Reviewerly/exHarmony huggingface dataset:
Unconstrained Motion Deblurring for Dual-Lens Cameras

Recently, there has been a renewed interest in leveraging multiple cameras, but under unconstrained settings. They have been quite successfully deployed in smartphones, which have become de facto choice for many photographic applications. However, akin to normal cameras, the functionality of multi-camera systems can be marred by motion blur which is a ubiquitous phenomenon in hand-held cameras. Despite the far-reaching potential of unconstrained camera arrays, there is not a single deblurring method for such systems. In this paper, we propose a generalized blur model that elegantly explains the intrinsically coupled image formation model for dual-lens set-up, which are by far most predominant in smartphones. While image aesthetics is the main objective in normal camera deblurring, any method conceived for our problem is additionally tasked with ascertaining consistent scene-depth in the deblurred images. We reveal an intriguing challenge that stems from an inherent ambiguity unique to this problem which naturally disrupts this coherence. We address this issue by devising a judicious prior, and based on our model and prior propose a practical blind deblurring method for dual-lens cameras, that achieves state-of-the-art performance.
A Data Security Scheme Using Image Steganography

This paper proposes a method for protecting data from unauthorized access with the use of both stenographic and cryptographic mechanisms. The system uses block pixel-value differencing (BPVD) and Advanced Encryption Standard (AES) for data protection. The original image is partitioned into 4 × 4 blocks and the data gets traversed in all the three directions and RGB channel. Based on the hiding capacity of each direction, the proposed method choose a color channel for embedding the encrypted data. AES method is used for encrypting the text data to be embedded. In this new hybrid method the concealing capacity of the image is improved as each direction obtain maximum data. As the data is encrypted, the security of the system is improved much. Using color images the concealing capacity of PVD method is identified and direction based analysis is provided in it.
A Security Scheme for Fog Computing Environment of IoT

As an extension of cloud computing, fog computing environment as well as fog node plays an increasingly important role in internet of things (IoT). This technology provides IoT with more distributed and efficient applications and services. However, IoT nodes have so much variety and perform poorly, which leads to more security issues. For this situation, we initially design a security scheme for the IoT fog environment. Based on the combination of Blockchain and Trusted Execution Environment (TEE) technologies, the security of data storage and transmission from fog nodes to the cloud are ensured, thus ensuring the trustworthiness of the data source in the fog environment.
Number of Minimal Hypergraph Transversals and Complexity of IFM with Infrequency: High in Theory, but Often Not so Much in Practice!

Hypergraph Dualization (also called as hitting set enumeration) is the problem of enumerating all minimal transversals of a hypergraph $${\mathcal {H}}$$, i.e., all minimal inclusion-wise hyperedges (i.e., sets of vertices) that intersect every hyperedge in $${\mathcal {H}}$$. Dualization is at the core of many important Artificial Intelligence (AI) problems. As a contribution to a better understanding of Dualization complexity, this paper introduces a tight upper bound to the number of minimal transversals that can be computed in polynomial time. In addition, the paper presents an interesting exploitation of the upper bound to the number of minimal transversals. In particular, the problem dealt with is characterizing the complexity of the data mining problem called $$\mathtt {IFM}_{\mathtt {I}}$$ (Inverse Frequent itemset Mining with Infrequency constraints), that is the problem of finding a transaction database whose frequent and infrequent itemsets satisfy a number of frequency/infrequency patterns given in input.
Region-Based and Feature Based Mammogram Enhancement Techniques

Region-based enhancement algorithms operate adaptively based on the availability of features and enhances them with respect to their background (irrespective of its shape or size). Region-based approach defines an adaptive region for processing (about a pixel); whose size is dependent upon the availability of features within that region (Pratt et al. in Image enhancement. PIKS Scientific Inside, pp. 247–305, ). In such a case, contrast manipulation algorithms can be then applied on a region rather than pixel basis.
Real-Time Recognition and Tracing of Moving Objects in Video Images using Background Subtraction, Kalman Filter and Particle Filter

Improving the reliability and performance of target detection and tracking in multi-field environments is one of the key areas for moving target detection and tracking. At present, the detection and tracking of moving targets are one of the difficult research hotspots in the field of image processing. Due to the limited detection capabilities of object detectors such as vehicles, they often fail to alert in the event of an accident, especially in open areas. Aiming at the problem of target detection and tracking in a large space environment, several target detections and tracking algorithms combined with multiple image features are proposed. This paper discusses several methods for detecting moving targets. The algorithm is based on a variety of visual features of the target in the image, extracting a small number of features to detect the existence of the target. Kalman filtering and particle filtering are tools for accurately estimating data values. Simulation results show the effectiveness of these methods.
Impact ofthe modulus switching technique on some attacks against learning problems

The modulus switching technique has been used in some cryptographic applications as well as in cryptanalysis. For cryptanalysis against the learning with errors (LWE) problem and the learning with rounding (LWR) problem, it seems that one does not know whether the technique is really useful or not. This work supplies a complete view of the impact of this technique on the decoding attack, the dual attack and the primal attack against both LWE and LWR. For each attack, the authors give the optimal formula for the switching modulus. The formulas get involved the number of LWE/LWR samples, which differs from the known formula in the literature. They also attain the corresponding sufficient conditions saying when one should utilise the technique. Surprisingly, restricted to the LWE/LWR problem that the secret vector is much shorter than the error vector, they also show that performing the modulus switching before using the so-called rescaling technique in the dual attack and the primal attack make these attacks worse than only exploiting the rescaling technique as reported by Bai and Galbraith at the Australasian conference on information security and privacy (ACISP) 2014 conference. As an application, they theoretically assess the influence of the modulus switching on the LWE/LWR-based second round NIST PQC submissions.
A Method of Making Video Trailer Based on Artificial Intelligence

Abstract Based on the flownet2 optical flow algorithm, cascade CNN face detection and facenet face recognition algorithm, the method of automatically generating video trailers by using the sum of the optical flow vector sum per second and the absolute value of the audio loudness amplitude per second and the character duration information is proposed. It can effectively edit the visual essence clip, the video essence clip corresponding to the audio, the main character clip and the story outline to generate a trailer.
‘Agaram’ – Web Application of Tamil Characters Using Convolutional Neural Networks and Machine Learning

This paper aims to explore the scope of these neural networks and apply them to try and recognize handwritten data which consists of Tamil characters written by various people and convert it to a computerized text document. Through our system we will be targeting one of the gaps in the currently available technology, which is to properly identify and distinguish the characters in ancient manuscripts.
Image Encryption using Edge Maps and Scrambling methods

Encryption is an effective way to avoid threats to data. Here we perform bit plane decomposition(e.g. Fibonacci P-code, Truncated Fibonacci P-code methods ) for the source image which is to be encrypted, where the number of bit-planes generated depends on the bit length of the pixel. Other than our source image we use another key image to which we perform edge mapping with randomly generated thresholds(e.g. Canny, Prewitt, Sobel, LoG, Roberts, Kirsh edge generators), where the number of edge maps generated depends on the number of bit planes. We perform a pixel scrambling method to the edge maps which generates scrambled edge maps. Then xor operation to each individual bit-plane with the scrambled edge map is done. These images are combined using a randomly generated sequence to form an encrypted image. The final encrypted image is then formed by scrambling the encrypted image. In this process the results of encrypted images are high with greater NPCR and UACI values and lower Correlation values, depicting the consistency of this algorithm and the security against differential attack for any image.
Establishing a User-Centered Design Process for Human-Machine Interfaces: Threats to Success

While user-centered design (UCD) processes have widely been established in domains like end-user electronics and business-to-consumer products, such processes still lack widespread adaptation for the development of industrial human-machine interfaces (HMIs). Over a period of more than two years, we have worked as part of a development team at a company from the manufacturing domain in a pilot project to introduce a UCD process. During this period, we have - via participant observation - collected a set of observed practices and behaviors that violate well-known UCD principles. Furthermore, we derived some root causes of these violations. Our insights are that introducing a UCD processes cannot be performed isolated for a single development team but impacts the entire organization including management and requires trust as well as changes with regard to mindset, methods, technologies, and team organization.
Domain-Aware Unsupervised Cross-dataset Person Re-identification

We focus on the person re-identification (re-id) problem of matching people across non-overlapping camera views. While most existing works rely on the abundance of labeled exemplars, we consider a more difficult unsupervised scenario, where no labeled exemplar is provided. One solution for unsupervised re-id that attracts much attention in the recent researches is cross-dataset transfer learning. It utilizes knowledge from multiple source datasets from different domains to enhance the unsupervised learning performance on the target domain. In previous works, much effect is taken on extraction of the generic and robust common appearances representations across domains. However, we observe that there also particular appearances in different domains. Simply ignoring these domain-unique appearances will misleading the matching schema in re-id application. Few unsupervised cross-dataset algorithms are proposed to learn the common appearances across multiple domains, even less of them consider the domain-unique representations. In this paper, we propose a novel domain-aware representation learning algorithm for unsupervised cross-dataset person re-id problem. The proposed algorithm not only learns a common appearances across-datasets but also captures the domain-unique appearances on the target dataset via minimization of the overlapped signal supports across different domains. Extensive experimental studies on benchmark datasets show superior performances of our algorithm over state-of-the-art algorithms. Sample analysis on selected samples also verifies the ability of diversity learning of our algorithm.
Direct Inference, Reichenbach's Principle, and the Sleeping Beauty Problem

Abstract A group of philosophers led by the late John Pollock has applied a method of reasoning about probability, known as direct inference and governed by a constraint known as Reichenbach's principle, to argue in support of ‘thirdism’ concerning the Sleeping Beauty Problem. A subsequent debate has ensued about whether their argument constitutes a legitimate application of direct inference. Here I defend the argument against two extant objections charging illegitimacy. One objection can be overcome via a natural and plausible definition, given here, of the binary relation ‘logically stronger than’ between two properties that can obtain even when the respective properties differ from one another in ‘arity’; given this definition, the Pollock group's argument conforms to Reichenbach's principle. Another objection prompts a certain refinement of Reichenbach's principle that is independently well-motivated. My defense of the Pollock group's argument has epistemological import beyond the Sleeping Beauty problem, because it both widens and sharpens the applicability of direct inference as a method for inferring single-case epistemic probabilities on the basis of general information of a probabilistic or statistical nature.
POSTER: Leveraging Run-Time Feedback for Efficient ASR Acceleration

In this work, we propose Locality-AWare-Scheme (LAWS) for an Automatic Speech Recognition (ASR) accelerator in order to significantly reduce its energy consumption and memory requirements, by leveraging the locality among consecutive segments of the speech signal. LAWS diminishes ASR's workload by up to 60% by removing most of the off-chip accesses during the ASR's decoding process. We furthermore improve LAWS's effectiveness by selectively adapting the amount of ASR's workload, based on run-time feedback. In particular, we exploit the fact that the confidence of the ASR system varies along the recognition process. When confidence is high, the ASR system can be more restrictive and reduce the amount of work. The end design provides a saving of 87% in memory requests, 2.3x reduction in energy consumption, and a speedup of 2.1x with respect to the state-of-the-art ASR accelerator.
Human Machine Joint Decision Making in Distorted Surveillance Scenario

There is plenty of human-machine joint decision-making scenarios in the real world applications, such as driving assistant, suspect identification, medical diagnosis, etc. Existing algorithms propose that machine should give a rejection option when having a high risk or uncertainty score so that the input can be passed to human to make the decision. This is an interesting algorithmic model of human-machine collaboration, but implicitly assumes that humans are more trustworthy than machines. Such an assumption ignores the bias and inconsistency of human, especially in scenarios where machines have superior recognition ability than humans. In this work, we investigate the human-machine joint decision-making problem in distorted surveillance videos, where machines experimentally prove to be comparable to human beings in tolerance to distortion, sometimes even stronger. We propose a new human-machine joint decision-making framework by considering both the confidences of machine and human. To obtain the confidence of human, we build a real-life human decision-making database and propose a deep neural network to estimate human's confidence. Then, confidence alignment method and decision rule are proposed to further output the final decision. Experiments demonstrate that the proposed framework can make less human intervention and more accurate decisions in several human-machine joint decision-making scenarios.
Analysis Based on Distributed Representations of Various Parts Images in Four-Scene Comics Story Dataset

Ways of understanding human creations with artificial intelligence have increased; however, those are still known as one of the most difficult tasks. Our challenge is to find ways to understand four-scene comics by AI. To achieve this aim, we used the novel dataset called"Four-scene Comics Story Dataset", which is the first dataset made by researchers and comic artists to develop AI creations. We focused on illustration touches of comics which are determined by comic artists. First, we applied convolutional autoencoder (CAE) models to this dataset to get distributed representations, then applied classifiers in order to classify the different touches. The prediction offers an indirect measure of the distributed representations. We already investigated the experiment using original scenes. To show the advantageous of the dataset, the proposed method is confirmed by computer simulations taking data of various pattern of removing image parts such as "eyes" in scene images of the four-scene comics story dataset structure as examples.
Rethinking Authentication

DONE? & IN TODAY’S ENVIRONMENT, there is little doubt that companies, organizations, and governments must make significant investments in developing, implementing, and supporting authentication for their digital systems. Perhaps because of this, an organization’s IT support center often takes a hard line when it comes to user’s authentication. Authentication tools typically take for granted that the user will do whatever it takes to authenticate. No surprise, users do not like being forced into a corner.1 They are resourceful and very creative in making it more manageable, usually by coming up with ways that circumvent the overall security of a system. Unfortunately, as the number and complexity of authentications for each user become greater, job satisfaction forworkers is likely to decrease.
Thermal Rendering Based on Thermal Diffusion Equation

Actuator arrays that consist of a planar arrangement of actuators are widely used for transportation of objects or shaping a rough surface by controlling the actuators cooperatively. In particular, thermal displays that consist of multiple heat sources spread over a surface have been developed for many years. Generally, the expression ability is determined by the number of actuators in actuator arrays, and accordingly, thermal displays require many heat sources to ensure a high spatial resolution of temperature distribution. However, the number of usable heat sources is limited because of mechanical issues. In order to solve this problem, a control method that employs a model derived from a thermal diffusion equation and is used between two heat sources, is proposed in this paper. By using the proposed method, any selected point of the heat conduction can be expressed using only one heat source. Therefore, infinite number of nodes of propagation can be reproduced by selecting a desired node. The method is expected to extend the technology for controlling multiple actuators or heat sources on a surface.
Summarizing Lecture Videos by Key Handwritten Content Regions

We introduce a novel method for summarization of whiteboard lecture videos using key handwritten content regions. A deep neural network is used for detecting bounding boxes that contain semantically meaningful groups of handwritten content. A neural network embedding is learnt, under triplet loss, from the detected regions in order to discriminate between unique handwritten content. The detected regions along with embeddings at every frame of the lecture video are used to extract unique handwritten content across the video which are presented as the video summary. Additionally, a spatiotemporal index is constructed from the video which records the time and location of each individual summary region in the video which can potentially be used for content-based search and navigation. We train and test our methods on the publicly available AccessMath dataset. We use the DetEval scheme to benchmark our summarization by recall of unique ground truth objects (92.09%) and average number of summary regions (128) compared to the ground truth (88).
A connected component-based approach for text-independent writer identification

Writer identification from off-line images of handwriting is a challenging task. In this work we assess the performance of textural descriptors for writer identification on different writing styles. The proposed method is different from the existing texture based methods: the earlier methods extract texture information at page, paragraph, fragment level to get a document descriptor, while the proposed method exploits the texture at a connected-component level. Specifically, an improvement of the textural features, the contour direction, angle, and length distributions are explored. Using these texture descriptors, the occurrence histograms are calculated in order to determine the similarities between different images. For the evaluation, the IFN/ENIT (411 writers) and IAM (657 writers) datasets were used. In our characterization of the individual and combination of textural features of connected-components we show that the proposed method outperforms the state-of-the-art algorithms and archives the best performance.
Optimizing Augmented Reality Outcomes in a Gamified Place Experience Application through Design Science Research

Nearly ubiquitous smartphone use invites research and development of augmented reality experiences promoting knowledge and understanding. However, there is a lack of design science research dissemination about developing these solutions. This paper adds to the information systems body of knowledge by presenting the second iteration of Design Science Research Methodology artefact and the process of its development in the form of a gamified place experience application about indigenous art, focusing on the optimization of AR integration and user interface enhancements. In testing the usability, we illustrate how the application was optimized for successful outcomes. The qualitative analysis results revealed the high level of usability of the mobile application leading to further testing of efficacy in creating Sense of Place where the art is curated and displayed.
Secure Outsourcing of Geospatial Vector Data

The objective of secure outsourcing is to enforce authorizations specified by the data owner on the outsourced data as service provider cannot be trusted. It protects the location data from attackers while allowing authorized users to issue spatial queries that are executed efficiently by service provider. In this paper, we are proposing secure outsourcing mechanism for geospatial vector data by utilizing Hilbert R tree for transformation and indexing of outsourced data and Merkle Hash tree for secure storage of outsourced data as well as for preserving integrity of the query results generated by service provider.
Graph Spectral Domain Features for Static Hand Gesture Recognition

The graph spectral processing is gaining increasing interest in the computer vision society because of its ability to characterize the shape. However, the graph spectral methods are usually high computational cost and one solution to simplify the problem is to automatically divide the graph into several sub-graphs. Therefore, we utilize a graph spectral domain feature representation based on the shape silhouette and we introduce a fully automatic divisive hierarchical clustering method based on the shape skeleton for static hand gesture recognition. In particular, we establish the ability of the Fiedler vector for partitioning 3D shapes. Several rules are applied to achieve a stable graph segmentation. The generated sub-graphs are used for matching purposes. Supporting results based on several datasets demonstrate the performance of the proposed method compared to the state-of-the-art methods by increment 0.3% and 3.8% for two datasets.