Reviewerly/exHarmony huggingface dataset:
ObliDC

Data privacy is becoming one of the most critical concerns in cloud computing. Several proposals based on Intel SGX such as VC3 [1] and M2R [2] have been introduced in the literature to protect data privacy during job execution in the cloud. However, a comprehensive formal proof of their security guarantees is still lacking. In this paper, we propose ObliDC, a general UC-secure SGX-based oblivious distributed computing framework. First, we model the life-cycle of a distributed computing job as data-flow graphs. Under the assumption of malicious, adaptive adversaries in the cloud, we then formally define data privacy of a distributed computing job by introducing a notion named ODC-privacy, which encompasses both semantic security (to protect data confidentiality during computation and transmission) and oblivious traffic (to prevent data leakage from traffic analysis). ObliDC is composed of four two-party protocols -- job deployment, job initialization, job execution, and results return, which allow for modular construction of concrete privacy-preserving job protocols in different distributed computing frameworks. Finally, inspired by a formal abstraction for trusted processors proposed by R. Pass et al. [3], we formally prove the security of ObliDC under the universal composability (UC) framework.
Adaptive multilayer level set method for segmenting images with intensity inhomogeneity

The level set method based on bias correction can segment images with gentle intensity inhomogeneity effectively. However, most level set methods fail to segment severe inhomogeneous images due to the use of fixed scale clustering criterion. To deal with this problem, an adaptive multilayer level set method is proposed to segment images with severe intensity inhomogeneity. First, an improved global adaptive scale operator and a local adaptive scale operator are designed to adaptively adjust the scale of clustering kernel function according to the degree of intensity inhomogeneity. Then, an adaptive multilayer level set structure is constructed with the two designed scale operators. The number of layers and the scale of each layer in the multilayer structure are adaptively determined based on the degree of intensity inhomogeneity, which not only provides appropriate candidate scales in each pixel but also allows the model to detect global contrast information. With the dual minimisation method, image segmentation and bias correction can be achieved simultaneously. In addition, a hybrid bias field initialisation procedure is proposed to enhance the robustness of the proposed method. Experimental results demonstrate the effectiveness and robustness of the proposed method in segmenting images with intensity inhomogeneity.
DiSSCo, iDigBio and the Future of Global Collaboration

Integrated Digitized Biocollections (iDigBio) is the United States’ (US) national resource and coordinating center for biodiversity specimen digitization and mobilization. It was established in 2011 through the US National Science Foundation’s (NSF) Advancing Digitization of Biodiversity Collections (ADBC) program, an initiative that grew from a working group of museum-based and other biocollections professionals working in concert with NSF to make collections' specimen data accessible for science, education, and public consumption. The working group, Network Integrated Biocollections Alliance (NIBA), released two reports (Beach et al. 2010, American Institute of Biological Sciences 2013) that provided the foundation for iDigBio and ADBC. iDigBio is restricted in focus to the ingestion of data generated by public, non-federal museum and academic collections. Its focus is on specimen-based (as opposed to observational) occurrence records. iDigBio currently serves about 118 million transcribed specimen-based records and 29 million specimen-based media records from approximately 1600 datasets. These digital objects have been contributed by about 700 collections representing nearly 400 institutions and is the most comprehensive biodiversity data aggregator in the US. Currently, iDigBio, DiSSCo (Distributed System of Scientific Collections), GBIF (Global Biodiversity Information Facility), and the Atlas of Living Australia (ALA) are collaborating on a global framework to harmonize technologies towards standardizing and synchronizing ingestion strategies, data models and standards, cyberinfrastructure, APIs (application programming interface), specimen record identifiers, etc. in service to a developing consolidated global data product that can provide a common source for the world’s digital biodiversity data. The collaboration strives to harness and combine the unique strengths of its partners in ways that ensure the individual needs of each partner’s constituencies are met, design pathways for accommodating existing and emerging aggregators, simultaneously strengthen and enhance access to the world’s biodiversity data, and underscore the scope and importance of worldwide biodiversity informatics activities. Collaborators will share technology strategies and outputs, align conceptual understandings, and establish and draw from an international knowledge base. These collaborators, along with Biodiversity Information Standards (TDWG), will join iDigBio and the Smithsonian National Museum of Natural History as they host Biodiversity 2020 in Washington, DC. Biodiversity 2020 will combine an international celebration of the worldwide progress made in biodiversity data accessibility in the 21 st century with a biodiversity data conference that extends the life of Biodiversity Next. It will provide a venue for the GBIF governing board meeting, TDWG annual meeting, and the annual iDigBio Summit as well as three days of plenary and concurrent sessions focused on the present and future of biodiversity data generation, mobilization, and use.
Recognition Times for 54 Thousand Dutch Words: Data from the Dutch Crowdsourcing Project

We present a new database of Dutch word recognition times for a total of 54 thousand words, called the Dutch Crowdsourcing Project. The data were collected with an internet vocabulary test. The database is limited to native Dutch speakers. Participants were asked to indicate which words they knew. Their response times were registered, even though the participants were not asked to respond as fast as possible. Still, the response times correlate around .7 with the response times of the Dutch Lexicon Projects for shared words. Also results of virtual experiments indicate that the new response times are a valid addition to the Dutch Lexicon Projects. This not only means that we have useful response times for some 20 thousand extra words, but we now also have data on differences in response latencies as a function of education and age. The new data correspond better to word use in the Netherlands.
An Assessment of the Effectiveness of CALL in Teaching English Language Writing Skills in Saudi Arabia

Computer-Assisted Language Learning (CALL) is playing a vital role in teaching English language to English as a Foreign Language (EFL) students.However, to best of my knowledge a little has been done in this regard to keep the students in line with the most recent advancements in this paradigm in Saudi Arabia.This paper evaluates the efficacy of CALL in improving students' writing skills and provides innovative techniques and robust strategies for long-lasting learning.The research seeks to fill in the knowledge gap regarding prospects of using CALL in the Kingdom with these main research questions; 1) how is the technology presently used for teaching the writing skills?; 2) what is the true impact of using CALL on students' writing skills?; 3) which area of the language (organization, structure, content, grammar) sees the most improvements by CALL to make them better writers?A quantitative research design was used for this study.The sample was sixty female students of a Saudi University divided equally into control and experimental groups.The elicited data analysis indicates that the performance scores of two groups differ significantly when taught through CALL.The research contends that using CALL can enhance students' writing skills over a short period of time when compared to the traditional ways of improving the writing skills.The current study also recommends that language classrooms should be equipped with all the latest technological facilities to encourage the use of CALL.
ConsenSGX: Scaling Anonymous Communications Networks with Trusted Execution Environments

Abstract Anonymous communications networks enable individuals to maintain their privacy online. The most popular such network is Tor, with about two million daily users; however, Tor is reaching limits of its scalability. One of the main scalability bottlenecks of Tor and similar network designs originates from the requirement of distributing a global view of the servers in the network to all network clients. This requirement is in place to avoid epistemic attacks , in which adversaries who know which parts of the network certain clients do and do not know about can rule in or out those clients from being responsible for particular network traffic. In this work, we introduce a novel solution to this scalability problem by leveraging oblivious RAM constructions and trusted execution environments in order to enable clients to fetch only the parts of the network view they require, without the directory servers learning which parts are being fetched. We compare the performance of our design with the current Tor mechanism and other related works to show one to two orders of magnitude better performance from an end-to-end perspective. We analyse the requirements to actually deploy such a scheme today and conclude that it would only require a small fraction ( &lt; 2.5%) of the relays to have the required hardware support; moreover, these relays can perform their roles with minimal network bandwidth requirements.
<i>CAVE</i>: Making Collective Virtual Narrative: <i>Best Paper Award</i>

CAVE is a shared narrative six degrees of freedom (6DoF) virtual reality experience. In 3.5 days, 1,927 people attended its premiere at SIGGRAPH 2018. Thirty participants at a time each saw and heard the same narrative from their own individual location in the room, as they would when attending live theater. CAVE set out to disruptively change how audiences collectively experience immersive art and entertainment. Inspired by the social gatherings of theater and cinema, CAVE resonated with viewers in powerful and meaningful ways. Its specific pairing of colocated audiences and physically shared immersive narrative suggests a possible future path for shared cinematic experiences.
A Sequential Two-Stage Track-to-Track Association Method in Asynchronous Bearings-Only Sensor Networks for Aerial Targets Surveillance

Successful track-to-track association (TTTA) in a multisensor and multitarget scenario is predicated on a reasonable likelihood function to evaluate the similarity of asynchronous mono tracks. To deal with the lack of synchronous data and prior knowledge of the targets in practical applications, this paper investigates a global optimization method with a novel likelihood function constructed by finite asynchronous measurements with joint temporal and spatial constraints (JTSC). For a scenario with more than two independent sensors, a sequential two-stage strategy is proposed to calculate the similarity of multiple asynchronous mono tracks. For the first stage, based on the temporal features of measurements from different sensors, a pairwise fusion model to estimate the position of the target with two mono tracks is established based on the asynchronous crossing location approach. For the other stage, to evaluate the similarity of the outputs, a pairwise similarity model is constructed by searching for the optimal matching points by setting temporal and spatial constraints. Thus, the likelihood of multiple asynchronous tracks is obtained. Simulations are performed to verify that the proposed method can achieve favorable performance without data-synchronization, and also demonstrate the superiority over the methods based on hinge angle differences (HADs) in some scenarios.
A Meta-MDP Approach to Exploration for Lifelong Reinforcement Learning

In this paper we consider the problem of how a reinforcement learning agent that is tasked with solving a sequence of reinforcement learning problems (Markov decision processes) can use knowledge acquired early in its lifetime to improve its ability to solve new problems. Specifically, we focus on the question of how the agent should explore when faced with a new environment. We show that the search for an optimal exploration strategy can be formulated as a reinforcement learning problem itself, albeit with a different timescale. We conclude with experiments that show the benefits of optimizing an exploration strategy using our proposed approach.
Secure storage and retrieval schemes for multiple encrypted digital holograms with orthogonal phase encoding multiplexing

Recent developments in 3D computational optical imaging such as digital holographic microscopy has ushered in a new era for biological research. Therefore, efficient and secure storage and retrieval of digital holograms is a challenging task for future cloud computing services. In this study, we propose a novel scheme to securely store and retrieve multiple encrypted digital holograms by using phase encoding multiplexing. In the proposed schemes, an encrypted hologram can only be accessed using a binary phase mask, which is the key to retrieve the image. In addition, it is possible to independently store, retrieve, and manage the encrypted digital holograms without affecting other groups of the encrypted holograms multiplexed using different sets of binary phase masks, due to the orthogonality properties of the Hadamard matrices with high autocorrelation and low cross-correlation. The desired encrypted holograms may also be searched for, removed, and added independently of other groups of the encrypted holograms. More and more 3D images or digital holograms can be securely and efficiently stored, retrieved, and managed.
An Intelligent Knowledge Extraction Framework for Recognizing Identification Information From Real-World ID Card Images

In this work, we study the problem of recognizing identification (ID) information from unconstrained real-world images of ID card, which has extensively applied in practical scenarios. Nonetheless, manual ways of processing the task are impractical due to the unaffordable cost of labor and time consumption as well as the unreliable quality of manual labeling. In this paper, we propose an intelligent framework for automatically recognizing ID information from images of the ID cards. Specifically, we first conduct marginal detection using a multi-operator algorithm and then localize the region of ID card from all the proposed candidate regions with SVM classifier. Furthermore, we segment linguistic characters from the card region by an improved projection algorithm. Finally, we recognize the specific characters by an eight-layer convolutional neural network. We perform extensive experiments on a Chinese ID card dataset to validate the effectiveness and efficiency of our proposed method. The experimental results demonstrate the superiority of proposal over other existing schemes.
CIM-CSS: A Formal Modeling Approach to Context Identification and Management for Intelligent Context-Sensitive Systems

Context modeling is often used to relate the context in which a system will operate to the entities of interest in the problem domain. It remains the case that context models are inadequate in emerging computing paradigms (e.g., smart spaces and the Internet of Things), in which the relevance of context is shaped dynamically by the changing needs of users. Formal models are required to fuse and interpret contextual information obtained from the heterogeneous sources. In this paper, we propose an integrated and formal context modeling approach for intelligent systems operating in the context-sensitive environments. We introduce a goal-driven, entity-centered identification method for determining which context elements are influential in adapting the system behavior. We then describe a four-layered framework for metamodeling the identification and management of context. First, the framework presents a formal metamodel of context. A formalization of context using the first-order logic with relational operators is then presented to specify formally the context information at different abstraction levels. The metamodel, therefore, prepares the ground for building a formal modeling language and automated support tool (https://github.com/metamodeler/CIM-CSS/). The proposed model is then evaluated using an application scenario in the smart meeting rooms domain, and the results are analyzed qualitatively.
Towards Automated Configuration of Stream Clustering Algorithms

Clustering is an important technique in data analysis which can reveal hidden patterns and unknown relationships in the data. A common problem in clustering is the proper choice of parameter settings. To tackle this, automated algorithm configuration is available which can automatically find the best parameter settings. In practice, however, many of our today’s data sources are data streams due to the widespread deployment of sensors, the internet-of-things or (social) media. Stream clustering aims to tackle this challenge by identifying, tracking and updating clusters over time. Unfortunately, none of the existing approaches for automated algorithm configuration are directly applicable to the streaming scenario. In this paper, we explore the possibility of automated algorithm configuration for stream clustering algorithms using an ensemble of different configurations. In first experiments, we demonstrate that our approach is able to automatically find superior configurations and refine them over time.
Use of Computing Devices as Sensors to Measure Their Impact on Primary and Secondary Students’ Performance

The constant innovation in new technologies and the increase in the use of computing devices in different areas of the society have contributed to a digital transformation in almost every sector. This digital transformation has also reached the world of education, making it possible for members of the educational community to adopt Learning Management Systems (LMS), where the digital contents replacing the traditional textbooks are exploited and managed. This article aims to study the relationship between the type of computing device from which students access the LMS and how affects their performance. To achieve this, the LMS accesses of students in a school comprising from elementary to bachelor's degree stages have been monitored by means of different computing devices acting as sensors to gather data such as the type of device and operating system used by the students.The main conclusion is that students who access the LMS improve significantly their performance and that the type of device and the operating system has an influence in the number of passed subjects. Moreover, a predictive model has been generated to predict the number of passed subjects according to these factors, showing promising results.
A Provable Secure Image Encryption Schema Based on Fractional Order Chaotic Systems

In the literature, many chaotic systems have been used in the design of image encryption algorithms. In this study, an application of fractional order chaotic systems is investigated. The aim of the study is to improve the disadvantageous aspects of existing methods based on discrete and continuous time chaotic systems by utilizing the features of fractional order chaotic systems. The most important advantage of the study compared to the literature is that the proposed encryption algorithm is designed with a provable security approach. Analyses results have been shown that the proposed method can be used successfully in many information security applications.
Noise-Resilient Similarity Preserving Network Embedding for Social Networks

Network embedding assigns nodes in a network to low-dimensional representations and effectively preserves the structure and inherent properties of the network. Most existing network embedding methods didn't consider network noise. However, it is almost impossible to observe the actual structure of a real-world network without noise. The noise in the network will affect the performance of network embedding dramatically. In this paper, we aim to exploit node similarity to address the problem of social network embedding with noise and propose a node similarity preserving (NSP) embedding method. NSP exploits a comprehensive similarity index to quantify the authenticity of the observed network structure. Then we propose an algorithm to construct a correction matrix to reduce the influence of noise. Finally, an objective function for accurate network embedding is proposed and an efficient algorithm to solve the optimization problem is provided. Extensive experimental results on a variety of applications of real-world networks with noise show the superior performance of the proposed method over the state-of-the-art methods.
A Hybrid Neural Network Model for Commonsense Reasoning

This paper proposes a hybrid neural network (HNN) model for commonsense reasoning. An HNN consists of two component models, a masked language model and a semantic similarity model, which share a BERT-based contextual encoder but use different model-specific input and output layers. HNN obtains new state-of-the-art results on three classic commonsense reasoning tasks, pushing the WNLI benchmark to 89%, the Winograd Schema Challenge (WSC) benchmark to 75.1%, and the PDP60 benchmark to 90.0%. An ablation study shows that language models and semantic similarity models are complementary approaches to commonsense reasoning, and HNN effectively combines the strengths of both. The code and pre-trained models will be publicly available at https://github.com/namisan/mt-dnn.
Technology for Training Creative Graduates in Engineering Bachelor’s Programs

This article is devoted to technologies of engineering education that produce the most in-demand professional qualities of graduates from Bachelor’s programs. A review of studies about introducing innovations to improve the educational process in practice-oriented Bachelor’s studies is carried out. The advantages and limitations of project-based and problem-based learning technologies are defined. The work presents the experience of developing and applying a unique teaching technology based on the integration of problemand project-based training approaches, designed to enhance the creativity of the study process in engineering Bachelor’s programs. It is described with a focus on the mechanisms for the integrated use of the advantages of methods of problemand project-based training in the formation of the professional competencies required for an engineer’s innovative activity in the development of a new product project. The data from an empirical study have been considered; they allow us to substantiate the conclusion that the integration of projectand problem-based learning in the form of a holistic technology is effective for developing students’ creative capacities.