Reviewerly/exHarmony huggingface dataset:
A Survey on Phishing Emails Detection Techniques

In the last few eras, phishing attacks have become more sensitive case for local users, organizations, and service providers. Phishing emails can also cause financial loss to organizations and individuals. Phishing attackers swindle the user or organization&#x2019;s information by sending them fake emails that resemble most of their work. Many researchers proposed different methods to avoid phishing emails, but this problem has not been fully solved. Over time, there is a rapid increase in technology and phishing attack methods and detection techniques. The network users want the complete solution to avoid phishing attacks, which is quite difficult; however, using many methods can avoid phishing emails. In this survey paper, we present an overview of the state-of-the-art techniques to detect phishing emails. We perform a comprehensive study of these techniques and evaluate them. This provides an understanding of the problem; its solution space and future research directions are also proposed.
Learning Beam Search: Utilizing Machine Learning to Guide Beam Search for Solving Combinatorial Optimization Problems

Beam search (BS) is a well-known incomplete breadth-first-search variant frequently used to find heuristic solutions to hard combinatorial optimization problems. Its key ingredient is a guidance heuristic that estimates the expected length (cost) to complete a partial solution. While this function is usually developed manually for a specific problem, we propose a more general Learning Beam Search (LBS) that uses a machine learning model for guidance. Learning is performed by utilizing principles of reinforcement learning: LBS generates training data on its own by performing nested BS calls on many representative randomly created problem instances. The general approach is tested on two specific problems, the longest common subsequence problem and the constrained variant thereof. Results on established sets of benchmark instances indicate that the BS with models trained via LBS is highly competitive. On many instances new so far best solutions could be obtained, making the approach a new state-of-the-art method for these problems and documenting the high potential of this general framework.
Comparative Study of J48 Decision Tree Classification Algorithm, Random Tree, and Random Forest on In-Vehicle CouponRecommendation Data

Coupons are one of the media used to increase sales and invite customers to repurchase products. A study to investigate the effectiveness of the distribution of coupons, especially coupons for restaurants and bar, can be carried out by collecting data through an in-vehicle survey. The data can then be analyzed using classification techniques in data mining. This paper presents a classification on the problem of in-vehicle coupon recommendation to determine the decision of coupon acceptance through the J48, Random Tree, and Random Forest decision tree classification algorithm. The dataset used consists of 23 attributes including the class Y attribute which indicates the receipt of coupons by customers. The performance of the three algorithms is evaluated to determine the best classification algorithm by looking at accuracy, time to build the model, and other variables that appear in the class classification experiment. The results reveal that the Random Tree classification algorithm takes the least amount of time (0.28 seconds) and has the lowest accuracy (67.38%). The J48 algorithm is more accurate than the Random Tree algorithm (72.79%) but takes significantly longer time (0.36 seconds). The Random Forest technique has the best accuracy (77.0%), but the time it takes for model creation is substantially longer than the Random Tree and J48 algorithms (10.89 seconds).
Entity alignment via summary and attribute embeddings

Abstract Entity alignment is the task of integrating heterogeneous knowledge among different knowledge graphs (KGs). KG is a popular way of storing facts about real-world entities. Unfortunately, a very limited number of the entities stored in different KGs are aligned. This paper presents an embedding-based entity alignment method that finds entity alignment by measuring the similarities between entity embeddings. Existing methods mainly focus on the relational structures and attributes information for the alignment process. Such methods fail while the entities have a fewer number of attributes or when the relational structure could not capture the meaningful representation of the entities. To address this problem, we propose EASAE, an entity alignment method using summary and attribute embeddings. We exploit the entity summary information available in KGs for entities’ summary embedding. To learn the semantics of the entity summary, we employ bidirectional encoder representations from transformers (BERT). Our model learns the representations of entities by using relational triples, attribute triples and summary as well. We perform experiments on real-world datasets, and the results indicate that the proposed approach outperformed the state-of-the-art models for entity alignment.
BPFL: A Blockchain Based Privacy-Preserving Federated Learning Scheme

Federated Learning (FL), which allows multiple participants to co-train machine Learning models without exposing local data, has been recognized as a promising method in the past few years. However, in the FL process, the server side may steal sensitive information of users, while the client side may also upload malicious data to compromise the training of the global model. Most existing privacy-preservation FL schemes seldom deal with threats from both of these two sides at the same time. In this paper, we propose a Blockchain based Privacy-preserving Federated Learning scheme named BPFL, which uses blockchain as the underlying distributed framework of FL. Homomorphic encryption and Multi-Krum technology are combined to achieve ciphertext-level model aggregation and model filtering, which can guarantee the verifiability of local models while realizing privacy-preservation. Security analysis and performance evaluation prove that the proposed scheme can achieve enhanced security and improve the performance of the FL model.
Survey on Learnable Databases: A Machine Learning Perspective

During the decades of development of artificial intelligence, a spectrum of applications involving image, speech, text data, etc. are successfully powered by machine learning. The advantages are mainly derived from the learning ability from data, which most traditional databases lack. Recent years have seen a surge in approaches that explore artificial intelligence to power traditional databases, i.e. learnable databases, making databases more adaptive and intelligent. Specifically, they can be automatically optimized according to historical metric statistics and current query workload, which significantly improves the database performance and relieves the trivial routine maintenance suffering. However, one of the major issues, especially for practitioners, is the lack of consensus in their definitions as well as a lack of clear categorization from a machine learning perspective. To alleviate these problems, this paper introduces concepts and algorithms related to learnable databases and investigates the progress in learnable databases in five aspects: database parameter configuration, data storage management, query optimization, query interface, and benchmark of learnable databases. Additionally, we survey AI-empowered technique development in commercial databases and new approaches to learning-based database security. We develop a categorizing framework in terms of input features, model selection, and output results (mostly being viewed as class labels). Finally, we conclude the current work and discuss future work on learnable databases.
Dehazing Underwater Images Using Encoder Decoder Based Generic Model-Agnostic Convolutional Neural Network

Enhancing degraded underwater images is a challenging problem for researchers. Deep learning approaches can significantly provide improvement in image restoration and enhancement. Moreover automatic analysis of marine digital data has a high research demand. This paper aims to introduce a fully connected convolutional neural network for dehazing underwater images. The low level and high level features are integrated by an encoder-decoder deep framework which helps to recover the hazy image. The proposed method is evaluated quantitatively and qualitatively on Underwater Image Enhancement Benchmark dataset (UIEB). The model outperforms existing methods in terms of metrics: SSIM, PSNR and MSE and has the ability to dehaze underwater images retaining fine details.
Making the most of comparable corpora in Neural Machine Translation: a case study

Abstract Comparable corpora can benefit the development of Neural Machine Translation models, in particular for under-resourced languages. We present a case study centred on the exploitation of a large comparable corpus for Basque-Spanish, created from independently-produced news by the Basque public broadcaster eitb , where we evaluate the impact of different techniques to exploit the original data, in order to complement parallel datasets for this language pair in both translation directions. Two efficient methods for parallel sentence mining are explored, which identified a common core of approximately half of the total number of aligned sentences, each one uniquely identifying valid parallel sentences not captured by the other method. Filtering the data via identification of length-difference outliers proved highly effective to improve the models, as was the use of tags to discriminate between comparable and parallel data in the training corpora. The use of backtranslated data is also evaluated in this work, with results indicating that alignment-based datasets remain the most beneficial, although complementary backtranslations should also be included to fully exploit the available comparable data. Overall, the results in this work demonstrate that this type of data needs to be carefully analysed prior to its use as training data for Neural Machine Translation, since issues such as information imbalance between source and target data can lead to unoptimal results for a given translation pair.
Design of Reinforced Fuzzy Model Driven to Feature Selection Through Univariable-Based Correlation and Multivariable-Based Determination Coefficient Analysis

In this article, we introduce a design methodology of reinforced fuzzy models based both on univariate analysis and multivariable analysis to cope with high-dimensional problems. This approach is aimed at reducing the design process and curbing computing overhead inherently associated with the increasing volume of data in terms of both their number and the dimensionality of the feature space. The critical features of the proposed fuzzy models are highlighted as follows: First, the essential input variables of the model are selected by running the univariable and multivariable analyses. In univariate analysis, input variables with a strong linear relationship with the output variable are selected through correlation analysis completed for each input space and output space. On the contrary, in multivariable analysis, input variables are chosen by comparing the determination coefficients obtained from the subsets of input variables. Second, according to the analysis of the input variable, we construct two different kinds of fuzzy models. The first fuzzy model comprises the design of the univariable-based fuzzy model (UFM) and its aggregation. The UFMs are made by the individual input variables selected from the univariable analysis using a correlation coefficient. The subspaces formed by correlation analysis are applied for determining the centers of the membership function (MF). The results produced by individual fuzzy models are aggregated through some <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</i> -conorms. The second fuzzy model is with the fuzzy clustering for improving the form of fuzzy space in the premise part of a fuzzy rule. To curb the dramatic increase in size of fuzzy rule in high-dimensional problems, the clustering space is employed as the fuzzy space, and the partition matrix produced by fuzzy provided the required degrees of the MF. Experimental studies include a suite of synthetic and publicly available data. The superiority of the proposed design methodology was demonstrated by using 34 publicly available datasets and also compared with the conventional models associated with the fuzzy rule-based models as well as the state-of-the-art models reported in the literature.
Secure delegated quantum computation based on Z-rotation encryption

Abstract Quantum computing on encrypted data allows a client who has limited quantum capacity to delegate his or her private computation to an untrusted quantum server, while the input and output are encrypted by the quantum one-time pad and only the client can correctly decrypt them. Generally, the client is required to have ability to prepare some single qubits and perform some basic gates. In this work, we consider a further restricted situation where the client can only prepare one single qubit and perform one basic gate. Specifically, we show that as long as the client can prepare a fixed qubit <?CDATA $\vert {+} \rangle$ ?> and perform a fixed phase gate P , then he or she can still achieve the secure delegated quantum computation. Besides, our protocol can provide a more rigorous security for any quantum computation. For example, even if some encryption keys about the computation are leaked, it can still guarantee the privacy of the input and output. Finally, our protocol experimentally has a great significance in reducing the device complexity of the client's side.
UrduThreat@ FIRE2021: Shared Track on Abusive Threat Identification in Urdu

With the growth of spread and importance of social media platforms, the effect of their misuse became more and more impactful. This shared task address the task of abusive and threatening language detection in Urdu language that has more than 230 million speakers worldwide. We presented two datasets: (i) Abusive and Non-Abusive language, (ii) Threatening and Non-Threatening language. The abusive dataset contains 1,187 tweets categorized as Abusive and 1,213 as Non-Abusive and the threatening dataset contains 4,929 tweets categorized as Non-Threatening and 1,071 as Threatening. In this shared task, 21 teams registered for participation from six countries (India, Pakistan, China, Malaysia, United Arab Emirates, Taiwan), 10 teams submitted their runs for Subtask A — Abusive Language Detection, 9 teams submitted their runs for Subtask B — Threatening Language detection, and seven teams submitted their technical reports. We provided one baseline system for Subtask A and three baseline systems for Subtask B. The best performing system achieved an F-score value of 0.88 for Subtask A and 0.545 for Subtask B. For both subtasks, m-Bert based transformer models showed the best performance.
Unifying Physical Interaction, Linguistic Communication, and Language Acquisition of Cognitive Agents by Minimalist Grammars

Cognitive agents that act independently and solve problems in their environment on behalf of a user are referred to as autonomous. In order to increase the degree of autonomy, advanced cognitive architectures also contain higher-level psychological modules with which needs and motives of the agent are also taken into account and with which the behavior of the agent can be controlled. Regardless of the level of autonomy, successful behavior is based on interacting with the environment and being able to communicate with other agents or users. The agent can use these skills to learn a truthful knowledge model of the environment and thus predict the consequences of its own actions. For this purpose, the symbolic information received during the interaction and communication must be converted into representational data structures so that they can be stored in the knowledge model, processed logically and retrieved from there. Here, we firstly outline a grammar-based transformation mechanism that unifies the description of physical interaction and linguistic communication and on which the language acquisition is based. Specifically, we use minimalist grammar (MG) for this aim, which is a recent computational implementation of generative linguistics. In order to develop proper cognitive information and communication technologies, we are using utterance meaning transducers (UMT) that are based on semantic parsers and a mental lexicon , comprising syntactic and semantic features of the language under consideration. This lexicon must be acquired by a cognitive agent during interaction with its users. To this aim we outline a reinforcement learning algorithm for the acquisition of syntax and semantics of English utterances. English declarative sentences are presented to the agent by a teacher in form of utterance meaning pairs (UMP) where the meanings are encoded as formulas of predicate logic. Since MG codifies universal linguistic competence through inference rules, thereby separating innate linguistic knowledge from the contingently acquired lexicon, our approach unifies generative grammar and reinforcement learning, hence potentially resolving the still pending Chomsky-Skinner controversy.
An Introduction to Blockchain Technology and Its Application in Libraries

The concept of blockchain technology came into existence from the first cryptocurrency, Bitcoin. It is a very new, complex and trusted technology, and researchers are working continuously to find a way to apply this disruptive technology in different areas. This paper discusses a brief concept about the blockchain and the application of blockchain technology in libraries & information centres to enhance better library and information services in the future, as well as at present. This paper provides a basic understanding of blockchain technology, like what blockchain and blockchain technology is, its features, advantages, types, and how it will be helpful in education and libraries? Blockchain is a very disruptive technology that can reconfigure all aspects of society and its operations. This technology is immutable and distributed, making it difficult for transactions to be changed, duplicated, or faked. The study is an initiative to make library professionals aware of this emerging disruptive technology and guide them in taking some actions for the appropriate application of blockchain technology in libraries.
Host Vulnerability Analysis Using Supervised Learning Based on Port Response

Vulnerability Scanning is one of the initial stages used in the practice of penetration testing (or pentesting), vulnerability scanning can be said to be a vital process because it can determine how the penetration testing process will be carried out later. The conventional method requires scanning to be done as a whole, which takes a long time and uses a large amount of resources. In this paper, the author proposes a method that applies the Gradient Boosting which is one of a few types from Boosting Algorithm to perform a vulnerability scan based on the port response of the target host. There are only 5 (five) types of ports that being used as a parameters, which all ports have been determined and considered from several books references. And from a several books references itself, it is stated that three of these five ports have a percentage of 65% the most frequent and vulnerable to exploitation activities, these three ports include TCP 22, TCP 80, TCP 443, whereas the two other ports is only an addition to increase exploitation rate percentage which also determined and considered from a book reference, the other two ports is UDP 53, and UDP 80. From the results of tests carried out in 15 times of testing using the CV (or Cross Validation) method, the model built by applying the Gradient Boosting Algorithm gets the results of accuracy, precision, and recall respectively by 98.810%, 98.903%, and 98.812% and with average error rate around 0.00260.
Tube-Embedded Transformer for Pixel Prediction

Multi-task pixel-level learning, which aims to exploit the inter-task interactions to improve the learning of each task, is an important but challenging issue in visual perception and multimedia applications. Measuring the inter-task correlation and intra-task specificity, we propose a tube-embedded transformer (TET) framework for robust multi-task pixel prediction. To facilitate inter-task interactions, we aggregate and project all tasks into a shared tube pool to generate the latent multi-task representation during the coarse-to-fine decoding stages. The resulting task-tube interactions replace the two-by-two task-task interactions to reduce the model complexity significantly. In addition, we introduce the transformer mechanism to adaptively transfer tube features to the target task. Concretely, on the one hand, multi-task features aggregate in the tube to generate the shared feature representation bases; on the other hand, based on the task-tube association and complementarity, the tube outputs the query entry and the weighting coefficients of the target task. Experimentally, on the joint learning of semantic segmentation, depth estimation, and surface normal estimation, the comparison experiments show the superiority of the TET multi-task learning method over other state-of-the-art approaches, and the ablation experiments verify the effectiveness of the TET mechanism.
An Information Security Method Based on Optimized High-Fidelity Reversible Data Hiding

With the rapid development of the Industrial Internet of Things, the social and commercial value of digital information is greatly increased, and the sensitive information of groups and individuals faces more and more problems. Due to providing accurate prediction errors, pixel value ordering (PVO)-based reversible data hiding is an active research topic. In this article, a new PVO-based embedding method is proposed first, in which the adaptive data embedding is adopted according to the difference between the maximum/minimum three pixels. Second, a pixel collecting and sorting model is designed to increase expandable prediction errors. By converting a shifting prediction error into an expandable one, the proposed PVO-based embedding method creates conditions for data embedding. In this way, the space location of pixels in the block can be better utilized to achieve improved capacity-distortion performance. Finally, an adaptive embedding is designed based on the content of the target block and its neighboring blocks. By measuring the complexity of the target pixel block, smoothing blocks are preferentially used and the appropriate embedding is determined by the complexity. The experimental results show that the proposed method outperforms a series of PVO-based multipass embedding methods. The proposed method is expected to address some security issues in the industrial and privacy fields, such as enhancing confidentiality and security during the transmission of sensitive information.