Reviewerly/exHarmony huggingface dataset:
Impact of Virtual Embodiment on the Perception of Virtual Heights

In Virtual Reality Exposure Therapy (VRET) for anxiety disorders, virtual reality is used to simulate threatening environments and stimuli, allowing patients to be exposed to their fear. Presence, the ‘sense of being there’, is widely assumed to be crucial for fear responses. It can be enhanced by full-body ownership and agency—the illusory perception that an artificial body is one’s own and that a person is himself the cause for the movements of that body. This study investigated the effects of a virtual full-body representation on the perception of virtual heights. Results revealed that coherent stimuli successfully enabled a virtual embodiment, while subjects with acrophobic tendencies showed fear responses to virtual heights. However, effects were neither found on fear, nor on presence nor on self-confidence nor on physiological responses. These findings suggest that virtual embodiment has no significant influence on the efficacy of a VRET for fear of heights.
Optical Flow Based Background Subtraction Method for Lung Nodule Segmentation

Image based Lung cancer diagnosis offers an effective and non-intrusive method for identification and diagnosis of lung cancer in a patient. This is done by segmentation of lung nodules from radiological images obtained from patients. This paper proposes a framework to segment nodule lesions in images. The novelty of the proposed framework is that we use optical flow information along with the morphological operations on images. The proposed framework applies the Farneback method of optical flow extraction algorithms for the nodule segmentation task. This work validates and demonstrates that optical flow information is useful for nodule segmentation and can be used in tandom with other features.
Personalized Recommendation Method of E-commerce Based on Fusion Technology of Smart Ontology and Big Data Mining

Abstract E-commerce personalized recommendation system needs to be able to respond to user page requests in real time and provide recommendation services for users according to different recommendation models chosen by users. In this paper, based on the fusion method of intelligent ontology and big data mining, the semantic access preference of the user is captured by the user interest acquisition algorithm of the semantic cluster, and the current session of the user is matched with the acquired user preference to obtain the recommendation set. The paper presents personalized recommendation method of E-commerce based on fusion technology of smart ontology and big data mining.
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Huffman (2013) [12] studied Fq-linear codes over Fqm and he proved the MacWilliams identity for these codes with respect to ordinary and Hermitian trace inner products. Let S be a finite commutative Fq-algebra. An Fq-linear code over S of length n is an Fq-submodule of Sn. In this paper, we study Fq-linear codes over S. We obtain some bounds on minimum distance of these codes, and some large classes of MDR codes are introduced. We generalize the ordinary and Hermitian trace products over Fq-algebras and we prove the MacWilliams identity with respect to the generalized form. In particular, we obtain Huffman's results on the MacWilliams identity. Among other results, we give a theory to construct a class of quantum codes and the structure of Fq-linear codes over finite commutative graded Fq-algebras.
Computer Graphics meets Estimation Theory: Parameter Estimation Lower Bounds for Plenoptic Imaging Systems

This work focuses on assessing the information- theoretic limits of parameter estimation in plenoptic imaging systems, which are capable of providing substantially more information about a given scene than conventional cameras. We present a framework to compute lower bounds for parameter estimation from noisy plenoptic observations, and our particular focus is on indirect imaging problems, where the observations do not contain line-of-sight (LOS) information about the parameter(s) of interest. Using computer graphics rendering software to synthesize the (often complicated) dependence among parameter(s) of interest and observations, we numerically evaluate the Hammersley-Chapman-Robbins bound to establish fundamental lower limits on the variance of any unbiased estimators of the unknown parameters. We demonstrate the utility of our proposed framework on a few canonical estimation tasks.
ADCMO: An Anomaly Detection Approach Based on Local Outlier Factor for Continuously Monitored Object

Since the existing works in data stream anomaly detection mainly locate abnormal objects from data sequences instead of detecting the abnormal state of a monitored object in real-time. And they need a set of empirical predefined thresholds, which may affect the flexibility of the detection models. These limitations make the existing solutions no longer suitable for the real-time detection of the changes in the status of the monitored object. In this paper, we propose a new Abnormal conditions Detection approach (ADCMO) on the Continuously Monitored Object. ADCMO incorporates the data distribution property to carry out the anomaly quantification for the data in the data stream, and then the abnormal conditions of the monitored object are quantified by the anomaly information of the current data and historical data. Finally, the anomaly state is adaptively determined by the distribution of anomaly coefficients. ADCMO can detect the changes in the status by calculating the outlier coefficient of the object at each moment and adaptively make the abnormal early warning. The experiments show that ADCMO can do this any-time, dynamically, efficiently and effectively.
Structured Summarization of Academic Publications

We propose SUSIE, a novel summarization method that can work with state-of-the-art summarization models in order to produce structured scientific summaries for academic articles. We also created PMC-SA, a new dataset of academic publications, suitable for the task of structured summarization with neural networks. We apply SUSIE combined with three different summarization models on the new PMC-SA dataset and we show that the proposed method improves the performance of all models by as much as 4 ROUGE points.
A computationally efficient method for Human Activity Recognition based on spatio temporal cuboid and Super Normal Vector

For the last three decades human activity recognition has shown a huge technological advancement due to less expensive RGB-D cameras and the increase in the large volume of video data. As a result of the increase in number of surveillance cameras, manual annotation becomes difficult and need for au tomatic recognition and annotation of video arises. In this paper, we introduce a computationally and storage efficient method for recognizing human activities from depth videos and a new frame selection method based on the mean value of motion energy. We extract normal vectors from the points in the boundary curve. Then polynormals are obtained by sequentially attaching the normals from a neighborhood of each of the points in the boundary curve. These polynormals from a spatio-temporal cuboid constructed from the input video and it is pooled to form the Super Normal vectors. These Super Normal vectors are the final feature vectors, which are given as input to the classifier. The classifier used is lib-linear SVM. The results on MSRAction3D dataset show that the algorithm we put forward is fast and the accuracy obtained is comparable with the existing methods. The method which we proposed here gives an accuracy of 88% while taking whole frames and 89.82% when frame selection method is applied. The proposed method is also tested on UTD-MHAD dataset.
A novel RDO based on perceptual algorithm

Rate distortion optimization (RDO) plays an important role in High Efficiency Video Coding (HEVC), as it is often utilized to select the best encoding parameters and control the tradeoff between bit rate and distortion of reconstructed video. For video coding, since the video signal is ultimately received by the human eye, the visual perception of the human eye cannot be ignored. Thus, we proposed a perceptual RDO scheme based on Lagrange multiplier by considering human visual system characteristics. The temporal perceptual features are extracted by considering the temporal perceptual correlations for each coding tree unit. These Lagrange multipliers are then adaptively adjusted using these perceptual features. Experiments demonstrate that with SSIM as metric, it can improve the subjective video coding performance. Compared to the HM16.9, it can achieve 5.6% in average bitrate reductions with negligible subjective quality loss.
SmartDHX: Diffie-Hellman Key Exchange with Smart Contracts

Blockchain technologies enable decentralized applications (DApps) that run on distributed infrastructures without any central authority. All transactions for communication and data storage are public and can be verified by all participants. DApps interacting with a smart contract typically require client-side code, which is not part of the smart contract, and therefore do not hold the same verifiability properties. Following the vision of a verifiable DApp, we propose SmartDHX, a Diffie-Hellman key exchange (DHKE) scheme, fully implemented as a smart contract. That is, SmartDHX communicates only via the Ethereum blockchain and provides both backend and client-side code with the smart contract. The application code can therefore be verified and deployed without external trust requirements. By executing DHKE on-chain, we gain a number of properties, including asynchronicity as well as message integrity and authenticity. We generalize the two-party SmartDHX to emphasize that our approach is able to handle complex cryptographic protocols. In our analysis, we expose an efficiency tradeoff when executed on chain. In particular, we provide a proof-of-concept implementation and analyze the runtime and transaction fees. Since DHKE is used by many cryptographic algorithms, SmartDHX contributes a fundamental building block in the domain of DApps.
A Systematic Algorithm for Moving Object Detection with Application in Real-Time Surveillance

Moving object detection and tracking from video sequences are a relevant research field since it can be used in many applications. While detection allows to return object shapes discovered in the image, tracking aims to individually identify and estimate individual trajectories of detected objects over time. Hence, detection can have a crucial impact on the overall tracking process. This paper focuses on detection. Currently, one of the leading detection algorithms includes frame difference method (FD), background subtraction method (BS), and optical flow method. Here, we present a detection algorithm based on the first two approaches since it is very adequate for fast real-time treatments, whereas optical flow has higher computation cost due to a dense estimation. A combination of FD and BS with Laplace filters and edge detectors is a way to achieve sparse detection fast. Thus, a main proposed contribution is the achievement of a systematic detection algorithm for moving target detection with a more elaborated combination of basic procedures used in real-time surveillance. Experimental results show that the proposed method has higher detection accuracy and better noise suppression than the current methods for standard benchmark datasets.
Fulva: Efficient Live Migration for In-Memory Key-Value Stores with Zero Downtime

A key-value store live migration approach migrates key-value tuples and their client requests from an overloaded machine (source) to an idle machine (destination), while still serving client requests. Existing migration approaches fall into two categories. First, a source-driven approach (e.g., DrTM-B) executes all client requests on the source and incrementally propagates the updated key-value tuples to the destination. This approach has an inevitable downtime to completely propagate the updated tuples at the end of a migration. Second, a destination-driven approach (e.g., RockSteady) executes all read and write requests on the destination, and pulls tuples from source for read requests on-demand. This approach has zero downtime, but incurs extra network round-trips due to the on-demand pull, greatly increasing request latency. Overall, a live migration approach that has zero downtime and no performance degradation during the migration is highly desirable but missing. The key observation of our Fulva system is that the source and destination can cooperatively drive the migration and serve requests, and we need only to design an efficient protocol to ensure linearizability (i.e., reads see the updates from the latest writes). To this end, when a migration starts, Fulva works by three steps. First, all write requests are redirected to the destination. Second, each client program uses a Fulva's RPC library to track the migration progress. For read requests accessing the already-migrated tuples, Fulva RPC library sends the requests to the destination. Third, for read requests accessing not-yet-migrated tuples, Fulva sends to both machines. The first step avoids downtime because all updated tuples are already on the destination. The second and third steps avoid the on-demand pull and ensure linearizability, making Fulva efficient. We implemented Fulva using DPDK and integrated it with RAMCloud, a popular in-memory key-value store. We compared Fulva with two notable systems, RockSteady (destination-driven approach) and RAMCloud's default source-driven approach. Extensive evaluation shows that Fulva had much higher throughput and lower latency than the two systems, and Fulva's network bandwidth usage is comparable with RockSteady. All Fulva's source code and raw evaluation results are released on github.com/hku-systems/fulva.
De Re, De Dicto, and Binding Modalities

In classical logic, the move from propositional to quantificational is profound but essentially takes one route, following a direction we are all familiar with. In modal logic, such a move shoots off in many directions at once. One can quantify over things or over intensions. Quantifier domains can be the same from possible world to possible world, shrink or grow as one moves from a possible world to an accessible one, or follow no pattern whatsoever. A long time ago, Kripke showed us how shrinking or growing domains are related to validity of the Barcan and the converse Barcan formulas, bringing some semantic order into the situation. But when it comes to proof theory, things get somewhat strange. Nested sequents for shrinking or growing domains, or for constant domains or completely varying domains, are relatively straightforward. But axiomatically some oddities are quickly apparent. A simple combination of propositional modal axioms and rules with standard quantificational axioms and rules proves the converse Barcan formula, making it impossible to investigate its absence. Kripke showed how one could avoid this, at the cost of using a less common axiomatization of the quantifiers. But things can be complicated and even here an error crept into Kripke’s work that wasn’t pointed out until 20 years later, by Fine. Justification logic was created by Artemov, with a system called $$\textsf {LP}$$ which is related to propositional $$\textsf {S4}$$. This was extended to a quantified version by Artemov and Yavorskaya, for which a possible world semantics was supplied by Fitting. Subsequently Artemov and Yavorskaya introduced what they called binding modalities, by transferring ideas back from quantified $$\textsf {LP}$$ to $$\textsf {S4}$$. In this paper, we continue the investigation of binding modalities, but for $$\textsf {K}$$, and show that they provide a natural intuition for Kripke’s non-standard axiomatization, and relate directly to the distinction between de re and de dicto. Unlike in Kripke’s treatment, the heavy lifting is done through a generalization of the modal operator, instead of a restriction on quantifier axiomatizations.
Few-shot Knowledge Reasoning Method based on Attention Mechanism

As a core issue of knowledge graph research, knowledge graph reasoning and complementation technology have always been a hot topic of current research. Existing knowledge graph reasoning techniques usually requires a large amount of training for each relationship, and training each relationship requires a large number of training samples. Inspired by meta-learning [1], this paper combines the idea of meta-learning [1] with the attention mechanism [5] to be applied to knowledge reasoning. On the one hand, it greatly reduces the number of samples required for each relationship training, and also reduces the scale of the problem, and the accuracy is higher than the model without the attention mechanism; On the other hand, the extensibility of knowledge has been greatly improved. After the model training is expected to be completed, when dealing with the newly added relationship, there is no need to retrain the model.
Face Phylogeny Tree Using Basis Functions

Photometric transformations, such as brightness and contrast adjustment, can be applied to a face image repeatedly resulting in a set of near-duplicate images. Identifying the original image from a set of such near-duplicates and deducing the relationship between them are important in the context of digital image forensics. This is commonly done by generating an image phylogeny tree (IPT)-a hierarchical structure depicting the relationship between a set of near-duplicate images. In this work, we utilize three different families of basis functions to model pairwise relationships between near-duplicate images. The basis functions used in this work are orthogonal polynomials, wavelet basis functions and radial basis functions. We perform extensive experiments to assess the performance of the proposed method across three different modalities, namely, face, fingerprint and iris; across different image phylogeny tree configurations; and across different types of photometric transformations. We also utilize the same basis functions to model geometric transformations and deep-learning based transformations. Finally, we utilize the concept of approximate von Neumann graph entropy to explain the success and failure cases of the proposed IPT generation algorithm. Experiments indicate that the proposed algorithm generalizes well across different scenarios thereby suggesting the merits of using basis functions to model the relationship between photometrically or geometrically modified images.
Distortion and Faults in Machine Learning Software

Machine learning software, deep neural networks (DNN) software in particular, discerns valuable information from a large dataset, a set of data, so as to synthesize approximate input-output relations. The outcomes of such DNN programs are dependent on the quality of both learning programs and datasets. However, the quality assurance of DNN software is difficult. The trained machine learning models, defining the functional behavior of the approximate relations, are unknown prior to its development, and the validation is conducted indirectly in terms of the prediction performance. This paper introduces a hypothesis that faults in DNN programs manifest themselves as distortions in trained machine learning models. Relative distortion degrees measured with appropriate observer functions may indicate that the programs have some hidden faults. The proposal is demonstrated with the cases of the MNIST dataset.
Digital Topographies. Using AR to Represent Archival Material in Urban Space

In this paper we discuss how Augmented Reality as a form of spatial computing can contribute to the concept of a “mediated city”, and tackle possible problems that we understand as conflicts between different modes of spatial perception. In reference to Spatial Sociology we assume that space includes a physical dimension as well as a social dimension and discuss a multidimensional framework to analyze AR applications. We argue that spatial computing provides a digital topography that creates new ways of understanding and orientation as well as multidimensional problems. As an example we present an immersive city tour we realized in Hamm, North Rhine-Westphalia in 2018 in collaboration with the city’s archive and the Ministry of Culture in NRW.
Generic hardness of inversion on ring and its relation to self-bilinear map

In this paper, we study the generic hardness of the inversion problem on a ring, which is a problem to compute the inverse of a given prime c by just using additions, subtractions and multiplications on the ring. If the characteristic of an underlying ring is public and coprime to c, then it is easy to compute the inverse of c by using the extended Euclidean algorithm. On the other hand, if the characteristic is hidden, it seems difficult to compute it. For discussing the generic hardness of the inversion problem, we first extend existing generic ring models to capture a ring of an unknown characteristic. Then we prove that there is no generic algorithm to solve the inversion problem in our model when the underlying ring is isomorphic to Zp for a randomly chosen prime p assuming the hardness of factorization of an unbalanced modulus. We also study a relation between the inversion problem on a ring and a self-bilinear map. Namely, we give a construction of a self-bilinear map based on a ring on which the inversion problem is hard, and prove that natural complexity assumptions including the multilinear computational Diffie-Hellman (MCDH) assumption hold w.r.t. the resulting sef-bilinear map.
Impact of CBL on Student's Learning and Performance

Case-Based Learning (CBL) is a well-known teaching methodology adopted across several disciplines for understanding problem domain from multiple perspectives. The CBL is also utilized in the Software Engineering domain to teach various concepts like Requirements Engineering (RE), Software Design and Testing. In this paper, we report the results of a replicated study conducted to assess the effectiveness of CBL methodology in facilitating the learning of several RE concepts. The evaluation was made based on postgraduate students' responses to a set of questions representing five learning principles and a set of case questions for the given case. We found that the use of CBL teaching methodology helps in improving students' learning and performance.
Accurate Ship Tracking in Maritime Images with Kernelized Correlation Filter and Curve Fitting

At present, the single tracking algorithm is generally used to achieve the single-target ship tracking task. Although the tracking result is ideal at this time, when the target ship is blocked by other obstacles, the tracking result will be greatly reduced. In order to solve this problem, this paper proposes a ship tracking framework based on nuclear correlation filter (KCF) and curve fitting. In the first step, the KCF algorithm is used to obtain accurate position data of the target target ship before and after occlusion and position data of the occluded stage. In the second step, according to the obtained position data, a trajectory conforming to the navigation of the target ship is obtained by a curve fitting method. In the third step, the proposed ship tracking framework is verified in several offshore video databases.