Reviewerly/exHarmony huggingface dataset:
Prix-LM: Pretraining for Multilingual Knowledge Base Construction

Knowledge bases (KBs) contain plenty of structured world and commonsense knowledge.As such, they often complement distributional text-based information and facilitate various downstream tasks.Since their manual construction is resource-and timeintensive, recent efforts have tried leveraging large pretrained language models (PLMs) to generate additional monolingual knowledge facts for KBs.However, such methods have not been attempted for building and enriching multilingual KBs.Besides wider application, such multilingual KBs can provide richer combined knowledge than monolingual (e.g., English) KBs.Knowledge expressed in different languages may be complementary and unequally distributed: this implies that the knowledge available in high-resource languages can be transferred to low-resource ones.To achieve this, it is crucial to represent multilingual knowledge in a shared/unified space.To this end, we propose a unified representation model, Prix-LM , for multilingual KB construction and completion.We leverage two types of knowledge, monolingual triples and cross-lingual links, extracted from existing multilingual KBs, and tune a multilingual language encoder XLM-R via a causal language modeling objective.Prix-LM integrates useful multilingual and KB-based factual knowledge into a single model.Experiments on standard entity-related tasks, such as link prediction in multiple languages, cross-lingual entity linking and bilingual lexicon induction, demonstrate its effectiveness, with gains reported over strong task-specialised baselines.
Copernicus data to boost raw material source management: Illustrations from the RawMatCop programme

Earth Observation (EO) data can become an essential tool in the transformation of a raw materials sector that aims to reconfigure its model of operation. The high demand for the mineral resources necessary for the transition to a carbon neutral and circular economy conflicts with the increasing difficulties of finding new deposits. As the sector heads towards embracing circularity and reducing the environmental impacts, a clear focus has been set on developing appropriate tools to boost the efficiency of mineral resource management, both technologically and economically. In this scenario, the Sentinel satellites of the European Copernicus program come into play. Despite being satellites considered medium resolution, they provide great temporal and spatial coverage in a continuous record, which makes them tools with great potential for the raw materials sector. However, the lack of applications in the raw materials sector suggests that these technological advances have remained underrated by sectoral actors. The RawMatCop program was designed to bridge this gap. This program, co-funded by the European Commission and EIT RawMaterials, aims to develop applications and promote the use of Copernicus data in the raw materials sector to contribute to a safe and sustainable supply of mineral resources. The presented applications can be grouped into three categories covering the whole mining cycle from exploration to exploitation and post-mining. Two of the presented case studies cover the study of primary sources including exploration of Iron Oxide Copper Gold mineralisations to identify high-potential mining areas and mapping of informal gold mining and its environmental impacts. Another project focused on secondary sources tackled data applications for grade mapping and sample optimisation in mining residues. And the forth project focused on monitoring ground stability related to mining activity. The results demonstrate the high cost-effectiveness of Sentinel 1 and 2 in extending ground-based measurements to larger areas, especially when these are hard-to-reach areas. Finally, the presented projects examine the industrial and social impacts of technological innovations, as well as contribute to the achievement of prominent European Union policy objectives and the United Nations Sustainable Development Goals.
Understanding the Overheads of Hardware and Language-Based IPC Mechanisms

A recent surge of security attacks has triggered a renewed interest in hardware support for isolation. Extended page table switching with VMFUNC, memory protection keys (MPK), and memory tagging extensions (MTE) are just a few of the hardware isolation mechanisms that promise support for low-overhead isolation in recent CPUs. Along with the restored interest in lightweight hardware isolation mechanisms, safe programming languages like Rust has made a leap towards practical, zero-overhead safety implemented without garbage collection.
Longitudinal Brain MR Image Modeling Using Personalized Memory for Alzheimer’s Disease

Longitudinal analysis of a disease is an important issue to understand its progression and design prognosis and early diagnostic tools. From the longitudinal images where data is collected from multiple time points, both the spatial structural information and the longitudinal variations are captured. The temporal dynamics are more informative than static observations of the symptoms, particularly for neurodegenerative diseases such as Alzheimer’s disease, whose progression spans over the years with early subtle changes. In this paper, we propose a new generative framework to predict the lesion progression over time. Our method first encodes images into the structural and longitudinal state vectors, where interpolation or extrapolation of feature vectors in the time axis can be performed for the manipulation of these feature vectors. These processed feature vectors can be decoded into image space to predict the image at the time point which we are interested. During the training, we force the model to encode longitudinal changes into longitudinal state features and capture the structural information in a separate vector. Moreover, we introduce a personalized memory for the online update scheme, which adapts the model to the target subject, which helps the model preserve fine details of brain image structures in each subject. Experimental results on the public longitudinal brain magnetic resonance imaging dataset show the effectiveness of the proposed method.
Quantum teleportation of a tripartite entangled coherent state

The present work proposes a scheme to teleport a tripartite coherent state using an unparalleled four-component state as a quantum channel. The scheme involves linear optical devices like beam splitters and phase shifters. It is shown that, even by taking an uncompromising quantum model, almost a complete teleportation can be achieved with an impressive number of photons. It is also shown that the teleportation fails only if zero photons are found in all the three output modes or zero in two output modes and a nonzero even/odd photon in one mode. However, the probability of getting these output modes is almost negligible.
Social group detection based on multi-level consistent behaviour characteristics

Groups are the basic elements which depict the complex social dynamics in crowded scenarios. Most researchers tend to partition people based on trajectories and lack sufficient utilization of group properties. To address this issue, we design a new group detection framework based on multi-level group descriptors. Both crowd trajectories and semantic information are utilized to profile group properties. A unified graph clustering model is given to model multi-feature consistency and inconsistency in a unified objective function. The public pedestrian datasets show that the proposed framework can achieve superior detection performance and density robustness when compared with existing methods. In addition, to verify the capacity of our method at group detection in other motion scenes, we construct a new non-vehicle dataset in real scenes. Extensive experiments on our non-vehicle dataset are performed, and the results demonstrate that our approach is effective and reliable for social group detection in various crowd scenarios.
Intelligent Shopping Cart Design Based on the Multi-Sensor Information Fusion Technology and Vision Servo Technology

In order to promote the intelligent transformation of service-oriented robot used in supermarkets, an intelligent shopping cart has been designed in this paper to realize the integration of auto following, shopping and payment based on the multi-sensor information fusion technology and vision servo technology. Firstly, a distributed sensor fusion structure was designed including photoelectric, ultrasonic sensors and monocular camera. Then, all the collected data were fused during auto following process of the intelligent shopping cart, and the commodity information could be identified to form the billing information by which the customer could complete the payment by scanning the payment QR code and the corresponding records also can be viewed by logging in the Wechat applet.
What Did You Add to My Additive Manufacturing Data?: Steganographic Attacks on 3D Printing Files

Additive Manufacturing (AM) adoption is increasing in home and industrial settings, but information security for this technology is still immature. Thus far, three security threat categories have been identified: technical data theft, sabotage, and illegal part manufacturing. In this paper, we expand to a new threat category: misuse of digital design files as a subliminal communication channel. We identify and explore attacks by which arbitrary information can be embedded steganographically in the most common digital design file format, the STL, without distorting the printed object. Because the technique will not change the manufactured object’s geometry, it is likely to remain unnoticed and can be exploited for data transfer. Further, even with knowledge of our methods, defenders cannot distinguish between actual data transfer and random manipulation of the files. This is the first info-hiding attack on this system, conducted despite the fact that random changes may spoil the physical artifact and result in detection.
BiblIA - a General Model for Medieval Hebrew Manuscripts and an Open Annotated Dataset

The paper presents Open Source generalized models for recognition and page segmentation, intended for use on the eScriptorium platform or kraken OCR engine, of Medieval Hebrew manuscripts in square script that arrive at a character accuracy of more than 97% on the validation set and a dataset consisting of 202 pages from almost 100 different literary manuscripts with layout annotation (regions and lines) as well as transcription. The manuscript pages are sourced from material in different script types, geographical, and chronological origins. In addition we describe the bootstrapping procedure that enabled us to create most of the dataset automatically through text-image alignment.
Construction and Research of Digital Archives Cloud Platform Based on Big Data Management

Abstract Cloud computing has the characteristics of super computing power, low cost and high security, as well as providing powerful data storage and network services. In the commercial field, it has begun to take shape. This paper attempts to introduce cloud computing technology into the construction of digital archives, analyzes the possibility and reality of the application of cloud computing technology services in the service and management of digital archives from the common point of view of cloud computing services and digital archives, and puts forward the corresponding ways of cloud computing application in digital archives. This paper analyzes and integrates all kinds of risks that digital archives may face under the cloud computing environment, and establishes the security evaluation index system including the security elements of information system and cloud computing technology. In this paper, fuzzy comprehensive evaluation method is used to evaluate the mathematical model of cloud digital archives security evaluation system, and a set of security evaluation system suitable for cloud digital archives is proposed.
A novel chaotic system based on coupled map lattice and its application in HEVC encryption

Video information is currently widely used in various fields. Compared with image and text data, video data has the characteristics of large data volume, strong data relevance, and large data redundancy, which makes traditional cryptographic systems no longer suitable for video encryption systems. The paper proposes a new chaotic system based on coupled map lattice (CML) and applies it to high efficiency video coding (HEVC) video encryption. The chaotic system logistic-iterative chaotic map with infinite collapses-coupled map lattice (L-ICMIC-CML), which is improved on the basis of the ICMIC system and combined with CML, generates stream ciphers and encrypts some syntax elements of HEVC. The experimental results show that the stream cipher generated by the L-ICMIC-CML system passes the SP800-22 Revla test and has strong randomness. Applying the stream cipher to the proposed HEVC encryption scheme, through the analysis of the encryption scheme's security, encryption time and encryption efficiency, it is better than other chaotic system encryption schemes. The video encryption system proposed in this paper is both safe and efficient.
Multi-view informed attention-based model for Irony and Satire detection in Spanish variants

Making machines understand language and reasoning on it has been one of the most challenging problems addressed by Artificial Intelligent researchers. This challenge increases when figurative language is used for communicating complex meanings, intentions, emotions and attitudes in creative and funny ways. In fact, sentiment analysis approaches struggle when facing irony, satire and other figurative languages, particularly those where the explanation of a prediction might arguably be as necessary as the prediction itself. This paper describes a new model MvAttLSTM based on deep learning for irony and satire detection in tweets written in distinct Spanish variants. The proposed model is based on an attentive-LSTM informed with three additional views learned from distinct perspectives. We investigate two strategies to pass these views into MvAttLSTM. We perform an extensive evaluation on three corpora, one for irony detection and two for satire detection. Moreover, in order to study the robustness of our proposed model, we investigate its performance on humor recognition. Experiments confirm that the proposed views help our model to improve its performance. Moreover, they show that affective information benefits our model to detect irony and satire. In particular, a first analysis of the results highlights the discriminating power of emotional features obtained from SenticNet and SEL lexicon. Overall, our system achieves the state-of-the-art performance in irony and satire detection in Spanish variants and competitive results in humor recognition.
Multi‐stream adaptive spatial‐temporal attention graph convolutional network for skeleton‐based action recognition

Skeleton-based action recognition algorithms have been widely applied to human action recognition. Graph convolutional networks (GCNs) generalize convolutional neural networks (CNNs) to non-Euclidean graphs and achieve significant performance in skeleton-based action recognition. However, existing GCN-based models have several issues, such as the topology of the graph is defined based on the natural skeleton of the human body, which is fixed during training, and it may not be applied to different layers of the GCN model and diverse datasets. Besides, the higher-order information of the joint data, for example, skeleton and dynamic information is not fully utilised. This work proposes a novel multi-stream adaptive spatial-temporal attention GCN model that overcomes the aforementioned issues. The method designs a learnable topology graph to adaptively adjust the connection relationship and strength, which is updated with training along with other network parameters. Simultaneously, the adaptive connection parameters are utilised to optimise the connection of the natural skeleton graph and the adaptive topology graph. The spatial-temporal attention module is embedded in each graph convolution layer to ensure that the network focuses on the more critical joints and frames. A multi-stream framework is built to integrate multiple inputs, which further improves the performance of the network. The final network achieves state-of-the-art performance on both the NTU-RGBD and Kinetics-Skeleton action recognition datasets. The simulation results prove that the proposed method reveals better results than existing methods in all perspectives and that shows the superiority of the proposed method.
AutoEval: A NLP Approach for Automatic Test Evaluation System

In this modern era, there is a need for automation in answer assessment systems as the world moves towards automation. Since online response evaluation is only available for option-based questions, the evaluator's job is made more difficult when evaluating theory answers. The teacher manually checks the answer and assigns the required grade. The current system necessitates more manpower and time to evaluate the response. In this paper, we demonstrate the working and operation of the ‘AutoEval - Automatic Test Evaluation System’, a computerized or automatic exam paper evaluation system. A manual paper evaluation is time-consuming and exhausting, but computerized exam paper evaluation aids in standardizing the paper correction process in an effective and reliable manner. The evaluation of response scripts is an essential part of evaluating students' results. In most cases, a response evaluation is performed manually, which can lead to bias. The assessment is based on several factors including the evaluator's mood swings and the inter-relationship between the student and evaluator. Furthermore, assessment and evaluation are a time-consuming and repetitive process. Natural language processing (NLP) methods are used to evaluate exam papers in a variety of ways. NLP is used to search for grammatical errors, as well as to perform syntactic analysis, semantic similarity and database storage.
Empirical Analysis and Privacy Implications in OAuth-based Single Sign-On Systems

Single sign-on authentication systems such as OAuth 2.0 are widely used in web services. They allow users to use accounts registered with major identity providers such as Google and Facebook to login to a wide variety of independent services (relying parties). These services can both identify users and access a subset of the user's data stored with the provider. We empirically investigate the end-user privacy implications of OAuth implementations by relying parties around the world. We collect data on the use of OAuth-based logins in the Alexa Top 500 sites per country for five countries. We categorize user data made available by four identity providers (Google, Facebook, Apple, and LinkedIn) and evaluate popular services accessing user data from the SSO platforms of these providers. Many services allow users to choose from multiple login options (with different identity providers). Our results reveal that services request different categories and amounts of personal data from different providers, often with at least one choice undeniably more privacy-intrusive. We find that privacy-friendly login choices tend to be listed last, suggesting a dark pattern favoring options that release more user data. These privacy choices (and their privacy implications) are highly invisible to users. Based on our analysis, we consider challenges (e.g., opposing goals of stakeholders) in addressing these concerns and discuss ideas for further exploration.
End-to-end style-conditioned poetry generation: What does it take to learn from examples alone?

In this work, we design an end-to-end model for poetry generation based on conditioned recurrent neural network (RNN) language models whose goal is to learn stylistic features (poem length, sentiment, alliteration, and rhyming) from examples alone.We show this model successfully learns the 'meaning' of length and sentiment, as we can control it to generate longer or shorter as well as more positive or more negative poems.However, the model does not grasp sound phenomena like alliteration and rhyming, but instead exploits lowlevel statistical cues.Possible reasons include the size of the training data, the relatively low frequency and difficulty of these sublexical phenomena as well as model biases.We show that more recent GPT-2 models also have problems learning sublexical phenomena such as rhyming from examples alone.
Interactive Multi-Dimension Modulation for Image Restoration

Interactive image restoration aims to generate restored images by adjusting a controlling coefficient which determines the restoration level. Previous works are restricted in modulating image with a single coefficient. However, real images always contain multiple types of degradation, which cannot be well determined by one coefficient. To make a step forward, this paper presents a new problem setup, called multi-dimension (MD) modulation, which aims at modulating output effects across multiple degradation types and levels. Compared with the previous single-dimension (SD) modulation, the MD setup to handle multiple degradations adaptively and relief data unbalancing problem in different degradation types. We also propose a deep architecture - CResMD with newly introduced controllable residual connections for multi-dimension modulation. Specifically, we add a controlling variable on the conventional residual connection to allow a weighted summation of input and residual. The values of these weights are generated by another condition network. We further propose a new data sampling strategy based on beta distribution together with a simple loss reweighting approach to balance different degradation types and levels. With corrupted image and degradation information as inputs, the network can output the corresponding restored image. By tweaking the condition vector, users can control the output effects in MD space at test time. Moreover, we also provide an estimation network to predict the condition vector, thus the base network could directly output the restored image without modulation from users. Extensive experiments demonstrate that the proposed CResMD achieves excellent performance on both SD and MD modulation tasks.
Three approaches to supervised learning for compositional data with pairwise logratios

Logratios between pairs of compositional parts (pairwise logratios) are the easiest to interpret in compositional data analysis, and include the well-known additive logratios as particular cases. When the number of parts is large (sometimes even larger than the number of cases), some form of logratio selection is needed. In this article, we present three alternative stepwise supervised learning methods to select the pairwise logratios that best explain a dependent variable in a generalized linear model, each geared for a specific problem. The first method features unrestricted search, where any pairwise logratio can be selected. This method has a complex interpretation if some pairs of parts in the logratios overlap, but it leads to the most accurate predictions. The second method restricts parts to occur only once, which makes the corresponding logratios intuitively interpretable. The third method uses additive logratios, so that K−1 selected logratios involve a K-part subcomposition. Our approach allows logratios or non-compositional covariates to be forced into the models based on theoretical knowledge, and various stopping criteria are available based on information measures or statistical significance with the Bonferroni correction. We present an application on a dataset from a study predicting Crohn's disease.