Reviewerly/exHarmony huggingface dataset:
A drug association based inventory control system for ambulatory care

The purpose of the research is to explore an innovated drug inventory control system for medical organization to improve the system performance in terms of daily patients’ prescriptions and drugs flows. It is not easy to well control drugs inventory due to large numbers of stock keeping units and huge quantities of them in a medical organization. In order to maintain both the daily jobs of ambulatory care and the rights of the patients, the availability of all drugs keeping at a reasonable stock level for medical staffs and patients is required. Also, to avoid increase of cost due to over stocking to overcome the prediction of possible shortage of drugs is a must. In traditional control system, cost is always be considered as the decision variable. Based on the total cost of each item in an inventory management within a planning cycle, and to calculate out the quantity for that particular item within the total inventory, the total amount of inventory ratio for that item will also be derived and be classified to make different inventory management strategies for different catalogues. There are hundreds of variable pharmaceutical goods in medical organization, hence, large number of prescription data and critical information will be ignored if the inventory classification is determined by the traditional cost approach. This study used association rules analysis to mining the patient treatments database, in order to derive prescription-oriented association rules for improving cost-oriented categories inventory management. In this research, data mining technology is utilized to detect hiddenly issues, which might cause drug shortages. Around 480,000 data, ranges from January to March, 2010, were extracted from Taiwan’s National Health Insurance files, and applied with multi-level association rule analysis, 19 association rules were obtained. In conclusion, the potential relationship in a huge database, found by data mining technique, can be used to support managing staff to adjust inventory policies accordingly.
Ontological Model for the Semantic Description of Syllabuses

The syllabus is a relevant document to organize how the teaching-learning process will be carried out during an academic course in Higher Education Institutions (HEI). Usually, this document is written in a human-readable format that do not enable automatic processing through intelligent services to support teaching and learning. Therefore, we created OntoSyllabus ontology for the representation of syllabuses applying the NeOn methodology. The semantic model of a syllabus will allow the comprehension for both: machines and humans, and it will facilitate the interchange of data between different services and applications. The ontology was created based on the results of our three previous studies, which helped us to determinate the terms and relations in the syllabus ontology. The documentation and the computable model are available on the Internet for their reuse.
Design and Evaluation of an FPGA-based Hardware Accelerator for Deflate Data Decompression

Data compression is an important technique for coping with the rapidly increasing volumes of data being transmitted over the Internet. The Deflate lossless data compression standard is used in several popular compressed file formats including the PNG image format and the ZIP and GZIP file formats. Consequently, several implementations of hardware accelerators for Deflate have been proposed. The recent availability of distributed field-programmable gate arrays (FPGAs) in the Internet cloud and the growing demand for decompressing compressed data that is streamed from remote servers make FPGA-based decompression accelerators commercially attractive. This paper describes an efficient implementation of the Deflate decompression algorithm using high-level synthesis from designs, specified in C++, down to optimized implementations for a Xilinx Virtex UltraScale+ class FPGA. When decompressing the Calgary corpus benchmark, our decompressor has average input (output) data throughputs of 70.7 (246.4) and 130.6 (386.6) MB/s for dynamically and statically encoded files, respectively. This performance is comparable to the 375 MB/s output throughput of Xilinx's state-of-the-art proprietary Deflate decompressor design.
Secure Application for Sharing Health Records using Identity and Attribute based Cryptosystems in Cloud Environment

Applications providing healthcare services are rapidly escalating towards the security, reliability and integrity challenges. Numerous applications tends to manage patient's health records by storing the data on cloud without prior encryption, which will lead to leakage and misuse of such sensitive information. Hence a mHealth i.e. mobile healthcare application is proposed to help the patients to share their health records with healthcare service providers and securely find friends with similar health conditions.
From Classical to Non-monotonic Deontic Logic Using ASPIC $$^+$$

In this paper we use formal argumentation to design non-monotonic deontic logics, based on two monotonic deontic logics. In particular, we use the structured argumentation theory ASPIC $$^+$$ to define non-monotonic variants of well-understood modal logics. We illustrate the approach using argumentation about free-choice permission.
Legal Question Answering System Using FrameNet

A central issue of yes/no question answering is the usage of knowledge source given a question. While yes/no question answering has been studied for a long time, legal yes/no question answering largely differs from other domains. The most distinguishing characteristic is that legal issues require precise analysis of predicate argument structures and semantical abstraction in these sentences. We have developed a yes/no question answering system for answering questions for a statute legal domain. Our system uses a semantic database based on FrameNet, which works with a predicate argument structure analyzer, in order to recognize semantic correspondences rather than surface strings between given problem sentences and knowledge source sentences. We applied our system to the COLIEE (Competition on Legal Information Extraction/Entailment) 2018 task. Our frame based system achieved better scores on average than our previous system in COLIEE 2017, and was the second best score among participants of Task 4. We confirmed effectiveness of the frame information with the COLIEE training dataset. Our result shows the importance of the points described above, revealing opportunities to continue further work on improving our system’s accuracy.
Facilitating PTZ Camera Auto-Calibration to Be Noise Resilient With Two Images

This paper presents a simple but effective technique to calibrate a PTZ (Pan/Tilt/Zoom) camera by using only two images for five intrinsic parameters: focal length, aspect ratio, the principle point coordinates and the distortion coefficient. In our approach, the SCC-SURF (Shape-Color Combined SURF) descriptor is first employed to obtain robust point correspondences in a pair of color images taken before and after the camera undergoing an arbitrary pan-tilt rotation respectively. Based on the radial lens distortion division model, the point correspondences between these two images are applied to calculate the homography and the distortion coefficient simultaneously. The estimated homography is proved more precise with our novel framework CWRLD (Covariance Weighted Ransac under Lens Distortion), which employs a covariance matrix in the presence of feature location noise. Finally, the remaining four intrinsic parameters are solved using directly decomposing estimated homography with a series of Givens rotations. Both synthetic and real data are provided to verify that our proposed technique is precise, convenient, and applicable for online calibration without regard for a specific imaged environment.
Improving Human Motion Classification by Applying Bagging and Symmetry to PCA-Based Features

This paper proposes a method for improving human motion classification by applying bagging and symmetry to Principal Component Analysis (PCA)-based features. In contrast to well-known bagging algorithms such as random forest, the proposed method recalculates the motion features for each “weak classifier” (it does not randomly sample a feature set). The proposed classification method was evaluated on a challenging (even to a human observer) motion capture recording dataset of martial arts techniques performed by professional karate sportspeople. The dataset consisted of 360 recordings in 12 motion classes. Because some classes of these motions might be symmetrical (which means that they are performed with a dominant left or right hand/leg), an analysis was conducted to determine whether accounting for symmetry could improve the recognition rate of a classifier. The experimental results show that applying the proposed classifiers’ bagging procedure increased the recognition rate (RR) of the Nearest-Neighbor (NNg) and Support Vector Machine (SVM) classifiers by more than 5% and 3%, respectively. The RR of one trained classifier (SVM) was higher when we did not use symmetry. On the other hand, the application of symmetry information for bagged NNg improved its recognition rate compared with the results without symmetry information. We can conclude that symmetry information might be helpful in situations in which it is not possible to optimize the decision borders of the classifier (for example, when we do not have direct information about class labels). The experiment presented in this paper shows that, in this case, bagging and mirroring might help find a similar object in the training set that shares the same class label. Both the dataset that was used for the evaluation and the implementation of the proposed method can be downloaded, so the experiment is easily reproducible.
A Teaching System of English Online Course Based on Artificial Intelligence

In comparison with common teaching auxiliary system, this paper applies artificial intelligence module combining with knowledge recommendation to the system and designs an online English course teaching system. The core modules in system adopt domain knowledge base and teaching management and they separates organizing management of knowledge from processing mechanism of knowledge. Through interface accessing knowledge base, processing mechanism promotes that the change of content organizing in knowledge base will not lead to large change in processing mechanism. Meanwhile, the realization of adaptive teaching planning is also put forward so that the teaching can be diversified. Test application shows the system can assist students to improve learning efficiency and learning contents will be more pertinent. Furthermore, the system provides similar methods with an example model so it also has referential significance.
Term-Sequence-Modal Logics

Term-modal logics, developed by Fitting et al., enable us to index a modal operator by a term of the first-order logic and even to quantify variables in the index of the modal operator. In this paper, we expand term-modal logics by allowing a modal operator to be indexed by a finite sequence of terms as well as a single term. The expanded logics are generalizations of both term-modal logics and quantified modal logics. We provide sound Hilbert-style axiomatizations (without Barcan-like axioms) for the logics and establish the strong completeness results for some of the logics. We also propose sequent calculi for the logics and show cut elimination theorems and Craig interpolation theorems for some of the calculi.
Target tracking with a dynamic and adaptive selection of radars based on entropy

In the modern rapidly changing battlefield, the resources of multiple radars are usually limited for multiple objects multiple missions. Moreover, the amount of target information acquired by different radars at different time index is different. Thus, as to target tracking, it is important to reasonably and dynamically allocate radars’ resources. In the study, based on information theory, the entropy is utilised to quantitatively measure the amount of target information observed from multiple radars. Corresponding, the lower bound (LB) of the given entropy is also derived. The smaller the value of the entropy, the more accurate the estimate of target motion state is. So based on minimising the entropy LB of target information acquired from radars at different times, a new fusion tracking method is proposed to dynamically adaptively choose radars with high amount of target information for target tracking. The simulation results show that the proposed method has higher tracking accuracy than the fusion tracking without optimal choice of radars.
Natural Language Processing for Associative Word Predictions

A potential of natural language processing to predict words based on their associative relations is assessed on a Serbian language dataset. The approach investigated is to use a number of different written materials, and see if a neural network can extract word associations just by reading regular texts. Evaluation is performed using ready made, publicly available games of association. The final result exceeds 70% certainty of a correct solution within 100 most probable words in a vocabulary of over 150,000. Results are evaluated using different dataset sizes and preparation techniques with the idea of identifying impact of a particular approach.
Smart Interactive Mirror Display

A suit of six applications are presented on the 32” high definition screen surrounded by a framed encasing that contains speakers and a webcam. These apps include weather, Twitter, news, to-do list, calendar, and music. The smart mirror application by is run from a central computer with Windows 8.1 which features a multi-core CPU, solid-state memory, and wireless connectivity. The application can be interacted with use of the Leap Motion controller. This controller allows for touch-free control of the smart mirror by use of finger swipes, taps, and circles. There is also the option to use voice control to play music, post a tweet, or add a task to the to-do list. Finally, the mirror monitors its own temperature and humidity levels so that if the bathroom becomes potentially harmful to the computer's hardware, it can execute preventive measures and shut the system down.
Modeling and control of nonlinear systems using an interactive software: A T-S fuzzy approach

This paper describes a Matlab-based interactive tool focused on the modeling and control of nonlinear systems using a Takagi-Sugeno (T-S) fuzzy approach. Noticeably the modeling of dynamical nonlinear systems plays a fundamental role in the field of automatic control, since it allows to apply well-established analysis and control synthesis tools based on the Lyapunov theory. The interactive tool presented in this work is dedicated to aid control practitioners to easily derive a fuzzy state-space representation which is locally equivalent to the original nonlinear system and also to obtain a controller meeting desired closed-loop requirements.
Detection of suspicious time windows in memory monitoring

Modern memory monitoring tools do not only offer analyses at a single point in time, but also offer features to analyze the memory evolution over time. These features provide more detailed insights into an application's behavior, yet they also make the tools more complex and harder to use.
Enabling Compressed Encryption for Cloud Based Big Data Stores

We propose a secure yet efficient data query system for cloud-based key-value store. Our system supports encryption and compression to ensure confidentiality and query efficiency simultaneously. To reconcile encryption and compression without compromising performance, we propose a new encrypted key-value storage structure based on the concept of horizontal-vertical division. Our storage structure enables fine-grained access to compressed yet encrypted key-value data. We further combine several cryptographic primitives to build secure search indexes on the storage structure. As a result, our system supports rich types of queries including key-value query and range query. We implement a prototype of our system on top of Cassandra. Our evaluation shows that our system increases the throughput by up to 7 times and compression ratio by up to 1.3 times with respect to previous works.
Edge‐aware image filtering using a structure‐guided CNN

Image filtering is a fundamental preprocessing step for accurate, robust computer vision applications such as image segmentation, object classification, and reconstruction. However, many convolutional neural network (CNN)-based methods tend to lose significant edge information in the output layer, and generate undesired artefacts in the feature extraction layers. This study presents a deep CNN model for edge-aware image filtering. The proposed network model consists of three sub-networks: (i) feature extraction, (ii) convolution artefact removal, and (iii) structure extraction networks. The proposed network model has an end-to-end trainable architecture that does not need any post-processing steps. Especially, the structure extraction network can successfully preserve significant edges. The proposed filter outperforms state-of-the-art denoising filters in terms of both objective and subjective measures, and can be used for various image enhancement and restoration problems such as edge-preserving smoothing, image denoising, deblurring, and deblocking.
Detailed 3D Face Reconstruction from a Single RGB Image

This paper introduces a method to obtain a detailed 3D reconstruction of facial skin from a single RGB image.To this end, we propose the exclusive use of an input image without requiring any information about the observed material nor training data to model the wrinkle properties.They are detected and characterized directly from the image via a simple and effective parametric model, determining several features such as location, orientation, width, and height.With these ingredients, we propose to minimize a photometric error to retrieve the final detailed 3D map, which is initialized by current techniques based on deep learning.In contrast with other approaches, we only require estimating a depth parameter, making our approach fast and intuitive.Extensive experimental evaluation is presented in a wide variety of synthetic and real images, including different skin properties and facial expressions.In all cases, our method outperforms the current approaches regarding 3D reconstruction accuracy, providing striking results for both large and fine wrinkles.
Pedestrian Attributes Recognition in Surveillance Scenarios Using Multi-Task Lightweight Convolutional Neural Network

Pedestrian attributes (such as gender, age, hairstyle, and clothing) can effectively represent the appearance of pedestrians. These are high-level semantic features that are robust to illumination, deformation, etc. Therefore, they can be widely used in person re-identification, video structuring analysis and other applications. In this paper, a pedestrian attributes recognition method for surveillance scenarios using a multi-task lightweight convolutional neural network is proposed. Firstly, the labels of the attributes for each pedestrian image are integrated into a label vector. Then, a multi-task lightweight Convolutional Neural Network (CNN) is designed, which consists of five convolutional layers, three pooling layers and two fully connected layers to extract the deep features of pedestrian images. Considering that the data distribution of the datasets is unbalanced, the loss function is improved based on the sigmoid cross-entropy, and the scale factor is added to balance the amount of various attributes data. Through training the network, the mapping relationship model between the deep features of pedestrian images and the integration label vector of their attributes is established, which can be used to predict each attribute of the pedestrian. The experiments were conducted on two public pedestrian attributes datasets in surveillance scenarios, namely PETA and RAP. The results show that, compared with the state-of-the-art pedestrian attributes recognition methods, the proposed method can achieve a superior accuracy by 91.88% on PETA and 87.44% on RAP respectively.
A survey on Image Indexing and Retrieval based on Content Based Image

Image indexing and retrieval became an interesting field of research nowadays due to the lack of advanced methodologies to index and retrieve images and to the existence of huge quantities of images available everywhere; especially on the web. The available solutions are able to find similar items having the exact shape but not the same item if it has a different shape. In this paper, we present different available technique concerning image indexing and retrieval. The first procedure that comes to mind is Content Based Image retrieval (CBIR). Many years of researching have been made on this topic using this methodology. Then, we explain the functionality of this technique and shows all work done in CBIR and also we discuss the Description Based Image Retrieval (DBIR).
Accelerated Quantum Computation by means of Evanescent Photons and its Prospects for Optical Quantum Hypercomputers and Artificial Intelligence

The realization of an hypercomputer system, namely an accelerated Turing machine able to perform a countable infinite number of computational steps within a finite time, is generally considered as forbidden by Heisenberg's uncertainty principle, due to the exponentially increasing energy required by computational steps. This limitation would not affect just conventional computation but also quantum algorithms. In this paper we shown it could be possible to overcome this “barrier” by considering a quantum system based on evanescent photons produced inside optical components made of metamaterials. This proposal opens very interesting perspectives towards the realization of quantum optical supercomputer systems based on this new kind of technology in particular with respect to its potential applications in the field of Artificial Intelligence where computational power can be considered as a primary goal.
Relevance of Polynomial Order in Takagi-Sugeno Fuzzy Inference Systems Applied in Diagnosis Problems

Nowadays Takagi Sugeno Fuzzy Inference Systems provide an interesting alternative to solve different kind of problems, and this is because this approach offers versatility and easy design. One of the applications of this kind of Fuzzy Inference Systems is in the classification area, specifically in diagnosis problems, in order to be used as computer aided systems, and this provides interesting results. This paper aims at evaluating the performance of this kind of systems in diagnosis problems, but modifying the order of the Sugeno polynomial. This polynomial is originally a first-order polynomial that relates the inputs with the firing force of the rules. However, with the emergence of high-order Sugeno polynomials is interesting to evaluate how this approach can improve the performance of the Takagi Sugeno Fuzzy Inference Systems. On the other hand, we evaluate the performance by changing the order of the Sugeno polynomial for Type-1, Interval Type-2 and General Type-2 Fuzzy Inference Systems, in order to obtain a tendency of the performance with respect to the order of the Sugeno polynomial.