Reviewerly/exHarmony huggingface dataset:
Robust Multisource Remote Sensing Image Registration Method Based on Scene Shape Similarity

Image registration is an indispensable component of remote sensing applications, such as disaster monitoring, change detection, and classification. Grayscale differences and geometric distortions often occur among multisource images due to their different imaging mechanisms, thus making it difficult to acquire feature points and match corresponding points. This article proposes a scene shape similarity feature (SSSF) descriptor based on scene shape features and shape context algorithms. A new similarity measure called SSSF ncc is then defined by computing the normalized correlation coefficient of the SSSF descriptors between multisource remote sensing images. Furthermore, the tie points between the reference and the sensed image are extracted via a template matching strategy. A global consistency check method is then used to remove the mismatched tie points. Finally, a piecewise linear transform model is selected to rectify the remote sensing image. The proposed SSSF ncc aims to extract the scene shape similarity between multisource images. The accuracy of the proposed SSSF ncc is evaluated using five pairs of experimental images from optical, synthetic aperture radar, and map data. Registration results demonstrate that the SSSF ncc similarity measure is robust enough for complex nonlinear grayscale differences among multisource remote sensing images. The proposed method achieves more reliable registration outcomes compared with other popular methods.
The adaptation of the $k$-means algorithm to solving the multiple ellipses detection problem by using an initial approximation obtained by the DIRECT global optimization algorithm

We consider the multiple ellipses detection problem on the basis of a data points set coming from a number of ellipses in the plane not known in advance, whereby an ellipse E is viewed as a Mahalanobis circle with center S, radius r, and some positive definite matrix Σ. A very efficient method for solving this problem is proposed. The method uses a modification of the k-means algorithm for Mahalanobis-circle centers. The initial approximation consists of the set of circles whose centers are determined by means of a smaller number of iterations of the DIRECT global optimization algorithm. Unlike other methods known from the literature, our method recognizes well not only ellipses with clear edges, but also ellipses with noisy edges. CPU-time necessary for running the corresponding algorithm is very short and this raises hope that, with appropriate software optimization, the algorithm could be run in real time. The method is illustrated and tested on 100 randomly generated data sets.
Gamification of Heritage through Augmented Reality

This paper focuses on a game on architectural heritage, possibilities for usinggamification for conveying information through the reanimation of an ancientcity. It proposes an immersive AR game involving the portrayal of culturalheritage through mobile devices. The game includes an AR application forAndroid devices which enabled rendering of 3D content in combination withcamera input. This application is an independent game, tracking targets throughGPS on a larger scale and using object recognition on a smaller scale. Ourresearch aims to propose implementing game mechanics on an AR system at anarchaeological site in order to increase visitors' interest, and promote thedissemination of cultural heritage.
Paley type partial difference sets in abelian groups

Abstract Partial difference sets with parameters are called Paley type partial difference sets. In this note, we prove that if there exists a Paley type partial difference set in an abelian group of order v , where v is not a prime power, then or , an odd integer. In 2010, Polhill constructed Paley type partial difference sets in abelian groups with those orders. Thus, combining with the constructions of Polhill and the classical Paley construction using nonzero squares of a finite field, we completely answer the following question: “For which odd positive integers , can we find a Paley type partial difference set in an abelian group of order ?”
Inductive Relation Prediction on Knowledge Graphs.

The dominant paradigm for relation prediction in knowledge graphs involves learning and operating on latent representations (i.e., embeddings) of entities and relations. However, these embedding-based methods do not explicitly capture the compositional logical rules underlying the knowledge graph, and they are limited to the transductive setting, where the full set of entities must be known during training. Here, we propose a graph neural network based relation prediction framework, GraIL, that reasons over local subgraph structures and has a strong inductive bias to learn entity-independent relational semantics. Unlike embedding-based models, GraIL is naturally inductive and can generalize to unseen entities and graphs after training. We provide theoretical proof and strong empirical evidence that GraIL can represent a useful subset of first-order logic and show that GraIL outperforms existing rule-induction baselines in the inductive setting. We also demonstrate significant gains obtained by ensembling GraIL with various knowledge graph embedding methods in the transductive setting, highlighting the complementary inductive bias of our method.
Spaceline: A Concept for Interaction in Cinematic Virtual Reality

Watching omnidirectional movies via head-mounted displays places the viewer inside the scene. In this way, the viewer attends an immersive movie experience. However, due to the free choice of the viewing direction, it is possible to miss details which are important for the story. On the other hand, the additional space component gives the filmmakers new opportunities to construct non-linear interactive stories. To assist this, we introduce the concept of a spaceline which connects movie sequences via interactive regions. This work explains the terms of the spaceline concept and introduces methods that make it easier for the viewer to follow the story, at their own pace with their own focus. We present a design space that supports filmmakers in designing interactive CVR experiences.
The Feasibility of Deep Learning Use for Adversarial Model Extraction in the Cybersecurity Domain

Machine learning algorithms found their way into a surprisingly wide range of applications, providing utility and allowing for insights gathered from data in a way never before possible. Those tools, however, have not been developed with security in mind. A deployed algorithm can meet a multitude of risks in the real world. This work explores one of those risks - the feasibility of an exploratory attack geared towards stealing an algorithm used in the cybersecurity domain. The process we have used is thoroughly explained and the results are promising.
Image Composition of Partially Occluded Objects

Abstract Image composition extracts the content of interest (COI) from a source image and blends it into a target image to generate a new image. In the majority of existing works, the COI is manually extracted and then overlaid on top of the target image. However, in practice, it is often necessary to deal with situations in which the COI is partially occluded by the target image content. In this regard, both tasks of extracting the COI and cropping its occluded part require intensive user interactions, which are laborious and seriously reduce the composition efficiency. This paper addresses the aforementioned challenges by proposing an efficient image composition method. First, we extract the semantic contents of the images by using state‐of‐the‐art deep learning methods. Therefore, the COI can be selected with clicks only, which can greatly reduce the demanded user interactions. Second, according to the user's operations (such as translation or scale) on the COI, we can effectively infer the occlusion relationships between the COI and the contents of the target image. Thus, the COI can be adaptively embedded into the target image without concern about cropping its occluded part. Therefore, the procedures of content extraction and occlusion handling can be significantly simplified, and work efficiency is remarkably improved. Experimental results show that compared to existing works, our method can reduce the number of user interactions to approximately one‐tenth and increase the speed of image composition by more than ten times.
Performance analysis of clustering internal validation indexes with asymmetric clusters

The present work evaluates the performance of a set of internal clustering indexes in artificial and real data sets regarding a specific structural characteristic. In particular, it deals with data sets whose clusters present asymmetric characteristics in their geometries. With this objective, the concept of symmetry is formalized with respect to the axes of maximum variance of the clusters by the definition of a new index. Then a novel methodology is proposed to evaluate the performance of the internal indexes for crisp clustering in sets with this specific structural characteristic. The new defined index is combined with the correlation analysis, allowing to evaluate dynamically the performance of 11 internal indexes well known in the literature, and of a recently proposed index, the Representative Tree Index (RTI). In this way, this methodology allows us to measure not only the absolute error of the indices in relation to a particular configuration of clusters, but also the degree to which the structural characteristic of interest influences the performance of the index, obtaining a more generic understanding of their behaviors.
Perceptual Video Coding Scheme Using Just Noticeable Distortion Model Based on Entropy Filter

Because perceptual video coding (PVC) can reduce bitrates with negligible visual quality loss in video compression, a PVC scheme based on just noticeable distortion (JND) model is proposed for ultra-high definition video. Firstly, the proposed JND model is designed, considering the spatial JND characteristics such as contrast sensitivity, luminance adaptation and saliency weight factor. Secondly, in order to perform precise JND suppression, the Gauss differential entropy (GDE) filter is designed to divide the image into smooth and complex texture region. Thirdly, through incorporating the proposed JND model into the encoding process, the transform coefﬁcients are suppressed in harmonization with the transform/quantization process of high efﬁciency video coding (HEVC). In order to achieve the JND suppression effectively, a distortion compensation factor and distortion compensation control factor are incorporated to control the extent of distortion in the rate distortion optimization process. The experimental results show that the proposed PVC scheme can achieve a remarkable bitrate reduction of 32.98% for low delay (LD) configuration and 28.61% for random access (RA) configuration with a negligible subjective quality loss. Meanwhile, the proposed method only causes about average 12.94% and 22.45% encoding time increase under LD and RA configuration compared with an HEVC reference software, respectively.
Confronting the tensions where UX meets AI

This new forum provides a space to engage with the challenges of designing for intelligent algorithmic experiences. We invite articles that tackle the tensions between research and practice when integrating AI and UX design. We welcome interdisciplinary debate, artful critique, forward-looking research, case studies of AI in practice, and speculative design explorations. --- Juho Kim and Henriette Cramer, Editors
A Multi-Attribute Blind Quality Evaluator for Tone-Mapped Images

High dynamic range (HDR) imaging enables capturing a wide range of luminance levels existing in real-world scenes. While HDR capturing devices become widespread in the market, the display technology is yet limited in representing full luminance ranges and standard low dynamic range (LDR) displays are currently more prevalent. To visualize the HDR content on traditional displays, tone mapping (TM) operators are introduced that convert HDR content into LDR. The dynamic range compression and different processing steps during TM can lead to loss of scene details, as well as luminance and chrominance changes. Such signal deviations will affect image naturalness and consequently disturb the visual quality of experience. Therefore, research into objective methods for quality evaluation of tone-mapped images has received attention in recent years. In this paper, we proposed a completely blind image quality evaluator for tone-mapped images based on a multi-attribute feature extraction scheme. Due to the diversity of TM distortions, various image characteristics are taken into account to develop an effective metric. The features are designed by considering spectral and spatial entropy, detection probability of visual information, image exposure, sharpness, and color properties. The quality-relevant features are then fed into a machine-learning regression framework to pool a quality score. The validation tests on two benchmark datasets reveal the superior performance of the proposed approach compared to the competing metrics.
Reducing Latency in a Collaborative Augmented Reality Service

We show how inter-device location tracking latency can be reduced in an Augmented Reality (AR) service that uses Microsoft's HoloLens (HL) devices for multi-user collaboration. Specifically, we have built a collaborative AR system for a research greenhouse that allows multiple users to be able to work collaboratively to process and record information about individual plants in the greenhouse. In this system, we have combined the HL "world tracking" functionality together with marker-based tracking to develop a one-for-all-shared-experience (OFALL-SE) dynamic object localization service. We compare this OFALL-SE service with the traditional Local Anchor Transfer (LAT) method for managing shared experiences and show that latency of data transmission throughout the server and users can be dramatically reduced. Our results indicate that OFALL-SE can support near-real-time collaboration when sharing the physical locations of the plants among users in a greenhouse.
Text Encryption Algorithm based on Chaotic Map

Abstract To maintain the confidential data, a scheme or algorithm is needed to protect the data from many attacks. Recently, many cryptography schemes and algorithms have been introduced. One of them is the cryptography algorithm based on chaotic maps. The nature of the chaotic maps gives many advantages to the algorithm. In this paper, an algorithm to encrypt the text is purposed. The algorithm utilizes the nature of the chaotic map by using the MS map as a keystream generator. The ciphertext found by the purposed algorithm is very difficult to be cracked by the bruce-force attack and known-plaintext attack. The decryption process has been tested using 3 different secret keys (that is very similar with the secret key on the encrypted process), and resulting the decrypted text is the same with plaintext only if the input is exactly the same secret key as in encrypted process.
Mimic and Rephrase: Reflective Listening in Open-Ended Dialogue

Reflective listening–demonstrating that you have heard your conversational partner–is key to effective communication. Expert human communicators often mimic and rephrase their conversational partner, e.g., when responding to sentimental stories or to questions they don’t know the answer to. We introduce a new task and an associated dataset wherein dialogue agents similarly mimic and rephrase a user’s request to communicate sympathy (I’m sorry to hear that) or lack of knowledge (I do not know that). We study what makes a rephrasal response good against a set of qualitative metrics. We then evaluate three models for generating responses: a syntax-aware rule-based system, a seq2seq LSTM neural models with attention (S2SA), and the same neural model augmented with a copy mechanism (S2SA+C). In a human evaluation, we find that S2SA+C and the rule-based system are comparable and approach human-generated response quality. In addition, experiences with a live deployment of S2SA+C in a customer support setting suggest that this generation task is a practical contribution to real world conversational agents.
Multivariate Triangular Quantile Maps for Novelty Detection

Novelty detection, a fundamental task in machine learning, has drawn a lot of recent attention due to its wide-ranging applications and the rise of neural approaches. In this work, we present a general framework for neural novelty detection that centers around a multivariate extension of the univariate quantile function. Our framework unifies and extends many classical and recent novelty detection algorithms, and opens the way to exploit recent advances in flow-based neural density estimation. We adapt the multiple gradient descent algorithm to obtain the first efficient end-to-end implementation of our framework that is free of tuning hyperparameters. Extensive experiments over a number of real datasets confirm the efficacy of our proposed method against state-of-the-art alternatives.
Fusion of Handcrafted and Deep Features for Medical Image Classification

Abstract Medical image classification has recently attracted increased attention. Effective feature extraction and learning are key means to improve classification performance. However, in the current study, handcrafted feature are mostly designed with intuitive mode, while the deep feature depends on a large amount of training samples and has weak interpretability. To capture more discriminative features for medical image, a novel feature fusion approach, termed multi-layer visual feature fusion (MLVSF), has been proposed on the basis of low-level, mid-level and deep features. More specifically, by fusing the handcrafted and deep features generated by local binary pattern variant, bag-of-visual-words, convolutional neural network, respectively, MLVSF can effectively enhance the discriminating power of features for medical image recognition. Experimental results on two medical image datasets show that MLVSF can improve convolutional neural networks, and achieve a better classification accuracy in comparison with some state-of-the-art methods.
A Proactive, Cost-aware, Optimized Data Replication Strategy in Geo-distributed Cloud Datastores

Geo-replicated cloud datastores adopt the replication methodology by placing multiple data replicas at suitable storage zones. This can provide reliable services to customers with high availability, low access latency, low system cost, and decreased bandwidth consumption. However, this has the potential to increase the whole system overheads of maintaining more resource replicas, and to also degrade the system utilization due to unnecessary storage space cost. Thus, it is important to determine the suitable replication zones on-the-fly to increase the availability of data resources and maximize the system utilization. Specifically, it is essential to determine the appropriate number of replicas for different data resources at each zone in a particular time interval. We propose Cost Optimization Replica Placement (CORP) algorithms to enable state-of-art proactive provisioning replication of data resources based on an one-step look-ahead workload behavior pattern forecast over the distributed data storage infrastructure using statistical techniques. The experimental results show the cost effectiveness of the proposed replication strategies.
Decentralized Informative Path Planning With Balanced Exploration-Exploitation for Swarm Robotic Search

Abstract Swarm robotic search is concerned with searching targets in unknown environments (e.g., for search and rescue or hazard localization), using a large number of collaborating simple mobile robots. In such applications, decentralized swarm systems are touted for their task/coverage scalability, time efficiency, and fault tolerance. To guide the behavior of such swarm systems, two broad classes of approaches are available, namely nature-inspired swarm heuristics and multi-robotic search methods. However, simultaneously offering computationally-efficient scalability and fundamental insights into the exhibited behavior (instead of a black-box behavior model), remains challenging under either of these two class of approaches. In this paper, we develop an important extension of the batch Bayesian search method for application to embodied swarm systems, searching in a physical 2D space. Key contributions lie in: 1) designing an acquisition function that not only balances exploration and exploitation across the swarm, but also allows modeling knowledge extraction over trajectories; and 2) developing its distributed implementation to allow asynchronous task inference and path planning by the swarm robots. The resulting collective informative path planning approach is tested on target search case studies of varying complexity, where the target produces a spatially varying (measurable) signal. Significantly superior performance, in terms of mission completion efficiency, is observed compared to exhaustive search and random walk baselines, along with favorable performance scalability with increasing swarm size.
Analysis on the Application of artificial Intelligence in classroom

Abstract With the continuous development and strength of information, the development of artificial intelligence has also moved towards a new stage, gradually living, but also only applied in a few fields and a few regions. Therefore, this paper puts forward a tentative idea whether artificial intelligence can be used in our classroom in the near future. With the smart phone, IPD and other intelligent items in the life of more and more widely used, its simplicity and intelligence, so that all the young and old, the use of a wide range of people, and easy to accept. With the diversity of modern life, the needs of students in the new era are becoming more and more widespread, while the traditional classroom teaching mode is being issued. Birth change, but the form is still relatively single, so for the traditional classroom, intelligent classroom will show a great advantage. It is possible to make students understand the content of the course before the beginning of the course by simulating the form of the real class, and choose the style and form of the course according to the students’ ability and interest in the learning process. In the era of the vigorous development of artificial intelligence, the development of intelligent classroom and intelligent classroom will not be underestimated, and intelligent classroom will also play a great role in the development of life in the future.
Efficient Office Assistant Robot System: Autonomous Navigation and Controlling Based on ROS

International Journal of Multidisciplinary Studies (IJMS) is published by the Faculty of Graduate Studies - University of Sri Jayewardenepura. IJMS provides a supportive environment for multidisciplinary researchers to publish their contribution to the academy of knowledge and wisdom. Focus of the journal is on multidisciplinary research which should cover at least two distinct disciplines.