Reviewerly/exHarmony huggingface dataset:
A Configurable Evaluation Framework for Node Embedding Techniques

While Knowledge Graphs (KG) are graph shaped by nature, most traditional data mining and machine learning (ML) software expect data in a vector form. Several node embedding techniques have been proposed to represent each node in the KG as a low-dimensional feature vector. A node embedding technique should preferably be task independent. Therefore, when a new method has been developed, it should be tested on the tasks it was designed for as well as on other tasks. We present the design and implementation of a ready to use evaluation framework to simplify the node embedding technique testing phase. The provided tests range from ML tasks, semantic tasks to semantic analogies.
A dynamic dyadic particle filter for price prediction

Financial forecasting deals with dynamically evolving phenomena through time. Accurate price prediction based on historical data is a challenging task in this field, because a large degree of uncertainty governs price evolution. Stock and flight price prediction are two cases studied here. A dynamic dyadic particle filter is proposed that is based on sequential importance resampling. To ensure the generation of efficient particles, a time-evolving structure is constructed by employing pairs of latent state vectors called dyads. The variance of the observation noise is treated as a random variable obeying a heavy-tailed distribution. For stock price prediction, the dyad consists of a latent state vector modeling each stock and a latent state vector modeling the group of companies of the same category. It is demonstrated by experiments that the dynamic evolution of latent state vectors leads to more efficient prediction compared to state-of-the-art techniques. For flight price prediction, the dyad consists of two latent state vectors, corresponding to route and arrival, respectively. Promising flight price predictions are disclosed given the wide price range in each route.
On the realization of the recognition-primed decision model for artificial agents

Abstract This work proposes a methodology to program an artificial agent that can make decisions based on a naturalistic decision-making approach called recognition-primed decision model (RPDM). The proposed methodology represents the main constructs of RPDM in the language of Belief-Desire-Intention logic. RPDM considers decision-making as a synthesis of three phenomenal abilities of the human mind. The first is one’s use of experience to recognize a situation and suggest appropriate responses. The main concern here is on situation awareness because the decision-maker needs to establish that a current situation is the same or similar to one previously experienced, and the same solution is likely to work this time too. To this end, the proposed modeling approach uses a Markov logic network to develop an Experiential-Learning and Decision-Support module. The second component of RPDM deals with the cases when a decision-maker’s experience becomes secondary because the situation has not been recognized as typical. In this case, RPDM suggests a diagnostic mechanism that involves feature-matching, and, therefore, an ontology (of the domain of interest) based reasoning approach is proposed here to deal with all such cases. The third component of RPDM is the proposal that human beings use intuition and imagination (mental stimulation) to make sure whether a course of action should work in a given situation or not. Mental simulation is modeled here as a Bayesian network that computes the probability of occurrence of an effect when a cause is more likely. The agent-based model of RPDM has been validated with real (empirical) data to compare the simulated and empirical results and develop a correspondence in terms of the value of the result, as well as the reasoning.
Sound and reusable components for abstract interpretation

Abstract interpretation is a methodology for defining sound static analysis. Yet, building sound static analyses for modern programming languages is difficult, because these static analyses need to combine sophisticated abstractions for values, environments, stores, etc. However, static analyses often tightly couple these abstractions in the implementation, which not only complicates the implementation, but also makes it hard to decide which parts of the analyses can be proven sound independently from each other. Furthermore, this coupling makes it hard to combine soundness lemmas for parts of the analysis to a soundness proof of the complete analysis. To solve this problem, we propose to construct static analyses modularly from reusable analysis components . Each analysis component encapsulates a single analysis concern and can be proven sound independently from the analysis where it is used. We base the design of our analysis components on arrow transformers , which allows us to compose analysis components. This composition preserves soundness, which guarantees that a static analysis is sound, if all its analysis components are sound. This means that analysis developers do not have to worry about soundness as long as they reuse sound analysis components. To evaluate our approach, we developed a library of 13 reusable analysis components in Haskell. We use these components to define a k -CFA analysis for PCF and an interval and reaching definition analysis for a While language.
A Machine Learning Approach to Modeling Satellite Behavior

To date, there are roughly 1,957 active satellites and 17,494 inactive and debris objects that are currently tracked orbiting the globe. Each satellite's orbit varies by shape, size, and orientation depending on the satellites distinct function. Due to their diverse orbits and quantity, it is not feasible for analysts to simultaneously monitor all satellites; however, machine learning approaches allow for the automation of satellite characterization for space situational awareness (SSA). This project presents preliminary evidence of how machine learning applications can be used to model satellite behavior. Satellite data were modeled using the machine learning approaches of supervised classification, unsupervised clustering, and supervised neural networks. Supervised models were used to determine satellite stability as a critical feature for satellite characterization. Similarly, unsupervised techniques provided insight into satellite characterization by geospatial location and detection of anomalous behavior and maneuvers. Deep learning, using a recurrent neural network (RNN), predicted sequential satellite maneuvers over time. Together, these satellite modeling results have implications for providing surveillance and security in space.
3D Medical Image Synthesis by Factorised Representation and Deformable Model Learning

In this paper we propose a model for controllable synthesis of 3D (volumetric) medical image data. The model is comprised of three components which are learnt simultaneously from unlabelled data through self-supervision: (i) a multi-tissue anatomical model, (ii) a probability distribution over deformations of this anatomical model, and, (iii) a probability distribution over ‘renderings’ of the anatomical model (where a rendering defines the relationship between anatomy and resulting pixel intensities). After training, synthetic data can be generated by sampling the deformation and rendering distributions. To encourage meaningful correspondence in the learnt anatomical model the renderer is kept simple during training, however once trained the (deformed) anatomical model provides dense multi-class segmentation masks for all training volumes, which can be used directly for state-of-the-art conditional image synthesis. This factored model based approach to data synthesis has a number of advantages: Firstly, it allows for coherent synthesis of realistic 3D data, as it is only necessary to learn low dimensional generative models (over deformations and renderings) rather than over the high dimensional 3D images themselves. Secondly, as a by-product of the anatomical model we implicitly learn a dense correspondence between all training volumes, which can be used for registration, or one-shot segmentation (through label transfer). Lastly, the factored representation allows for modality transfer (rendering one image in the modality of another), and meaningful interpolation between volumes. We demonstrate the proposed approach on cardiac MR, and multi-modal abdominal MR/CT datasets.
A novel pruning algorithm for mining long and maximum length frequent itemsets

Frequent itemset mining is today one of the most popular data mining techniques. Its application is, however, hindered by the high computational cost in many real-world datasets, especially for smaller values of support thresholds. In many cases, moreover, the large number of frequent itemsets discovered is overwhelming. In some real-world applications, it is sufficient to find a smaller subset of frequent itemsets, such as identifying the frequent itemsets with a maximum length. In this paper, we present a pruning algorithm, called LengthSort, that reduces the search space effectively and improves the efficiency of mining frequent itemsets with a maximum length. LengthSort prunes both the items and the transactions before constructing a Frequent Pattern tree structure. Our experiments on several datasets show that the proposed pruning techniques reduce the time needed to discover the frequent itemsets with a maximum length. The proposed pruning algorithm can also be applied to efficiently discover frequent itemsets that are longer than a user-specified threshold.
Landmark-Guided Deformable Image Registration for Supervised Autonomous Robotic Tumor Resection

Oral squamous cell carcinoma (OSCC) is the most common cancer in the head and neck region, and is associated with high morbidity and mortality rates. Surgical resection is usually the primary treatment strategy for OSCC, and maintaining effective tumor resection margins is paramount to surgical outcomes. In practice, wide tumor excisions impair post-surgical organ function, while narrow resection margins are associated with tumor recurrence. Identification and tracking of these resection margins remain a challenge because they migrate and shrink from pre-operative chemo or radiation therapies, and deform intra-operatively. This paper reports a novel near-infrared (NIR) fluorescent marking and landmark-based deformable image registration (DIR) method to precisely predict deformed margins. The accuracy of DIR predicted resection margins on porcine cadaver tongues is compared with rigid image registration and surgeon's manual prediction. Furthermore, our tracking and registration technique is integrated into a robotic system, and tested using ex vivo porcine cadaver tongues to demonstrate the feasibility of supervised autonomous tumor bed resections.
An introduction to data stream processing

Most of information technologies courses are focused on exposing the traditional static data (e.g., SGBD) processing model and lack on presenting the data stream model. Such model can be applied to fields such as Internet of Things, busyness and finances, logistics and smart cities) of the industry. Complex Event Processing consists of a programming approach to handle Data Stream Processing. It provide primitives to process and detect the occurrence of patterns in data streams. This short course has the objective of presenting the complex event processing (CEP) programming model as a means of dealing with the specificities of data flows.
Text Summarization: An Essential Study

The proliferation of data from diverse sources makes humans insufficient in utilizing the knowledge properly at some instance. To quickly have an overview of abundant information, Text Summarization (TS) comes into play. TS will effectively extract the candidate sentences from the source and represent the saliency of whole knowledge. Over the decades Text Summarization techniques have been transformed by the usage of linguistics to advanced machine learning models, this study explores summarization approaches along with their recent state-of-art models in single and multi-document summarization. This survey is intended to make an extensive study from features representation to sentence selection and summary generation using machine learning, recent graph and evolutionary based methods. The overall investigation will help the researchers to effectively handle large quantities of data in building effective Natural Language Processing applications. Eventually, this study draws popular abstractive mechanisms and observations that would be helpful for the intended research.
Neighbor-Induction and Population-Dispersion in Differential Evolution Algorithm

The differential evolution (DE) optimization algorithm predominantly relies on elite individuals and random difference to direct evolution. Although the strategy is clear and easy to implement, identifying a suitable direction for the DE mutation strongly depends on the direction information provided beforehand. To address this, we present a neighbor-induced mutation operator that simulates the neighbor-induced movement of Antarctic krill to guide the evolution direction in a natural manner. Additionally, center dispersion is proposed to disperse the population and redistribute individual positions to escape search stagnation, inspired by the spreading out of krill around newly discovered food. Comprising the new operator and the center dispersion pattern, this paper proposes a neighbor-induced DE algorithm with dispersion pattern (NDEd). The results of the comparative experiments verify the effectiveness of the neighbor-induced mutation operator and the dispersion pattern. Further, experimental results from 28 test functions of CEC2013 demonstrate that NDEd performs better compared to the other classic DE algorithms.
Recurrent Sub-volume Analysis of Head CT Scans for the Detection of Intracranial Hemorrhage

Non-contrast head/brain CT (NCHCT) is the initial imaging study of choice in patients visiting any emergency services and could be the only investigation to guide management in patients with head trauma or stroke symptoms. Immediate preliminary radiology reports to trigger appropriate level of care is paramount in the emergency department. Our proposed solution comprises of an efficient method for the detection of intracranial hemorrhage, by creating multiple 2-dimensional (2D) composite images of sub-volumes from the original scan. We also propose a recurrent neural network which combines the sub-volume features and takes into consideration the contextual information across sub-volumes to give a scan level prediction. We achieve an overall AUROC of 0.914.
Optimized Algorithms and Architectures for Montgomery Multiplication for Post-quantum Cryptography

Finite field multiplication plays the main role determining the efficiency of public key cryptography systems based on RSA and elliptic curve cryptography (ECC). Most recently, quantum-safe cryptographic systems are proposed based on supersingular isogenies on elliptic curves which require large integer multiplications over extended prime fields. In this work, we present two Montgomery multiplication architectures for special primes used in a post-quantum cryptography system known as supersingular isogeny key encapsulation (SIKE). We optimize two existing Montgomery multiplication algorithms and develop area-efficient and time-efficient Montgomery multiplication architectures for hardware implementations of post-quantum cryptography. Our proposed time-efficient architecture is $$32\%$$ to $$42\%$$ faster than the leading one (depending on the prime size) available in the literature which has been used in original SIKE submission to the NIST standardization process. The area-efficient architecture is $$42\%$$ to $$50\%$$ smaller than the counterparts and is about $$3\%$$ to $$11\%$$ faster depending on the NIST security level.
Korean Tourist Spot Multi-Modal Dataset for Deep Learning Applications

Recently, deep learning-based methods for solving multi-modal tasks such as image captioning, multi-modal classification, and cross-modal retrieval have attracted much attention. To apply deep learning for such tasks, large amounts of data are needed for training. However, although there are several Korean single-modal datasets, there are not enough Korean multi-modal datasets. In this paper, we introduce a KTS (Korean tourist spot) dataset for Korean multi-modal deep-learning research. The KTS dataset has four modalities (image, text, hashtags, and likes) and consists of 10 classes related to Korean tourist spots. All data were extracted from Instagram and preprocessed. We performed two experiments, image classification and image captioning with the dataset, and they showed appropriate results. We hope that many researchers will use this dataset for multi-modal deep-learning research.
Overview of 3D Scene Viewpoints evaluation method

The research on 3D scene viewpoints has been a frontier problem in computer graphics and virtual reality technology. In a pioneering study, it had been extensively used in virtual scene understanding, image-based modeling, and visualization computing. With the development of computer graphics and the human-computer interaction, the viewpoint evaluation becomes more significant for the comprehensive understanding of complex scenes. The high-quality viewpoints could navigate observers to the region of interest, help subjects to seek the hidden relations of hierarchical structure, and improve the efficiency of virtual exploration. These studies later contributed to research such as robot vision, dynamic scene planning, virtual driving and artificial intelligence navigation.The introduction of visual perception had The introduction of visual perception had contributed to the inspiration of viewpoints research, and the combination with machine learning made significant progress in the viewpoints selection. The viewpoints research also has been significant in the optimization of global lighting, visualization calculation, 3D supervising rendering, and reconstruction of a virtual scene. Additionally, it has a huge potential in novel fields such as 3D model retrieval, virtual tactile analysis, human visual perception research, salient point calculation, ray tracing optimization, molecular visualization, and intelligent scene computing.
A Survey on Human Activity Recognition Using Accelerometer Sensor

Human Activity Recognition (HAR) has gained significant attention due to its wide range of applications and its promises in remote and automated health domains. Many studies in HAR used different types of sensors. In this paper, we are providing a comprehensive survey on HAR. This paper focuses on accelerometer (ACC) sensor as a primary source of data for activity detection. A survey that is a comparative study comparing various techniques and algorithms in the literature using ACC for data generation, is provided. The learning module for HAR is explored. The factors impacting the HAR when ACC is on usage are explored in addition of the challenges of HAR and future trends of research.
Software Defect Prediction Based on Cost-Sensitive Dictionary Learning

Software defect prediction technology has been widely used in improving the quality of software system. Most real software defect datasets tend to have fewer defective modules than defective-free modules. Highly class-imbalanced data typically make accurate predictions difficult. The imbalanced nature of software defect datasets makes the prediction model classifying a defective module as a defective-free one easily. As there exists the similarity during the different software modules, one module can be represented by the sparse representation coefficients over the pre-defined dictionary which consists of historical software defect datasets. In this study, we make use of dictionary learning method to predict software defect. We optimize the classifier parameters and the dictionary atoms iteratively, to ensure that the extracted features (sparse representation) are optimal for the trained classifier. We prove the optimal condition of the elastic net which is used to solve the sparse coding coefficients and the regularity of the elastic net solution. Due to the reason that the misclassification of defective modules generally incurs much higher cost risk than the misclassification of defective-free ones, we take the different misclassification costs into account, increasing the punishment on misclassification defective modules in the procedure of dictionary learning, making the classification inclining to classify a module as a defective one. Thus, we propose a cost-sensitive software defect prediction method using dictionary learning (CSDL). Experimental results on the 10 class-imbalance datasets of NASA show that our method is more effective than several typical state-of-the-art defect prediction methods.
Small vehicles detection based on UAV

In recent years, with the development of deep neural networks, object detection has been widely used in many fields such as video surveillance, face recognition, and unmanned aerial vehicles (UAVs). However, small objects detection is still a challenge for the most of existing methods due to its low resolution and limited information. Here, the authors use an effective object detector single shot multi-box detector (SSD) as base architecture to detect small vehicles shot by UAV platform. First, in order to improve the performance for small objects, an extended-layer feature method is proposed for introducing more small-scale information. Second, the shapes of default boxes on specific feature maps are adjusted to balance the performance loss caused by adding feature maps. Experimental results show that this method reached the accuracy of 78.6% on PASCAL VOC object-detection dataset, which has a 1.8% bonus compared with the baseline. Moreover, in order to show the effectiveness on small vehicles, an additional experiment is conducted on the UAV123 dataset, which gets a 3.1% bonus. The speed of the proposed method has a 39 fps on a NVIDIA GTX Titan X.
Development Of A Scalable Coding For The Encryption Of Images Using Block Truncation Code

In today's technology, especially in Lossy Compression image reconstruction which is identical to the original image transmitted is highly unattainable, protection of digital data between buyer and seller especially from intruders and hackers which requires encryption and also to save space and increase speedy transmission which requires image compression has arisen as an important factor of research. This paper proposes a most efficient way of encrypting, compressing and recovering the original image at the receiver side with high PSNR value. The input image is encrypted by using the pseudo random number and compressed using Block Truncation Coding(BTC). The images are transmitted more securely using pseudo random number, which acts as a secret key and it is shared between sender and receiver. The original gray level pixel value is compressed using Block Truncation Coding(BTC). The encrypted image is obtained by adding compressed BTC pixel value with pseudo random number value and then transmitted. At the receiver side, the decryption process is done to recover the compressed pixel value and original image is reconstructed using BTC.