Reviewerly/exHarmony huggingface dataset:
Deep Learning and Lexical Analysis Combined Rubbing Character Recognition

Rubbings are among the oldest ancient literatures and presumably contain a lot of knowledge yet to be discovered. However, as of now, the recognition process of rubbing characters is now completely done manually. Such process, due to varying styles of characters, takes an enormous amount of effort, and can be done by a small number of specialists. Still, the aging process, which may damage characters or add noises, further increases the difficulty of recognition. In this paper, we propose a method by combining deep learning and lexical analysis for increasing the accuracy of rubbing character recognition. This work is currently in progress, and the experimentation uses rubbing character database developed by Kyoto University. In the experiment, we use a rubbing image for recognition, and the experimental results verify the effectiveness of the proposed method.
A responsible approach towards user and personal voice assistants interaction

This paper presents the ongoing efforts in constructing a low cost open source voice controlled smart home hub for Portuguese language. It also takes the opportunity to raise some considerations concerning security and privacy for users of Digital Personal Assistants. The smart hub was developed using Snowboy library for hotword detection, CMUSphinx tool for speech recognition and Home Assistant toolkit for integration among intelligent devices. A prototype has been developed, and is currently been tested in order to evaluate its functionalities.
Proof of Existence Using Blockchain

In the past years, we have been hearing a lot about cryptocurrency and Blockchain which has brought new scope and technology into the hands of developers and common man. Blockchain technology was first introduced in a whitepaper which went like this: “Bitcoin: A Peer-to-Peer Electronic Cash System,” by Satoshi Nakamoto in 2008. Though Blockchain being a completely new field, many companies are trying to implement Blockchain too. A Blockchain contains of a block which contains a hash of cryptography of the previous block, timestamp and transaction data. The main purpose of this project is to create a Proof-Of-Existence of any type of file or record on the platform of Blockchain. Once submitted, we shall be uploading the file's unique signature with its associated information into the Private Decentralized Public Ledger. Records stored here can be extracted later to prove that this particular file existed on particular date & time with the associated information uploaded. It is impossible to duplicate the document's signature or modify the past records both technologically and computationally. Therefore POE can be used to prove any file's existence in legal matters. It can also be an important part in any organization which will obviously have records of deals, bonds, customer details, employee details etc which if made to generate a POE for each of them, then will be secure and safe because this is something which cannot be hacked.
What Should Cognitive Science Look Like? Neither a Tree Nor Physics

While pointing out important features of cognitive science, Núñez et al. (2019) also argue prematurely for the end of cognitive science. I discuss problematic analytic features in the application of hierarchical cluster analysis to journal citation data. On the conceptual side, I argue that the research programs framework of Lakatos may not be so wisely applied to cognitive science. Further, the diversity of structure in cognitive science departments may represent a rational, strategic adaptation by an interdisciplinary department to cognitive and other resource challenges rather than the sign of low progress on a discipline.
Polynomial Event Semantics

We propose a simple extension of event semantics that naturally supports the compositional treatment of quantification. Our analyses require neither quantifier raising or other syntactic movements, nor type-lifting. Denotations are computed strictly compositionally, from lexical entries up, and quantifiers are analyzed in situ. We account for the universal, existential and counting quantification and the related distributive coordination, with the attendant quantifier ambiguity phenomena. The underlying machinery is not of lambda-calculus but of much simpler relational algebra, with straightforward set-theoretic interpretation. The source of quantifier ambiguity in our approach lies in two possible analyses for the existential (and counting) quantification. Their inherent ambiguity however becomes apparent only in the presence of another, non-existential quantification.
High- and Low-Level Feature Enhancement for Medical Image Segmentation

The fully convolutional networks (FCNs) have achieved state-of-the-art performance in numerous medical image segmentation tasks. Most FCNs typically focus on fusing features in different levels to improve the learning ability to multi-scale features. In this paper, we explore an alternative direction to improve network performance by enhancing the encoding quality of high- and low-level features, so as to introduce two feature enhancement modules: (i) high-level feature enhancement module (HFE); (ii) low-level feature enhancement module (LFE). HFE utilizes attention mechanism to selectively aggregate the optimal feature information in high- and low-levels, enhancing the ability of high-level features to reconstruct accurate details. LFE aims to use global semantic information of high-level features to adaptively guide feature learning of bottom networks, so as to enhance the semantic consistency of high- and low-level features. We integrate HFE and LFE into a typical encoder-decoder network, and propose a novel medical image segmentation framework (HLF-Net). On two challenging datasets of skin lesion segmentation and spleen segmentation, we prove that the proposed modules and network can improve the performance considerably.
Self-Dual Binary $[8m,\,\,4m]$ -Codes Constructed by Left Ideals of the Dihedral Group Algebra $\mathbb{F}_2[D_{8m}]$

Let m be an arbitrary positive integer and D <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">8m</sub> be the dihedral group of order 8m, i.e., D <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">8m</sub> = (x, y | x <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4m</sup> = 1, y <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 1, yxy = x <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> ). Left ideals of the dihedral group algebra F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> [D <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">8m</sub> ] are called binary left dihedral codes of length 8m, and abbreviated as binary left D <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">8m</sub> -codes. In this paper, we give an explicit representation and enumeration for all distinct self-dual binary left D <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">8m</sub> -codes. These codes make up an important class of self-dual binary [8m, 4m]-codes such that the dihedral group D <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">8m</sub> is necessarily a subgroup of the automorphism group of each code. In particular, we provide recursive algorithms to solve congruence equations over finite chain rings for constructing all distinct self-dual binary left D <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">8m</sub> -codes and obtain a Mass formula to count the number of all these self-dual codes. As a preliminary application, we obtain the extremal self-dual binary [48, 24, 12]-code and an extremal self-dual binary [56, 28, 12]code from self-dual binary left D <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">48</sub> -codes and left D <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">56</sub> -codes respectively.
Refinement kinds: type-safe programming with practical type-level computation

This work introduces the novel concept of kind refinement , which we develop in the context of an explicitly polymorphic ML-like language with type-level computation. Just as type refinements embed rich specifications by means of comprehension principles expressed by predicates over values in the type domain, kind refinements provide rich kind specifications by means of predicates over types in the kind domain. By leveraging our powerful refinement kind discipline, types in our language are not just used to statically classify program expressions and values, but also conveniently manipulated as tree-like data structures, with their kinds refined by logical constraints on such structures. Remarkably, the resulting typing and kinding disciplines allow for powerful forms of type reflection, ad-hoc polymorphism and type-directed meta-programming, which are often found in modern software development, but not typically expressible in a type-safe manner in general purpose languages. We validate our approach both formally and pragmatically by establishing the standard meta-theoretical results of type safety and via a prototype implementation of a kind checker, type checker and interpreter for our language.
Deep Learning based Ancient Literature Recognition and Preservation

This paper introduces an deep learning based ancient literature recognition method for culture heritage preservation by deep learning. The model of Single Shot Multibox Detector is used for detecting and recognizing the ancient characters. This paper only focuses on the recognition of Oracle Bone Inscriptions which is the one of the oldest and most mysterious ancient characters, used about 3000 years ago in china. The experimental results show that Precision achieves and Recall achieves 0.86 and 0.97, respectively, and prove the effectiveness of Single Shot Multibox Detector in ancient characters recognition. By analyzing the experimental results, we found that in the case of heavy blurred image or tilted characters, the Single Shot Multibox Detector can not achieve a exciting results. Hence, we apply the pre-processing of binarization, changing brightness and contrast, and rotation in the test images, and then achieve accuracy improvement of recognition by using pre-processed images.
Integration of Autonomous Heterogeneous Systems for Decision Making Autonomy in Naval Defence: A Position Paper

In this article we are presenting a state of the art in the Artificial Intelligence (AI) and Robotics fields, so as to address the problem of autonomy and integration of autonomous heterogeneous systems for decision making autonomy. This state of the art is followed by the presentation of some avenues addressing the issue in order to lead the autonomy of heterogeneous robotic systems in the naval defence environment possible.
Hand-based interactions in Virtual Reality: No better feeling than the real thing!

Manipulating tools or crafting objects using one's bare hands is a recurring activity in many immersive training applications. Yet, high technical challenge and costly haptic hardware confine such tasks to surgery training and specialised simulators of the like, while more general immersive applications settle for less natural experiences using standard game controllers and/or relying on more abstract interactions. We designed a low-cost prototype were a physical object is used to provide a natural haptic feedback to a barehanded virtual manipulation task, hypothesising that nothing better than a real object would provide an accurate feeling of touch and control. With VR and AR high-end technologies now reaching out to broad audiences, we wondered if it was yet possible to design a compelling experience using only consumer market products and APIs. In this paper we demonstrate indeed that these technologies are mature enough to build a working prototype, but we failed to reach a level of quality suitable for more widespread usage.
Deep Depth from Uncalibrated Small Motion Clip

We propose a novel approach to infer a high-quality depth map from a set of images with small viewpoint variations. In general, techniques for depth estimation from small motion consist of camera pose estimation and dense reconstruction. In contrast to prior approaches that recover scene geometry and camera motions using pre-calibrated cameras, we introduce in this paper a self-calibrating bundle adjustment method tailored for small motion which enables computation of camera poses without the need for camera calibration. For dense depth reconstruction, we present a convolutional neural network called DPSNet (Deep Plane Sweep Network) whose design is inspired by best practices of traditional geometry-based approaches. Rather than directly estimating depth or optical flow correspondence from image pairs as done in many previous deep learning methods, DPSNet takes a plane sweep approach that involves building a cost volume from deep features using the plane sweep algorithm, regularizing the cost volume, and regressing the depth map from the cost volume. The cost volume is constructed using a differentiable warping process that allows for end-to-end training of the network. Through the effective incorporation of conventional multiview stereo concepts within a deep learning framework, the proposed method achieves state-of-the-art results on a variety of challenging datasets.
Multi-Paths Generation for Structural Rule Quests

Existing quest generation systems that use structural rules have an important limitation. A quest generated by such systems are not guaranteed to be flexible such that players can finish the quest in a variety of ways. In this paper, we extend such systems by replacing action-based quests with game state-based quests. A quest generated from our system is filtered for conflicting scenarios and then analyzed using Prolog to guarantee multiple paths of completion. Ensuring that quests always have multiple ways to complete improves quest quality and players' experience.
LSB Elimination based Feature Extraction for Outsourced Image Retrieval in Encrypted Images

This paper proposes an image retrieval approach in encrypted images that are outsourced to the cloud network using LSB (Least significant bit) elimination based feature extraction. To preserve the privacy of the image that is outsourced to the cloud, the images are encrypted. Retrieving the encrypted images that are outsourced to cloud is considered as a challenging task. The proposed method eliminates the LS B' s, and the histogram is estimated to obtain the necessary features. Initially, the image owner encrypts the original image by using a key and uploads the encrypted image to the cloud. The cloud server extracts the features from the encrypted original image and stores the features in the cloud. To perform image retrieval, the user has to encrypt the Query image using a key, and it must be uploaded to the cloud. The cloud server extracts the features from the Query image and matches with the features that are already stored in the cloud server. The encrypted images correspond to the top-k matched features are returned to the user, from which the user has to decrypt it using the key. The performance of the proposed method was measured in terms of Precision, time of feature extraction, time of index construction and time of image retrieval. Experimental results reveal that the proposed feature extraction outperforms than the traditional methods.
3aRAM: A 3-Layer AHP-Based Risk Assessment Model and its Implementation for an Industrial IoT Cloud

To assist in the automation of factory processes, the amount of valuable data flow in the industrial cyber-physical system (CPS), Internet of things (IoT) and cloud computing will also have to increase accordingly. Thus, developing a risk assessment system which is specialized for an industrial IoT system is necessary, especially for the cloud platform that the data mainly flow on. In this paper, we revise the AHP (Analytic Hierarchy Process) method and propose a 3-layer AHP-based risk assessment model (3aRAM) for an Industrial IoT cloud (PaaS platform) to allow the cloud system to self-benchmark its own security status. The model is composed of three phases: data collection, data analysis and risk assessment. To refrain from unnecessary experts involving into the risk assessment phase, a feedback mechanism is designed in the proposed model. We realize the risk assessment system and apply it to an industrial IoT cloud system. Finally, we estimate the practicality of our system by injecting different degrees of noises and launching DoS attacks, and show the change of integrity and availability scores, which are generated by the proposed risk assessment model.
Measure of bipartite correlation for quantum metrology

Recently, researchers have found that not only entanglement but also quantum discord and even classical correlation can enhance the precision of parameter estimation. Thus the correlation which contributes quantum metrology should be treated as a new type of resource. In this paper, we directly construct a measure of the resource named as the relative quantum Fisher information (rQFI). It quantifies the improvement of QFI caused by correlations naturally. Operationally, rQFI quantifies the improvement of optimal precision achieved by joint measurement on a composite system, instead of the local measurement on the subsystem. Furthermore, rQFI itself is an alternative type of total correlation which captures effects of quantum entanglement, quantum discord, and classical correlation. It reduces to an entanglement measure for pure states. A detailed study for general two-qubit states is presented. Additionally, an alternative generalized discord measure can be extracted out of the rQFI.
Modeling Supply Chain Quality Management using Multi-Layer Fuzzy Cognitive Maps

Supply Chain Quality Management (SCQM) modeling is one of the main research avenues in both academic and practitioner fields. Therefore, a SCQM model using a Multi-Layer Fuzzy Cognitive Map (ML-FCM) is developed in this paper. The ML-FCM model developed in this work allows expanding and decomposing SCQM concepts by grouping them as Fuzzy Cognitive Maps (FCMs) in multi-layers. The model was applied in a three-echelon supply chain in the sector of personal care products. The model construction and its capacity to represent the studied problem are analyzed through density and strength metrics. The inference process results validated the initial selection of the concepts in every FCM and allowed to quantify the effect of each echelon on SCQM performance. The most important decision making variables for SCQM improvement in the studied supply chain were total profit, quality costs, suppliers rejections and returns, productivity, and customer rejections and returns .
Instance Segmentation of Nematode Cysts in Microscopic Images of Soil Samples

Nematodes are plant parasites that cause damage to crops. In order to quantify nematode infestation based on soil samples, we propose an instance segmentation method that will serve as the basis of automatic quantitative analysis. We consider light microscopic images of cluttered object collections as they occur in realistic soil samples. We introduce an algorithm, LMBI (Local Maximum of Boundary Intensity) to propose instance segmentation candidates. In a second step, a SVM classifier separates the nematode cysts among the candidates from soil particles. On a data set of soil sample images, the LMBI detector achieves near-optimal recall with a limited number of candidate segmentations, and the combined detector/classifier achieves recall and precision of 0.7. The pipeline only requires simple dot annotations and ≈moderately sized training data, which enables quick annotating and training in laboratory applications.
Applying maximally stable extremal regions and local binary patterns for guide‐wire detecting in percutaneous coronary intervention

Intervention surgery strongly requires information on the guide-wire position under the monitoring of X-ray video. Hence, the related researches such as guide-wire detecting or tracking have become widespread. However, most of the existing methods require a lot of resources for computing or large data for training since the X-ray videos have internal physicals such as anatomical skeleton contours and organs that are quite similar to a guide-wire. This work presents a practical method that only requires a moderate number of training data for detecting a guide-wire tip in an X-ray video sequence during the percutaneous coronary intervention surgery. The method applies maximally stable extremal regions (MSER) combine with modified multi-filters (region area range filter and stroke width variation filter) for region detection and local binary patterns (LBP) for guide-wire recognition. The motivation for applying MSER and LBP are the robust efficacy and the low requirement of resources. The approach evaluated 20 different sequences of X-ray videos, a total of 1295 frames. 50 selected frames were used as training templates and others to experiment. The method was successfully performed to the detecting guide-wires with p-value < 0.01 compared with conventional MSER methods, 93.7% average detection accuracy, and 21 fps average speed.
Comparing Feature Learning Methods for Human Activity Recognition: Performance study in new user scenario

Designing robust features for human activity recognition (HAR) that performs well across a wide range of users is a hard task. Therefore, more attention is being given to feature learning techniques, to automatically learn features from raw data. In this paper, we present a comparison study among feature learning methods for HAR. Using accelerometer data, we compare four methods for feature learning from raw-sensor data (PCA-based, clustering, matrix factorization, and LSTM networks) to the traditional hand-crafted feature extraction method. We focus on the performance degradation when each model is evaluated using a new user. According to our results, features learned with Principal Component Analysis are the more robust to the new user scenario. Our results evidence the importance of evaluation in unseen user, since the performance difference compared to a random split testing is big.
An AutoML Approach for the Prediction of Fluid Intelligence from MRI-Derived Features

We propose an AutoML approach for the prediction of fluid intelligence from T1-weighted magnetic resonance images. We extracted 122 features from MRI scans and employed Sequential Model-based Algorithm Configuration to search for the best prediction pipeline, including the best data pre-processing and regression model. In total, we evaluated over 2600 prediction pipelines. We studied our final model by employing results from game theory in the form of Shapley values. Results indicate that predicting fluid intelligence from volume measurements is a challenging task with many challenges. We found that our final ensemble of 50 prediction pipelines associated larger parahippocampal gyrus volumes with lower fluid intelligence, and higher pons white matter volume with higher fluid intelligence.