Reviewerly/exHarmony huggingface dataset:
Improving information extraction from visually rich documents using visual span representations

Along with textual content, visual features play an essential role in the semantics of visually rich documents. Information extraction (IE) tasks perform poorly on these documents if these visual cues are not taken into account. In this paper, we present Artemis - a visually aware, machine-learning-based IE method for heterogeneous visually rich documents. Artemis represents a visual span in a document by jointly encoding its visual and textual context for IE tasks. Our main contribution is two-fold. First, we develop a deep-learning model that identifies the local context boundary of a visual span with minimal human-labeling. Second, we describe a deep neural network that encodes the multimodal context of a visual span into a fixed-length vector by taking its textual and layout-specific features into account. It identifies the visual span(s) containing a named entity by leveraging this learned representation followed by an inference task. We evaluate Artemis on four heterogeneous datasets from different domains over a suite of information extraction tasks. Results show that it outperforms state-of-the-art text-based methods by up to 17 points in F1-score.
Learning a Deformable Registration Pyramid

We introduce an end-to-end unsupervised (or weakly supervised) image registration method that blends conventional medical image registration with contemporary deep learning techniques from computer vision. Our method downsamples both the fixed and the moving images into multiple feature map levels where a displacement field is estimated at each level and then further refined throughout the network. We train and test our model on three different datasets. In comparison with the initial registrations we find an improved performance using our model, yet we expect it would improve further if the model was fine-tuned for each task. The implementation is publicly available ( https://github.com/ngunnar/learning-a-deformable-registration-pyramid ).
Recommendation System with Reasoning Path Based on DQN and Knowledge Graph

Recommendation system is a popular research field. In the age of information explosion, a reliable recommendation system is necessary for users. There are a certain number of approaches to do recommendation work. Reinforcement learning is one of the methods used in recommendation system. In this paper, we use reinforcement learning to recommend items to target users, and achieved a rather good result. To give a better user experience, we have added explanations for recommended items. The explanation is realized by Knowledge Graph. We use TransE to embed target users and items, and it helps manage the information of users and items. Our method KGDQN combines Knowledge Graph and reinforcement learning, which can decide the proper recommendation items, and find the reasoning paths from target users to recommended items. Redundant edges are pruned and the DQN model renders a reward function which gives back the result of recommended items, and the explanation paths of the recommendation. Experiments are conducted on Amazon datasets which show the superior performance of KGDQN.
Benchmarking Live Migration Performance Under Stressed Conditions

Live migration is a technology that seamlessly relocates a virtualized service between physical hosts, which allows services to rapidly adapt to environmental changes. Despite the large amount of research, there is still a lack of understanding of its performance. Towards a better understanding of live migration, we build a testbed and use it to migrate a computation-intensive application with docker and KVM. We evaluate the service downtime, migration time, and network usage under different conditions. The results show that KVM outperforms docker in most of the scenarios, with some critical exceptions where only docker manages to perform miaration.
Personalized image observation behavior learning in fixation based personalized salient object segmentation

Fixation as representation of one viewer’s attention are very intuitive to reflect the viewer’s observation procedure. The viewer’s observation behavior can be further revealed by analyzing fixations features. In this paper, we propose a fixation based personalized salient object segmentation method involving personal observation behavior learning. Concretely, we design three neural networks and deploy a meta-learning method. The first network is a base segmentation network that can be converted into a meta-segmentation network by meta-learning. The meta- segmentation network can learn one viewer’s observation behavior from only one sample and then generates the viewer’s segmentation network to segment the other samples. Moreover, a fusion network plays an important role in alleviating an unsuitable transmission problem and generating a final segmentation result. The experimental results demonstrate the reasonability of our observation behavior learning and the effectiveness of the three proposed neural networks.
Construction of document feature vectors using BERT

In recent years, the performance of various natural language processing systems has been greatly improved by pre-training models such as Bidirectional Encoder Representations from Transformers (BERT). The BERT model obtains word em-beddings in context by the multi-head attention of a Transformer. In document-classification tasks, document feature vector can be constructed from the BERT output. However, the length of the BERT input sequence is upper-limited, which reduces the amount of information obtainable from BERT by the standard method. This limitation is problematic when handling long documents. Against this background, we propose a method that constructs document feature vector in BERT by embedding all words in long documents.
Orienteering Mobile App

The article discusses the development of a system for sport orienteering. Sport orienteering is beginning to gain popularity among a huge mass of people, especially teenagers. But the organization of this event is a laborious process. For that you need one person for each control point, for tracking participants ways. Due to the human factor, the mark that the participant has passed this point takes a lot of time and because of this, queues appear at checkpoints. This slows down of competition and worsens the results of the participants themselves. This sport has been given close attention because the sport federation is interested in attaching more and more young people to sports. An information system is an information processing system that works in conjunction with organizational resources such as people, hardware, and financial resources that provide and distribute information. It should reduce the burden on participants passing checkpoints. Solving this problem by developing a mobile application will optimize this process.
Transfer Learning for Test Time Reduction of Parameter Extraction in MEMS Accelerometers

Parameter extraction during the final test of MEMS sensors poses a highly time-critical challenge. The progressing miniaturization, test stimuli and structural complexity lead to nonlinear couplings and inhomogeneity in system differential equations, which cannot be linearized and are therefore dependent on either slow numerical solution methods or machine learning algorithms requiring many labeled data. A transfer learning approach is presented making use of high complexity ASIC-MEMS models for Monte-Carlo generation of simulated devices, which are used to pre-train neural networks on the task of parameter extraction. In a first step, it is shown that for both, high quality factor and low quality factor systems, neural networks are not only able to fit the relation between time-series recorded during final testing and the two performance parameters natural frequency and damping factor but also to extract Brownian noise, mass, and epitaxial layer thickness. Subsequently, it is shown that the transfer learning approach is particularly useful for the determination of parameters, which cannot be measured directly during the final test and for which it is expensive to record labeled data like Brownian noise for systems in a harsh production test environment. If only very few labeled samples are available - in the performed experiments, 25 devices under test were sufficient - the transfer learning approach outperforms a neural network purely trained on measured data. These findings emphasize the practical advances of the transfer learning approach and motivate the evaluation of further applications in the field of parameter extraction in MEMS sensors.
Tag recommendation model using feature learning via word embedding

Tag recommendation models serve as extracting metadata for target objects like images, videos and Web pages. However, these models tackle cold start problem due to absence of initial tags. To improve tag quality in tag recommendation services, most of previous works exploit the statistical properties such as co-occurrence patterns or term frequency to predict the candidate tags to a target object. Yet, these tag recommendation methods fail to be effective when initial tags are absent or low quality texts are available for objects. Recently, sentence modeling via word embeddings achieves successes in many natural language processing tasks. Therefore, this paper aims to introduce a novel tag recommendation algorithm that can analyze the relation between words in a text associated with target object using word embedding. In fact, we involve grammatical relations between words in a text or sentence with focus on feature learning methods. Skip-gram model is used to optimize feature values and learn the representation vector of words for tag recommendation. Our method shows improvements to previous research methods with gains of up to 10 percent in precision using real data from Movielens dataset.
Classification of Thalassemia Patients Using a Fusion of Deep Image and Clinical Features

It is difficult to diagnose different types of diseases of the same category using only clinical reports, which is why doctors diagnose this type of disease using clinical reports as well as blood smear image of patients. This paper, therefore, come up with a technique that uses both clinical report and blood smear image of patients for detection of thalassemia. Clinical features are extracted by the blood analyzer, and image features are extracted from the blood smear image via the Deep Confusional Neural Network (CNN) and then both features are combined to make a relevant feature set. But this set of features has some redundant features that increase computational complexity. This work uses principal component analysis (PCA) to overcome redundancy in features, resulting in lower computational complexity. This paper used machine learning classification algorithms such as Naive Bayes, random forest (RF) and KNN to classify patients into a thalassemic and normal person with the help of combined features and got accuracy 99 ±1%, specificity 100%, and sensitivity 100%.
BCI-CFI: A context-sensitive control-flow integrity method based on branch correlation integrity

As part of the arms race, one emerging attack methodology has been control-hijacking attacks, e.g., return-oriented programming (ROP). Control-flow integrity (CFI) is a generic and effective defense against most control-hijacking attacks. However, existing CFI mechanisms have poor security as demonstrated by their equivalence class (EC) sizes, which are sets of targets that CFI policies cannot distinguish. Adversaries can choose an illegitimate control transfer within an EC that is included in the resulting CFG and incorrectly allowed by CFI protection policies. The paper introduces a context-sensitive control-flow integrity method, which aims to improve the security of CFI and prevent ROP attacks. The paper presents BCI-CFI, a context-sensitive CFI technique based on branch correlation integrity (BCI), which can effectively break down EC sizes and improve the security of CFI. BCI-CFI takes the branch correlation relationship (i.e., a new type of context for CFI) as contextual information to refine the CFI policy and identify the BCI pairs in the target program via static analysis. Furthermore, the paper introduces a state machine MCFI for BCI-CFI to conduct target validation for the indirect control-flow transfer (ICT) instructions in the target program at runtime. Our results show that, (i) BCI-CFI prevented adversaries from manipulating the control data and launching ROP attacks, (ii) protected both forward and backward ICT in the target program, and improved the security and effectiveness of CFI, and (iii) BCI-CFI introduced a 19.67% runtime overhead on average and a maximum runtime overhead of 31.2% BCI-CFI is a context-sensitive CFI technique aiming to prevent adversaries from manipulating the control data of the target program to launch ROP attacks. BCI-CFI can reduce EC sizes and improve the security of CFI while incurring a moderate runtime overhead on average.
A Fast Spatial Pool Learning Algorithm of Hierarchical Temporal Memory Based on Minicolumn’s Self-Nomination

As a new type of artificial neural network model, HTM has become the focus of current research and application. The sparse distributed representation is the basis of the HTM model, but the existing spatial pool learning algorithms have high training time overhead and may cause the spatial pool to become unstable. To overcome these disadvantages, we propose a fast spatial pool learning algorithm of HTM based on minicolumn’s nomination, where the minicolumns are selected according to the load-carrying capacity and the synapses are adjusted using compressed encoding. We have implemented the prototype of the algorithm and carried out experiments on three datasets. It is verified that the training time overhead of the proposed algorithm is almost unaffected by the encoding length, and the spatial pool becomes stable after fewer iterations of training. Moreover, the training of the new input does not affect the already trained results.
How Blockchain and AI Enable Personal Data Privacy and Support Cybersecurity

Recent increases in security breaches and digital surveillance highlight the need for improved privacy and security, particularly over users’ personal data. Advances in cybersecurity and new legislation promise to improve data protection. Blockchain and distributed ledger technologies provide novel opportunities for protecting user data through decentralized identity and other privacy mechanisms. These systems can allow users greater sovereignty through tools that enable them to own and control their own data. Artificial intelligence provides further possibilities for enhancing system and user security, enriching data sets, and supporting improved analytical models.
Frequency-Based Haze and Rain Removal Network (FHRR-Net) with Deep Convolutional Encoder-Decoder

Removing haze or rain is one of the difficult problems in computer vision applications. On real-world road images, haze and rain often occur together, but traditional methods cannot solve this imaging problem. To address rain and haze problems simultaneously, we present a robust network-based framework consisting of three steps: image decomposition using guided filters, a frequency-based haze and rain removal network (FHRR-Net), and image restoration based on an atmospheric scattering model using predicted transmission maps and predicted rain-removed images. We demonstrate FHRR-Net’s capabilities with synthesized and real-world road images. Experimental results show that our trained framework has superior performance on synthesized and real-world road test images compared with state-of-the-art methods. We use PSNR (peak signal-to-noise) and SSIM (structural similarity index) indicators to evaluate our model quantitatively, showing that our methods have the highest PSNR and SSIM values. Furthermore, we demonstrate through experiments that our method is useful in real-world vision applications.
Empirical Study on Cyber Range Capabilities, Interactions and Learning Features

Emerging technologies and the globalization require constant investment in people and their performance in actual and virtual environments. New technologies such as autonomous systems, machine learning and artificial intelligence (AI) radically re-contextualize the human dimension of the organization. Technological developments are changing the ways people experience the physical and the virtual environments. Strategic changes have revealed new critical vulnerabilities such as social media-based disinformation campaigning with impact on the human aspects at state, societal, organizational and individual levels. Scenarios of gathering information, committing fraud or getting access to critical systems are often used for follow-up actions. Cybersecurity education and training aim to raise the level of expertise, skills and competences and ensure better performance in complex situations in cyber space. Researchers have addressed assumptions, models, concepts and cognitive aspects of humans performing in the cyber domain. However, the human cognitive learning and human performance approaches in Virtual Reality (VR), Augmented Reality (AR) and Mixed Reality (XM) and Cyber Range (CR) learning platform design are only partly touched. CRs are becoming crucial means in acquisition of skills and knowledge but also to augment and mimic human cognitive behaviour for cognitive agents. Empirical studies and evaluations of the capabilities, tools and techniques for enhancing organizational cyber resilience by the human performance to better face cyber-attacks are needed. The purpose of this paper is to provide a literature review, suggest viewpoints on cybersecurity training and education and to study the current capabilities of CRs. This paper includes a literature review to support human performance and provides empirical findings on CR capabilities, interactions and learning features. The results of this study can be used as a baseline for future initiatives towards the development of CRs in accordance with human cognitive learning and future improvements in design. Furthermore, the intention is to constructively promote discussion on current issues about humans in cyber physical systems and cybersecurity domain and thus boost multidisciplinary studies to enhance cyber awareness in different sectors.
Dynamic Cognitive-Social Particle Swarm Optimization

Particle Swarm Optimization (PSO) is a heuristic optimization algorithm based on the modeling of the behavior of fishes and birds flock. This paper proposes a better version of PSO, named Dynamic Cognitive-Social PSO “DCS-PSO”, for global minima search by introducing optimal and dynamic cognitive and social scaling parameters without taking into consideration the inertia term. Furthermore, the velocity of each particle is controlled systematically at each iteration to avoid local minimum traps and to converge quickly and reliably towards the global minimum. The proposed algorithm is more suitable for high dimensional optimization problems and it has gotten over the limitations of classical Particle Swarm Optimization. Several experiments have been carried out, using the proposed DCS-PSO, to optimize thirteen benchmark functions and an important improvement has been observed, not only in terms of reaching the best global solutions but also in terms of convergence speed, compared to the existing benchmark approaches.
Pixel P Air-Wise Fragile Image Watermarking Based on HC-Based Absolute Moment Block Truncation Coding

In this paper, we first designed Huffman code (HC)-based absolute moment block truncation coding (AMBTC). Then, we applied Huffman code (HC)-based absolute moment block truncation coding (AMBTC) to design a pixel pair-wise fragile image watermarking method. Pixel pair-wise tampering detection and content recovery mechanisms were collaboratively applied in the proposed scheme to enhance readability even when images have been tampered with. Representative features are derived from our proposed HC-based AMBTC compression codes of the original image, and then serve as authentication code and recovery information at the same time during tamper detection and recovery operations. Recovery information is embedded into two LSB of the original image with a turtle shell-based data hiding method and a pre-determined matrix. Therefore, each non-overlapping pixel-pair carries four bits of recovery information. When the recipient suspects that the received image may have been tampered with, the compressed image can be used to locate tampered pixels, and then the recovery information can be used to restore the tampered pixels.
Randomized block Krylov subspace methods for trace and log-determinant estimators

We present randomized algorithms based on block Krylov subspace methods for estimating the trace and log-determinant of Hermitian positive semi-definite matrices. Using the properties of Chebyshev polynomials and Gaussian random matrix, we provide the error analysis of the proposed estimators and obtain the expectation and concentration error bounds. These bounds improve the corresponding ones given in the literature. Numerical experiments are presented to illustrate the performance of the algorithms and to test the error bounds.
Criminal Network Community Detection in Social Media Forensics

Nowadays, Online Social Networks (OSNs) has created a breeding ground for criminals to engage in cyber–crime activities, and the legal enforcement agencies (LEAs) are facing significant challenges since there is no consistent and generalized framework built specifically to analyse users' misbehaviour and their social activity on these platforms. Data exchanged over these platforms represent an important source of information, even their characteristics such as unstructured nature, high volumes, velocity, and data inter–connectivity, become an obstacle for LEAs to analyse these data using traditional methods in order to provide it to the legal domain. Although numerous researches have been carried out on digital forensics, little focus has been employed on developing appropriate tools to exhaustively meet all the requirements of crime investigation targeting data integration, information sharing, collection and preservation of digital evidences. To bridge this gap, in our preliminary work we presented a generic digital evidence framework, called CISMO as a semantic tool that is able to support LEAs in detecting and preventing different type of crimes happening on OSNs. This paper gives details of the knowledge extraction layer of the framework. Specially, we mainly focus on analyses criminal social graph structures proving the effectiveness of CISMO in a case study with real criminal dataset. Experimental results reveal that applying appropriate Social Network Analyses (SNA), CISMO framework should be able to query and discover the criminal networks, empowering the criminal investigator to see the connections between people.