially in the area of condition monitoring, it is often prohibited due to data privacy issues. To tackle this situation, we propose an Autoencoder-based Federated Learning method utilizing vibration sensor data from rotating machines, that allows for a distributed training on edge devices, located on-premise and close to the monitored machines. Preserving data privacy and at the same time exonerating possibly unreliable network connections of remote sites, our approach enables knowledge transfer across organizational boundaries, without sharing the monitored data. We conducted an evaluation utilizing two real-world datasets as well as multiple testbeds and the results indicate that our method enables a competitive performance compared to previous results, while significantly reducing the resource and network utilization.
Design and Implementation of House Rental Management System Based on SSM Framework

Today's widespread use of the Internet has brought a lot of convenience to life. Therefore, by integrating the housing lease management with the closely related network in life, and using mature JSP technology to develop and implement the housing lease management system, the diversification and informatization of the housing lease management system can play a significant role in further improving the development of the leasing industry and enriching the housing lease management experience. The system chooses a feasible development scheme according to the actual situation, and realizes all the functions of the system with the help of JSP, SSM framework, MySQL database and other cutting-edge technologies. Next, it tests the system, including whether the system has loopholes, whether the user permission functions are abnormal, and so on, so as to improve the system and finally make the system meet the relevant development standards.
CYBERCRIME MONEY LAUNDERING CASES AND DIGITAL EVIDENCE

The widespread dependence on digital systems and increased value of digital commerce in the metaverse boosted cyber vulnerability. The cybercrime will be more profitable than the global trade of all major illegal drugs combined, while Cybersecurity Ventures expects global cybercrime costs to grow by 15 percent annually by 2025. Cybercrime changed traditional money laundering methods which is difficult to detect since it could be committed from anywhere in the world. The threat posed by cybercrime money laundering methodologies has been aggravated by the Covid-19 pandemic.&#x0D; To investigate cyber laundering the e-evidence are crucial, which is confirmed by the EU Commission estimate that 85 percent of criminal investigations require electronic evidence. Additional challenge for law enforcement authorities presents the fact that the organised cybercrime is joining forces and their likelihood of detection and prosecution is estimated to be 0.05 percent in the USA. Furthermore, the digital evidence is often held by service providers as private companies based in another country, which causes many obstacles to access to those data by investigative and law enforcement authorities. &#x0D; The paper identifies impact of Covid-19 on cybercrime and increased risks of cyber laundering. In relation to investigation and prosecution of cyber money laundering, the paper analyzes challenges for investigative authorities to gather data and evidence in cyber money laundering cases and efforts of EU and USA authorities to facilitate access to digital evidence and relevant data stored by service providers. The paper refers to possible shortcomings of proposed instruments and need for efficient response and adaptation to changes in the cybercrime.
An Enhanced MRC Framework with Triggers as Explanations for Named Entity Recognition

An important challenge in named entity recognition (NER) is how to distinguish the correct meanings of polysemy entities in specific contexts. With the emergence of a large number of emerging entities and various new surface forms of entities, this challenge becomes more and more prominent. Another important challenge is that collecting annotations of named entities is usually expensive and laborious, especially in technical domains such as biomedical publications, financial reports, academic literature, etc. The key to solving above two challenges is that the model should be able to recognize entities utilizing information beyond entities. Entity trigger refers to a group of words that can act as explanation to recognize the positions and categories of entities. In view of the above two challenges, we propose an enhanced machine reading comprehension (MRC) framework with Triggers as Explanations for NER (i.e. MRC-TE). Specifically, our framework combines the information contained in entities and triggers into the same model through a multi-task learning method. Experimental results on two dataset CONLL2003 and BC5CDR demonstrates the effectiveness of our framework.
Saisiyat Is Where It Is At! Insights Into Backdoors And Debiasing Of Cross Lingual Transformers For Named Entity Recognition

Deep learning and, in particular, Transformer-based models are revolutionizing natural language processing (NLP). As a result, NLP models can now be pre-trained and fine-tuned by anyone with sufficient resources, and subsequently shared with the world at large. This is an unprecedented approach that helps level the AI playing field and improves productivity. However, this new AI sharing approach presents novel and largely unaddressed challenges involving bias and backdoors. This study has four objectives related to better understanding these issues and their causes: 1) determine if there is bias in a cross lingual (XL) Transformer model such as RoBERTa XLM (XLM-R) for named entity recognition (NER), 2) provide a predictive explainabilty (interpretability) framework that addresses the reasons why the XL model may or may not have bias, 3) test this explainability (interpretability) framework on different scenarios to evaluate its predictive capabilities, and 4) consider implications of any insights and future research directions. Based on experimental results, we find that XLM- R is not significantly biased in the NER task. The results suggest that name related subwords heavily influence NER performance and that cross-lingual transfer learning is reasonably effective in Transformer models. Finally, we discuss a general framework for debiasing or backdooring of Transformer models based on subword embedding representations. In general, by knowing the values of the embeddings of subwords in a Transformer model, one can select triggers (subwords) that will impact the overall performance of the model task either positively or negatively. As such, a broad-use backdoor scheme was developed and tested that significantly affects the recall in both a NER task and a masked-based sentiment analysis task. The results are intriguing and promising.
Parallel mlp architecture for Polyp Segmentation

Colonoscopy is a common medical technique used to diagnose colon polyps which are highly related to colon cancer. In clinical practice, the accurate detection and location of polyps is very important, because it can provide valuable information for doctors' follow-up diagnosis. However, there are many problems in polyp segmentation. Firstly, the size, shape and color of polyps are different. Secondly, there is no clear boundary between polyps and the surrounding tissue. For the above reasons, it is very easy to miss detection in clinical diagnosis. To solve these problems, we propose a new parallel mlp network to improve the accuracy of polyp segmentation. Specifically, the proposed network uses a parallel MLP decoder module to capture more global information and local information. Then, an adaptive feature aggregate module is used to fusion different level features, which can capture the differences between different levels of features. In addition, in order to get more boundary information, we use the RA module. We have evaluated our model on five public datasets, and we have better segmentation results than other latest methods.
Federated Learning Task Scheduling Algorithm For Trusted Sharing Of Power Data

In order to realize the data sharing of isolated power system and support the intelligent decision-making of power grid, a data sharing scheme based on blockchain and federated learning has emerged. However, the lack of computing attributes in blockchain makes it difficult to guarantee the trust of federated learning process. And due to the lack of federated learning task scheduling algorithm, data sharing based on federated learning in power grid is inefficient. To solve above problems, this paper proposes a state channel-based federated learning architecture which establishes a soft computing sandbox through the state channel technology to realize the supervision of the federated learning training process. To match federated learning tasks with heterogeneous device nodes in power grid, we propose a task scheduling algorithm. After testing, it is found that the algorithm proposed in this paper has outstanding performance in many indicators. The average task response delay is shortened by 38.96 %. The response latency of tasks deployed by high priority users is reduced by at least 27.52 %; Under high load conditions, the system blocking time can be delayed by 10.47%.
A Research on Application Server-Oriented Digital Forensics in New Types of Network Crime

In the process of application server investigation and evidence collection of new types of network crime, this paper summarizes the analysis of common server clusters, virtual private network servers and IP-based voice transmission servers in public security investigation and forensic identification. Based on the case analysis and the actual process of case investigation, a new type of network crime application server inspection method is proposed, which can be used to analyze new types of application servers in the process of law enforcement inspection and digital forensics practice.
Architecture design of industrial Internet identity analysis secondary node platform in electric power industry

Industry Internet analytical system identification is an important part of the Internet network architecture, this article put forward the electric power industry industrial Internet identity resolution secondary node platform design framework, this paper expounds the industrial marking the overall architecture of the Internet, application architecture, security protection, gives the logo registration, identification, analysis, business management, data management and implementation of security, Finally, the deployment method of industrial Internet logo is described.