Reviewerly/exHarmony huggingface dataset:
An Approach to Improve Apriori Algorithm for Extraction of Frequent Itemsets

The amount of data generated today regarding volume, generation velocity, and variety is quite immense. This, in turn, has created a great challenge for scientists and researchers. To devise a solution, researchers have suggested a variety of schemes to help alleviate this problem. One of the suggested schemas is Association Rule Mining, and it is primarily focused on finding the associations in transactionlike data. To assist in finding such associations, Frequent Itemsets should be discovered first. Therefore, this research is a new approach to finding Frequent Itemsets and it is based on the Apriori algorithm and Apache Spark distributed platform. Further, we introduce an extended version of Apriori which tends to find Maximal Frequent Itemsets first to help speed up the mining process. The results and comparison to algorithms like YAFIM and HFIM and the original Apriori show the suggested algorithm outperforms them in dense datasets by an average of 38 percent.
Weighted Model Counting Without Parameter Variables

Weighted model counting (WMC) is a powerful computational technique for a variety of problems, especially commonly used for probabilistic inference. However, the standard definition of WMC that puts weights on literals often necessitates WMC encodings to include additional variables and clauses just so each weight can be attached to a literal. This paper complements previous work by considering WMC instances in their full generality and using recent state-of-the-art WMC techniques based on pseudo-Boolean function manipulation, competitive with the more traditional WMC algorithms based on knowledge compilation and backtracking search. We present an algorithm that transforms WMC instances into a format based on pseudo-Boolean functions while eliminating around 43 % of variables on average across various Bayesian network encodings. Moreover, we identify sufficient conditions for such a variable removal to be possible. Our experiments show significant improvement in WMC-based Bayesian network inference, outperforming the current state of the art.
Leveraging Intra-Domain Knowledge to Strengthen Cross-Domain Crowd Counting

Unsupervised cross-domain counting research using synthetic datasets becomes imminent when considering the laborious labeling for supervised methods. However, the existing methods only focus on learning domain shared knowledge to narrow the gap between the source domain and target domain (inter-domain gap). Nevertheless, these methods do not consider the enormous distribution gap among the target domain data itself (intra-domain gap). In this paper, we propose a two-step domain adaptation method with multi-level feature response branches, which further uses the intra-domain knowledge to strengthen the target domain’s adaptability. Specifically, we first use different feature response branches to learn inter-domain knowledge more robustly, reducing the prediction inconsistency of different scenarios. Subsequently, the trained model is used to generate pseudo-labels for the target domain. The entire model was retrained by using pseudo-labels. Various experiments on synthetic dataset GCC and three real public datasets validate our proposed method’s availability with higher accuracy.
Developing Reading Skills through Effective Reading Approaches

The research paper explores that how the students can develop their reading skills by using effective reading approaches. It is acknowledged that the reading comprehension is one of the most important parts in the English curriculum in all education level of Bangladesh. It is observant that teaching reading approaches are considered as an important procedure to develop the skills of the Bangladeshi students. In Bangladesh most of the teachers do not have the idea of teaching reading approaches. For this reason, the teachers should need to enhance their skills, knowledge and gathering proper idea about effective reading approaches as well as need to prepare themselves to utilize their practical experiences and knowledge on to their students. So the prime purpose of this study to show the effective reading approaches in order to develop studentâ€Ÿs reading skills in English. From June to December in 2018, an action research has been applied to a number of 40 students at higher secondary level in Manikganj, Bangladesh. The most important question of the study is "could the reading approaches help student's English reading comprehension studies?" The outcome of the study specifies that students who have been tutored about the reading strategies have a development to a great level.
Visualization Support for Multi-criteria Decision Making in Software Issue Propagation

Finding the propagation scope for various types of issues in Software Product Lines (SPLs) is a complicated Multi-Criteria Decision Making (MCDM) problem. This task often requires human-in-the-loop data analysis, which covers not only multiple product attributes but also contextual information (e.g., internal policy, customer requirements, exceptional cases, cost efficiency). We propose an interactive visualization tool to support MCDM tasks in software issue propagation based on the user's mental model. Our tool enables users to explore multiple criteria with their insight intuitively and find the appropriate propagation scope.
SGAIN, WSGAIN-CP and WSGAIN-GP: Novel GAN Methods for Missing Data Imputation

Real-world datasets often have missing values, which hinders the use of a large number of machine learning (ML) estimators. To overcome this limitation in a data analysis pipeline, data points may be deleted in a data preprocessing stage. However, an alternative better solution is data imputation.
Sequent Calculi for Intuitionistic Gödel–Löb Logic

This paper provides a study of sequent calculi for intuitionistic Gödel–Löb logic (iGL), which is the intuitionistic version of the classical modal logic GL, known as Gödel–Löb logic. We present two different sequent calculi, one of which we prove to be the terminating version of the other. We study those systems from a proof-theoretic point of view. One of our main results is a syntactic proof for the cut-admissibility result for those systems. Finally, we apply this to prove Craig interpolation for intuitionistic Gödel–Löb logic.
A Multi-Agent Communication Based Model for Nested Named Entity Recognition

Named entity recognition (NER) is a basic task in natural language processing. Traditionally, sequence labeling methods are applied to named entity recognition and achieve good performance. However, sequence labeling methods can not be straightly applied to recognize nested named entities where an entity is included in another entity. Recently, some new methods are proposed for nested named entity recognition. Most of them ignore that entity type information can help recognize entity boundaries or ignore that entity boundary information can help recognize entity type, which limits the performance of nested NER. Considering the effect of entity type information and entity boundary information, in this paper, we propose a multi-agent communication module to utilize these two kinds of information. Our multi-agent communication module contains a type labeling agent and a boundary labeling agent. The type labeling agent can utilize boundary information from boundary labeling agent to recognize entity type. And the boundary labeling agent can utilize type information from type labeling agent to recognize entity boundaries. They communicate and collaborate iteratively to finish the entity boundary recognition. Compared with previous methods, with the assist of entity type information and entity boundary information, the performance of boundary recognition improves. The improvement of boundary recognition is beneficial to recognizing nested named entities, which improves the performance of nested named entity recognition. Empirical experiments are conducted on three nested NER datasets. And the experimental results show the effectiveness of our model.
Design of a Customer-Centric Surveillance System for ATM Banking Transactions using Remote Certification Technique

Automated Teller Machine (ATM) is a major tool for electronic banking (e-Banking). It enables the availability of banking services anytime and anywhere. Presently, most ATMs communicate only with the banking system networks for security monitoring and enforcements. This paper presents a customer-aware surveillance system for all attempted ATM banking activities. Employing a graphical modeling tool (Unified Modeling Language), the design integrates an additional input, through an inbuilt IP Camera that stealthily captures the ATM user facial image, which is automatically transmitted to the mobile device of the bank account owner, through some dedicated artificial intelligent agents, for remote certification, which either authorizes the transaction appropriately or signals a security-violation alert to the banking security system. Thus, online and real-time monitoring of ATM banking transactions is enabled, reporting suspected unusual attempts to account owner and bank security unit. Hence, customer-level visibility of ATM banking security, through remote certification of a proxy ATM user, is made possible.
Towards Better Valorisation of Industrial Minerals and Rocks in Serbia—Case Study of Industrial Clays

The improvement in valorisation of industrial minerals and rocks in Serbia is related to improvement of legislation, and to adoption of best possible techniques for geological exploration and estimation of resources/reserves. Therefore, we analyzed the actual practice in the field of geological exploration which has not changed significantly in the last 30 years, as well as legislation related to the mining industry and access to land. The improvement of legislation mostly depends on the state authorities. Contrary to that, the enhancement of geological exploration methodology should be the issue of experts working in that area of the mining industry but is also very much related to the existing legislation. Serbia still has very strictly prescribed procedures, which are generally good; however, it is an overly complicated and restrictive system for performing geological exploration and evaluation of mineral deposits. The most important improvements in legislation in the last twenty years are presented and discussed, as well as problems that still need to be solved and solutions found to fully understand the potential of industrial minerals and rocks in Serbia.
Cayley graphs of dihedral groups having perfect edge state transfer

Perfect state transfer plays a crucial role in quantum information processing and quantum computation. However, perfect state transfer is a rare phenomenon. As a generalization of perfect state transfer, perfect edge state transfer overcomes this disadvantage and there are more graphs admitting perfect edge state transfer than admitting perfect state transfer. In this paper, we reveal the relations between perfect state transfer and perfect edge state transfer on Cayley graphs. Furthermore, we present necessary and sufficient conditions for the existence of perfect edge state transfer on Cayley graphs of dihedral groups. With these conditions, several concrete constructions of Cayley graphs admitting perfect edge state transfer are proposed. Notably, this is the first attempt at perfect edge state transfer on Cayley graphs.
Multi-source Non-malleable Extractors and Applications

We introduce a natural generalization of two-source non-malleable extractors (Cheragachi and Guruswami, TCC 2014) called as multi-source non-malleable extractors. Multi-source non-malleable extractors are special independent source extractors which satisfy an additional non-malleability property. This property requires that the output of the extractor remains close to uniform even conditioned on its output generated by tampering several sources together. We formally define this primitive, give a construction that is secure against a wide class of tampering functions, and provide applications. More specifically, we obtain the following results:
The Challenge of Only One Flow Problem for Traffic Classification in Identity Obfuscation Environments

As encrypted traffic grows, network flow classification has become a significant issue because of the impossibility to parse the payload in an encrypted packet. A possible packet sniffing location for organizations is an under control gateway between intranet and internet to inspect network traffic. However, when an intranet user uses an identity obfuscation protocol such as VPN or TOR, the packet IP and port would be rewritten to preserve user privacy. The same user's packet sniffed between a user and TOR entry node/VPN proxy always has the same 5-tuples (packets with the same source IP, destination IP, source port, destination port, and IP protocol defined as flow). Thus, we cannot rely on the 5-tuples rule to split traffic into flows. This challenge is called the “only one flow problem” and poses an obstacle for flow classification. A previous solution uses timeout value to determine flow separation points to address this issue. However, the predefined static time threshold cannot fit all user habits, which leads to separation errors. To overcome timeout limitations, we propose a flexible method called AI-FlowDet by leveraging the scene change concept and a CNN model to find behavior change points of traffic based on learning data. AI-FlowDet can apply to the traffic of the identity obfuscation protocols. Next, we propose 294 size-based and direction-based features that can be used with AI-FlowDet to evaluate flow type classification performance. Every experiment leverages different machine learning algorithms. The results show that AI-FlowDet with the proposed features can achieve 98.5% weighted accuracy, which is increased by 12.6% versus the previous timeout method with baseline features. We proved that the proposed splitting methods for the only one flow problem and proposed features for flow type classification are effective based on the good results obtained for both the VPN and TOR datasets.
Affective Teacher Tools: Affective Class Report Card and Dashboard

While using online learning software, students demonstrate many reactions, various levels of engagement, and emotions (e.g. confusion, excitement, frustration). Having such information automatically accessible to teachers (or digital tutors) can aid in understanding how students progress and suggest when and who needs further assistance. We developed the Affective Teacher Tools, a report card and dashboard that present teachers measures of students’ engagement and affective states as they use an online tutoring system, MathSpring.org, which supports students as they practice mathematics problem-solving at the middle school level. We conducted two development and research studies – one that assesses teachers perception of the affective report card and a second study that assesses a live affective dashboard, which senses students’ affect and performance in a live class that is using MathSpring. We use computer vision techniques to measure students’ engagement and affective states from their facial expressions while they use the tutoring system. In this paper, we summarize both the report card and affective dashboard, the research studies and results, and we also discuss implications, and future planned experiments for the next phase of this research.
Similarity Based Collaborative Filtering Model for Movie Recommendation Systems

Recommendation systems are information filtering tools and techniques which provide valuable suggestions to users about items they may like by considering various attributes. In Recommendation algorithms, similarity metrics are the main components and performances of these will have direct influence on the recommendations generated. Selecting suitable Similarity measures will be a major factor in improving performance of data mining techniques. These are mainly used for finding the similarity between the users or items as per the requirement for different purposes. In this paper, performance of various similarity metrics applied in recommendation tasks are compared by considering MovieLens dataset for developing a recommendation system for movies using user-based and item-based.
MS-AFF: a novel semantic segmentation approach for buried object based on multi-scale attentional feature fusion

Infrared technology is widely used in buried object detection since it can capture the heat radiated outward from the target objects. Compared to the visible images, infrared images present poor resolution, low contrast, and fuzzy visual effect, making it challenging to detect the target objects, specifically in complex backgrounds. In recent years, deep learning-based methods have made significant improvements in detection tasks. However, infrared images of buried objects are difficult to obtain. Less training samples, worse performance of deep-learning based detection. We raise a multi-scale attentional feature fusion module for infrared image semantic segmentation to solve this problem. Precisely, we integrate a series of feature maps from different levels by an atrous spatial pyramid structure. In this way, the model can obtain rich representation ability on the infrared images. Besides, a global spatial information attention module is employed to let the model focus on the target region and reduce disturbance in infrared images’ background. In addition, we propose an infrared segmentation dataset based on the infrared thermal imaging system. Finally, we use state-of-the-art object detection methods to train the buried object dataset and compare them with our proposed method. Extensive experiments on infrared segmentation dataset demonstrate the superiority of our method.
Review Polarity-Wise Recommender

The de facto review-involved recommender systems, using review information to enhance recommendation, have received increasing interest over the past years. Thereinto, one advanced branch is to extract salient aspects from textual reviews (i.e., the item attributes that users express) and combine them with the matrix factorization (MF) technique. However, the existing approaches all ignore the fact that semantically different reviews often include opposite aspect information. In particular, positive reviews usually express aspects that users prefer, while the negative ones describe aspects that users dislike. As a result, it may mislead the recommender systems into making incorrect decisions pertaining to user preference modeling. Toward this end, in this article, we present a review polarity-wise recommender model, dubbed as RPR, to discriminately treat reviews with different polarities. To be specific, in this model, positive and negative reviews are separately gathered and used to model the user-preferred and user-rejected aspects, respectively. Besides, to overcome the imbalance of semantically different reviews, we further develop an aspect-aware importance weighting strategy to align the aspect importance for these two kinds of reviews. Extensive experiments conducted on eight benchmark datasets have demonstrated the superiority of our model when compared with several state-of-the-art review-involved baselines. Moreover, our method can provide certain explanations to real-world rating prediction scenarios.
Asymmetric Stereo Color Transfer

Dual-camera systems containing a color camera and a monochrome camera are widely equipped on smartphones. The color camera captures chrominance information while the monochrome camera captures fine details, which causes asymmetry across spectral and spatial dimensions. In these imaging systems, the chrominance information of low-resolution (LR) color images and the spatial information of high-resolution (HR) monochrome images are highly complementary. In this paper, we propose an elaborate convolutional neural network to recover HR color images by transferring color information from LR color images to HR monochrome images. The network contains a novel feature extraction module named U-ASPP and an asymmetric parallax attention module (APAM). Our network achieves state-of-the-art performance on the Flickr1024 stereo dataset with high efficiency. Moreover, the effectiveness of our trained network is validated in real-world asymmetric image pairs captured by a smartphone, which demonstrates that our method has high generalization capability in real-world imaging systems.
Multimodal-Semantic Context-Aware Graph Neural Network for Group Activity Recognition

Group activities in videos involve visual interaction contexts in multiple modalities between actors, and co-occurrence between individual action labels. However, most of the current group activity recognition methods either model actor-actor relations based on the single RGB modality, or ignore exploiting the label relationships. To capture these rich visual and semantic contexts, we propose a multimodal-semantic context-aware graph neural network (MSCA-GNN). Specifically, we first build two visual sub-graphs based on the appearance cues and motion patterns extracted from RGB and optical-flow modalities, respectively. Then, two attention-based aggregators are proposed to refine each node, by gathering representations from other nodes and heterogeneous modalities. In addition, a semantic graph is constructed based on linguistic embeddings to model label relationships. We employ a bi-directional mapping learning strategy to further integrate the information from both multimodal visual and semantic graphs. Experimental results on two group activity benchmarks show the effectiveness of the proposed method.
ClaimLinker: Linking Text to a Knowledge Graph of Fact-checked Claims

We present ClaimLinker, a Web service and API that links arbitrary text to a knowledge graph of fact-checked claims, offering a novel kind of semantic annotation of unstructured content. Given a text, ClaimLinker matches parts of it to fact-checked claims mined from popular fact-checking sites and integrated into a rich knowledge graph, thus allowing the further exploration of the linked claims and their associations. The application is based on a scalable, fully unsupervised and modular approach that does not require training or tuning and which can serve high quality results at real time (outperforming existing unsupervised methods). This allows its easy deployment for different contexts and application scenarios.
Underwater Object Detection model based on YOLOv3 architecture using Deep Neural Networks

While analysing the strategic areas of underwater surveillance as well as resource exploration or scrutiny, object detection plays a crucial role. The capability of analysing the objects along with extracting the inherent information emphasizes the high research value of object detection in the field of underwater as well as the low light medium. The conventional systems serving this objective utilizes traditional handcrafting algorithms and computational methodologies which is highly inefficient. This brings out the need of computer vision based systems which are basically automated and will be a learning based model. This paper aims to propose a model to automatically detect underwater object using YOLOv3 architecture with darknet framework and deep learning. This paper also explores the possibility of custom training of YOLOv3 based underwater object detection models using Fish 4 Knowledge dataset.