Reviewerly/exHarmony huggingface dataset:
Improving Named Entity Recognition in Vietnamese Texts by a Character-Level Deep Lifelong Learning Model

Named entity recognition (NER) is a fundamental task which affects the performance of its dependent task, e.g. machine translation. Lifelong machine learning (LML) is a continuous learning process, in which the knowledge base accumulated from previous tasks will be used to improve future learning tasks having few samples. Since there are a few studies on LML based on deep neural networks for NER, especially in Vietnamese, we propose a lifelong learning model based on deep learning with a CRFs layer, named DeepLML–NER, for NER in Vietnamese texts. DeepLML–NER includes an algorithm to extract the knowledge of “prefix-features” of named entities in previous domains. Then the model uses the knowledge in the knowledge base to solve the current NER task. Preprocessing and model parameter tuning are also investigated to improve the performance. The effect of the model was demonstrated by in-domain and cross-domain experiments, achieving promising results.
Dynamic Fusion of Convolutional Features based on Spatial and Temporal Attention for Visual Tracking

Convolutional neural networks (CNN) based trackers have been widely employed in visual object tracking due to their powerful representations. Features from different CNN layers encode different information. Deeper layers contain more semantic information, while the resolution is too coarse to localize the target. Shallower layers carry more detail information but are less robust for appearance variations. In this paper, we propose an algorithm which incorporates the Spatial and Temporal attention to take full advantage of the Hierarchical Convolutional Features for Tracking (STHCFT). We firstly learn correlation filters on each convolutional layer. Based on the spatial attention inspired by the paraventricular thalamus (PVT) in the brain, we choose the most important layer to build the base response, and the others to be the auxiliary responses. In addition, we make full use of the temporal attention to determine the weights of the auxiliary responses. Finally, the target is located by the maximum value of the fused responses. Extensive experimental results on the benchmark OTB-2013 and OTB-2015 have shown the proposed algorithm performs favorably against several state-of-the-art trackers.
Visual Skeleton and Reparative Attention for Part-of-Speech image captioning system

In this paper, we propose a novel system for image caption generation that can adapt the language models for word generation to specific syntactic structure of sentences and visual skeleton of image. Moreover, it is capable of capturing the attentive regions of image corresponding to the predicting words. Our system consists of three modules: Visual Skeleton Vector (VSV) generation module, Visual Reparative Attention (VRA) mechanism and Part-of-Speech (POS) language model. We adopt Faster R-CNN to generate an objective sentence, which can reflect the visual skeleton (the salient objects and their relations) of an image. And we propose an encode–decode model to the procedure of generating VSV for each image, which is used to initialize our language model. VRA is a two-step strategy to analyze what and where our visual perception system should attend to. Besides, we first adopt the Stanford Parser to work out the syntactic structure of sentences, and explicitly utilize this syntactic information to our POS language model. Each of these modules can enhance the relationships between textual and visual information. The effectiveness of our entire system is verified on two classical datasets (MSCOCO and Flickr30k). Our system is on par with or better than compared state-of-the-art published methods and achieve superior performance on COCO captioning Leaderboard.
An Efficient Parallel High Utility Sequential Pattern Mining Algorithm

High utility sequential pattern mining (HUSPM) is an emerging research topic in pattern mining. Aiming at this problem, many efficient algorithms have been proposed in recent years. However, most of them are serial algorithms, which assume that the entire database (and the data structures) can completely fit into memory. Thus, two conspicuous drawbacks attract our attention. Firstly, in the big data era, it is hard to put the large scale database completely into the memory. Secondly, the serial algorithms are unable to process the real-time data quickly. To address these shortcomings, in this paper, we propose an efficient distributed algorithm for HUSPM. The proposed algorithm takes the advantages of multiple machines and multi-core processer, so it can quickly compete the mining task. The major contributions are summarized as follows. First, we propose an algorithm based on the SLST strategy to partition the original database. Second, we propose a multi-threading algorithm for HUSPM. Finally, based on the two points, an efficient distributed algorithm, named PHUSP, is proposed. Our experiments show the proposed algorithm is much faster than the state-of-the-art algorithms.
Personalizing Session-based Recommendation with Dual Attentive Neural Network

Session-based recommendation which aims to recommend the next item in an anonymous session for users, becomes one of the most popular tasks in recommendation area. Traditional methods such as matrix factorization and item-to-item perform very poorly because they only take into account the last click of the session and ignore the information of the whole click sequence. On the other hand, Recurrent Neural Network (RNN) based methods have performed excellently in session-based recommendation. However, they only consider the user's sequential behavior in the current session or only use cross-session information to track user's interests over time, whereas the user preference is not emphasized in cross-session. Therefore, in this work, we design a novel neural network framework for personalized session-based recommendation, named Dual Attentive Neural Network (DANN). DANN considers user's main purpose of current session and user's personalized preference of cross-session. Specifically, in DANN we exploit a user-level attention mechanism to model user's personalized preference and capture user's main purpose in the current session via a session-level attention mechanism. The experimental results on two real-world datasets show that our DANN model outperforms other baseline models. Furthermore, we find that DANN achieves improvement when modeling user personalized preferences, which shows the advantage of modeling user's preference and user's purpose simultaneously.
The Operational Cost of Ethereum Airdrops

Efficient transfers to many recipients present a host of issues on Ethereum. First, accounts are identified by long and incompressible constants. Second, these constants have to be stored and communicated for each payment. Third, the standard interface for token transfers does not support lists of recipients, adding repeated communication to the overhead. Since Ethereum charges resource usage, even small optimizations translate to cost savings. Airdrops, a popular marketing tool used to boost coin uptake, present a relevant example for the value of optimizing bulk transfers. Therefore, we review technical solutions for airdrops of Ethereum-based tokens, discuss features and prerequisites, and compare the operational costs by simulating 35 scenarios. We find that cost savings of factor two are possible, but require specific provisions in the smart contract implementing the token system. Pull-based approaches, which use on-chain interaction with the recipients, promise moderate savings for the distributor while imposing a disproportional cost on each recipient. Total costs are broadly linear in the number of recipients independent of the technical approach. We publish the code of the simulation framework for reproducibility, to support future airdrop decisions, and to benchmark innovative bulk payment solutions.
Cognitive services to improve user experience in searching for academic information based on chatbot

In this paper, we propose a cognitive system to improve the user experience in searching for academic information with a chatbot. The difference between traditional search systems and the proposed cognitive system is to improve the user experience (UX) through optimization factors such as response time, ease of use, friendly interface, and user interaction through cognitive services cloud computing. This can go beyond the interaction between a chatbot and human since the user experience is very important and can define the success or failure of a system. Validation of the proposed cognitive system was made with potential users of a Peruvian educational institution through a chatbot prototype. Validation consisted of obtaining results and user opinions of the current search system of the institution and compared them with results and user opinions of the implementation of the prototype chatbot. The results showed an 89.5% reduction in the average response expected and acceptance level of 63.2% within the target population.
Detecting Malicious Domains Using Modified SVM Model

The existing machine learning algorithms are costly when it comes to retraining the detection model. However, domains used in different types of malicious activities vary a lot depending on their complex purposes. In order to detect malicious domains in large scale traffic data in real time, a modified SVM algorithm (F-SVM) is proposed in this paper. F-SVM draws lessons from the idea of feedback learning, and it is able to solve the problem of insufficient labeled samples and high cost of updating models. Experimental results show that F-SVM algorithm is capable to keep a high precision rate through whole detection process while simple SVM model has a detection fall as the detection process goes on. F-SVM model's high efficiency in updating detection model makes it suitable for online detection.
Research of the Possibility of Hidden Embedding of a Digital Watermark Using Practical Methods of Channel Steganography

The purpose of the study of the authors of this article are the two most popular network protocols of the network and transport level – TCP and IP, for the presence of the steganographic potential of their packets. The article describes and argues the choice of certain working tools. A study was made of possible ways of channel steganography in data packets of protocols. The argumentation is given for the choice of certain working tools. The implementation of the attachment of information was made. Described the feasibility of the application, developed software, and appropriate conclusions were made throughout the study.
Collusion Resistant Trace-and-Revoke for Arbitrary Identities from Standard Assumptions

A traitor tracing scheme is a multi-user public-key encryption scheme where each user in the system holds a decryption key that is associated with the user's identity. Using the public key, a content distributor can encrypt a message to all of the users in the system. At the same time, if a malicious group of users combine their respective decryption keys to build a "pirate decoder," there is an efficient tracing algorithm that the content distributor can use to identify at least one of the keys used to construct the decoder. A trace-and-revoke scheme is an extension of a standard traitor tracing scheme where there is an additional key-revocation mechanism that the content distributor can use to disable the decryption capabilities of compromised keys. Namely, during encryption, the content distributor can encrypt a message with respect to a list of revoked users such that only non-revoked users can decrypt the resulting ciphertext. Trace-and-revoke schemes are challenging to construct. Existing constructions from standard assumptions can only tolerate bounded collusions (i.e., there is an a priori bound on the number of keys an adversary obtains), have system parameters that scale exponentially in the bit-length of the identities, or satisfy weaker notions of traceability that are vulnerable to certain types of "pirate evolution" attacks. In this work, we provide the first construction of a trace-and-revoke scheme that is fully collusion resistant and capable of supporting arbitrary identities (i.e., the identities can be drawn from an exponential-size space). Our scheme supports public encryption and secret tracing, and can be based on the sub-exponential hardness of the LWE problem (with a super-polynomial modulus-to-noise ratio). The ciphertext size in our construction scales logarithmically in the size of the identity space and linearly in the size of the revocation list. Our scheme leverages techniques from both combinatorial and algebraic constructions for traitor tracing.
A Word Segmentation Method of Ancient Chinese Based on Word Alignment

Since there are no public tagged corpora available for ancient Chinese word segmentation (CWS), the state-of-the-art CWS methods cannot be used for ancient Chinese. To address this problem, this paper proposes a word segmentation method based on word alignment (WSWA). Specifically, the method segments words according to the word alignment between modern Chinese words and ancient Chinese characters. If multiple consecutive characters in ancient Chinese align to the same modern Chinese word, they are considered as one word. Because many modern Chinese words are derived from ancient Chinese, the method also exploits the co-occurring characters between modern and ancient Chinese to extract words for CWS. Moreover, to reduce the effect of alignment errors, the method removes the word alignments easily leading to CWS errors. We quantitatively analyze the effects of modern CWS and word alignment on WSWA method using hand-annotated corpora. Our method outperforms the state-of-the-art methods on the WSA experiment on Shiji with a large margin, which demonstrates the effectiveness of using word alignment to perform ancient CWS.
Taking the Middle Path: Learning About Security Through Online Social Interaction

Integrating security into software development involves more than learning principles or applying techniques. Security can be integrated into software development practice by following a middle path, through which developers draw together knowledge received through training and software development techniques.
Predicting Performance using Approximate State Space Model for Liquid State Machines

Liquid State Machine (LSM) is a brain-inspired architecture used for solving problems like speech recognition and time series prediction. LSM comprises of a randomly connected recurrent network of spiking neurons. This network propagates the non-linear neuronal and synaptic dynamics. Maass et al. have argued that the non-linear dynamics of LSM is essential for its performance as a universal computer. Lyapunov exponent (μ), used to characterize the non-linearity of the network, correlates well with LSM performance. We propose a complementary approach of approximating the LSM dynamics with a linear state space representation. The spike rates from this model are well correlated to the spike rates from LSM. Such equivalence allows the extraction of a memory metric (τ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</sub> ) from the state transition matrix. τ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</sub> displays high correlation with performance. Further, high τ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</sub> systems require fewer epochs to achieve a given accuracy. Being computationally cheap (1800× time efficient compared to LSM), the τ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</sub> metric enables exploration of the vast parameter design space. We observe that the performance correlation of the τ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</sub> surpasses that of Lyapunov exponent (μ), (2 - 4× improvement) in the high-performance regime over multiple datasets. In fact, while μ increases monotonically with network activity, the performance reaches a maxima at a specific activity described in literature as the edge of chaos. On the other hand, τ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</sub> remains correlated with LSM performance. Hence, τ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</sub> captures the useful memory of network activity that enables LSM performance. It also enables rapid design space exploration and fine-tuning of LSM parameters for high performance.
A Neural Topic Model Based on Variational Auto-Encoder for Aspect Extraction from Opinion Texts

Aspect extraction is an important task in ABSA (Aspect Based Sentiment Analysis). To address this task, in this paper we propose a novel variant of neural topic model based on Variational Auto-encoder (VAE), which consists of an aspect encoder, an auxiliary encoder and a hierarchical decoder. The difference from previous neural topic model based approaches is that our proposed model builds latent variable in multiple vector spaces and it is able to learn latent semantic representation in better granularity. Additionally, it also provides a direct and effective solution for unsupervised aspect extraction, thus it is beneficial for low-resource processing. Experimental evaluation conducted on both a Chinese corpus and an English corpus have demonstrated that our model has better capacity of text modeling, and substantially outperforms previous state-of-the-art unsupervised approaches for aspect extraction.
Narrator’s Name Recognition with Support Vector Machine for Indexing Indonesian Hadith translations

The narrator's name in the Hadith is one of the most important components in determining the validity of a hadith, but with the large number of Hadiths that exist, causing the process of determining the validity of a Hadith manually becomes difficult, especially in the Indonesian Hadith translation. Named Entity Recognition (NER) is a method that aims to find entities in a text document, in this case the entity includes the name of the person, location, organization, etc. This study will discuss the implementation of the Named Entity Recognition to the Indonesian translation of the Hadith collection to find the names of narrators from each Hadith. In this study 200 Hadiths from 9 different books consisting of 31010 tokens and 2241 narrator name entities will be used as datasets. Because of the variety of entity forms and the amount of data used, this study will use a supervised-learning approach, and to maximize performance from the NER system, Support Vector Machine (SVM) is chosen as a classifier model that is known to have good generalization capabilities in classifying data and ability to deal with high-dimensional data. Some combinations of test scenarios on the NER model showed the highest F-1 results of 0.9 with training data totaling 140 Hadiths consisting of 1564 entities and testing 60 Hadiths consisting of 677 entities. The narrator name produced by the NER system will then be used as an index of the Hadiths that have been narrated by the narrator using the Inverted Index method.
Triple attention network for sentimental visual question answering

Visual Question Answering (VQA) and Visual Sentiment Analysis (VSA) are recently popular research fields in multimedia analysis using deep learning, but little effort has been put in attempting to close the gap between them. Better image understanding can be achieved by analyzing sentimental attributes from the different regions of an image. This paper proposes the Triple Attention Network (TANet) that attends to features of an image, a text question and a string of distinct multiple localized sentimental visual attributes, in a triple attention mechanism, in order to generate a fully affective answer. The separate experiments demonstrate how two customized image datasets can be used to train a VQA model that employs Long short-term memory (LSTM) and convolutional neural network (CNN) feature attention techniques for the text question and the sentimental attributes. The additional attention to the sentimental attributes causes the model to focus on more relevant regions of the image, which results in better image understanding and improved quality of the answer. The Hadamard product is modified to handle the three attended variables during feature fusion. The results of the experiments clearly show that high classification accuracy levels can be achieved together with a multi-attribute affective answer, and our model outperforms recent VSA and VQA baseline models. The proposed model is a significant step towards the realization of machines that can comprehend perfect natural language just like humans.
Optimization based interesting region identification for video watermarking

The advancements in multimedia watermarking technologies have gained lots of attention in the past few years. A digital video watermarking is the data embedded in the video and sent to the recipient. Privacy is considered as the major issue in digital media while hiding the data by maintaining the video quality. This paper presents an optimization algorithm for video watermarking based on interesting regions. Here, the optimal regions for the video watermarking are selected using the proposed Chronological-Moth Search (Chronological-MS) approach, which is established by modifying the Moth Search (MS) algorithm using chronological concept. The employed fitness function contains cost function, which uses several parameters, like energy, edge, pixel intensity, brightness, and coverage. At first, the input video file is subjected to feature extraction, and the extracted features are employed for selecting the optimal regions using the proposed Chronological-MS. After that, the wavelet transform is given to the original image to obtain the wavelet coefficients. Here, the privacy message is divided into binary images using bit plane technique. Thus, the embedding process is carried out for hiding the secret message with the use of interesting regions identified using the proposed Chronological-MS algorithm and is retrieved at the extraction phase. The performance of the proposed Chronological-MS shows superior performance with correlation coefficient and PSNR of 1.00 and 98.741 dB, respectively.
Sentiment Analysis Application and Natural Language Processing for Mobile Network Operators’ Support on Social Media

Social Media have become a mixed platform of emotional expressions on services and products reviews. While network operators focus on quality of service and customer experience, mostly built upon complaints and performance indicators, subscribers and/or followers are mostly expressing their emotions on twitter and other social media. On one side, understanding followers' sentiment and perception on offered and applied services can help the South African mobile network operators to anticipate network and customer problems, embracing proactive measures rather than reactive ones on improving service and network quality. On the other side, the rise of text mining, sentiment analysis and the global Natural Language Processing expands the needs of Data Analysis across textual statistics. In this paper, we leverage on Natural Language Processing (NLP), using sentiment analysis and text mining to analyze mobile network operators, in this case CellC followers' using the R platform. We use the polarity model of sentiment analysis to determine the level of potential detraction and promotion across South African Mobile Network Operator (MNO), based on public tweets. The orientation of the study in this paper creates a bi-directional link between customers or followers and the MNO, in the goal of extracting relevant signification from sets of social media's unstructured information.