Reviewerly/exHarmony huggingface dataset:
Hypothesis-driven Adaptation of Business Models based on Product Line Engineering

The continuous innovation of its business models is an important task for a company to stay competitive. During this process, the company has to validate various hypotheses about its business models by adapting to uncertain and changing customer needs effectively and efficiently. This adaptation, in turn, can be supported by the concept of Software Product Lines (SPLs). SPLs reduce the time to market by deriving products for customers with changing requirements using a common set of features, structured as a feature model. Analogously, we support the process of business model adaptation by applying the engineering process of SPLs to the structure of the Business Model Canvas (BMC). We call this concept a Business Model Decision Line (BMDL). The BMDL matches business domain knowledge in the form of a feature model with customer needs to derive hypotheses about the business model together with experiments for validation. Our approach is effective by providing a comprehensive overview of possible business model adaptations and efficient by reusing experiments for different hypotheses. We implement our approach in a tool and illustrate the usefulness with an example of developing business models for a mobile application.
Service Evolution Analytics: Change and Evolution Mining of a Distributed System

Changeability and evolvability analysis can aid an engineer tasked with a maintenance or an evolution task. This article applies change mining and evolution mining to evolving distributed systems. First, we propose a Service Change Classifier based Interface Slicing algorithm that mines change information from two versions of an evolving distributed system. To compare old and new versions, the following change classification labels are used: inserted, deleted, and modified. These labels are then used to identify subsets of the operations in our newly proposed Interface (WSDL) Slicing algorithm. Second, we proposed four Service Evolution Metrics that capture the evolution of a system's Version Series VS = {V1, V2,...,VN}. Combined the two form the basis of our proposed Service Evolution Analytics model, which includes learning during its development phase. We prototyped the model in an intelligent tool named AWSCM (Automatic Web Service Change Management). Finally, we present results from experiments with two well-known cloud services: Elastic Compute Cloud (EC2) from the Amazon Web Service (AWS), and Cluster Controller (CC) from Eucalyptus. These experiments demonstrate AWSCM's ability to exploit change and evolution mining.
An efficient certificateless public auditing scheme in cloud storage

Abstract Cloud storage is a mode of online storage in which data are stored on multiple virtual servers usually hosted by third parties. Cloud service providers manage and operate data storage as services. The major concern in cloud storage is the integrity of outsourced data. Many public auditing schemes which authorize the third party auditor (TPA) to check the integrity of outsourced data have been proposed. However, most of them are based on traditional public key cryptosystems. This paper proposes an efficient certificateless public auditing Scheme (CL‐PAS) in cloud storage. In the process of challenge and auditing, CL‐PAS ensures that malicious cloud servers cannot forge auditing evidences to deceive TPA to pass the verification, and prevents curious TPA from recovering original data from auditing evidences. So it strengthens privacy preserving. Security analysis shows that CL‐PAS scheme meets the predetermined security requirements. The simulation and performance analysis results show that, compared with similar schemes, CL‐PAS scheme has the higher efficiency.
SeqDynamics: Visual Analytics for Evaluating Online Problem‐solving Dynamics

Abstract Problem‐solving dynamics refers to the process of solving a series of problems over time, from which a student's cognitive skills and non‐cognitive traits and behaviors can be inferred. For example, we can derive a student's learning curve (an indicator of cognitive skill) from the changes in the difficulty level of problems solved, or derive a student's self‐regulation patterns (an example of non‐cognitive traits and behaviors) based on the problem‐solving frequency over time. Few studies provide an integrated overview of both aspects by unfolding the problem‐solving process. In this paper, we present a visual analytics system named SeqDynamics that evaluates students ‘problem‐solving dynamics from both cognitive and non‐cognitive perspectives. The system visualizes the chronological sequence of learners’ problem‐solving behavior through a set of novel visual designs and coordinated contextual views, enabling users to compare and evaluate problem‐solving dynamics on multiple scales. We present three scenarios to demonstrate the usefulness of SeqDynamics on a real‐world dataset which consists of thousands of problem‐solving traces. We also conduct five expert interviews to show that SeqDynamics enhances domain experts’ understanding of learning behavior sequences and assists them in completing evaluation tasks efficiently.
Are CUIs Just GUIs with Speech Bubbles?

Conversational User Interfaces (CUIs) have become ubiquitous in recent years from mobile assistants like Apple's Siri to standalone products such as the Amazon Echo. However, as we move from established models of Human Computer Interaction (HCI), namely the Graphical User Interface (GUIs) prevalent for several decades to more dynamic, socially aware forms of interaction with technology, namely CUIs, we need to fundamentally rethink how we conduct user research. Simply applying existing methodologies to these new interaction methods will not likely tackle the unique challenges posed by them.
Towards Blockchainizing Land Valuation Certificate Management Procedures in Vietnam

In this paper, we concentrate on the process of managing the land valuation certificate using blockchain in Vietnam. To approach this problem, we base on the e-government application framework published by the Ministry of Information and Communication and Ministry of Natural Resources and Environment to develop a blockchain-based component which will be linked to the framework. Our component has three layers corresponding to three main layers of the architecture. After that, we develop a decentralized application serving the procedures of land valuation certificate management. With our application, certificates are stored and can be traced from the blockchain network. Our component hence helps create the transparency of management processes. We also carry out preliminary experiments using the proposed system to prove its feasibility during test operation processes. Our research also paves the way for applying blockchain technology to the state management field in general.
Fairness and Efficiency in DAG-Based Cryptocurrencies

Bitcoin is a decentralised digital currency that serves as an alternative to existing transaction systems based on an external central authority for security. Although Bitcoin has many desirable properties, one of its fundamental shortcomings is its inability to process transactions at high rates. To address this challenge, many subsequent protocols either modify the rules of block acceptance (longest chain rule) and reward, or alter the graphical structure of the public ledger from a tree to a directed acyclic graph (DAG). Motivated by these approaches, we introduce a new general framework that captures ledger growth for a large class of DAG-based implementations. With this in hand, and by assuming honest miner behaviour, we (experimentally) explore how different DAG-based protocols perform in terms of fairness, as well as efficiency. To do so, we isolate different parameters of the network (such as k, the number of pointers to previous blocks) and study their effect on those performance metrics. Our results demonstrate how the DAG-based ledger protocols described by our framework cope with a high transaction load. More specifically, we show that even in a scenario where every miner on the system is honest in terms of when they publish blocks, what they point to, and what transactions each block contains, fairness and efficiency of this kind of ledgers can break down at specific hash rates if miners have differing levels of connectivity to the P2P network sustaining the protocol. (The full version of this paper can be found in [2]).
Adversarial learning based attentional scene text recognizer

In this paper, we propose an adversarial learning based attentional scene text recognizer to solve the distortion problem of scene text image. We choose a rectification module which can rectify images in both horizontal and vertical directions, and use a recognizer based on the attention mechanism. Through the adversarial learning of the rectification network and the recognition network, we iteratively improve the rectification effect and the recognition performance. The entire network is trained with weak supervision, so only images and corresponding text labels are needed. Our method achieves high performance for both regular and irregular scene text images, and the experimental results tested on multiple benchmarks prove that our method achieves the performance of state-of-the-art.
A Visual Analytics Approach to Facilitate Crime Hotspot Analysis

Abstract Computer‐based technology has played a significant role in crime prevention over the past 30 years, especially with the popularization of spatial databases and crime mapping systems. Police departments frequently use hotspot analysis to identify regions that should be a priority in receiving preventive resources. Practitioners and researchers agree that tracking crime over time and identifying its geographic patterns are vital information for planning efficiently. Frequently, police departments have access to systems that are too complicated and excessively technical, leading to modest usage. By working closely together with domain experts from police agencies of two different countries, we identified and characterized five domain tasks inherent to the hotspot analysis problem and developed SHOC, a visualization tool that strives for simplicity and ease of use in helping users to perform all the domain tasks. SHOC is included in a visual analytics system that allows users without technical expertise to annotate, save, and share analyses. We also demonstrate that our system effectively supports the completion of the domain tasks in two different real‐world case studies.
Flip It or Click It: Equivalent Learning of Vocabulary From Paper, Laptop, and Smartphone Flashcards

Students frequently utilize digital devices and applications to assist studying. Past research has yielded mixed results on their effectiveness, and scant research has compared portable technologies with each other or focused on smartphones specifically. We compared college students’ learning of vocabulary on paper, laptop, and smartphone. Students were randomly assigned to one platform for reviewing vocabulary flashcards, and then they completed a memory quiz and answered questions regarding perceptions of these technologies. Results showed equivalency in students’ learning across platforms, with the only exception being that students spent longer reviewing paper flashcards. Self-reported cognitive load and satisfaction were equal across platforms. Quantitative and qualitative data also suggested that, though students quickly label both laptops and smartphones as helpful for education, they see more educational value in laptops. This research suggests that smartphones present new ways of learning, but care must be taken with how smartphones are utilized in educational settings.
VWorld

The growing development and commercialization of Virtual Reality (VR) allow more children to get access to this technology. VR features a new, more emotional relevant experience with a sense of presence and high interactivity. In this paper, we present VWorld, an immersive VR system designed to boost children's creativity and computational thinking skill. VWorld enables children to create their own virtual world by putting 3D objects on a miniature map, then explore the world and control the chosen objects by constructing program sequences. We present the design and implementation of VWorld system, with the design considerations of children in our VR environment, and conduct the preliminary evaluation and the future plan of the study.
Thai Spelling Correction and Word Normalization on Social Text Using a Two-Stage Pipeline With Neural Contextual Attention

Text correction systems (e.g., spell checkers) have been used to improve the quality of computerized text by detecting and correcting errors. However, the task of performing spelling correction and word normalization (text correction) for Thai social media text has remained largely unexplored. In this paper, we investigated how current text correction systems perform on correcting errors and word variances in Thai social texts and propose a method designed for this task. We have found that currently available Thai text correction systems are insufficiently robust for correcting spelling errors and word variances, while the text correctors designed for English grammatical error correction suffer from overcorrections (text rewrites). Thus, we proposed a neural-based text corrector with a two-stage structure to alleviate issues of overcorrections while exploiting the benefits of a neural Seq2Seq corrector. Our method consists of a neural-based error detector and a Seq2Seq neural error corrector with contextual attention. This novel architecture allows the Seq2Seq network to produce corrections based on both the erroneous text and its context without the need for an end-to-end structure. Our method outperformed all the other evaluated text correction systems. When compared to the second-best result (copy-augmented transformer), our method further reduced the word error rate (WER) from 2.51% to 2.07%, improved the generalized language evaluation understanding (GLEU) score from 0.9409 to 0.9502 on the Thai text correction task, and improved the GLEU score from 0.7409 to 0.7539 on the English spelling correction task.
Is credential stuffing the new phishing?

Sometimes it seems as though, in a moment of weakness or just wishing for an easier life, the average security pro might wish that Clifford Stoll, writer of an (in)famous 1995 Newsweek article titled ‘The Internet? Bah!' (or ‘Why the web won't be Nirvana') had been right. 1 As the title suggests, Stoll contended that, while fun, the Internet was a bubble set to burst.
A Bidirectional Iterative Algorithm for Nested Named Entity Recognition

Nested named entity recognition (NER) is a special case of structured prediction in which annotated sequences can be contained inside each other. It is a challenging and significant problem in natural language processing. In this paper, we propose a novel framework for nested named entity recognition tasks. Our approach is based on a deep learning model which can be called in an iterative way, expanding the set of predicted entity mentions with each subsequent iteration. The proposed framework combines two such models trained to identify named entities in different directions: from general to specific (outside-in), and from specific to general (inside-out). The predictions of both models are then aggregated by a selection policy. We propose and evaluate several selection policies which can be used with our algorithm. Our method does not impose any restrictions on the length of entity mentions, number of entity classes, depth, or structure of the predicted output. The framework has been validated experimentally on four well-known nested named entity recognition datasets: GENIA, NNE, PolEval, and GermEval. The datasets differ in terms of domain (biomedical, news, mixed), language (English, Polish, German), and the structure of nesting (simple, complex). Through extensive tests, we prove that the approach we have proposed outperforms existing methods for nested named entity recognition.
Modeling for Three-Subset Division Property without Unknown Subset

A division property is a generic tool to search for integral distinguishers, and automatic tools such as MILP or SAT/SMT allow us to evaluate the propagation efficiently. In the application to stream ciphers, it enables us to estimate the security of cube attacks theoretically, and it leads to the best key-recovery attacks against well-known stream ciphers. However, it was reported that some of the key-recovery attacks based on the division property degenerate to distinguishing attacks due to the inaccuracy of the division property. Three-subset division property (without unknown subset) is a promising method to solve this inaccuracy problem, and a new algorithm using automatic tools for the three-subset division property was recently proposed at Asiacrypt2019. In this paper, we first show that this state-of-the-art algorithm is not always efficient and we cannot improve the existing key-recovery attacks. Then, we focus on the three-subset division property without unknown subset and propose another new efficient algorithm using automatic tools. Our algorithm is more efficient than existing algorithms, and it can improve existing key-recovery attacks. In the application to Trivium, we show a 842-round key-recovery attack. We also show that a 855-round key-recovery attack, which was proposed at CRYPTO2018, has a critical flaw and does not work. As a result, our 842-round attack becomes the best key-recovery attack. In the application to Grain-128AEAD, we show that the known 184-round key-recovery attack degenerates to a distinguishing attack. Then, the distinguishing attacks are improved up to 189 rounds, and we also show the best key-recovery attack against 190 rounds. In the application to ACORN, we prove that the 772-round key-recovery attack at ISC2019 is in fact a constant-sum distinguisher. We then give new key-recovery attacks mounting to 773-, 774- and 775-round ACORN. We verify the current best key-recovery attack on 892-round Kreyvium and recover the exact superpoly. We further propose a new attack mounting to 893 rounds.
Unsupervised Network Quantization via Fixed-Point Factorization

The deep neural network (DNN) has achieved remarkable performance in a wide range of applications at the cost of huge memory and computational complexity. Fixed-point network quantization emerges as a popular acceleration and compression method but still suffers from huge performance degradation when extremely low-bit quantization is utilized. Moreover, current fixed-point quantization methods rely heavily on supervised retraining using large amounts of the labeled training data, while the labeled data are hard to obtain in the real-world applications. In this article, we propose an efficient framework, namely, fixed-point factorized network (FFN), to turn all weights into ternary values, i.e., {-1, 0, 1}. We highlight that the proposed FFN framework can achieve negligible degradation even without any supervised retraining on the labeled data. Note that the activations can be easily quantized into an 8-bit format; thus, the resulting networks only have low-bit fixed-point additions that are significantly more efficient than 32-bit floating-point multiply-accumulate operations (MACs). Extensive experiments on large-scale ImageNet classification and object detection on MS COCO show that the proposed FFN can achieve about more than 20× compression and remove most of the multiply operations with comparable accuracy. Codes are available on GitHub at https://github.com/wps712/FFN.
A novel method for fast sky conditions identification from global solar radiation measurements

Sky conditions, and the corresponding luminance and radiance distribution patterns are essential to daylighting, energy and thermal environmental studies. The CIE Standard Skies define the overcast, partly cloudy, and clear sky conditions intuitively by rigorous luminance distributions, which are, however, usually determined by sophisticated and uncommon measurements (e.g. radiation in multiple vertical directions). This study proposes a simple approach to identifying the hourly sky conditions from the global horizontal irradiance (EHG) that is most widely accessible in weather stations with basic radiation measurement facilities. The sophisticated measurements are, therefore, avoided for simple applications. The partly cloudy sky, especially, is identified by the notable disparity of its EHG measurement from the theoretical cloudless condition. The proposed approach interprets the ISO/CIE Standard Sky conditions and their diffuse luminance and radiance distributions successfully. From the datasets at two sites with different climates, the approach correctly identifies 7% more sky conditions than a previous work when the direct beam and diffuse radiation measurements were not accessible. The model can thus be essential to solar energy studies for places with ground-based solar radiation measurements only.
On Enabling Attribute-Based Encryption to Be Traceable Against Traitors

Abstract Attribute-based encryption (ABE) is a versatile one-to-many encryption primitive, which enables fine-grained access control over encrypted data. Due to its promising applications in practice, ABE schemes with high efficiency, security and expressivity have been continuously emerging. On the other hand, due to the nature of ABE, a malicious user may abuse its decryption privilege. Therefore, being able to identify such a malicious user is crucial towards the practicality of ABE. Although some specific ABE schemes in the literature enjoys the tracing function, they are only proceeded case by case. Most of the ABE schemes do not support traceability. It is thus meaningful and important to have a generic way of equipping any ABE scheme with traceability. In this work, we partially solve the aforementioned problem. Namely, we propose a way of transforming (non-traceable) ABE schemes satisfying certain requirements to fully collusion-resistant black-box traceable ABE schemes, which adds only $O(\sqrt{\mathcal{K}})$ elements to the ciphertext where ${\mathcal{K}}$ is the number of users in the system. And to demonstrate the practicability of our transformation, we show how to convert a couple of existing non-traceable ABE schemes to support traceability.