Reviewerly/exHarmony huggingface dataset:
Gated Sequential Recommendation System with Social and Textual Information Under Dynamic Contexts

Recommendation systems are undergoing plentiful practices in research and industry to improve consumers’ satisfaction. In recent years, many research papers leverage abundant data from heterogeneous information sources to grasp diverse preferences and improve overall accuracy. Some noticeable papers proposed to extract users’ preference from information along with ratings such as reviews or social relations. However, their combinations are generally static and less expressive without considerations on dynamic contexts in users’ purchases and choices. In this paper, we propose Heterogeneous Information Sequential Recom-mendation System (HISR), a dual-GRU structure that builds the sequential dynamics behind the customer behaviors, and combines preference features from review text and social attentional relations under dynamics contexts. A novel gating layer is applied to dynamically select and explicitly combine two views of data. Moreover, in social attention module, temporal textual information is brought in as a clue to dynamically select friends that are helpful for contextual purchase intentions as an implicit combination. We validate our proposed method on two large subsets of real-world local business dataset Yelp, and our method outperforms the state of the art methods on related tasks including social, sequential and heterogeneous recommendations.
Misper-Bayes: A Bayesian Network Model for Missing Person Investigations

Bayesian Networks are probabilistic graph models that can be used for classification, prediction, diagnosis and parameter learning. Probabilities can be inferred from the models and missing values can be imputed, based on probability theory. Missing person cases place a strain on the already overstretched resources of Police Forces. Such cases predominantly come from at risk groups such as children in care and people suffering from depression or dementia. Current approaches for dealing with such cases are manual and rely upon empirical studies and domain knowledge. This paper proposes the use of a Bayesian Network model, which can be used to predict the likely location of a missing person (misper) for a number of at risk groups. The model is evaluated using a set of misper cases and results compare very favourably with those of the manual processes currently used by UK Police forces. The novel approach described provides both a theoretical foundation and a practical framework for the future development of a decision support system. In addition to the model, a contribution is made through guidelines, which recount experiences in learning a Bayesian Network from data.
Data-Driven Construction of User Utility Functions from Radio Connection Traces in LTE

In recent years, the number of services in mobile networks has increased exponentially. This increase has forced operators to change their network management processes to ensure an adequate Quality of Experience (QoE). A key component in QoE management is the availability of a precise QoE model for every service that reflects the impact of network performance variations on the end-user experience. In this work, an automatic method is presented for deriving Quality-of-Service (QoS) thresholds in analytical QoE models of several services from radio connection traces collected in an Long Term Evolution (LTE) network. Such QoS thresholds reflect the minimum connection performance below which a user gives up its connection. The proposed method relies on the fact that user experience influences the traffic volume requested by users. Method assessment is performed with real connection traces taken from live LTE networks. Results confirm that packet delay or user throughput are critical factors for user experience in the analyzed services.
BIRL: Bidirectional-Interaction Reinforcement Learning Framework for Joint Relation and Entity Extraction

Joint relation and entity extraction is a crucial technology to construct a knowledge graph. However, most existing methods (i) can not fully capture the beneficial connections between relation extraction and entity extraction tasks, and (ii) can not combat the noisy data in the training dataset. To overcome these problems, this paper proposes a novel Bidirectional-Interaction Reinforcement Learning (BIRL) framework, to extract entities and relations from plain text. Especially, we apply a relation calibration RL policy to (i) measure relation consistency and enhance the bidirectional interaction between entity mentions and relation types; and (ii) guide a dynamic selection strategy to remove noise from training dataset. Moreover, we also introduce a data augmentation module for bridging the gap of data-efficiency and generalization. Empirical studies on two real-world datasets confirm the superiority of the proposed model.
Mixture-Based Open World Face Recognition

Face Recognition (FR) is a challenging task, especially when dealing with unknown identities. While Open-Set Face Recognition (OSFR) assigns a single class to all unfamiliar subjects, Open-World Face Recognition (OWFR) employs an incremental approach, creating a new class for each unknown individual. Current OWFR approaches still present limitations, mainly regarding the accuracy gap to standard closed-set approaches and execution time. This paper proposes a fast and simple mixture-based OWFR algorithm that tackles the execution time issue while avoiding accuracy decay. The proposed method uses data curve representations and Universal Background Models based on Gaussian Mixture Models. Experimental results show that the proposed approach achieves competitive performance, considering accuracy and execution time, in both closed-set and open-world scenarios.
GIS cloud computing based government Big Data analysis platform

Transparency, wisdom and responsibility are new requirements for government work in the era of Big Data. Government Big Data is changing the traditional mode of government work, which needs corresponding technical means for auxiliary analysis and application. This paper first analyzes the spatial related government Big Data, then introduces association analysis method of government Big Data, based on GIS cloud computing technology, it proposes the government Big Data platform architecture and main functions, and finally explores the application analysis case based on traffic accident data.
Improving relation extraction beyond sentence boundaries using attention

Reation Extraction(RE) is the subprocess of Information Extraction(IE) which focuses on determining and extracting the reation between two participating entities. Most of the past work focus on extracting relations within a sentence. Nowadays, research on relation extraction focuses on identifying and determining relationship between participating entities across sentences. This paper proposes a bi-directional GRU model with self attention mechanism for inter-sentential relation extraction. First, a bi-directional GRU with self attention mechanism is used to capture the information about the relation from intermediary terms between two entities. Then a bi-directional GRU is used to capture the information represented by entities, which plays a vital role in relation extraction. Finally, the proposed model combines both word embeddings and entity embeddings for extracting a relation. Experimental results show that the proposed Bi-directional GRU model can deliver state-of-the-art results on relation classification. Application of self attention mechanism on intermediary terms improves the performance of relation extraction. Experimental results show that F-measure of the proposed inter-sentential relation extraction is 0.75, which is better than state-of-the-art systems of inter-sentential relation extraction with the same dataset.
Sketch Face Recognition via Cascaded Transformation Generation Network

Sketch face recognition refers to matching photos with sketches, which has effectively been used in various applications ranging from law enforcement agencies to digital entertainment. However, due to the large modality gap between photos and sketches, sketch face recognition remains a challenging task at present. To reduce the domain gap between the sketches and photos, this paper proposes a cascaded transformation generation network for cross-modality image generation and sketch face recognition simultaneously. The proposed cascaded transformation generation network is composed of a generation module, a cascaded feature transformation module, and a classifier module. The generation module aims to generate a high quality cross-modality image, the cascaded feature transformation module extracts high-level semantic features for generation and recognition simultaneously, the classifier module is used to complete sketch face recognition. The proposed transformation generation network is trained in an end-to-end manner, it strengthens the recognition accuracy by the generated images. The recognition performance is verified on the UoM-SGFSv2, e-PRIP, and CUFSF datasets; experimental results show that the proposed method is better than other state-of-the-art methods.
Query Spelling Correction

In this chapter we will focus on the discussion of an important type of query understandings: Query spelling correction, especially on the web search queries. Queries issued by web search engine users usually contain errors and misused words/phrases. Although a user might have a clear intent in her mind, inferring the query’s intent in this case becomes difficult because of the edit errors or vocabulary gap between the user’s ideal query and the query issued to the search engine. Because of this, query spelling correction is a crucial component of modern search engines. The performance of the query spelling correction component will affect all other parts of the search engine. In this chapter we will first introduce early works on query spelling correction based on edit distance. Then we will discuss the noisy channel model to the problem. After that we will introduce modern approaches to more complex and realistic problem setup where it involves multiple types of spelling errors. Finally we will also summarize other components needed to support a modern large-scale query spelling correction system.
Mastering by Students of the Second Grades of the Universal Educational Action of Classification

The article discusses the problem of the formation of a universal educational action of classification among junior schoolchildren of the second grade, which refers to intellectual operations of a cognitive nature. Three stages of the formation of the classification action are revealed: the choice of an essential basis for grouping objects; classification according to a given basis; classification on an independently chosen basis. Examples of assignments developed on the basis of the content of various elementary school subjects are given, in the process of completing which students of the second grade consistently master the logical actions of classification.
Image-Enhanced Multi-Modal Representation for Local Topic Detection from Social Media

Detecting local topics from social media is an important task for many applications, ranging from event tracking to emergency warning. Recent years have witnessed growing interest in leveraging multi-modal social media information for local topic detection. However, existing methods suffer great limitation in capturing comprehensive semantics from social media and fall short in bridging semantic gaps among multi-modal contents, i.e., some of them overlook visual information which contains rich semantics, others neglect indirect semantic correlation among multi-modal information. To deal with above problems, we propose an effective local topic detection method with two major modules, called IEMM-LTD. The first module is an image-enhanced multi-modal embedding learner to generate embeddings for words and images, which can capture comprehensive semantics and preserve both direct and indirect semantic correlations. The second module is an embedding based topic model to detect local topics represented by both words and images, which adopts different prior distributions to model multi-modal information separately and can find the number of topics automatically. We evaluate the effectiveness of IEMM-LTD on two real-world tweet datasets, the experimental results show that IEMM-LTD has achieved the best performance compared to the existing state-of-the-art methods.
Effective Multimodal Opinion Mining Framework Using Ensemble Learning Technique for Disease Risk Prediction

Multimodal sentiment analysis framework is tremendously applied in the medical and healthcare sectors. Identifying the depression, differently disabled person speech recognition, Alzheimer, low pressure or heart problems and those kinds of impairments are widely addressed. Video data is processed for mining feature polarity from acoustic, linguistic and visual data upon extraction from the same. The feature data set extracted from the YouTube videos contains comments, likes, views and shares for expressing the polarity of information conveyed through the streaming videos. Static information from a video file is extracted in the form of linguistic representation. Musical data extracted and transformed in the linguistic form is used for polarity classification using ensemble-based random forest algorithm which has encountered with the error rate of 4.76%. Short feature vectors are expressed in the visualizing musical data and trending YouTube videos data set for utilizing the transformed and SF vectors from video and musical data. Accuracy of the ensemble-based learning is obtained as 91.6% which is tougher than any other algorithms to achieve using the same set of machine learning algorithms. Proper filter wrapping of batch data is used for the split ratio of 5 percentage split window. When SVM is used in discrimination with ensemble random forest algorithm, the predicted results contain an error rate of 2.64% which improves the accuracy of the classifier along the soft margin with an accuracy of 96.53% accuracy.
Homomorphic commitment scheme with constant output locality

Output locality is the complexity measure of computational efficiency for Boolean functions. The function can be eficient if the function has a low output locality. Both security and eficiency are crucial but trade-off concepts of any cryptographic protocol. Recently, a hash function with output locality 4 and still secure under (M, δ)-bSVP assumption was pro-posed. This result block the mold. Further, in 2019, a low constant output locality commitment scheme was constructed. Constant output locality means that the output locality does not depend on the input. Since commitment schemes are fundamental building blocks of various secure protocols, it is useful if a homomorphic commitment with a low output locality is realized. In this paper, we propose a homomorphic commitment scheme with output locality 3. We also give parameter suggestion for our scheme.
Dynamically Weighted Ensemble Models for Automatic Speech Recognition

In machine learning, training multiple models for the same task, and using the outputs from all the models helps reduce the variance of the combined result. Using an ensemble of models in classification tasks such as Automatic Speech Recognition (ASR) improves the accuracy across different target domains such as multiple accents, environmental conditions, and other scenarios. It is possible to select model weights for the ensemble in numerous ways. A classifier trained to identify target domain, a simple averaging function, or an exhaustive grid search are the common approaches to obtain suitable weights. All these methods suffer either in choosing sub-optimal weights or by being computationally expensive. We propose a novel and practical method for dynamic weight selection in an ensemble, which can approximate a grid search in a time-efficient manner. We show that a combination of weights always performs better than assigning uniform weights for all models. Our algorithm can utilize a validation set if available or find weights dynamically from the input utterance itself. Experiments conducted for various ASR tasks show that the proposed method outperforms the uniformly weighted ensemble in terms of Word Error Rate (WER) in our experiments.
The role of governance in SDG through public trust in government: Study in selected OIC member states

Abstract This study’s focus addresses how governance—the leading indicators in the 16th Goal of Sustainable Development Goals (SDG)—affects the government public trust that plays an essential role in SDG achievement. This study examines the effect of governance on public trust in the government using six governance indicators from the World Governance Index for 24 Islamic Cooperation Organizations (OIC) member countries during the 2011-2018 period. This study also examines the effect of two macroeconomic indicators, namely real GDP growth and the unemployment rate, to determine the potential influence of a country’s economic conditions in determining the level of public trust in the government. This study uses the Fixed and Random Effect Regression for panel data. The results show that two governance indicators, Political Stability and Absence of Violence/Terrorism and Control of Corruption, proved to influence the level of public trust in the government positively. However, Voice and Accountability indicator and the unemployment rate are found to negatively affect public trust in government. Standardized Coefficients Regression is also used to compare the effects of governance and macroeconomic indicators and it is found that macroeconomic conditions’ influence is slightly above the governance aspects in determining the level of public trust in government.
An Optimal Approach for E-Commerce Application Service on to the Public Cloud Environment

Recently, the demand for cloud computing increases rapidly due to its multiple benefits to the customers like scalability, efficiency, user friendly, 24x7 availability, and so on. Cloud becomes very popular due to its salient features which can incorporate various domains such as the Internet of Things (IoT), Big Data, Machine Learning for creating easy and flexible computation mechanisms. The concept of Enterprise Resource Planning (ERP) having the mechanism in doing the operations in various domains in the field of research. Generally, we define only these services but when the services came into mind whatever the cloud provides it is treated as a service such as a network as a service, storage as a service, data as a service, security as a service, Privacy as a service, etc., whatever the services offered by the cloud are undefined There are various clouds such as Public clouds, Private clouds, Hybrid clouds, and Community clouds.The concept of ERP technology is merged with the various services offered by the cloud such as SaaS, PaaS, and IaaS. The working mechanism of the cloud is quite different compared to normal applications over ERP applications. However, the cloud services definitions were based upon a person to person or organization to organization in terms of flexibility, application usage, portability, reliability, scalability, etc., the future of cloud computing technology is very huge in all the sectors like education, clinical, pharmacy, industries, etc.
Meta-tracking and Dominant Motion Patterns at the Macroscopic Crowd Level

This paper presents a method for crowd motion segmentation and generating dominant motion patterns at the macroscopic crowd level, where a crowd is treated as an entity. In this approach, the dominant motion patterns, as a base for behaviour analysis of a mass of people, are the focus of interest. Dominant motion patterns are generated based on meta-trajectories. A meta-trajectory is defined as a set of tracklets and/or trajectories of entities in the crowd. The entities are particles initially organized as a uniform grid which is overlaid on a flow field. To estimate the flow field, a dense optical flow is used. Based on advection of the particles, tracklets/trajectories are obtained. They are grouped by a graph-based clustering algorithm and meta-trajectories are obtained. By overlapping meta-trajectories with the quantized orientation of the average optical flow field dominant motion patterns are obtained. The preliminary experimental results of the proposed method are given for a subset of UCF dataset, a subset of Crowd Saliency Detection dataset, our own FER dataset and computer crowd simulation videos of characteristic behaviour.
Dynamic matching of a 300-mm-aperture high-precision interference cavity via a double-ring-point support

The precision of a large-aperture interference cavity is limited by the deformation of the flat owing to support and gravity. In this study, we proposed a double-ring-point support scheme for dynamic matching. The theory was derived from elastic deformation. To obtain an optimal support structure and force operation, the support locations, number of discrete support points, and force distribution were analysed quantitatively using finite element method. Furthermore, matching measurements were analysed in detail, and dynamic matching was implemented by matching different transmission flats to achieve high-precision interference cavity. The identical matching precision demonstrated the effectiveness of the proposed scheme. Finally, with the dynamic matching, the precision of interference cavity is improved from λ/7 to λ/27 (peak-to-valley) for a 300-mm aperture.
Implementing CALL System using Natural Language Processing Tools

As it seems that computer and the similar devices are the part of our day to day activities and they are widespread in schools, organizations and our homes. In the similar way if we are looking the today’s scenario the knowledge of any second (foreign) language seems to be essential for students basically those are doing their education.Computer Assisted Language Learning (CALL) is actually a way to provide the facility to use the language resources and learning material in a formal manner so that a person can easily attain the required skill.Now a day’s CALL is being used at different levels of education. Normally in CALL it is providing the features for a single language, so if a bilingual function to be included in CALL it will be more beneficial. Here it is to be explored that how Natural language processing tools are helpful in the development and the implementation of a CALL system that is having bilingual features and also the impact of implementing CALL over the traditional method of language teaching.
Enhancing Learning Object Repositories with Ontologies

In this paper, we present a review on the use of ontologies in learning object repositories systems for searching and suggestion purposes, considering its adoption for the seaThings project that aims to promote the ocean literacy. We also describe the use case of the Cognix system and Agent-based Learning Objects - OBAA metadata standard for learning objects which is being implemented on a new learning objects repository. This system includes concepts from artificial intelligence such as agents and ontologies that aim to improve the search and so making the system more responsive. This paper also sugests how an ontology can be implemented, using metadata in learning object repositories to provide relevant aspects, such as interoperability, reuse, and searching.