Reviewerly/exHarmony huggingface dataset:
App store mining for iterative domain analysis: Combine app descriptions with user reviews

Summary Compared with traditional software, the domain analysis of apps is conducted not only in the early stage of software development to gain knowledge of a particular domain but also runs throughout each iteration of apps to help developers understand evolution trends of the domain for maintaining their competitiveness. In this paper, we propose an approach to analyze app descriptions combined with reviews in App stores automatically and construct a feature‐based domain state model (FDSM) in the form of state machine to support the domain analysis of apps. In FDSM, the domain knowledge up to a certain moment together is defined as a state. Initial state summarizes the high‐level knowledge by gaining topics of app descriptions, whereas each transition is generated based on the information gained within one period of time and describes the change from the current state to the next one. Furthermore, user opinions in reviews are introduced into the model to quantify the value of information for helping developers get key domain knowledge efficiently. To validate the proposed approach, we conducted a series of experiments based on Google Play. The results show that FDSM can provide valuable information for supporting domain analysis, especially in the evolution process of apps.
Multinomial processing trees with response times: Changing speed and accuracy by selectively influencing a vertex

In many experiments a person performs a task, such as identifying a letter, and an experimental factor, such as brightness, is manipulated. Empirically, changing the level of a factor often produces a relation, stochastic dominance, on the response time cumulative distribution functions. Specifically, for levels 1 and 2 of the factor, let H1(t) and H2(t) be the cumulative distribution functions of the correct response time. Then one often finds that for all times t, H1(t) > H2(t). We consider a Multinomial Processing Tree in which arcs have a probability of being selected and require time to be selected. It is natural to consider the effect of a factor on products of probability and time. At levels 1 and 2 of the factor, let π(1) and π(2) be the probability of a correct response. The factor produces weighted stochastic dominance if π(1)H1(t) > π(2)H2(t) for all times t. An experimental factor selectively influences a vertex in a Multinomial Processing Tree if changing the level of the factor changes parameters at a single vertex, leaving all else invariant. We consider conditions under which a factor selectively influencing a vertex in a Multinomial Processing Tree produces weighted stochastic dominance. Our assumptions allow parameters in a Multinomial Processing Tree to vary from trial to trial, and to be correlated through dependence on a common random variable. Further, the same Multinomial Processing Tree need not be used on every trial, there may be a mixture of Multinomial Processing Trees. We demonstrate results of theorems with a simulation.
On Improving the Effectiveness of Adversarial Training

Machine learning models, including neural networks, are vulnerable to adversarial examples, which are adversarial inputs generated from legitimate examples by applying small perturbations to fool machine learning models to misclassify. Algorithms that are used to generate adversarial examples are called adversarial example generation methods. As the state-of-the-art defense approach, adversarial training improves the robustness of machine learning models by augmenting the training data with adversarial examples. However, adversarial training is far from being perfect yet, and a deeper understanding of it is always needed for further improving its effectiveness. In this paper, we propose to investigate two research questions. The first question is: whether Method-Based Ensemble Adversarial Training (MBEAT) could be beneficial, i.e., whether leveraging the adversarial examples generated by multiple methods could help increase the effectiveness of adversarial training. The second question is: whether Round Gap Of Adversarial Training (RGOAT) could exist, i.e., whether a neural network model adversarially trained in one round would not be robust against the adversarial examples further generated from this model itself. We design an adversarial training experimental framework to answer these two research questions. We find that MBEAT is indeed beneficial, indicating that it has some important value in practice. We also find that RGOAT indeed exists, indicating that adversarial training should be an iterative process.
A Résumé Evaluation System Based on Text Mining

This study explored the application of interview robots on recruitment process. By adopting techniques including web crawling, text mining, and natural language processing, this study developed an effective system that matches job candidates with recruiters. The designed system analyzed electronic résumés in Traditional Chinese, on which the words were graded according to the job market on the Internet and implemented with techniques related to big data. The results demonstrated that the designed system identified the current demand on talent-seeking and quickly presented candidate rankings for a specific position, thereby fulfilling the needs of both job-hunting candidates and talent-seeking recruiters.
Force on the lips of a trumpet player

When modeling a brass instrument such as the trumpet, an estimate of the pressure at the player's lips is essential, since the resulting force drives the oscillations of the lips which are needed to produce a musical tone. In most work to date, the calculation of the force on the lips has relied on values of the pressure derived from the Bernoulli equation, even though that relation assumes steady flow in contrast to the pulsating flow caused by vibrations of the lips. This paper uses a quantitative application of the Navier-Stokes equations to calculate the flow through a model of vibrating lips attached to a toy model of the trumpet. The results are used to explore when the Bernoulli equation can and cannot be used. The Bernoulli equation is found to fail badly during significant portions of each oscillation cycle of the lips. The reasons for this breakdown are elucidated.
Defending AUTOSAR Safety Critical Systems Against Code Reuse Attacks

With the emergence of AUTOSAR as the dominant software platform for deeply embedded automotive systems, securing AUTOSAR against software based attacks becomes essential to securing most vehicles. However security countermeasures have to fit within the constraints of automotive systems, such as available system resources, legacy code reuse and sensitivity to cost. Since the primary goal of such systems is to control safety critical vehicle functions, any security countermeasures need to harmonize the safety and security response. In this paper, we investigate the susceptibility of safety critical automotive systems to code reuse attacks that aim to violate the vehicle safety. We perform a case study using the vehicle diagnostics protocol with the aim to bypass the security access checks and execute a safety critical Routine Control service. Then we propose an HSM based monitoring system to detect or prevent such attacks.
Research on investigation and evidence collection of cybercrime Cases

Based on the actual data, cases of cyber ceimes and the author's experience of attending crime investigation, this article analyzes the characteristics and tendency of new cybercrimes, talks about the requirements of cybercrime prevention and control, and shows us the problems of cybercrime investigation for obtaining evidence. The investigation department should improve the ability to investigate and collect evidence from the material evidence to the electronic evidence. There're some problems that must be paid attention in the process of electronic evidence collection. The readers will get to know the techniques and methods of cybercrime scene protection, site investigation and online tracking in the investigation of cybercrime cases. The basic principles of extracting electronic evidence are expounded. The cybercrime investigation process is refined. The model of cybercrime investigation and evidence collection is constructed.
Modal transition system encoding of featured transition systems

Featured transition systems (FTSs) and modal transition systems (MTSs) are two of the most prominent and well-studied formalisms for modeling and analyzing behavioral variability as apparent in software product line engineering. On one hand, it is well-known that for finite behavior FTSs are strictly more expressive than MTSs, essentially due to the inability of MTSs to express logically constrained behavioral variability such as persistently exclusive behaviors. On the other hand, MTSs enjoy many desirable formal properties such as compositionality of semantic refinement and parallel composition. In order to finally consolidate the two formalisms for variability modeling, we establish a rigorous connection between FTSs and MTSs by means of an encoding of one FTS into an equivalent set of multiple MTSs. To this end, we split the structure of an FTS into several MTSs whenever it is necessary to denote exclusive choices that are not expressible in a single MTS. Moreover, extra care is taken when dealing with infinite behavior: loops may have to be unrolled to accumulate FTS path constraints when encoding them into MTSs. We prove our encoding to be semantic-preserving (i.e., the resulting set of MTSs induces, up to bisimulation, the same set of derivable variants as their FTS counterpart) and to commute with modal refinement. We further give an algorithm to calculate a concise representation of a given FTS as a minimal set of MTSs. Finally, we present experimental results gained from applying a tool implementation of our approach to a collection of case studies.
A program for Victory Boogie Woogie

The paintings of Piet Mondrian have been attractive for programmers and researchers writing programs that produce compositions resembling the originals. In earlier work published in Leonardo, a generic framework was proposed, which turned out flexible and could be adapted to generate many different Mondrian types. Until recently, there were hardly any attempts to target Mondrian's Victory Boogie Woogie, which is more difficult than most of the earlier Mondrian types. This article describes an extension of the Leonardo approach by including Victory Boogie Woogie. This work poses different challenges because of its uniqueness, its complexity and the fact that it is unfinished. In the extension, three principles for modelling and programming are used: (1) working with cells, (2) nesting, and (3) object-orientation. The program entered and won a Dutch national competition on programming Victory Boogie Woogie in 2013.
Extraction of Fact Tables from a Relational Database: An Effort to Establish Rules in Denormalization

Relational databases are supported by very well established models. However, some neglected problems can occur with the join operator: semantic mistakes caused by the multiple access path problem and faults when connection traps arise. In this paper we intend to identify and overcome those problems and to establish rules for relational data denormalization. Two denormalization forms are proposed and a case study is presented.
Effect of Phase Errors on a Quantum Control Protocol Using Fast Oscillations

It has been recently shown that fast oscillating control fields can be used to speed up an otherwise slow adiabatic process, making the system always follow an instantaneous eigenvector closely. In applying this method though, one typically assumes perfect phase relations among the control fields. In this work, we discuss the effect of potential static phase errors. We show that the latter can in some cases produce higher fidelities, leading to an unexpected improvement of the method. This is shown numerically and explained via a perturbative expansion of the error produced by the control strategy. When high-precision phase control is accessible, the results suggest that the phases of the control field can be used as free parameters whose optimization can be beneficial for the control protocol.
Towards Safety and Security Co-engineering

The emergence of systems identified as both safety and security critical has motivated research and industry to search for novel approaches to conduct multi-concern engineering (co-engineering). But several aspects and issues have arisen during the process what has limited the advances. Among them, there are the specificities found in concepts, methods and development cycles, the current standalone practices of safety and security, and the lack of consolidated metrics for safety-security assessment. This paper presents synthetic discussions on referred topics along with some suggestions for solutions and perspectives.
Behind Enemy Lines

Data Stream Processing Systems (DSPSs) execute long-running, continuous queries over transient streaming data, often making use of outsourced, third-party computational platforms. However, third-party outsourcing can lead to unwanted violations of data providers' access controls or privacy policies, as data potentially flows through untrusted infrastructure. To address these types of violations, data providers can elect to use stream processing techniques based upon computation-enabling encryption. Unfortunately, this class of solutions can leak information about underlying plaintext values, reduce the possible set of queries that can be executed, and come with detrimental performance overheads. To alleviate the concerns with cryptographically-enforced access controls in DSPSs, we have developed \system, a DSPS that makes use of Intel's Software Guard Extensions (SGX) to protect data being processed on untrusted infrastructure. We show that \system can execute arbitrary queries while leaking no more information than an idealized \baseline system. At the same time, an extensive evaluation shows that the overheads associated with stream processing in \system are comparable to its computation-enabling encryption counterparts for many queries.
Batched Sparse Matrix Multiplication for Accelerating Graph Convolutional Networks

Graph Convolutional Networks (GCNs) are recently getting much attention in bioinformatics and chemoinformatics as a state-of-the-art machine learning approach with high accuracy. GCNs process convolutional operations along with graph structures, and GPUs are used to process enormous operations including sparse-dense matrix multiplication (SpMM) when the graph structure is expressed as an adjacency matrix with sparse matrix format. However, the SpMM operation on small graph, where the number of nodes is tens or hundreds, hardly exploits high parallelism or compute power of GPU. Therefore, SpMM becomes a bottleneck of training and inference in GCNs applications. In order to improve the performance of GCNs applications, we propose new SpMM algorithm especially for small sparse matrix and Batched SpMM, which exploits high parallelism of GPU by processing multiple SpMM operations with single CUDA kernel. To the best of our knowledge, this is the first work of batched approach for SpMM. We evaluated the performance of the GCNs application on TSUBAME3.0 implementing NVIDIA Tesla P100 GPU, and our batched approach shows significant speedups of up to 1.59x and 1.37x in training and inference, respectively.
Active Online Learning for Social Media Analysis to Support Crisis Management

People use social media (SM) to describe and discuss different situations they are involved in, like crises. It is therefore worthwhile to exploit SM contents to support crisis management, in particular by revealing useful and unknown information about the crises in real-time. Hence, we propose a novel active online multiple-prototype classifier, called AOMPC. It identifies relevant data related to a crisis. AOMPC is an online learning algorithm that operates on data streams and which is equipped with active learning mechanisms to actively query the label of ambiguous unlabeled data. The number of queries is controlled by a fixed budget strategy. Typically, AOMPC accommodates partly labeled data streams. AOMPC was evaluated using two types of data: (1) synthetic data and (2) SM data from Twitter related to two crises, Colorado Floods and Australia Bushfires. To provide a thorough evaluation, a whole set of known metrics was used to study the quality of the results. Moreover, a sensitivity analysis was conducted to show the effect of AOMPC's parameters on the accuracy of the results. A comparative study of AOMPC against other available online learning algorithms was performed. The experiments showed very good behavior of AOMPC for dealing with evolving, partly-labeled data streams.
One-shot conclusive multiport quantum dense coding capacities

We introduce a probabilistic version of the one-shot quantum dense coding protocol in both two- and multiport scenarios in which the sender(s) transmit(s) only complete messages unambiguously, to a single receiver, and we refer to it as conclusive quantum dense coding. We derive the relevant form of the mutual information between encoded information and measurement results in the scenario considered. We analyze the corresponding capacities of shared states between two, three, and more qubits, and two qutrits. We identify cases where Pauli and generalized Pauli operators are not sufficient as encoders to attain the optimal one-shot conclusive quantum dense coding capacities. We find that there is a rich connection between the capacities and the bipartite as well as multipartite entanglements of the shared state.
“Like a Robot”: Designing Library Websites for New and Returning Users

The Walter W. Stiern Library conducted a major redesign to conform the library website to modern web standards. While conducting think-aloud usability testing on the new page, we found that our users were disoriented, especially students who had previously used the site or undergone library information literacy instruction. Returning users indicated that the pathway they had been taught as a first-year student was the same pathway they used every time they needed to find research articles. They used the same databases and resources, and the same series of links to get to those resources. As clean, organized, and attractive as we felt the new site was, returning users felt unsettled in their information-seeking process. These findings suggest that as academic libraries forge ahead with new designs, directions, and products in various redesign projects, our websites should help returning users acclimate to new interfaces as much as possible. Further, librarians must consider their approach to information literacy and practice a less prescriptive form of instruction.
VR Macintosh Museum: Case Study of a WebVR Application

Virtual reality (VR) applications have spread in the entertainment sector and are being introduced in other areas. Popularity of VR headsets as well as mobile headsets has created a lot of opportunities for VR applications, which can also be played in web pages thanks to HTML5’s WebVR. We propose a case study of a VR museum of Macintosh computers, which can be played using VR and non-VR interfaces as a web application. Participants are invited to explore the virtual world and interact with historic Apple Macintosh computers in an immersive, virtual world. The system has been implemented using A-Frame, a Mozilla-backed WebVR library that enabled us to create a multiplatform interface. User behavior is recorded in real time leveraging the experience API (xAPI) and may be analyzed both in real time and offline, so we can understand it better. This data, plus feedback received as part of the case study are discussed in the end of this article.
Neural Diverse Abstractive Sentence Compression Generation

In this work, we have contributed a novel abstractive sentence compression model which generates diverse compressed sentence with paraphrase using a neural seq2seq encoder decoder model. We impose several operations in order to generate diverse abstractive compressions at the sentence level which was not addressed in the past research works. Our model jointly improves the information coverage and abstractiveness of the generated sentences. We conduct our experiments on the human-generated abstractive sentence compression datasets and evaluate our system on several newly proposed Machine Translation (MT) evaluation metrics. Our experiments demonstrate that the methods bring significant improvements over the state-of-the-art methods across different metrics.
INTERACTIVE POSSIBILITIES OF E-LEARNING COURSE DEVELOPED ON THE BASIS OF THE MOODLE SYSTEM

The article substantiates the need for the introduction of digital educational innovations in higher school teaching process. Attention is paid to one of important approaches to specialists’ training at higher school - transition to a “blended model” of training, which combines and interpenetrates full-time and e-learning with the opportunity for students to choose time, place, pace and trajectory of studying independently. The innovative advantages of the Moodle virtual learning environment are highlighted. By the example of the author’s e-learning course “Basics of Mathematical Information Processing”, the interactive possibilities of e-learning are considered, which contribute to enhancing higher school students’ independent work.
Risk Assessment Method for Cable System Construction of Long-Span Suspension Bridge Based on Cloud Model

Cable system construction is one of the most risky construction stages of long-span suspension bridges, and a reliable risk assessment is an important means to ensure the construction safety. This study proposes a risk assessment method for cable system construction of suspension bridges based on the cloud model, which can combine randomness and fuzziness of risk information effectively. First, a multilevel evaluation index system is built by disassembling the process of cable system construction. Next, the index weights are calculated by the uncertain analytic hierarchy process (AHP). Then, according to the cloud model, a risk assessment model for cable system construction of the suspension bridge is established by realizing the mutual transformation between qualitative language and quantified data. Finally, an illustrative example concerning the risk of cable system construction of Wuhan Yang-Si-Gang Yangtze River Bridge is provided to demonstrate the feasibility and objectivity of the proposed method.
Big data and cumulation in the social sciences

Research using big data has become popular in the social sciences, raising many new questions. This essay focuses on the question of cumulation, and why the kind of cumulation that is characteristic of social data science is more akin to cumulation in the natural sciences. The reasons for this include how research teams are organized and how they compete to exploit certain data sets to improve upon the work of other teams. There are other factors, however, that mitigate against cumulation, including the lack of access to certain datasets and a lack of building on existing findings in the social sciences. Some of these factors pertain to fundamental philosophical issues in social science, including new ideas about the workings of causal explanation. Others relate to the collaboration or absence of collaboration between different disciplines and to the difference between more applied and more academic research. The essay reviews these factors and develops an account of cumulation anchored in a realist philosophy of science and in the practices and tasks of social science research. It concludes with a call for big data research to be more integrated with already ongoing cumulative findings in the social sciences while recognizing that there are several obstacles to such an integration.