Miracle-dz/UniFAR-FTUs huggingface dataset:
Thematic Question Generation over Knowledge Bases

Automatic generation of questions has recently received attention, as an indirect consequence of the renewed interest in Question Answering systems. Yet, most Automatic Question Generation systems focused on tasks like question selection, verbalization, distractor generation and difficulty assessment. In this paper, we come up with a novel task for Question Generation systems: thematic question generation. Inspired by a famous trivia board game, we aim at solving the problem of generating meaningful questions and their distractors for common knowledge topics. In this paper, we develop an end-to-end system that tackles these issues. We use the Wikipedia structure and content to determine the topics and their boundaries. We developed a template based approach to generate questions, allowing complex questions generation from binary and n-ary statements. To automatically generate templates, we developed an approach that reverts templates used for questions answering, allowing us to import more than 2000 templates. Our experimental campaign reports a success in topic assignment of 0.69 and very high scores (>0.9) for questions and distractors quality along with high inter-rater agreements.
Stand to Research the Privacy Limitations of the Internet of Things in the DID's Perspective

Accelerated growth of the Internet of Things (IoT) expands the implementation of it in our everyday life. The IoT connects artificial intelligence and different physical applications or devices, in order to expand the usage scope and make cities more innovative. A great amount of data produced by the IoT is an example of Data Intensive Domains (DID). The first two parts of this article is a background overview of the IoT and areas, where the IoT is developing. The third part describes the privacy limitations and possible solutions. The fourth part is about the level of IoT privacy limitations in the era of DID. In Conclusion the Research Stand was developed and aims that are planned to achieve.
From Genre Classification to Aspect Extraction: New Annotation Schemas for Book Reviews

In this paper, new schemas for feature categorization in different kinds of reviews, in the domain of books, are presented, so aspect extraction techniques could be later applied. We deal here with two types of reviews: formal reviews about scholarly books, written by experts, and informal ones about fiction books, written by readers which are not necessarily highly qualified. Our final goal is to extract the most relevant aspects or features to which any opinion is expressed in these reviews, along with the sentiment associated, for later integrating it to book recommender systems, improving the quality of the recommendations. Throughout this paper, the need for different annotation schemas is proved, by developing a new review classification system, as well as making an analysis at lexical and semantic levels on both kinds of reviews, for finally concluding with the presentation of the new categorization schemas.
Pedestrian Detection in Infrared Images Using Fast RCNN

Compared to visible spectrum image the infrared image is much clearer in poor lighting conditions. Infrared imaging devices are capable to operate even without the availability of visible light, acquires clear images of objects which are helpful in efficient classification and detection. For image object classification and detection, CNN which belongs to the class of feed-forward ANN, has been successfully used. Fast RCNN combines advantages of modern CNN detectors i.e. RCNN and SPPnet to classify object proposals more efficiently, resulting in better and faster detection. To further improve the detection rate and speed of Fast RCNN, two modifications are proposed in this paper. One for accuracy in which an extra convolutional layer is added to the network and named it as Fast RCNN type 2, the other for speed in which the input channel is reduced from three channel input to one and named as Fast RCNN type 3.Fast RCNN type 1 has better detection rate than RCNN and compare to Fast RCNN, Fast RCNN type 2 has better detection rate while Fast RCNN type 3 is faster.
Rating Prediction in Review-Based Recommendations via Adversarial Auto-Encoder

Recommendation methods usually use users' historical ratings on items to predict ratings on their unrated items to make recommendations. However, the sparse rating data limit the recommendation quality. In order to solve the sparsity problem, other auxiliary information is combined to mine users' preferences for higher recommendation quality. This paper proposes a novel recommendation model, which harnesses an adversarial learning among auto-encoders to improve recommendation quality by minimizing the gap of rating and review relation of users and items. The empirical studies on real-world datasets prove that the proposed method improves recommendation performance.
Desire Path-Inspired Procedural Placement of Coins in a Platformer Game

Many games feature collectible items that are manually placed by a designer. In this work, we developed an algorithm, inspired by desire paths, for automatically placing collectible coins in a platformer game. Desire paths are paths naturally formed where people walk, rather than those laid down artificially, and are often the shortest or easiest route between an origin and destination. Our algorithm uses player trajectories to find paths along which to place the coins for each level. We ran an experiment comparing path-based placement to other placement methods. Although we did not find a difference in total time spent playing or likelihood of finishing the game, our results suggest that path-based placement leads to players collecting more coins in less time than with designer or randomly placed coins. Further, we found that players played similarly when coins were either pathbased or there were no coins, and similarly when coins were either placed by a designer or randomly.
A New Generative Adversarial Network for Texture Preserving Image Denoising

In this paper, a new generative adversarial networks (GAN) is proposed for image denoising. The proposed GAN has a new generator network to produce denoised images with noisy images as input, and the entire network is trained using a new loss to represent the distance between the data distribution of clean images and denoised images. Based on quantitative and qualitative evaluating criteria, we made comparisons between our method and other denoising methods which shows the superiority of our approach.
I See What You See: Integrating Eye Tracking into Hanabi Playing Agents

Humans’ eye movements convey a lot of information about their intentions, often unconsciously. Intelligent agents that cooperate with humans in various domains can beneﬁt from interpreting this information. This paper contains a prelimi-nary look at how eye tracking could be useful for agents that play the cooperative card game Hanabi with human players. We outline several situations in which an AI agent can utilize gaze information, and present an outlook on how we plan to integrate this with reimplementations of contemporary Han-abi agents.
Microwave computational imaging in frequency domain with reprogrammable metasurface

Abstract. Recently, microwave computational imaging systems have had various applications ranging from security screening to biomedical diagnosis. However, existing methods are sensitive to noise and have a heavy computational burden in a three-dimensional (3-D) imaging scene. A computational imaging method approached in frequency domain is proposed, which improves imaging quality under noisy conditions and reduces computation complexity. The signal-to-noise ratio of the echo signal is improved by dechirping pulse compression method, which obtains the range resolution concurrently. According to the information of range resolution, the scene is divided into some range bins. With computational imaging algorithms, the azimuth and elevation resolution are obtained in each range bin by spatially diverse patterns of reprogrammable metasurface. A sparse 3-D image can be obtained by combining the reconstructed subimages. The simulation result shows that the proposed method outperforms the conventional methods with better antinoise ability and lower computational complexity in sparse 3-D scene imaging.
A Distributed Scalable Approach for Rule Processing: Computing in the Fog for the SWoT

The development of the Semantic Web of Things (SWoT) is challenged by the nature of IoT deployment architectures, where constrained devices collect data processed remotely by powerful Cloud servers. Such a deployment pattern introduces bottlenecks constituting a hurdle for scalability, and increases response time. This hinders the development of a number of critical and time-sensitive applications. Enabling the deployment of the Semantic Web stack closer to the constrained devices of the IoT may foster the development of time-sensitive interoperable applications, while reducing forwarding the user data to remote third party Cloud servers. The approach we develop in this paper is a contribution towards this direction, and aims to enable rule-based reasoning closer to sensors producing IoT data. For this purpose, we define a distributed scalable semantic processing algorithm by dynamically propagating deduction rules on Fog nodes. Our goal is to shorten the time needed to deliver high level information deduced from the collected data. This approach is evaluated on a smart building use case where both distribution and scalability have been considered.
Precise Description Generation for Knowledge Base Entities with Local Pointer Network

Verbalization of knowledge base (KB) facts about an entity allows users to absorb information from KB more easily. The drawback of most previous work is that they cannot generalize to unseen frames. This work introduces the task of precise KB verbalization that is aimed at generating an exact description for the given factual triples. We propose a novel sequence-to-sequence (seq2seq) model with the local pointer network to deal with this task. The approach to training data construction is also explored. Experimental results show our method improves the performances in terms of Meteor and slot error rates. Human evaluation is also performed to confirm the effectiveness of our model.
Integration of Ontological Case-Based Reasoning with Principal Component Analysis: Application to the IT Support Service

In present paper we propose an original approach to the indexing of cases by ontology concepts, as a result of which the special semantic data matrix is generated. The elements of this matrix are semantic links between cases and terminal concepts of the ontology. This matrix contains knowledge about the most stable, non-trivial relationships between the ontology concepts that determine the most frequently used cases. To identify these groups of concepts we propose and approve an approach based on modification of the principal component analysis with use of combination of polychoric correlations and correlation ratio. Interpretation of the loadings matrix on the principal components allows us to identify groups of interrelated concepts from different hierarchical branches of the ontology. Thus, problems that are at the junction of different concepts can be identified. The proposed method is implemented in the knowledge management system for IT support service.
Visual Analytics Interface for Time Series Data Based on Trajectory Manipulation

In recent years, time series data have been collected in many fields, and a visual analytics (VA) interface is expected to be useful for utilizing such data. When developing such interfaces for time series data, several problems arising from the property of time series data need to be resolved. For example, the temporal trend of data is usually visualized with animation. However, with this approach, a collision would occur between the movement of the time series data itself and that caused by interaction with users. As a result, visual clutter often occurs on a display. To solve these problems, this paper focuses on trajectories, which can handle temporal and spatial changes uniformly, and proposes a VA interface that enables direct manipulation of trajectories. The usefulness of the proposed interface is demonstrated through experiments.
Hybrid Question Answering Using Heuristic Methods and Linked Data Schema

The emergence of linked data in the form of knowledge graphs in RDF has been one of the most recent evolutions of the Semantic Web. This led to the development of natural language question answering systems that automatically translates a question into SPARQL based on these RDF knowledge graphs. In particular, hybrid question answering, the task of question answering by combining both structured (RDF) and unstructured knowledge sources (text) has emerged as an important challenge. This paper tackles hybrid question answering based on natural language questions. We present HAWK_R, a question answering system that improves an open source system called HAWK. We identify its limitations and propose enhancements using heuristic-based methods based on RDF and text search. Our results show a clear improvement of the F-score.
A novel method for content-based image retrieval to improve the effectiveness of the bag-of-words model using a support vector machine

The advancements in the multimedia technologies result in the growth of the image databases. To retrieve images from such image databases using visual attributes of the images is a challenging task due to the close visual appearance among the visual attributes of these images, which also introduces the issue of the semantic gap. In this article, we recommend a novel method established on the bag-of-words (BoW) model, which perform visual words integration of the local intensity order pattern (LIOP) feature and local binary pattern variance (LBPV) feature to reduce the issue of the semantic gap and enhance the performance of the content-based image retrieval (CBIR). The recommended method uses LIOP and LBPV features to build two smaller size visual vocabularies (one from each feature), which are integrated together to build a larger size of the visual vocabulary, which also contains complementary features of both descriptors. Because for efficient CBIR, the smaller size of the visual vocabulary improves the recall, while the bigger size of the visual vocabulary improves the precision or accuracy of the CBIR. The comparative analysis of the recommended method is performed on three image databases, namely, WANG-1K, WANG-1.5K and Holidays. The experimental analysis of the recommended method on these image databases proves its robust performance as compared with the recent CBIR methods.
Detection and identification method of medical label barcode based on deep learning

The widespread use of barcode technology has led to the complexity of the application scenario. In the traditional barcode recognition method, there is no universal solution to the problems of uneven illumination, distortion, and sheltered. In this paper, the deep learning theory is used to solve the problem of barcode detection under the above situation. And on this basis, the problem of correcting linear distortion Data Matrix code is solved, and the key technology of barcode recognition under complex situation is broken through. After testing, the recognition speed reached 125ms, and the recognition accuracy reached about 93%. The system uses CCD camera to collect pictures, adopts the HALCON to build the processing algorithm, and uses Visual Studio platform to build the software, which realizes the Date Matrix code, Drug Electronic Supervision Code and Product bar code fast and accurate identification on pharmaceutical packaging. The developed system can also detect the rotation angle of Barcode and Data Matrix code, which is favorable for reading the barcode information. The whole process is real-time.
Grail: A Framework for Adaptive and Believable AI in Video Games

We describe a framework - called Grail - which aims at providing developers with tools for implementing AI in games. There is a whole variety of games and the role of AI in them can vary from case to case. Thus, the main challenge is to create a system allowing for meeting various design goals with relatively easy to use interfaces. We present the conceptual architecture of Grail and algorithms chosen by us to cover a wide spectrum of use cases: Planning, Utility System, Simplified Games with Tree Search and scripting. We believe that together they fulfill the requirements of a flexible AI engine.
Notions of at-issueness

Upon hearing the sentence Messi, who once scored a goal with his hand, won the Ballon d’Or, the addressee is likely to interpret the main clause as conveying the “main point” and view the appositive relative clause as contributing secondary information. The intuition that some part of the utterance conveys the main point has recently been discussed in formal semantics and pragmatics under the label of “at-issueness”. However, this label has been used in a variety of ways and there is often little clarity as to what is meant by it. This survey tries to clear things up by identifying and spelling out three specific notions of at-issueness, i.e. Q(uestion)-atissueness, P(roposal)-at-issueness, and C(oherence)-at-issueness. After looking into what they say about similar kinds of data, I conclude that while these notions appear to capture facets of the same broad intuition, they are truly distinct. The paper also discusses potential connections of at-issueness to projection and commitment strength.
ECG Monitoring and Anomaly Detection Based on Compressed Measurements

Long-term monitoring systems based on wearable devices and local devices with computational capabilities -smartphone, smartwatch- could be used in the prevention of cardiovascular disease in risk subjects or during the follow-up for increasing the quality of life. In this paper we propose a lightweight solution that firstly exploits compressive sensing for locally reducing the amount of raw data, and successively employs a detection algorithm operating directly on the compressed domain for extracting only meaningful information to send at the medical staff. Performances of the proposed solution have been assessed under different conditions. Results show that the algorithm is able of identifying with a good precision and sensitivity the ECG features -QRS complexes and T, P waves- even with high compression ratios of about 20-50%.
A qualitative study on usability and acceptability of Yubico security key

Individual concerns about account takeover and subversion are well-documented. Surveys indicate that concerns for the privacy and security of online accounts are widely shared. Adopting Two-Factor Authentication (2FA) is an action that individuals can take to secure their own accounts, including many popular consumer-facing services. Given that, why is two-factor hardware not more widely adopted? What usability and acceptability factors drive the adoption, or lack of adoption of 2FA in the form of trusted hardware? Passwords are inherently misaligned with human cognition, and hardware keys designed for ease of use are readily available in the marketplace. Yet passwords remain the dominant online authentication method. In order to better understand relevant issues driving or impinging adoption of Two-Factor Authentication, we implemented a two-phase study of the Yubico FIDO U2F security key. The Yubico security key is a 2FA device designed to be user friendly. We examined the usability of the device by implementing a think-aloud protocol, and documented the halt and confusion points. We provided this analysis to Yubico, who implemented many of the recommended changes. We then repeated the study in the same context; noting significant improvements in usability. However, increase in usability did not affect the acceptability of the device, affecting the prolonged usage of the device. In both phases we interviewed the study participants about the acceptability of the device, finding similar concerns about lack of benefits and the invisibility of risk. A source of opposition to adoption is the concern for loss of access, with participants prioritizing availability over confidentiality. Another concern is that these do not lessen or simplify interaction with services as passwords are still required. We close with open questions for additional research, and further recommendations to encourage online safety through the adoption of 2FA.
AtNAGNAG: a comprehensive database for NAGNAG alternative splicing in Arabidopsis thaliana

NAGNAG alternative splicing is widely present in animal genomes. In plants, there has been little large-scale exploration of NAGNAG splicing, even in model plants such as Arabidopsis thaliana. To address this, here we describe the first database of NAGNAG motifs and their expression ratios between two splicing sites in A. thaliana. Using 435 RNA-seq datasets, we systematically identified and quantified 1,890 unique NAGNAG motifs as well as the variation of expression ratio between two splicing sites from six tissues, including 200 motifs with two novel splicing sites and 1,208 motifs containing one novel splicing site. Using this database, it is now possible to browse, search, visualize and download Arabidopsis NAGNAG motifs. The collected information can also be used to retrieve the related tissue-specific transcript and amino acid information. The database largely expands our knowledge about NAGNAG events. Placing these data in a unified browsing platform should enable high throughput functional validation of the identified NAGNAG events and understand the regulatory role of NAGNAG splicing motifs through the variation of expression ratios. Database URL: http://deepbiology.cn/nagnag.
3D Detection of Spheric and Aspheric Femoral Heads in Coronal MR Images of Patients with Legg-Calve-Perthes Disease Using the Spherical Hough Transform

This paper presents a study of 3D femoral head detection in coronal magnetic resonance (MR) imaging sequences to detect the spheric and aspheric femoral heads using the Spherical Hough Transform (SHT). Shape structure and functionality of the femoral head can be affected by a wide range of hip disorders. In most of the computerized analysis and assessment cases of the hip joint, segmentation of the proximal femur and femoral head is performed on the image sequences that are provided by the several medical imaging modalities. Determination of initial points, detection of the tissue boundaries and segmentation of the tissues are important challenges in automatic or semi-automatic proximal femur segmentation methods. In this paper, we aimed to perform a preliminary study based on 3D femoral head detection in bilateral MR hip images in coronal plane of patients with Legg-Calve-Perthes disease (LCPD). Firstly, MR sections are divided vertically into two equal halves automatically to separate the healthy and pathological hip joints and then Canny's edge detection method is performed to obtain edge images. Finally, 3D SHT is performed to detect the spherical structure of femoral heads. Automatic detection of the femoral head with SHT provides a starting point for the segmentation of the femoral head ball and proximal femur. Furthermore, it also allows for 3D segmentation of the femoral head sphere with the radius information that it provides with the 3D sphere center coordinates. Performance evaluations and experimental results observed on 8 bilateral MR imaging sequences belong to 6 subjects show that proposed method has promising results.