Reviewerly/exHarmony huggingface dataset:
Enriching Smart Contracts with Temporal Aspects

Blockchain technology provides a platform for the decentralized execution of smart contracts. A smart contract is an agreement that is automatically executed when certain conditions are met. Current smart contracts use business rules to express conditions in which transactions occur but lack the capability to reason over time. We present a model-driven approach for creating blockchain applications that can run temporal logic. We present the new concepts required at the model level and explain how these concepts can be translated to a blockchain solution applying Hyperledger Fabric, Hyperledger Composer, and PROTON complex event processing. We illustrate the approach using a pharma cold chain scenario. Cold chain scenarios are typical examples of blockchain cases in which temporal reasoning is required. Here, the blockchain provides transparency, a non-repudiation process, full traceability and trackability of the transactions (provenance), shorter lead times, and authentication and verification of the parties in the supply chain network at each asset transfer. The temporal reasoning is required since decisions about pharmaceutical quality is based on time windows in which the goods have been exposed to certain conditions. Our solution includes the model, the blockchain backend, and a mobile user interface that allows each certified and authorized party in the network to initiate actions, finish their transactions, and track progress. It also includes temperature monitoring to ensure compliance with the proper conditions for transportation and asset transfer.
TuFast: A Lightweight Parallelization Library for Graph Analytics

Recently, there has been significant interest in large-scale graph analytics systems. However, most of the design efforts focus on accelerating graph analytics on giant graphs and/or in a distributed environment. Little attention focuses on the programmer usability perspective, which is critical to implementing ad-hoc analytics on moderate size graphs. In this paper, we present a lightweight transactional memory (TM) library TuFast which provides easy-to-use primitives for the end-user to agilely develop fast shared memory graph parallelization on a multi-core server. TuFast exploits recent CPU instructions set Hardware Transactional Memory (HTM), which has been available in off-the-shelf CPUs. HTM offers free transactional semantic but also suffers from capacity limitation. Our framework resolves the capacity challenge and efficiently utilizes HTM on graph parallelization by exploiting the graph degree information. Large scale graphs have a power-law degree distribution: a large proportion of the vertices with a small degree, fits in single HTM transactions; a small proportion of vertices with a big degree fits a pessimistic approach like locking; other vertices with a moderate degree can be processed with an optimistic approach with HTM acceleration. Our hybrid approach automatically adapts to the degree of graphs dynamically during the processing. The graph analytical jobs expressed via our library are straightforward and concise and outperform state-of-the-art distributed and multi-core graph analytical systems by up to 4 orders of magnitude.
Solving Agile Software Development Problems with Swarm Intelligence Algorithms

This paper outlines a short overview of swarm intelligence algorithms that are used within the software engineering area. Swarm intelligence algorithms have been used in many software engineering tasks, e.g., grammatical inference or mutation testing. However, their presence in the agile software development field is still awakening. As there are some promising results of solving different problems of agile software development with swarm intelligence, this paper discusses such problems and the proposed solutions within the last decade. Based on the results we propose a systematic classification of swarm intelligence algorithms according to problems within agile software development, i.e., next release problem, risk, software design, software cost estimation, and software effort estimation. Afterwards, we present papers that fall in the scope of the proposed classification, and provide highlights of each paper for researchers, conducting research in this and associated fields. In this manner, we provide some conclusions for each of the classified problem groups, and, in the end, we review the guidelines for the future.
(Identity-based) dual receiver encryption from lattice-based programmable hash functions with high min-entropy

Dual receiver encryption (DRE) is an important cryptographic primitive introduced by Diament et al. at CCS'04, which allows two independent receivers to decrypt a same ciphertext to obtain the same plaintext. This primitive is quite useful in designing combined public key cryptosystems and denial of service attack-resilient protocols. In this paper, we obtain some results as follows.
Constructing High Precision Knowledge Bases with Subjective and Factual Attributes

Knowledge bases (KBs) are the backbone of many ubiquitous applications and are thus required to exhibit high precision. However, for KBs that store subjective attributes of entities, e.g., whether a movie is kid friendly, simply estimating precision is complicated by the inherent ambiguity in measuring subjective phenomena. In this work, we develop a method for constructing KBs with tunable precision--i.e., KBs that can be made to operate at a specific false positive rate, despite storing both difficult-to-evaluate subjective attributes and more traditional factual attributes. The key to our approach is probabilistically modeling user consensus with respect to each entity-attribute pair, rather than modeling each pair as either True or False. Uncertainty in the model is explicitly represented and used to control the KB's precision. We propose three neural networks for fitting the consensus model and evaluate each one on data from Google Maps--a large KB of locations and their subjective and factual attributes. The results demonstrate that our learned models are well-calibrated and thus can successfully be used to control the KB's precision. Moreover, when constrained to maintain 95% precision, the best consensus model matches the F-score of a baseline that models each entity-attribute pair as a binary variable and does not support tunable precision. When unconstrained, our model dominates the same baseline by 12% F-score. Finally, we perform an empirical analysis of attribute-attribute correlations and show that leveraging them effectively contributes to reduced uncertainty and better performance in attribute prediction.
Modeling of video projectors in OpenGL for implementing a spatial augmented reality teaching system for assembly operations

Teaching complex assembly and maintenance skills to human operators usually requires extensive reading and the help of tutors. In order to reduce the training period and avoid the need for human supervision, an immersive teaching system using spatial augmented reality was developed for guiding inexperienced operators. The system provides textual and video instructions for each task while also allowing the operator to navigate between the teaching steps and control the video playback using a bare hands natural interaction interface that is projected into the workspace. Moreover, for helping the operator during the final validation and inspection phase, the system projects the expected 3D outline of the final product. The proposed teaching system was tested with the assembly of a starter motor and proved to be more intuitive than reading the traditional user manuals. This proof of concept use case served to validate the fundamental technologies and approaches that were proposed to achieve an intuitive and accurate augmented reality teaching application. Among the main challenges were the proper modeling and calibration of the sensing and projection hardware along with the 6 DoF pose estimation of objects for achieving precise overlap between the 3D rendered content and the physical world. On the other hand, the conceptualization of the information flow and how it can be conveyed on-demand to the operator was also of critical importance for ensuring a smooth and intuitive experience for the operator.
Discriminative Learning in the Model Space for Symbolic Sequence Classification

Learning in the model space is an approach that represents sequences with state space models and performs the learning procedure in the space spanned by the “model points.” To enhance the discriminative capacity and preserve the simplicity and effectiveness of model-based kernel, a discriminative learning method in the model space is proposed for symbolic sequence classification in this paper. Compared with string kernels, the discriminative model-based kernel not only models the temporal information of sequence by a generative mechanism but also considers label information of sequence in the similarity measurement, which is beneficial to sequence classification. On the other hand, although learning in the model space approach has been successfully applied in the cognitive fault diagnosis and the time-series analysis, it is actually unclear how it might perform on the symbolic sequence classification. This paper extends the learning in the model space approach to the symbolic sequence classification problem with a coding scheme on categorical variables. Experimental results and the related analysis confirm the effectiveness of discriminative learning in the model space approach for the symbolic sequence classification. Moreover, we investigate several characteristics related to the performance of the proposed approach, including parameter analysis, Lyapunov analysis etc., and present a guideline on exploiting the discriminative learning in the model space approach in real-world applications.
Android-based applications on teaching skills based on TPACK analysis

Abstract Teaching skills learned in micro-teaching are important competencies in the learning management. Dependence of other parties, both lecturers and colleagues teaching during the assessment process of teaching practice and lack of media that integrates the concept of material, examples, and practices are obstacles in mastering teaching skills. This article describes the specifications needed of an Android-based learning tool (media) for microteaching (“Microteaching”) courses and analyzes the performance of these learning media. The development phase begins with Analysis, Design, Development, and Evaluation. The results of the analysis referring to TPACK and the design showed that the specifications of the Android-based media consisted of two main menus namely material (containing various multimedia elements) and assessment (besides text-based assessment also contained speech recognition elements). The performance of “Microteaching” in functionality is 100% successful, while the feasibility test at the evaluation stage not only for media experts, but material experts and users indicates that the media is suitable for use. Furthermore, results of the assessment show that the media is able to depend on the assessment, except from the aspect of verbal aspects. Integration of learning concept material through mind maps, technical examples related to verbal teaching skills and videos to provide concrete description of how to teach (both verbal and non-verbal).
Evaluation of augmented reality (AR) displays performance based on human visual perception

Conventional display metrics<sup>1</sup> are currently used to evaluate the performance of augmented reality (AR) displays: the maximum luminance it can reach, the image uniformity, the contrast ratio and others. However, these metrics initially defined for non-transmissive displays, do not consider the impact of the see-through environment<sup>2</sup> as it exists in AR applications. These measurements (usually obtained in a dark laboratory) are used to quantify the performance of AR display devices, but do not necessary relates to the perceived quality of images rendered by such devices, due to the non-linearity of human perception. Upon this, the projected image (Fig. 1) is an overlay upon the see-through environment. The intensity balance between these two “layers” becomes critical as the perceived scene (Fig. 2) is resulting from color mixing. Here, we want to highlight the fact that the brightness (perceived intensity) and color uniformity<sup>2</sup> of the projected image becomes less important whereas the contrast ratio between the image and the environment becomes very critical. Other metrics are also less significant once the outside scene is added such as pixel sharpness<sup>2</sup> (MTF) and the generated halo effects. These metrics are still important to evaluate the display performance for comparison purpose, but higher thresholds should be permitted for an AR display. The performance requirements must be adjusted in accordance to the AR application itself and not as it is done for conventional displays.
Playing with delay

Virtual reality is now used across a range of applications, from entertainment to clinical purposes. Although the rendered visualisations have better temporal and spatial resolutions than ever, several technological constraints remain - and people still suffer side-effects. With this demonstration, we address the temporal constraints of virtual reality. We invite participants to play a game where they are in charge of the presented delay, facilitating their first-hand experience of the consequences of delay. Furthermore, the demonstration serves as a platform for future explorations into short- and long-term effects of virtual reality constraints.
Combined training strategy for low‐resolution face recognition with limited application‐specific data

Application-specific data for certain biometric applications are often not sufficiently available. The authors present a solution for face recognition with limited application-specific data. Existing methods often use a classifier with convolutional neural networks (CNNs) as feature extractors. The CNNs are trained with massive general (i.e. not application specific) data and the classifier is trained with application-specific data. Alternatively, the authors propose a combined training strategy to train the classifier on a balanced mixture of general and application-specific data, such that the recognition performance is maximised. The proposed method largely alleviates the needs for application-specific data. To prove its effectiveness, they apply the proposed method to low-resolution face recognition. Specifically, they use the heterogeneous joint Bayesian (HJB) classifier that is capable of comparing features from the same modality but with different characteristics. To further boost performance, the authors augment the training data by pre-processing it to resemble application-specific data. They conducted extensive experiments on challenging datasets, namely, SCface and COX. The results show that the proposed method improves the true match rate on SCface at a false match rate of 10% by ∼11% and the true match rate on COX at a false match rate of 1% by ∼12%.
Towards a Sustainable Ecosystem of Intelligent Transportation Systems

It is difficult to overstate how large a role Intelligent Transportation Systems (ITS) technology has played in advancing safety, mobility, and productivity in our daily lives. ITS encompasses a broad range of technologies, including information and communication technologies, transportation and communication infrastructures, connected vehicles, and emerging technologies such as Internet-of-Things (IoT). It has been studied extensively in many different disciplines, including transportation, communication, database and management communities. Unfortunately, there are still many unsolved challenges that hinder the large deployment of advanced ITS systems. Recent studies have proposed using Blockchain, an emerging technology that enables decentralized coordination, to address inherent challenges in ITS such as security and scalability. However, these studies did not address a key question: how can we achieve a sustainable ITS ecosystem? This paper presents our preliminary study where we first point out the limitations of prior Blockchain-based ITS systems and then outline an architecture to support a sustainable ITS ecosystem. Our main goal is to stimulate further effort and cross-disciplinary collaboration by providing guidance and reference for future studies.
Improvements in a Wearable Device for Sign Language Translation

Nowadays a commercial product for sign language translation is still not available. This paper presents our latest results towards this goal, presenting a functional prototype called Talking Hands. Talking Hands uses a data-glove to detect the hand movements of the user, and a smartphone application to gather all the data and translates them into voice, using a speech synthesizer. Talking Hands implements the most suitable solutions for a massive production without penalizing its reliability. This paper presents the improvements of the last prototype in terms of hardware, software and design, together with a preliminary analysis for the translation of dynamic gestures through this device.
Data Center Network Design for Internet‐Related Services and Cloud Computing

Data center networks provide the physical infrastructure that hosts Internet‐related services and cloud computing. Designing data center networks properly is imperative for Internet‐related service and cloud computing providers to gain competitive edges through cost efficiency and service quality. In this study, we formulate a mathematical programming model to address the data center network design problem, in which the objective is to minimize total operating cost and the service delay penalty by optimizing data center location, footprint allocation, and resource provisioning decisions, while incorporating essential features, such as latency, power, multiple resources, configuration limits, and interdependent footprints. We employ a queueing model to approximate the service latency and provide tractable reformulations. To enhance computational efficiency for large‐scale problems, we further develop Lagrangian relaxation methods and generate strengthening cuts by exploiting the structural properties of the problem. Our numerical studies demonstrate that the proposed model, which jointly optimizes location, allocation, and resource provisioning, can achieve significant cost reductions and improvements in service quality compared with a hierarchical approach that optimizes these decisions sequentially. Moreover, our proposed solution methods outperform state‐of‐the‐art commercial software in terms of computational efficiency. Based on real‐world datasets, the proposed model selects data centers that have been chosen by major cloud computing infrastructure providers. We also draw managerial insights that can be used as design guidelines in practice.
Evaluation of Move Method Refactorings Recommendation Algorithms: Are We Doing It Right?

Previous studies introduced various techniques for detecting Move Method refactoring opportunities. However, different authors have different evaluations, which leads to the fact that results reported by different papers do not correlate with each other and it is almost impossible to understand which algorithm works better in practice. In this paper, we provide an overview of existing evaluation approaches for Move Method refactoring recommendation algorithms, as well as discuss their advantages and disadvantages. We propose a tool that can be used for generating large synthetic datasets suitable for both algorithms evaluation and building complex machine learning models for Move Method refactoring recommendation.
Random-opposition-based Learning for Computational Intelligence

In this paper, random-opposition-basedBairathi, Divya learning (ROBL) is proposed. ROBL is a generalized version of opposition-based learning (OBL). ROBLGopalani, Dinesh introduces randomness in OBL. ROBL is applied for some metaheuristics and artificial neural network. The examples are provided with preliminary results.
Cyclic codes with generalized cyclotomic cubic classes

This paper is study of cyclotomic classes of order 3r for r ≥ 1 to construct a class of cyclic codes of length pm. We obtain cube-root or a similar bound on the minimum odd weight of these codes.
Developing a Data-Literate Workforce through BLUE: Biodiversity Literacy in Undergraduate Education

The biodiversity sciences have experienced a rapid mobilization of data that has increased our capacity to investigate large-scale issues of critical importance (e.g., climate change and its impacts, zoonotic disease transmission, sustainable resource management, impacts of invasive species, and biodiversity loss). Several initiatives are underway to aggregate and mobilize these biodiversity, environmental, and ecological data resources (iDigBio, NEON, GBIF, iNaturalist, etc.). This requires a new set of skills for the 21st century biodiversity scientist; who is required to be fluent in integrative fields spanning evolutionary biology, systematics, ecology, geology, and environmental science and possess the quantitative, computational, and data skills to conduct research using large and complex datasets. The biodiversity science community has recognized a need to unite biodiversity and data sciences and improve data literacy in the emerging science workforce. The NSF-funded Biodiversity Literacy in Undergraduate Education (BLUE; biodiversityliteracy.com) is working to bridge a gap between efforts that currently exists to promote data literacy pre-college and professional development for those pursuing careers in biodiversity science. The BLUE network is developing strategies and materials to infuse biodiversity data into the core of the undergraduate science curriculum, facilitating broad-scale adoption of biodiversity data literacy competencies, and improving undergraduate biology training to meet increasing workforce demands in data and biodiversity sciences. The BLUE network has four major goals: 1) Cultivate a diverse and inclusive network of biodiversity researchers, data scientists, and biology educators focused on undergraduate data-centric biodiversity education; 2) build community consensus on core biodiversity data literacy competencies; 3) develop strategies and exemplar materials to guide the integration of biodiversity data literacy competencies into introductory undergraduate biology curricula; and 4) extend the network to engage a broader community of undergraduate educators in biodiversity data literacy efforts. The BLUE community continues to grow and build new partnerships and initiatives across the biodiversity science community. In year two of the BLUE network we have been focusing efforts building the community, developing and disseminating exemplar educational materials, and defining core biodiversity data literacy skills and competencies. We will present our current and ongoing work and ways in which members of the biodiversity_next community can be involved in shaping the biodiversity science of the future, while addressing the needs of a changing planet.