Reviewerly/exHarmony huggingface dataset:
Improved integral cryptanalysis of SPNbox in digital rights management systems

As an access control technology of digital material, digital rights management systems have a profound effect on the copyright protection of digital content. To address the threat of key exposure, applying white-box ciphers is effective to provide a security guarantee for digital rights management systems. SPNbox, proposed at Asiacrypt’16 is such a white-box cipher that fulfils comprehensive resistance against key exposure for digital rights management systems, including black-box security on the server-side and white-box security on the client-side. So far, the previous integral cryptanalysis of SPNbox employs a general 2-round distinguisher without considering the details of SPNbox. The properties of SPNbox are carefully explored and a novel 2-round integral distinguisher is introduced. On this basis, we propose new competitive 3-round key recovery attacks with lower complexities. Particularly, the improved attack on 3-round SPNbox-32 only requires 232 chosen plaintexts, whereas the current best attack necessitates 262 chosen plaintexts. In addition, integral attacks on 4- and 5-round SPNbox-8 are presented for the first time. Thus, the security margin of SPNbox-8 is narrowed by two rounds. These results indicate that the capability of SPNbox resisting integral cryptanalysis is inferior to the designers' claim.
Model Based Demand Order Estimation by Using Optimal Architecture Artificial Neural Network with Metaheuristic Optimizations

With the increase of e-commerce volumes in recent years, it is useful to estimate daily demand order numbers in order to improve the demand forecasts, production-distribution planning and sales services. In this manner, data-driven modeling and machine learning tools have been preferred to enhance demand order predictions, timely delivery, incomes and customer satisfaction in electronic trading because real-time data collection is possible in e-commerce platforms. Artificial Neural Networks (ANNs) are widely used for data-driven modeling and prediction problems. Since affecting the approximation performance of neural network function, the modeling performance of ANNs strongly depends on the architecture of neural networks, and architectural optimization of ANN models has become a main topic in the neuroevolution field. This study presents an architecture optimization method that implements Particle Swarm Optimization (PSO) and Differential Evolution (DE) algorithms to optimize ANN model architecture for the estimation of total demand order numbers from the sparse demand order data. In this approach, PSO and DE algorithm only optimizes neural model architecture according to an effective network search policy and the training of ANN models is carried out by using backpropagation algorithm. This neural architecture model optimization approach considers generalization of data, reducing neuron and training epoch numbers and it can yield an optimal architecture data-driven neural model for estimation of the daily total orders. In the experimental study, optimal architecture ANN models are obtained according to the daily order forecasting dataset.
Leveraging Wikipedia Knowledge for Distant Supervision in Medical Concept Normalization

The majority of recent research has approached the Medical Concept Normalization (MCN) task as supervised text classification. However, combining all of the currently available training datasets for this task (CADEC, PsyTAR, COMETA) only covers a small fraction of the concepts contained in the Systematized Nomenclature of Medical-Clinical Terms (SNOMED-CT). In this work, we propose a distant supervision approach to broaden the training data coverage of the SNOMED-CT concepts by tapping into Wikipedia as a source of informal medical phrases. Based on our observations, components of Wikipedia articles (article summaries, Wikipedia's redirect pages, wikilinks data) contain informal medical terms that can be generalized to those used in social media posts. We extract the article summaries, Wikipedia's redirect pages, and wikilinks data from the Wikipedia articles relating to medical information. We pair this data with corresponding SNOMED-CT concepts. Our distant supervision approach was able to double the concept coverage from the public MCN data sets. Our experiments show that the proposed distant supervision data approach improved the model performance on the three publicly available MCN datasets.
Query Expansion, Argument Mining and Document Scoring for an Efficient Question Answering System

In the current world, individuals are faced with decision making problems and opinion formation processes on a daily basis. Nevertheless, answering a comparative question by retrieving documents based only on traditional measures (such as TF-IDF and BM25) does not always satisfy the need. In this paper, we propose a multi-layer architecture to answer comparative questions based on arguments. Our approach consists of a pipeline of query expansion, argument mining model, and sorting of the documents by a combination of different ranking criteria. Given the crucial role of the argument mining step, we examined two models: DistilBERT and an ensemble learning approach using stacking of SVM and DistilBERT. We compare the results of both models using two argumentation corpora on the level of argument identification task, and further using the dataset of CLEF 2021 Touché Lab shared task 2 on the level of answering comparative questions.
Using Entities in Knowledge Graph Hierarchies to Classify Sensitive Information

Text classification has been shown to be effective for assisting human reviewers to identify sensitive information when reviewing documents to release to the public. However, automatically classifying sensitive information is difficult, since sensitivity is often due to contextual knowledge that must be inferred from the text. For example, the mention of a specific named entity is unlikely to provide enough context to automatically know if the information is sensitive. However, knowing the conceptual role of the entity, e.g. if the entity is a politician or a terrorist, can provide useful additional contextual information. Human sensitivity reviewers use their prior knowledge of such contextual information when making sensitivity judgements. However, statistical or contextualized classifiers cannot easily resolve these cases from the text alone. In this paper, we propose a feature extraction method that models entities in a hierarchical structure, based on the underlying structure of Wikipedia, to generate a more informative representation of entities and their roles. Our experiments, on a test collection containing real-world sensitivities, show that our proposed approach results in a significant improvement in sensitivity classification performance (2.2% BAC, McNemar's Test, p < 0.05) compared to a text based sensitivity classifier.
Analysing Moral Beliefs for Detecting Hate Speech Spreaders on Twitter

The Hate and Morality (HaMor) submission for the Profiling Hate Speech Spreaders on Twitter task at PAN 2021 ranked as the 19th position - over 67 participating teams - according to the averaged accuracy value of $$73\%$$ over the two languages - English ( $$62\%$$ ) and Spanish ( $$84\%$$ ). The method proposed four types of features for inferring users attitudes just from the text in their messages: HS detection, users morality, named entities, and communicative behaviour. In this paper, since the test set is now available, we were able to analyse false negative and false positive prediction with the aim of shed more light on the hate speech spreading phenomena. Furthermore, we fine-tuned the features based on users morality and named entities showing that semantic resources could help in facing Hate Speech Spreaders detection on Twitter.
Rhythmic and Psycholinguistic Features for Authorship Tasks in the Spanish Parliament: Evaluation and Analysis

Among the many tasks of the authorship field, Authorship Identification aims at uncovering the author of a document, while Author Profiling focuses on the analysis of personal characteristics of the author(s), such as gender, age, etc. Methods devised for such tasks typically focus on the style of the writing, and are expected not to make inferences grounded on the topics that certain authors tend to write about. In this paper, we present a series of experiments evaluating the use of topic-agnostic feature sets for Authorship Identification and Author Profiling tasks in Spanish political language. In particular, we propose to employ features based on rhythmic and psycholinguistic patterns, obtained via different approaches of text masking that we use to actively mask the underlying topic. We feed these feature sets to a SVM learner, and show that they lead to results that are comparable to those obtained by a BETO transformer, when the latter is trained on the original text, i.e., potentially learning from topical information. Moreover, we further investigate the results for the different authors, showing that variations in performance are partially explainable in terms of the authors' political affiliation and communication style.
ImageCLEF 2021 Best of Labs: The Curious Case of Caption Generation for Medical Images

As part of Best of Labs, we have been invited to conduct further investigation on the ImageCLEFmed Caption task of 2021. The task required participants to automatically compose coherent captions for a set of medical images. The most popular means of doing this is with an encoder-to-decoder model. In this work, we investigate a set of choices with regards to aspects of an encoder-to-decoder model. Such choices include what pre-training data should be used, what architecture should be used for the encoder, whether a natural language understanding (e.g., BERT) or generation (e.g., GPT2) checkpoint should be used to initialise the parameters of the decoder, and what formatting should be applied to the ground truth captions during training. For each of these choices, we first made assumptions about what should be used for each choice and why. Our empirical evaluation then either proved or disproved these assumptions—with the aim to inform others in the field. Our most important finding was that the formatting applied to the ground truth captions of the training set had the greatest impact on the scores of the task's official metric. In addition, we discuss a number of inconsistencies in the results that others may experience when developing a medical image captioning system.
Data-Centric and Model-Centric Approaches for Biomedical Question Answering

Biomedical question answering (BioQA) is the process of automated information extraction from the biomedical literature, and as the number of accessible biomedical papers is increasing rapidly, BioQA is attracting more attention. In order to improve the performance of BioQA systems, we designed strategies for the sub-tasks of BioQA and assessed their effectiveness using the BioASQ dataset. We designed data-centric and model-centric strategies based on the potential for improvement for each sub-task. For example, model design for the factoid-type questions has been explored intensely but the potential of increased label consistency has not been investigated (data-centric approach). On the other hand, for list-type questions, we apply the sequence tagging model as it is more natural for the multi-answer (i.e. multi-label) task (model-centric approach). Our experimental results suggest two main points: scarce resources like BioQA datasets can be benefited from data-centric approaches with relatively little effort; and a model design reflecting data characteristics can improve the performance of the system. The scope of this paper is majorly focused on applications of our strategies in the BioASQ 8b dataset and our participating systems in the 9th BioASQ challenges. Our submissions achieve competitive results with top or near top performance in the 9th challenge (Task b - Phase B).
The biggest challenges of manually managing PKI certificates

Business processes have always been built on trust, and as digital transformation moves more devices and people online, the need for digital trust is rapidly increasing. Today, being able to trust the identity of endpoints, the people using them, and their data and documents, is fundamental to society.
BaPipe: Balanced Pipeline Parallelism for DNN Training

The size of deep neural networks (DNNs) grows rapidly as the complexity of the machine learning algorithm increases. Distributed deep learning based on model parallelism has been widely used to satisfy the requirements of DNN training related to computation and memory. In this paper, we propose a training framework for pipeline parallelism called BaPipe (Balanced Pipeline) that can automatically explore methods to schedule pipeline parallelism and balanced partition strategies for DNN training on heterogeneous accelerator clusters. In BaPipe, each accelerator calculates the forward and backward propagation for the assigned partition of networks to implement an intra-batch pipeline parallelism strategy. By considering the parameters of DNN models as well as the computation, memory, and communication resources of each accelerator, BaPipe automatically selects the most suitable method of pipeline scheduling from among multiple proposed scheduling modes. It also uses a novel strategy to automatically investigate load balancing in the context of inter-layer partition, intra-layer partition, and coarse-grained partition. We trained such DNNs as VGG-16, ResNet-50, and Google’s Neural Machine Translation (GNMT) on GPU clusters, and simulated the training-related performance of FPGA clusters. Compared with the state-of-the-art frameworks for data parallelism (DP) and pipeline parallelism, BaPipe provides a speedup of [Formula: see text] and [Formula: see text] of memory reduction on various homogeneous and heterogeneous platforms.
Imprecise Learning from Misclassified and Incomplete Categorical Data with Unknown Error Structure

This article addresses the problem of learning from potentially misclassified and incomplete categorical data when the error structure is unknown and no prior information about the distribution of the data is available. We propose to use the knowledge gained from the well-known practice of double sampling to accomplish two goals; First, we estimate the unknown error structure. Then, under the framework of imprecise probability, we derive a prior Dirichlet distribution that expresses a state of quasi-near-ignorance about the data. Updating this prior using sample data leads to a quasi-near-ignorance posterior distribution that produces non-trivial estimates.
Using home monitoring technology to examine changes in routines after a fall

Home monitoring systems can support older people to age in place, and data from these systems can provide insight into an older person's activities at home. The aim of this study was to examine changes in sensor variables before and after a fall. Participants were recruited from clients of a home monitoring company. Fall history was obtained by interview. Sensor data from the participants' homes were used to calculate total activity, transitions, active time, wake-up time and night-time bathroom visits. Linear regression was used to examine changes in the three months before and after the fall. Five participants reported exact fall dates with adequate sensor data surrounding the fall. Individual analyses showed that four out of the five participants had a significant change in slope of at least one sensor variable after the fall. Digital biomarkers related to home activity can change after a fall, but the specific digital biomarker that best captures the change may vary by person. Future work in a larger sample should explore whether sensor data can inform recovery to usual routines after a fall.
Kebijakan Open Access Repositori Institusi di Perpustakaan Perguruan Tinggi: Kajian Best Practice Studi Literature

The open-access policy of the repository institutions becomes a guideline.The spirit of open access must be support by information, preservation and copyright.This study discusses transparency and open access to repositories.This research uses the literature study method using qualitative.The policy of open access has not yet been had by most formal university libraries.
Instance elimination strategy for non-convex multiple-instance support vector machine

Multiple-instance support vector machine (SVM) is a classic and effective algorithm to address the classification of bags. However, training multiple-instance SVM is expensive. To deal with this issue, the safe screening rule is introduced to solve optimization problem, which can significantly mitigate the storage burden by identifying inactive instances. Specifically, an instance elimination strategy is designed for the inner solver of a signal concave–convex procedure (CCCP). Then, between the iterations of CCCP, due to the label of instance may change, to cope with this difficulty, a dual screening method with variational inequalities (DVI) is used to identify the part of inactive instances. To further speed up the solution efficiency, a smart dual coordinate descent method (SDCDM) is introduced for the inner solver, which skips over many updates that result in no change to the current iteration. Experiments on 35 benchmark datasets significantly exhibit the outstanding performance of the proposed strategy.
HRET: Heterogeneous Information Network for Recommendation in testing and inspection

With the help of the sufficiency of heterogeneous information, heterogeneous information network(HIN) has been treated as the most advanced method to extract complex semantic data in recommender system. But it is still an empty field for some traditional industries such as testing and inspection industry, which mainly adopt the similarity-based collaborative filtering(CF) method. But it will make a huge waste of the rich heterogeneous auxiliary data, which could be fully utilized by HIN based method. Especially for testing and inspection industry, the profession will help the model to find a more accurate match between the user and business. In this work, we succeeded in building up a HIN embedding approach for recommendation, and design a unique network structure for testing and inspection industry, which both utilize the rich underlying information and properly solve the specialty problem in a professional industry, different from normal recommender scenario. An intensive experiment on the real world data set shows the performance of the model.
A DTP and SoLiD based Service for Multi-Source Semantically-Heterogeneous Personal Data Management

Privacy preservation is drawing growing concern. Due to personal data of massive users is centralizedly owned/managed by service providers at present, it is very easy to lead to privacy disclosure and personal data abuse, therefore many countries and regions put forward privacy protection laws and regulations and researchers also propose some de-centralized personal data management systems. Nevertheless, the implementation of decentralized data management model means a radical change in current service development and service model. For service providers, it is too difficult and costly to realize these personal data management systems, and there are also no additional economic benefits. These personal data management systems are too ideal to popularize from a business model perspective, so the development of decentralized models proceeds slowly. In response to this situation, we propose a new personal data management service which realizes the transition stage between reality and ideal where users&#x2019; data can exist both in users&#x2019; personal data cloud and service providers. To address the challenges of data collection and data semantic uniformity caused by personal data cloud at this transition stage, we propose a personal data management architecture based on SoLiD (Social Linked Data) and DTP (Data Transfer Project). The proposed architecture could transfer users&#x2019; data from service providers to personal data cloud and fuse heterogeneous data through data fusion technology so as to provide users with the ability to manage their own data and a unified view of data. The feasibility and efficiency of the proposed service are practically proved through conducting a case study.
Security Evaluation of Smart Contracts based on Code and Transaction - A Survey

As a computer program running on top of blockchain, smart contract not only proliferates the diversity of applications but also brings a myriad of security issues that lead to huge financial losses. As a result, security evaluation of smart contracts, such as vulnerability identification and attack detection, has received extensive attention in recent years. Given that various types of approaches have been proposed for smart contract security analysis, a systematization of knowledge for this domain is needed. To this end, in this paper, we systematically review the related literature in recent years and describe the mainstream approaches to the security evaluation of smart contracts. Specifically, we classify state-of-the-art analysis techniques for smart contract analysis into two categories, namely, code-based approaches and transaction-based approaches. Further, we elaborate on the key techniques adopted by these works respectively. We highlight and summarize the key challenges in future research for smart contract security analysis. Our research provides a more in-depth understanding of the state-of-the-art works for securing smart contracts, which may shed light on future research in this area.
Probing the Mystery of Cryptocurrency Exchange: The Case Study Based on Mt.Gox

The birth of Bitcoin has created the cryptocurrency exchange, the average daily trading volume of cryptocurrency exchanges is now more than 100 billion. Cryptocurrency exchanges serve as a place for users to exchange cryptocurrencies, acting as a bridge between the blockchain ecosystem and the real world. Based on the transaction mechanism, cryptocurrency exchanges can be divided into centralized exchanges(CEXs) and decentralized exchanges(DEXs). CEXs still hold the dominant position, and we focus on Mt.Gox with the leaked dataset. By preprocessing the data, a usable internal dataset was obtained. To better study CEX, we further provide a comprehensive analysis of Mt.Gox based on three types of records and conclude its characteristics. Finally, we propose a matching method for on-chain and off-chain data, which restores the complete transaction path of the transaction account and some strange transaction phenomena are discovered. The results of this experiment showed that our algorithm can find addresses on blockchain and de-anonymize to a certain extent.