Reviewerly/exHarmony huggingface dataset:
Secure Outsourcing Algorithms of Modular Exponentiations in Edge Computing

As one of the most expensive computations in public-key cryptosystems, modular exponentiation is typically out-sourced to the cloud servers. Traditional cloud-based outsourcing algorithms depend on multiple untrusted servers to guarantee the security, which may lead to vulnerability to the collusion attack. Although recent single-server multiple-requests outsourcing algorithms are more secure, they have to perform multiple requests to the single untrusted server to guarantee the security and checkability of the data, which will incur unacceptable latency and local computational costs. In comparison, the edge computing paradigm enhances security since it has multiple computational nodes, including some highly secure local computational nodes. In this paper, we propose the secure outsourcing algorithm of modular exponentiation for the edge computing paradigm. To address the dilemma that the computational resources of different nodes vary significantly, we design two lightweight algorithms to adaptively separate the modular exponentiation to the nodes based on the computational resources. To guarantee the outsourcing checkability, we propose a protocol verify the result returned from each node. We formally prove the security and checkability of our algorithm and validate the efficiency of our algorithm based on experiments and case studies.
A Novel Face Super-Resolution Method Based on Parallel Imaging and OpenVINO

Face image super-resolution refers to recovering a high-resolution face image from a low-resolution one. In recent years, due to the breakthrough progress of deep representation learning for super-resolution, the study of face super-resolution has become one of the hot topics in the field of super-resolution. However, the performance of these deep learning-based approaches highly relies on the scale of training samples and is limited in efficiency in real-time applications. To address these issues, in this work, we introduce a novel method based on the parallel imaging theory and OpenVINO. In particular, inspired by the methodology of learning-by-synthesis in parallel imaging, we propose to learn from the combination of virtual and real face images. In addition, we introduce a center loss function borrowed from the deep model to enhance the robustness of our model and propose to apply OpenVINO to speed up the inference. To the best of our knowledge, it is the first time to tackle the problem of face super-resolution based on parallel imaging methodology and OpenVINO. Extensive experimental results and comparisons on the publicly available LFW, WebCaricature, and FERET datasets demonstrate the effectiveness and efficiency of the proposed method.
Visual question answering from another perspective: CLEVR mental rotation tests

Different types of mental rotation tests have been used extensively in psychology to understand human visual reasoning and perception. Understanding what an object or visual scene would look like from another viewpoint is a challenging problem that is made even harder if it must be performed from a single image. We explore a controlled setting whereby questions are posed about the properties of a scene if that scene was observed from another viewpoint. To do this we have created a new version of the CLEVR dataset that we call CLEVR Mental Rotation Tests (CLEVR-MRT). Using CLEVR-MRT we examine standard methods, show how they fall short, then explore novel neural architectures that involve inferring volumetric representations of a scene. These volumes can be manipulated via camera-conditioned transformations to answer the question. We examine the efficacy of different model variants through rigorous ablations and demonstrate the efficacy of volumetric representations.
Hybrid Control for Learning Motor Skills

We develop a hybrid control approach for robot learning based on combining learned predictive models with experience-based state-action policy mappings to improve the learning capabilities of robotic systems. Predictive models provide an understanding of the task and the physics (which improves sample-efficiency), while experience-based policy mappings are treated as “muscle memory” that encode favorable actions as experiences that override planned actions. Hybrid control tools are used to create an algorithmic approach for combining learned predictive models with experience-based learning. Hybrid learning is presented as a method for efficiently learning motor skills by systematically combining and improving the performance of predictive models and experience-based policies. A deterministic variation of hybrid learning is derived and extended into a stochastic implementation that relaxes some of the key assumptions in the original derivation. Each variation is tested on experience-based learning methods (where the robot interacts with the environment to gain experience) as well as imitation learning methods (where experience is provided through demonstrations and tested in the environment). The results show that our method is capable of improving the performance and sample-efficiency of learning motor skills in a variety of experimental domains.
Implementation Strategies for Adopting Digital Library Research Support Services in Academic Libraries in Indonesia

Digital libraries play an essential role in supporting higher educations research. Therefore, Indonesia's academic libraries require digital library research support services and strategies to adopt such services quickly. Strategy design is carried out in three stages: a literature review using guidelines from the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) to determine indicators of research support services; a digital library survey to find out the implemented research support services in the top nine digital academic libraries in Indonesia; and gap analysis to establish strategies using the Technology-Organization-Environment (TOE) framework. The investigation shows Indonesian academic libraries have adopted few research support services. Libraries in implementing research support services experienced various obstacles. The recommended organization strategy focuses on research support services and partnerships with university research units. Regarding technology, academic libraries require developing a prototype of a digital library with research support service features. As for the environmental area, libraries need to assess user expectations for digital library research support services. Further research is required to validate the relationship between factors in an adoption model. A further study is conducted to develop a prototype of digital library research support services.
Drug Verification System Using Quick Response Code

Drug errors and abuses are the most frequently reported deficiencies in the healthcare sector worldwide. In the US alone over $3.5 billion has been expended on treatment related to drug errors that concern more than 1.5 million individuals. The drug is an important part of livelihood has faced the problem of authentication because medicines have to be tested to differentiate between the real and the fake. Drug code detection will reduce the risk of these mistakes by supplying the first responders with accurate information that can quickly decode this information using a code scanner on their smartphones and thus take the necessary steps against their use. The previous study implemented a desktop application system that checks for standardized drugs by scanning the Quick Response codes on the pack. Recently, lots of improvements have taken place in terms of smartphone development with various tools like cameras, which can be used to scan drug barcode. Therefore, the study developed a mobile application to scan the drugs' barcode and verify authenticity. The application designed using an integrated database for real-time drug authentications. The application was implemented using SQL running on a server and interacted with an Application Programming Interface (API) to serve as an intermediary between the application and the browser API built with an Object-relational mapping (ORM) called Sequelize. After code is scanned to gets its serial code, the API validates the serial code and releases a quick response code through a JavaScript Object Notation (JSON). The proposed system can be used by doctors, pharmacists and patients for the identification of fakes and harmful drugs, hence reduced the calculations of fakes or harmful drugs.
Research on Design Method and Construction Key Points of Prefabricated Building Structure Combined with Machinery

Abstract The design method of prefabricated building structure combined with machinery and the technology in the research of construction key points effectively solve the problem of cost control in the process of building construction through application sensitivity. The successful development of the design method and construction key research of the combination of prefabricated building structure and machinery will enable many branches to converge, thus benefiting the world.
Photovoltaic Panel Construction Change Monitoring Based on LSTM Models

Satellite and Aerial Image Time Series contain tremendous amounts of information of ground targets in space and time and have been commonly used in temporal pattern analysis tasks of ground targets. It has a wide range of applications including environment monitoring, urban planning, hazard assessment, etc. PV (photovoltaic) field construction monitoring is a new topic with increasing attentions. In this paper, we proposed a simple yet effective network based on LSTM (long short term memory) to detect PV field construction event. We build image time-series dataset from Sentinel-2 data of an Egypt PV field under construction to monitor the project progress. Compared with clustering model and CNNs (convolutional neural networks), our network achieves the better accuracy. Although low resolution and mislabeled pixels limit the accuracy cap, the simple network is still robust and easy to transfer for other applications.
Characterizing Bottlenecks in Scheduling Microservices on Serverless Platforms

Datacenters are witnessing an increasing trend in adopting microservice-based architecture for application design, which consists of a combination of different microservices. Typically these applications are short-lived and are administered with strict Service Level Objective (SLO) requirements. Traditional virtual machine (VM) based provisioning for such applications not only suffers from long latency when provisioning resources (as VMs tend to take a few minutes to start up), but also places an additional overhead of server management and provisioning on the users. This led to the adoption of serverless functions, where applications are composed as functions and hosted in containers. However, state-of-the-art schedulers employed in serverless platforms tend to look at microservice-based applications similar to conventional monolithic black-box applications. To detect all the inefficiencies, we characterize the end-to-end life cycle of these microservice-based applications in this work. Our findings show that the applications suffer from poor scheduling of microservices due to reactive container provisioning during workload fluctuations, thereby resulting in either in SLO violations or colossal container over-provisioning, in turn leading to poor resource utilization. We also find that there is an ample amount of slack available at each stage of application execution, which can potentially be leveraged to improve the overall application performance.
Playing by Ear: Designing for the Physical in a Sound-Based Virtual Reality Narrative

We present two proof-of-concept narrative VR experiences with a focus on sound-based physical interactions. Responding to a call to expand upon current design conceptualizations, we draw on tangible sound-based design in order to develop considerations for the body and physical environments within VR narratives. We propose that a focus on the actions the player is asked to perform (e.g., touch, stand, kneel, grasp, walk, listen, reach, dance) can contribute to an understanding of VR as a sensory, embodied medium that offers ways to playfully engage with physical reality rather than simulate it entirely.
Adversarial Examples Detection Using Random Projections of Residuals

The adversarial images make the deep neural network misclassify the original label and thus fool successive artificial intelligence system. The emergency of the adversarial images attracts researchers' attentions to the security of machine learning. In this paper, we employ a steganalysis based method to detect adversarial images which are generated by the typical attacks including BIM and DEEPFOOL. Unlike the previous steganalysis-based methods, we project the residuals onto randomly neighborhood and extract the histogram as the feature. Compared with steganalysis-based methods which calculates the co-occurrence on the truncated residual as feature, the proposed method does not truncate the residual thus avoiding loss information. Experimental results show that the proposed method obtained better performance than SPAM and SRM based methods for BIM and DEEPFOOL attack.
Exploiting Knowledge Hierarchy for Finding Similar Exercises in Online Education Systems

In education systems, Finding Similar Exercises (FSE) is the key step for both exercise retrieval and duplicate detection. Recently, more and more attention has been drawn into this area and several works have been proposed, to utilize the exercise content (e.g., texts or images) or the labeled knowledge concepts. Such approaches, however, have failed to take knowledge hierarchy into account. To this end, we advance a novel knowledge-aware multimodal network, namely KnowNet, for finding similar exercises in large-scale online education systems by integrating the knowledge hierarchy into the heterogeneous exercise data and learning a relation-aware semantic representation. Specifically, we first propose a Content Representation Layer (CRL) to learn a unified semantic representation of the heterogeneous exercise content. Then, we design a Hierarchy Fusion Layer (HFL) to exploit the knowledge hierarchy. By combining the knowledge hierarchy, HFL can not only retrieve the relation-aware semantic representation but also provide an interpretable view to investigate the similarity of exercises. Finally, we adopt a Similarity Score Layer (SSL) for returning similar exercises. Extensive experiments demonstrate the effectiveness and interpretability of KnowNet.
Combatting Foreign Election Interference: Canada's Electoral Ecosystem Approach to Disinformation and Cyber Threats

Foreign election interference presents a significant threat to electoral fairness, democratic legitimacy, and public confidence in elections. This article argues for an “electoral ecosystem” approach to combatting the threat of foreign intervention in elections. Under an electoral ecosystem approach, the electoral system is viewed as an interconnected network of institutions, processes, and actors, all of which must coordinate together to ensure electoral effectiveness and legitimacy. An electoral ecosystem is comprised of multiple institutions and actors, including governments, political parties, voters, third parties, online platforms, and electoral management bodies. Given the interdependence and interconnected nature of an electoral system, there are multiple points of vulnerability that must be defended. An electoral ecosystem approach does not depend on any one single line of defense but instead relies on a multiplicity of strategies that protect the institutions and individuals that comprise the ecosystem. To further explore the electoral ecosystem approach, this article focuses on Canada's response to foreign interference in elections. The ecosystem approach consists of three principal strategies. The first strategy involves a set of new campaign finance regulations which directly prevents the influence of foreign individuals and groups on the election. The second strategy consists of new measures to reduce disinformation and to lessen its distorting impact on democratic discourse. To the extent that foreign election interference takes place through social media, efforts to reduce disinformation will also reduce the impact of foreign interference. The third strategy involves strengthening Canadian cybersecurity. A number of steps have been taken recently, including the coordination of Canada's security agencies and the adoption of new measures to prevent computer hacking and privacy intrusions. These three strategies, and the multiple measures within each, provide protection across the electoral ecosystem.
Semantic Analysis of Cultural Heritage Data: Aligning Paintings and Descriptions in Art-Historic Collections

Art-historic documents often contain multimodal data in terms of images of artworks and metadata, descriptions, or interpretations thereof. Most research efforts have focused either on image analysis or text analysis independently since the associations between the two modes are usually lost during digitization. In this work, we focus on the task of alignment of images and textual descriptions in art-historic digital collections. To this end, we reproduce an existing approach that learns alignments in a semi-supervised fashion. We identify several challenges while automatically aligning images and texts, specifically for the cultural heritage domain, which limit the scalability of previous works. To improve the performance of alignment, we introduce various enhancements to extend the existing approach that show promising results.
Annotating Motion Primitives for Simplifying Action Search in Reinforcement Learning

Reinforcement learning in large-scale environments is challenging due to the many possible actions that can be taken in specific situations. We have previously developed a means of constraining, and hence speeding up, the search process through the use of motion primitives; motion primitives are sequences of pre-specified actions taken across a state series. As a byproduct of this work, we have found that if the motion primitives’ motions and actions are labeled, then the search can be sped up further. Since motion primitives may initially lack such details, we propose a theoretically viewpoint-insensitive and speed-insensitive means of automatically annotating the underlying motions and actions. We do this through a differential-geometric, spatio-temporal kinematics descriptor, which analyzes how the poses of entities in two motion sequences change over time. We use this descriptor in conjunction with a weighted-nearest-neighbor classifier to label the primitives using a limited set of training examples. In our experiments, we achieve high motion and action annotation rates for human-action-derived primitives with as few as one training sample. We also demonstrate that reinforcement learning using accurately labeled trajectories leads to high-performing policies more quickly than standard reinforcement learning techniques. This is partly because motion primitives encode prior domain knowledge and preempt the need to re-discover that knowledge during training. It is also because agents can leverage the labels to systematically ignore action classes that do not facilitate task objectives, thereby reducing the action space.
A Linked Data Model for Data Scopes

With the rise of data driven methods in the humanities, it becomes necessary to develop reusable and consistent methodological patterns for dealing with the various data manipulation steps. This increases transparency, replicability of the research. Data scopes present a qualitative framework for such methodological steps. In this work we present a Linked Data model to represent and share Data Scopes. The model consists of a central Data scope element, with linked elements for data Selection, Linking, Modeling, Normalisation and Classification. We validate the model by representing the data scope for 24 articles from two domains: Humanities and Social Science.
A self-sovereign identity management scheme using smart contracts

The existing self-sovereign identity management schemes have some problems, such as weak availability and security risks. To solve these problems, we proposed a cross-domain self-sovereign identity management scheme using smart contracts. This scheme takes into account the entire lifecycle of identity, especially including the cross-domain use and recovery. To preserve the privacy data of users on the blockchain, we proposed a data storage method of anchoring on blockchain. Finally, we implemented this scheme using the Solidity programming language for smart contract. This scheme has been experimentally verified to be capable of maintaining the expenditure of resources under control and having good usability. Compared with other self-sovereign identity management schemes, this scheme has better performance in terms of controllability, security and portability.
Finite-Word Hyperlanguages

Formal languages are in the core of models of computation and their behavior. A rich family of models for many classes of languages have been widely studied. Hyperproperties lift conventional trace-based languages from a set of execution traces to a set of sets of executions. Hyperproperties have been shown to be a powerful formalism for expressing and reasoning about information-flow security policies and important properties of cyber-physical systems. Although there is an extensive body of work on formal-language representation of trace properties, we currently lack such a general characterization for hyperproperties. We introduce hyperlanguages over finite words and models for expressing them. Essentially, these models express multiple words by using assignments to quantified word variables. Relying on the standard models for regular languages, we propose hyperregular expressions and finite-word hyperautomata (NFH), for modeling the class of regular hyperlanguages. We demonstrate the ability of regular hyperlanguages to express hyperproperties for finite traces. We explore the closure properties and the complexity of the fundamental decision problems such as nonemptiness, universality, membership, and containment for various fragments of NFH.
Body Part Localization and Pose Tracking by Using Deepercut Algorithm for King Cobra's BBL (Biting Behavior Learning)

During the recent years, much attention has been given to understand the behavior of king cobra. However, there is no report regarding pose tracking and usage of mathematical model that explain the mechanism of biting dynamics of king cobra. In this study a new method called body part localization technique which is often used in computer vision field is applied for detail behavior learning of king cobra. We explored the method of minimizing the time of direct observation by using deep learning technique. Firstly, we applied the approach of body part localization in order to explore the pose data leading to behavior analysis. Secondly, we formulated a Markov chain model to explain the dynamics of king cobra biting behavior. This model describes the significant potential states of biting behavior by using mathematical model leading to predict the behavioral transition from one state to another. From our investigation we found that king cobra at a normal state will also be irritated if the charmer put external pressure and might have chances of getting injured by 18%. Therefore, even a veteran charmer needs to be very careful while handling king cobra during entertainment show. We also concluded that the body part localization by using body landmarks in freely condition is more effective method to identify the king cobra's body part leading to behavior analysis as the disturbance of its habitation is sharply decreased than that of the repeated direct observation method.