Reviewerly/exHarmony huggingface dataset:
Cut elimination and complexity bounds for intuitionistic epistemic logic

Abstract The formal system of intuitionistic epistemic logic (IEL) was proposed by S. Artemov and T. Protopopescu. It provides the formal foundation for the study of knowledge from an intuitionistic point of view based on Brouwer–Heyting–Kolmogorov semantics of intuitionism. We construct a cut-free sequent calculus for IEL and establish that polynomial space is sufficient for the proof search in it. We prove that IEL is PSPACE-complete.
Multi-Intentional Description of Learning Semantic Web Services

E-learning systems use web service technology to develop distributed applications. Therefore, with the tremendous growth in the number of web services, finding the proper services while ensuring the independence and reusability of the learning objects in a different context has become an important issue and has attracted much interest. This article first proposes an extension of the Ontology Web Language for Services Learning Object (OWLS-LO) model to describe a multi-intentional learning object. This description ensures accessibility to learning objects. This research then presents a service discovery mechanism that uses the new semantic model for service matching. Experimental results show that the proposed semantic discovery mechanism using multi-intention model performs better than discovery mechanism based on single intention.
Dialogue Environments are Different from Games: Investigating Variants of Deep Q-Networks for Dialogue Policy

The dialogue manager is an important component in a task-oriented dialogue system, which focuses on deciding dialogue policy given the dialogue state in order to fulfill the user goal. Learning dialogue policy is usually framed as a reinforcement learning (RL) problem, where the objective is to maximize the reward indicating whether the conversation is successful and how efficient it is. However, even there are many variants of deep Q-networks (DQN) achieving better performance on game playing scenarios, no prior work analyzed the performance of dialogue policy learning using these improved versions. Considering that dialogue interactions differ a lot from game playing, this paper investigates variants of DQN models together with different exploration strategies in a benchmark experimental setup, and then we examine which RL methods are more suitable for task-completion dialogue policy learning <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
What counts as an exemplar model, anyway? A commentary on Ambridge (2020)

Ambridge calls for exemplar-based accounts of language acquisition. Do modern neural networks such as transformers or word2vec – which have been extremely successful in modern natural language processing (NLP) applications – count? Although these models often have ample parametric complexity to store exemplars from their training data, they also go far beyond simple storage by processing and compressing the input via their architectural constraints. The resulting representations have been shown to encode emergent abstractions. If these models are exemplar-based then Ambridge’s theory only weakly constrains future work. On the other hand, if these systems are not exemplar models, why is it that true exemplar models are not contenders in modern NLP?
Maximum Likelihood Estimation using Word Based Statistical Machine Translation

Machine Translation is the programmed interpretation of one natural language to another natural language. Human languages can be processed by computers via grammars/rules or via statistics/data. There are many machine translation techniques which are either Rule-based or Knowledge-based. In this paper we will discuss the Statistical Machine Translation (SMT) techniques. The Statistical Machine Translation makes use of statistics or data to find the translation of a given source text. In word primarily based SMT, the simple unit of translation is a word. As a consequence words in an input sentence are translated phrase by phrase individually and these words are than sooner or later organized in a particular way to get the target sentence. The paper discusses numerous fashions for the word based Statistical Machine translation. The paper is written with reference to Kashmiri Language, as not much work has been done in this regard.
Enhancement of supply chain management by integrating Blockchain technology

Due to its wide involvement in different fields: industry, maritime industry, trade, supply chain management has been greatly expanded to cover a large and complex network of stakeholders in the production and distribution process. The multitude of intermediaries in this process leads difficulties in communication, control, time saving... In this paper, we propose the Blockchain technology as a solution for decentralization and disintermediation of operations in the supply chain, and its effect on the maritime industry.
Interval Valued Intuitionistic Fuzzy Evaluations for Analysis of Students’ Knowledge

In the paper is proposed a method for evaluation of student’s knowledge obtained in the university e-learning courses. For the assessment of the student’s solution of the respective assessment units the theory of intuitionistic fuzzy and interval valued intuitionistic fuzzy sets is used. The obtained intuitionistic fuzzy estimations reflect the degree of each student’s good performances, or poor performances, for each assessment unit and for all units.
DeepLite: Real-Time Deep Learning Framework for Neighborhood Analysis

In this paper, we propose a new framework, DeepLite, for real-time deep learning on the edge. In DeepLite, a network of multiple deep learning models is designed to conduct the context-aware inferencing using real-time deep learning technologies. Comprehensively, DeepLite has several innovative concepts as follows: 1) an inference network of deep learning models/containers, 2) invocation of a new model based on the output of the previous model’s inferencing, 3) intelligent containers for models, 4) plug-and-play model/container, and 5) microservice architecture on the edge. DeepLite has been evaluated via a case study, NeighborNets which was based on an inference network of several deep learning models deployed to edge devices for computer vision, e.g., object detection algorithms to compute a diverse range of aspects, such as house types, level of greenery, house age, traffic conditions, and the types of recreational facilities.
Robust, Extensible, and Fast: Teamed Classifiers for Vehicle Tracking in Multi-Camera Networks

As camera networks have become more ubiquitous over the past decade, the research interest in video management has shifted to analytics on multi-camera networks. This includes performing tasks such as object detection, attribute identification, and vehicle/person tracking across different cameras without overlap. Current frameworks for management are designed for multi-camera networks in a closed dataset environment where there is limited variability in cameras and characteristics of the surveillance environment are well known. Furthermore, current frameworks are designed for offline analytics with guidance from human operators for forensic applications. This paper presents a teamed classifier framework for video analytics in heterogeneous many-camera networks with adversarial conditions such as multi-scale, multi-resolution cameras capturing the environment with varying occlusion, blur, and orientations. We describe an implementation for vehicle tracking and surveillance, where we implement a system that performs automated tracking of all vehicles all the time. Our evaluations show the teamed classifier framework is robust to adversarial conditions, extensible to changing video characteristics such as new vehicle types/brands and new cameras, and offers real-time performance compared to current offline video analytics approaches.
SENSE: Semantically Enhanced Node Sequence Embedding

Effectively representing graph node sequences in the form of vector embeddings is critical to many applications. We achieve this by (i) first learning vector embeddings of single graph nodes and (ii) then composing them to compactly represent node sequences. Specifically, we propose SENSE-S (Semantically Enhanced Node Sequence Embedding - for Single nodes), a skip-gram based novel embedding mechanism, for single graph nodes that co-learns graph structure as well as their textual descriptions. We demonstrate that SENSE-S vectors increase the accuracy of multi-label classification tasks by up to 50% and link-prediction tasks by up to 78% under a variety of scenarios using real datasets. Based on SENSE-S, we next propose generic SENSE to compute composite vectors that represent a sequence of nodes, where preserving the node order is important. We prove that this approach is efficient in embedding node sequences, and our experiments on real data confirm its high accuracy.
Chinese shallow semantic parsing based on multilevel linguistic clues

With rapid development of artificial intelligence and Chinese information processing technology, research related to natural language processing have reached the level of semantic understanding gradually, while Chinese Shallow Semantic Parsing is the key technique in the semantic understanding field. In this paper, a further improvement is conducted on the basic model of Chinese semantic role labeling for linear classification based on conditional random fields. In this paper, a method of combination of linguistic clues, combining with the existing linear sequence labeling algorithm and integrating some multilevel linguistic clues, such as morphology is related to syntax, in the model training to reconstruct and improve the Chinese semantic role labeling model of linear sequence. Through the experimental comparison and linguistic assistant analysis, this paper puts forward a targeted improvement method to significantly improve the accuracy of model labeling and proves that the integration of related linguistic clues in the semantic role labeling model based on linear sequence can improve the effect of model labeling.
Automated Goal Score Detection in Football Match Using Key Moments

Automated goal score detection in a football match is a comprehensive work and a challenging task as well. In this paper, we proposed a methodology for goal score detection using key moments of the match. Key moments in any sports are referred as actions that stimulate excitement or attraction of the audience. So, key moments can be identified using computer vision. Extracting one key moment involves many steps to happen at a time and it is necessary to integrate all the steps to identify it as a key moment. It is necessary to identify the goalpost region and track the ball to calculate the goal in football match. The proposed work analyses the various tracking algorithms and finalize MIL (Multiple Instance Learning) tracker technique to track the ball. So, the proposed work identifies the goal post region using sequence of image processing operations, tracks the ball using multiple instance learning tracker and confirms whether the goal has been occurred or not. The results of the proposed work have been shown.
Intelligent Camera Using a Finite-State Machine (FSM)

Virtual reality (VR), a technology field that implements human- computer interaction, creates a reality that does not really exist; provides new and changed environments for humans to recognize. A significant amount of VR content is produced in Korea by small businesses or start-ups, and more than 50% of VR content companies are involved in developing content related to video entertainment, such as games [11]. The game screen depicts scenes seen through a virtual camera installed in a 3D space. Cameras in the game are virtual, without substance, but they work on the same principle as real cameras. Various technologies and testing processes for camera editing have been developed for both "real" cameras and VR camera work. Nevertheless, depending on momentary changes in characters' various actions and situations, it still takes a lot of effort and time for filming directors to set up, edit, and provide camera locations to users. This has resulted in an increased need for intelligent camera functions, as well as heightened technical demands to automatically judge behavior, dialogue, and blind spots in camera images-intelligent control. Accordingly, considerable research has been focused recently on applying intelligent cameras to story creation to determine and analyze the behavior between real interacting with simulated VR. This paper proposes an intelligent camera imaging technique using artificial intelligence's FSM technology within the Unity game engine to control the camera. The solution is based on the Hitchcock rule widely used in camera-shooting technique. The FSM accepts inputs from the outside and changes the current state over time, and it can also change the current state according to certain rules by which input is entered from the outside.
Automatic annotation of liver computed tomography images based on a vessel‐skeletonization method

Abstract Anatomical analysis of liver region is an essential and key step for liver‐related disease diagnosis and treatment. One of the challenging issues is to annotate the functional regions of liver automatically or semi‐ automatically by analyzing Computed Tomography (CT) images. The present study developed a complete liver annotation system with an improved vessel‐skeletonization method is proposed for CT images. In the first step, an automatic level set method and a customized region‐growing method are applied to extract the liver region including vessels and tumors. Next, a modified iterative thinning method is developed to obtain the geometric structure of liver vessels and mark a vessel skeleton. The three‐dimensional information is transformed into a tree data structure for storage. Based on the branch distribution of portal vein skeleton, a model‐based method with a modified nearest neighbor segment approximation (NNSA) algorithm is adopted for the functional liver anatomy. Three experiments involving five 64‐row liver CT datasets are performed. The accuracies of segmentation and annotation results were validated by an experienced doctor. Compared with different methods, our proposed vessel skeletonization method can simultaneously preserve the connectivity of the vasculature topology and generate the skeleton in a shorter time. Furthermore, our proposed annotation system can provide both visual and measurable information of livers. These experimental results demonstrate the usefulness and effectiveness of our proposed method. Our liver annotation system is helpful to evaluate the function of liver system and support diagnosis of liver disease.
Mathematical Word Problem Solving Using Natural Language Processing

Natural language processing (NLP) is generally done on large data. Due to limited data word problem solving is challenging using NLP. There are some approaches proposed which could solve basic arithmetic problems like addition/subtraction. Knowledge representation is the main task to be done by NLP. Each kind of problem has its own approach. In this paper three types of mathematical word problems have been solved. Two of them are aptitude problems while the other two are reasoning problems. The spacy library has been used for effective use of Named Entity Recognition (NER) and word vectors. Stepwise solution has been generated instead of just answers which helps in improving understanding. The quite generic rather intuitive approach can be extended to solve some other kind of aptitude problems.
Improved Res2Net Model for Person re-Identification

Person re-identification has become a very popular research topic in the computer vision community owing to its numerous applications and growing importance in visual surveillance. Person re-identification remains challenging due to occlusion, illumination and significant intra-class variations across different cameras. In this paper, we propose a multi-task network base on an improved Res2Net model that simultaneously computes the identification loss and verification loss of two pedestrian images. Given a pair of pedestrian images, the system predicts the identities of the two input images and whether they belong to the same identity. In order to obtain deeper feature information of pedestrians, we propose to use the latest Res2Net model for feature extraction of each input image. Experiments on several large-scale person re-identification benchmark datasets demonstrate the accuracy of our approach. For example, rank-1 accuracies are 83.18% (+1.38) and 93.14% (+0.84) for the DukeMTMC and Market-1501 datasets, respectively. The proposed method shows encouraging improvements compared with state-of-the-art methods.
Joint Face Completion and Super-Resolution Using Multi-Scale Feature Relation Learning

This paper proposes a multi-scale feature graph generative adversarial network (MFG-GAN) to carry out face restoration in contexts in which both LR and occluded degradation modes coexist, and also to repair images with a single type of degradation. Based on the GAN, the MFG-GAN integrates the graph convolution and feature pyramid networks to restore occluded low-resolution face images to non-occluded high-resolution face images. The MFG-GAN uses a set of customized losses to ensure that high-quality images are generated. In addition, we designed the network in an end-to-end format. We conduct experiments on general face image restoration and facial expression restoration. Experimental results on the public-domain databases show that the proposed approach outperforms state-of-the-art methods in performing face super resolution (up to 4x or 8x) and face completion simultaneously and can recover better facial expression details.
The Power of AI in IoT : Cognitive IoT-based Scheme for Web Spam Detection

In the modern era, Internet of Things(IoT) plays an important role in connecting the people across the globe. The IoT objects enable the communication and data exchange among each other irrespective of their geographical locations. In such an environment, the Web of Things (WoT) provides the Internet service to the IoT objects. The Internet is mostly accessed by the search engines. The success of search engine depends upon the ranking algorithm. Although, Google is preferred by the maximum Internet users, but still the Google's ranking algorithm, PageRank experiences the occurrence of spam web pages. In this paper, the webpage filtering algorithm is proposed which automatically detects the spam web pages. The spam webpages are detected before these are processed by the ranking module of search engines. The machine learning model, i.e., decision tree is used for the validation of the proposed scheme. The ten fold cross validation approach is used to improve the accuracy of model, i.e., 98.2%. The results obtained demonstrate that the proposed scheme has the power of preventing the spam web pages in Cognitive Internet of Things (CIoT) environment.
Simulation for Cyber Risk Management – Where are we, and Where do we Want to Go?

There is a dearth of simulation environments that conduct a comprehensive cyber risk assessment and provide insights that are accessible for decision makers and operational folks. During the Winter Simulation Conference 2019, a group of experts will discuss the challenges and opportunities for developing simulation platforms for cyber risk management. The panel will focus on issues with integrating technologies into simulation platforms, loss of fidelity due to lack of access to cyber datasets and complexity involved in representing cyber-physical systems. This paper is a collection of position papers of the participating experts supporting their viewpoints that will be captured in the panel discussion.
A hybrid model using LSTM and decision tree for mortality prediction and its application in provider performance evaluation

The risk adjusted mortality rate, which is also called standardized mortality ratio (SMR), is one widely used quality measure to evaluate healthcare provider performance. Logistic regression and decision tree are two traditional risk models for mortality rate calculation. Though some machine learning based approaches could achieve higher accuracy, they are hard to interpret and may have poor calibration scores. In this paper, we evaluated multiple machine learning approaches with different formats of longitudinal data, and proposed a hybrid approach based on long short-term memory (LSTM) model and decision tree. The new hybrid method provides a comparable area under the receiver operating characteristic curve (AUC) performance as LSTM with a better calibration score. Using a set of 3,473 patients with 10 months of data from historical, large scale ESRD patient data, the LSTM with long format data approach achieved AUC for prediction of mortality of 0.772 compared to 0.758 for logistic regression and 0.726 for a decision tree model. The hybrid approach could reach 0.783, a little higher than both LSTM and decision tree model. The hybrid approach has the best calibration performance based on the Hosmer Lemeshow test.
Fireworks Algorithm (FWA) with Adaptation of Parameters Using Interval Type-2 Fuzzy Logic System

The main goal of this paper is to improve the performance of the Fuzzy Fireworks Algorithm (FFWA), which is a variation of conventional Fireworks Algorithm (FWA). In previous work the FFWA was proposed on Type-1 Fuzzy Logic to adjust parameters dynamically and the difference now in this work is that we use the Interval Type-2 Fuzzy Logic for and adjust the parameter of the explosion amplitude of each firework, and this variation, we called as Interval Type-2 Fuzzy Fireworks Algorithm and we denoted as IT2FFWA. To evaluate the performance of FFWA and IT2FFWA we tested both algorithms with 12 mathematical Benchmark functions.
Influence of Neighborhood on the Preference of an Item in eCommerce Search

Surfacing a ranked list of items for a search query to help buyers discover inventory and make purchase decisions is a critical problem in eCommerce search. Typically, items are independently predicted with a probability of sale with respect to a given search query. But in a dynamic marketplace like eBay, even for a single product, there are various different factors distinguishing one item from another which can influence the purchase decision for the user. Users have to make a purchase decision by considering all of these options. Majority of the existing learning to rank algorithms model the relative relevance between labeled items only at the loss functions like pairwise or list-wise losses [1] -[3]. But they are limited to point-wise scoring functions where items are ranked independently based on the features of the item itself. In this paper, we study the influence of an item's neighborhood to its purchase decision. Here, we consider the neighborhood as the items ranked above and below the current item in search results. By adding delta features comparing items within a neighborhood and learning a ranking model, we are able to experimentally show that the new ranker with delta features outperforms our baseline ranker in terms of Mean Reciprocal Rank (MRR) [4]. The ranking models with proposed delta features result in 3 - 5% improvement in MRR over the baseline model. We also study impact of different sizes for neighborhood. Experimental results show that neighborhood size 3 perform the best based on MRR with an improvement of 4 - 5% over the baseline model.