Reviewerly/exHarmony huggingface dataset:
Vector Separability Measurement Based Fast Feature Selection for Detecting Images Information Hiding

After careful consideration, we have revised the abstract to “Images are adopted as information hiding carries in recent years, which has brought challenges to multimedia forensics and security. In order to improve the feature selection speed while maintaining the detection accuracy and reducing the feature dimension, this paper proposes a fast feature selection method for detecting stego image with hiding information. First, a feature component separability criterion is proposed based on the inter-distance and inner-distance difference to measure the separability of steganalysis feature components, providing a basis for constructing candidate feature components. Second, the high-dimensional steganalysis feature is extracted from the training set containing the cover images and corresponding stego images. Then the separability value of each feature component is measured based on the above criterion. And candidate feature vectors are constructed based on the separability values of feature components. Third, a feature vector separability measurement, providing a direct basis for the feature component selection to measure the contribution of the candidate feature vector to the classified image classification. The candidate feature vector with the largest value is chosen as the final selection feature. The dimension of the feature selected by this method is exceedingly smaller than the original feature dimension, so the time cost of the subsequent classification training can be significantly reduced. A series of experiments based on the Bossbase image database have shown that: compared with the recent steganalysis-α method and the FSSG method, the proposed method can significantly improve the feature selection speed while maintaining the detection accuracy and reducing the feature dimension.”
Strategies for Pattern-Based Detection of Architecturally-Relevant Software Vulnerabilities

Software vulnerabilities expose a system to security breaches. In this paper, we focus on vulnerabilities rooted in a system's architecture. Specifically, we describe our attempt at developing ways of depicting and detecting architectural vulnerabilities. Our guiding observation was that vulnerabilities that belong to the same category result in commonalities in the source code. This observation led us to hypothesize that it is possible to define patterns that can be used to detect similar vulnerabilities. To test this hypothesis, we collected a dataset of vulnerabilities reported for the Tomcat web server that spanned 20 different categories and 90 unique vulnerabilities. We represented each individual vulnerability with a Program Dependence Graph (PDG) and employed two approaches we believed to be especially promising based on the results they yielded when applied to similar problems. The first approach relied on graph-theory research to identify shared subgraphs in vulnerability PDGs. The second approach performed a multi-level hierarchical clustering on the PDGs to account for cases in which vulnerabilities of the same category exhibit more than one pattern. In the end, neither approach yielded successful results: the former was too computationally expensive to be practically applicable and had limited applicability, while the latter generated patterns that performed poorly when applied on real examples of vulnerabilities. Even though the two approaches were ultimately unsuccessful, we report on several important lessons that emerged from this endeavor.
Vectors of temporally correlated snippets for temporal action detection

Detection of human actions in long untrimmed videos is an important but challenging task due to the unconstrained nature of actions present in untrimmed videos. We argue that untrimmed videos contain multiple snippets from actions and the background classes having significant correlation with each other, which results in imprecise detection of start-end times for action regions. In this work, we propose Vectors of Temporally Correlated Snippets (VTCS) which addresses this problem by finding the snippet-centroids from each class which are discriminant for their own class. For each untrimmed video, non-overlapping snippets are temporally correlated with the snippet-centroids using VTCS encoding to find the action proposals. We evaluate the performance of VTCS on the Thumos14 and ActivityNet datasets. For Thumos14, VTCS achieves a significant gain in mean Average Precision (mAP) at temporal Intersection over Union (tIoU) threshold 0.5, improving from 41.5% to 44.3%. For the sports-subset of ActivityNet dataset, VTCS obtains 38.5% mAP @0.5 tIoU threshold.
It's Not Always Better When We're Together: Effects of Being Accompanied in Virtual Reality

Virtual reality (VR) enables immersive applications that make rich content available independent of time and space. By replacing or supplementing physical face-to-face meetings, VR could also radically change how we socially interact with others. Despite this potential, the effect of transferring physical collaborative experience into a virtual one is unclear. Therefore, we investigated the experience differences between a collaborative virtual environment (CVE) and a physical environment. We used a museum visit as a task since it is a typical social experience and a promising use case for VR. 48 participants experienced the task in real and virtual environments, either alone or with a partner. Despite the potential of CVEs, we found that being in a virtual environment has adverse effects on the experience which is reinforced by being in the environment with another person. Based on quantitative and qualitative results, we provide recommendations for the design of future multi-user virtual environments.
Augmented Reality in the Registration Flow for Enterance Eximination at State Polytechnic of Sriwijaya Based on Android

Abstract Augmented Reality is a technology that aims to combine digital content made by computers with the real world in real time. Augmented Reality can be applied in almost all areas of life. One of the applications is in the field of providing information. The publication of registration flow for entrance examination at State Polytechnic of Sriwijaya is still using paper media that makes it less interesting on the prospective applicants. Therefore it requires an application based on android to implement the information distribution of registration flow for entrance examination at State Polytechnic of Sriwijaya. This augmented reality application consists of two menus namely information menu and location menu. Information menu contains motion graphic video of registration flow for entrance examination at State Polytechnic of Sriwijaya and location menu contains 3d object of entrance examination at State Polytechnic registration buildings. The research method is divided into several stages, namely design, asset creation, implementation and testing. At the testing stage is divided into two, namely application testing by testing the functionality of the features provided by the application and testing of respondents to determine the effectiveness of registration flow at State Polytechnic of Sriwijaya compared to paper media seen in the respondent's answer of 88.7%.
I Wish You Were Smart(er): Investigating Users' Desires and Needs Towards Home Appliances

In this work, we present findings from an online survey (N=77) in which we assessed situations of users wishing for features or devices in their home to be smart(er). Our work is motivated by the fact that on one hand, several successful smart devices and features found their way into users' homes (e.g., smart TVs, smart assistants, smart toothbrushes). On the other hand, a more holistic understanding of when and why users would like devices and features to be smart is missing as of today. Such knowledge is valuable for researchers and practitioners to inform the design of future smart home devices and features, in particular with regards to interaction techniques, privacy mechanisms, and, ultimately, acceptance and uptake. We found that users would appreciate smart features for various use cases, including remote control and multi-tasking, and are willing to share devices. We believe our work to be useful for designers and HCI researchers by supporting the design and evaluation of future smart devices.
New Constructions of Identity-Based Dual Receiver Encryption from Lattices

Dual receiver encryption (DRE), being originally conceived at CCS 2004 as a proof technique, enables a ciphertext to be decrypted to the same plaintext by two different but dual receivers and becomes popular recently due to itself useful application potentials such secure outsourcing, trusted third party supervising, client puzzling, etc. Identity-based DRE (IB-DRE) further combines the bilateral advantages/facilities of DRE and identity-based encryption (IBE). Most previous constructions of IB-DRE are based on bilinear pairings, and thus suffers from known quantum algorithmic attacks. It is interesting to build IB-DRE schemes based on the well-known post quantum platforms, such as lattices. At ACISP 2018, Zhang et al. gave the first lattice-based construction of IB-DRE, and the main part of the public parameter in this scheme consists of 2 n + 2 matrices where n is the bit-length of arbitrary identity. In this paper, by introducing an injective map and a homomorphic computation technique due to Yamada at EUROCRYPT 2016, we propose another lattice-based construction of IB-DRE in an even efficient manner: The main part of the public parameters consists only of 2 p n 1 p + 2 matrices of the same dimensions, where p ( ≥ 2 ) is a flexible constant. The larger the p and n, the more observable of our proposal. Typically, when p = 2 and n = 284 according to the suggestion given by Peikert et al., the size of public parameters in our proposal is reduced to merely 12% of Zhang et al.'s method. In addition, to lighten the pressure of key generation center, we extend our lattice-based IB-DRE scheme to hierarchical scenario. Finally, both the IB-DRE scheme and the HIB-DRE scheme are proved to be indistinguishable against adaptively chosen identity and plaintext attacks (IND-ID-CPA).
Succinct Non-Interactive Arguments via Linear Interactive Proofs

Abstract Succinct non-interactive arguments (SNARGs) enable verifying NP statements with lower complexity than required for classical NP verification. Traditionally, the focus has been on minimizing the length of such arguments; nowadays, researchers have focused also on minimizing verification time, by drawing motivation from the problem of delegating computation. A common relaxation is a preprocessing SNARG, which allows the verifier to conduct an expensive offline phase that is independent of the statement to be proven later. Recent constructions of preprocessing SNARGs have achieved attractive features: they are publicly-verifiable, proofs consist of only O (1) encrypted (or encoded) field elements, and verification is via arithmetic circuits of size linear in the NP statement. Additionally, these constructions seem to have “escaped the hegemony” of probabilistically-checkable proofs (PCPs) as a basic building block of succinct arguments. We present a general methodology for the construction of preprocessing $$\text{ SNARG } $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mspace /><mml:mtext>SNARG</mml:mtext><mml:mspace /></mml:mrow></mml:math> s, as well as resulting new efficiency features. Our contribution is threefold: (1) We introduce and study a natural extension of the interactive proof model that considers algebraically-bounded provers; this new setting is analogous to the common study of algebraically-bounded “adversaries” in other fields, such as pseudorandomness and randomness extraction. More concretely, in this work we focus on linear (or affine) provers, and provide several constructions of (succinct two-message) linear interactive proofs (LIPs) for NP. Our constructions are based on general transformations applied to both linear PCPs (LPCPs) and traditional “unstructured” PCPs. (2) We give conceptually simple cryptographic transformations from LIPs to preprocessing SNARGs, whose security can be based on different forms of linear targeted malleability (implied by previous knowledge assumptions). Our transformations convert arbitrary (two-message) LIPs into designated-verifier SNARGs, and LIPs with degree-bounded verifiers into publicly-verifiable SNARGs. We also extend our methodology to obtain zero-knowledge LIPs and SNARGs. Our techniques yield SNARGs of knowledge and thus can benefit from known recursive composition and bootstrapping techniques. (3) Following this methodology, we exhibit several constructions achieving new efficiency features, such as “single-ciphertext preprocessing SNARGs.” We also offer a new perspective on existing constructions of preprocessing SNARGs, revealing a direct connection of these to LPCPs and LIPs.
Image Forgery Detection Based on the Convolutional Neural Network

With the advent of the artificial intelligence era, a series of deep learning networks have shown great advantages in the field of image processing. The traditional method based on feature extraction has been replaced by the deep learning technology used for image forgery detection. And the latter is becoming the current research hotspot. This paper proposes an image forgery detection algorithm based on convolutional neural network. Compared with the traditional feature of extracting related features based on image content, this paper firstly uses SRM filtering and high-pass filtering to achieve image pre-processing for Columbia University image mosaic detection dataset. Then the processing of training and verification were realized by the convolutional neural network. The effects of pre-processing and the number of convolution layers on the classification results are fully compared. Experiments show that the convolutional neural network method mentioned in this paper has certain effectiveness and robustness for image forgery classification.
Bugs as Features: Describing Patterns in Student Code through a Classification of Bugs

Code puzzles can be an engaging way to learn programming concepts, but getting stuck in a puzzle can be discouraging when no help or feedback is available. Intelligent tutoring systems can provide automatic individualized help, but they rely on having a robust and useful representation of student state. One common challenge for Intelligent tutoring systems in the programming domain is a large state space of possible students states. We propose a constrained set of features of student code based on detecting and classifying the bugs present in the code.
Manufacturing Change: The Impact of Virtual Environments on Real Organizations

Manufacturing workplaces are becoming sites of intense change as technologies like IoT and AR/VR are beginning to make deep inroads into how complex products are engi-neered and assembled. These categories of technologies are becoming prominent in manufacturing because they offer potential solutions to the problems of unskilled labor and workforce shortages. Technology has the potential to shift manufacturing in both large and small ways, to better un-derstand how a manufacturing organization might appropri-ate VR, we ran a study with a global aviation manufacturer headquartered the United States. To document the changing nature of work via this class of technologies we conducted a VR study which facilitated access to participant observation and interviews (n=21). Our findings provide initial insights into the organizational impact of VR on human perfor-mance augmentation and skill acquisition revealing the larger infrastructural challenges facing the adoption of con-sumer grade smart technologies in industrial workplace settings.
Detecting Red-Light Runners (RLR) and Speeding Violation through Video Capture

The rise in the number of vehicles entails a higher chance of increase in traffic accidents. In that case, it is necessary to catch traffic violators to lessen accidents and guarantee safety in the road. Studies show that the leading cause of road collisions is traffic violations such as red-light violation and speeding. The proponents present a method for detecting vehicles in video clips using a single deep neural network called Single Shot Detector (SSD). Since this system detects violators faster than the human eyes, it makes the responsibilities of officers more effective and efficient. Traffic violation detection system helps to recognize the most common traffic violations whether it is over-speeding or red-light-running. In this work, the researchers employ a camera to acquire data from an intersection. The data shall be fed to the prototype to compare the results of three different convolutional neural network (CNN) features by applying it to the base network of SSD in detecting traffic violations in captured videos. Based on the results, the prototype can achieve up to 100 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> accuracy on all feature extractors for red light runner (RLR) and 92.1% accuracy for speeding with the consideration of the best set-up.
RDF Reasoning on Large Ontologies: A Study on Cultural Heritage and Wikidata

Large ontologies are available as linked data, and they are used across many domains, but to process them considerable resources are required. RDF provides automation possibilities for semantic interpretation, which can lower the effort. We address the usage of RDF reasoning in large ontologies, and we test approaches for solving reasoning problems, having in mind use cases of low availability of computational resources. In our experiment, we designed and evaluated a method based on a reasoning problem of inferring Schema.org statements from cultural objects described in Wikidata. The method defines two intermediate tasks that reduce the volume of data used during the execution of the RDF reasoner, resulting in an efficient execution taking on average 10.3 ± 7.6 ms per RDF resource. The inferences obtained in the Wikidata test were analysed and found to be correct, and the computational resource requirements for reasoning were significantly reduced. Schema.org inference resulted in at least one rdf:type statement for each cultural resource, but the inference of Schema.org predicates was below expectations. Our experiment on cultural data has shown that Wikidata contains alignment statements to other ontologies used in the cultural domain, which with the application of RDF and OWL reasoning can be used to infer views of Wikidata expressed in cultural domain’s data models.
Sketch-based Video&amp;A#58; Storytelling for UX Validation in AI Design for Applied Research

User Experience (UX) Design of Artificial Intelligence (AI) applications that assist knowledge-intensive tasks are usually the result of an iterative and fuzzy process. Although the combination of state-of-the-art technology with human expertise is undoubtedly valuable in the industry context, designing a user-centered tool that fits in the everyday activities of knowledge workers – supporting and augmenting their potential – is not a trivial work. User research uncovers pain points and opportunities, bringing the insights that inspire designers and Human Computer Interaction (HCI) experts. It allows a clear understanding of the context, which is the essence of a consistent interaction experience. Nevertheless, revealing the UX of the application and how AI in this context reshapes knowledge workers' everyday tasks is not straightforward and might also be an iterative process. In this paper, we present findings from a case study of the use of a storytelling method to design and validate the UX of AI assistants for the Oil & Gas industry by using an animated sketch-based video.
Reconfiguring quantum error-correcting codes for real-life errors

High error-rates preclude the preparation of fully error-corrected logical qubit state on noisy intermediate scale quantum (NISQ) computers. When operand logical qubits inherit large state-preparation noise, it is difficult to show that subsequent logical gate fails less frequently than its physical (unprotected) version. We articulate a scheme of decoupling transversal logical gate errors from state-preparation noise and experimentally validate its use-case for IBM Q quantum processors. We find that in the absence of state preparation noise, the IBM Q processors significantly raise the likelihood of certain two-qubit errors in the operand(s) of [[7, 1, 3]] transversal gates. Yet, encoding can still be shown to improve the gate fidelity provided that the gate operands are strategically decoded/corrected for the likely two-qubit errors in lieu of their less likely single-qubit counterparts. This trade-off enables quantum CSS code to principally correct longer strings of errors without increasing the codeword size and paves new avenues of investigating fault-tolerance in NISQ computers.
Research on Design of Localization Learning Platform Based on Blended Learning

Blended learning for combination online with offline cannot be inseparable from the support of localization learning platforms. Massive Open Online Courses (MOOCs) are not suitable for blended learning because of insufficient functions of management and monitoring offline learning activities on campus. The research presents the user permission for functions of a localization learning platform based on blended learning from the user requirement and describes the functionality design from the technology for implementation. A localization learning platform of AHMOOC in Chuzhou University is designed and applied for blended learning as an application case, and significant effect is obtained. Furthermore, further work is put forward. Firstly, unified external interface for other application systems should be designed to integrate with the localization learning platform under the background of smart campus. Secondly, personalized learning resource based on integrable ware should be designed and reused on localization learning platform for blended learning.
A comprehensive analysis of constant-time polynomial inversion for post-quantum cryptosystems

Post-quantum cryptosystems have currently seen a surge in interest thanks to the current standardization initiative by the U.S.A. National Institute of Standards and Technology (NIST). A common primitive in post-quantum cryptosystems, in particular in code-based ones, is the computation of the inverse of a binary polynomial in a binary polynomial ring. In this work, we analyze, realize in software, and benchmark a broad spectrum of binary polynomial inversion algorithms, targeting operand sizes which are relevant for the current second round candidates in the NIST standardization process. We evaluate advantages and shortcomings of the different inversion algorithms, including their capability to run in constant-time, thus preventing timing side-channel attacks.
A Blockchain-Based Deposit System to Reduce WEE

Nowadays, electronics/electrical appliances are becoming less and less appreciated. Due to ever shorter innovation cycles, especially in the field of consumer electronics, more and more electrical waste is being produced. Motivated by this fact we decided to face this challenge in a course called programming project course. There we introduced the problem and further the blockchain technology. The task was to design a blockchain-based solution to reduce Waste Electrical & Electronic Equipment (WEE) and so to improve sustainability. The final idea here was to establish a deposit system for WEE. In this paper, we present the programming project from the development process of the first ideas to the completion of the running prototype and discuss the results.