Reviewerly/exHarmony huggingface dataset:
Beyond Accuracy: Behavioral Testing of NLP models with CheckList

Although measuring held-out accuracy has been the primary approach to evaluate generalization, it often overestimates the performance of NLP models, while alternative approaches for evaluating models either focus on individual tasks or on specific behaviors. Inspired by principles of behavioral testing in software engineering, we introduce CheckList, a task-agnostic methodology for testing NLP models. CheckList includes a matrix of general linguistic capabilities and test types that facilitate comprehensive test ideation, as well as a software tool to generate a large and diverse number of test cases quickly. We illustrate the utility of CheckList with tests for three tasks, identifying critical failures in both commercial and state-of-art models. In a user study, a team responsible for a commercial sentiment analysis model found new and actionable bugs in an extensively tested model. In another user study, NLP practitioners with CheckList created twice as many tests, and found almost three times as many bugs as users without it.
Tool for Generating Behavior-Driven Development Test-Cases

Background: Testing using Behavior-Driven Development (BDD) techniques is one of the practices of Agile software development. This technique composes a test-case based on a use case scenario, for web application acceptance tests.Objective: In this study, we developed a tool to generate test case codes from BDD scenario definitions to help and facilitate practitioners to conduct testing.Methods: The generated test case code is made according to the codeception framework format so that it can be directly executed by the tester. The procedure is performed as follows: map the correlation of the language used in BDD (gherkin language) and the code syntax of the test code in the codeception framework, designed the GUIs in such a way that users can easily transform the Use Case Scenario, built the tool so that it can generate test cases codes. Evaluation is done by gathering respondents; ask to run the application and gathering feedback from respondents.Results: This tool can generate a codeception test-case file based on the BDD scenario. Generated test cases can be directly used on codeception tools. The results of the evaluation show that the tools can help entry-level programmers in developing automated tests.Conclusion: The tool can help user especially entry-level programmers to generate BDD test-case and make easy for the users for testing the web applications.
Beyond MeSH: Fine-grained semantic indexing of biomedical literature based on weak supervision

In this work, we propose a method for the automated refinement of subject annotations in biomedical literature at the level of concepts. Semantic indexing and search of biomedical articles in MEDLINE/PubMed are based on semantic subject annotations with MeSH descriptors that may correspond to several related but distinct biomedical concepts. Such semantic annotations do not adhere to the level of detail available in the domain knowledge and may not be sufficient to fulfil the information needs of experts in the domain. To this end, we propose a new method that uses weak supervision to train a concept annotator on the literature available for a particular disease. We test this method on the MeSH descriptors for two diseases: Alzheimer's Disease and Duchenne Muscular Dystrophy. The results indicate that concept-occurrence is a strong heuristic for automated subject annotation refinement and its use as weak supervision can lead to improved concept-level annotations. The fine-grained semantic annotations can enable more precise literature retrieval, sustain the semantic integration of subject annotations with other domain resources and ease the maintenance of consistent subject annotations, as new more detailed entries are added in the MeSH thesaurus over time.
Verbal Linguistic Intelligence of the First-Year Students of Indonesian Education Program: A Case in Reading Subject

&lt;p style="text-align:justify"&gt;This study aimed to describe seven indicators of students’ verbal linguistic intelligence in reading subject. It used a qualitative research method. The subjects of this study were 30 students consisted of 9 male and 21 female students. They took the reading subject in the second semester of the first year. They were given a test of verbal-linguistic intelligence. Seven students were selected to be interviewed because they have verbal-linguistic intelligence and good communication. To find out the validity of the data, the researchers used triangulation of the test results and the results of interviews and triangulation of the second researcher and research assistants. Furthermore, the data were analyzed using the content analysis method which consisted of three steps, they were data reduction, data presentation, and conclusion drawing/verification. The results of the study show that there were seven indicators of verbal-linguistic intelligence of students in reading subject, first, having excellent initial knowledge in mentioning words, second, enjoying wordplay with Scrabble, third, entertaining themselves and other students by playing tongue twisters, fourth, explaining the meaning of the words written and discussed, fifth, having difficulties in mathematics lesson, sixth, their conversation refers to something they have read and heard, and the last, having the ability to write poetry based on personal experience.&lt;/p&gt;
Topic Modeling: How and Why to Use in Management Research

Objective : To exemplify how topic modeling can be used in management research, my objectives are two-fold. First, I introduce topic modeling as a social sciences research tool and map critical published studies in management and other social sciences that employed topic modeling in a proper manner. Second, I illustrate how to do topic modeling by applying topic modeling in an analysis of the last five years of published research in this journal: the Iberoamerican Journal of Strategic Management (IJSM). Methodology : I analyze the last five years (2014 to 2018) of published articles in the IJSM. The sample is 164 articles. The abstracts were subjected to a standard topic modeling text pre-processing routine, generating 1,252 unique tokens. Originality/Relevance : By proposing topic modeling as a valid and opportunistic methodology for analyzing textual data, it can shift the old paradigm that textual data belongs only to the qualitative realm. Furthermore, allowing textual data to be labeled and quantified in a reproducible manner that mitigates (or closely fully eliminates) researcher bias. Main Results : Six topics were generated through Latent Dirichlet Allocation (LDA): Topic 1 – Strategy and Competitive Advantage; Topic 2 – International Business and Top Management Team; Topic 3 – Entrepreneurship; Topic 4 – Learning and Cooperation; Topic 5 – Finance and Strategy; and Topic 6 – Dynamic Capabilities. Theoretical/methodological Contributions : I present the state of the art of the literature published in IJSM and also show how the reader can perform their own topic modeling. The full data and code that was used are available in free open science repositories in Open Science Framework (OSF) and GitHub.
Exploring the role of intrinsic motivation in ISSP compliance: enterprise digital rights management system case

Purpose Employee compliance with information system security policies (ISSPs) has been emphasized as a key factor in protecting information assets against insider threats. Even though previous studies have identified extrinsic factors (in the form of external pressure, rewards and social norms) influencing employee compliance, the functioning of employees' intrinsic motivation has not been clearly analyzed. Thus, the aim of this study is to explore the influence of intrinsic motivations on employees' ISSP compliance. Design/methodology/approach This study follows a survey approach and conducts structural equation modeling using WarpPLS 5.0 to test the research model and hypotheses. The survey respondents are users of an enterprise digital rights management (EDRM) system. Findings The analysis results demonstrate that work impediments, perceived responsibility and self-efficacy significantly influence the intention to comply with ISSP. Additionally, autonomy significantly affects self-efficacy and perceived responsibility. Furthermore, autonomy plays a moderating role in the relationship between work impediment and ISSP compliance intentions. Originality/value This study initiatively explores the effect of intrinsic motivations on ISSP compliance intention of employees for a specific information security system (i.e. the EDRM system). This study clarifies the enabling role of intrinsic motivations in ISSP compliance and helps organizations to understand that employee's self-motivated intention, i.e. autonomy, is an essential factor that achieves a higher level of ISSP compliance in the workplace.
FACTORS INFLUENCING THE EMPLOYEES SERVICE PERFORMANCES IN HOSPITALITY INDUSTRY CASE STUDY AQBA FIVE STARS HOTEL

Amies-The present study aims at understanding and exploring factors that may effect on staffs of the employees in Aqaba hotels.Methodology -The study population consisted of all spectrums of the community interested in tourism in Aqaba city.Findings -The study shows that hotel administrations don't take into consideration the importance of training for new employees to offer the best service.The training courses guide new employees to the best and correct techniques which improve their skills, knowledge, and abilities to wok within one teamwork.The multiple correlation coefficient R=0.652 indicates that there is a positive correlation between the independent variables (training, transformational leadership, teamwork, work environment and performance appraisal) and service performance.The R2 indicated the generalizability of the model.It allows us to generalize the results taken from the respondents to the whole population.In this case it equals 0.425.The results showed that F-ratio for these data is equal to 113.561, which is statistically significant at p<0.05.Contribution : This study offers deeper understanding about the Service Performance in Hospitality Industry Case Study Aqaba Five stars Hotels, to create a balanced sustainable tourism development in Aqaba.
Lempel–Ziv-Like Parsing in Small Space

Lempel–Ziv (LZ77 or, briefly, LZ) is one of the most effective and widely-used compressors for repetitive texts. However, the existing efficient methods computing the exact LZ parsing have to use linear or close to linear space to index the input text during the construction of the parsing, which is prohibitive for long inputs. An alternative is Relative Lempel–Ziv (RLZ), which indexes only a fixed reference sequence, whose size can be controlled. Deriving the reference sequence by sampling the text yields reasonable compression ratios for RLZ, but performance is not always competitive with that of LZ and depends heavily on the similarity of the reference to the text. In this paper we introduce ReLZ, a technique that uses RLZ as a preprocessor to approximate the LZ parsing using little memory. RLZ is first used to produce a sequence of phrases, and these are regarded as metasymbols that are input to LZ for a second-level parsing on a (most often) drastically shorter sequence. This parsing is finally translated into one on the original sequence. We analyze the new scheme and prove that, like LZ, it achieves the kth order empirical entropy compression $$n H_k + o(n\log \sigma )$$ with $$k = o(\log _\sigma n)$$ , where n is the input length and $$\sigma$$ is the alphabet size. In fact, we prove this entropy bound not only for ReLZ but for a wide class of LZ-like encodings. Then, we establish a lower bound on ReLZ approximation ratio showing that the number of phrases in it can be $$\Omega (\log n)$$ times larger than the number of phrases in LZ. Our experiments show that ReLZ is faster than existing alternatives to compute the (exact or approximate) LZ parsing, at the reasonable price of an approximation factor below 2.0 in all tested scenarios, and sometimes below 1.05, to the size of LZ.
The JDDC Corpus: A Large-Scale Multi-Turn Chinese Dialogue Dataset for E-commerce Customer Service

Human conversations are complicated and building a human-like dialogue agent is an extremely challenging task. With the rapid development of deep learning techniques, data-driven models become more and more prevalent which need a huge amount of real conversation data. In this paper, we construct a large-scale real scenario Chinese E-commerce conversation corpus, JDDC, with more than 1 million multi-turn dialogues, 20 million utterances, and 150 million words. The dataset reflects several characteristics of human-human conversations, e.g., goal-driven, and long-term dependency among the context. It also covers various dialogue types including task-oriented, chitchat and question-answering. Extra intent information and three well-annotated challenge sets are also provided. Then, we evaluate several retrieval-based and generative models to provide basic benchmark performance on the JDDC corpus. And we hope JDDC can serve as an effective testbed and benefit the development of fundamental research in dialogue task
FeW: A Lightweight Block Cipher

In this paper, we propose a new lightweight block cipher called FeW 1 which encrypts 64-bit plaintext using key size 80/128 bits and produces 64-bit ciphertext. FeW is a software oriented design with the aim of achieving high efficiency in software based environments. We use a mix of Feistel and generalised Feistel structures (referred as Feistel-M structure hereinafter) to enhance the security of our design against basic cryptanalytic attacks like differential, linear, impossible differential and zero correlation attacks. Security analysis of this scheme proves its strength against present day cryptanalytic attacks.
The Importance of Suppressing Domain Style in Authorship Analysis

The prerequisite of many approaches to authorship analysis is a representation of writing style. But despite decades of research, it still remains unclear to what extent commonly used and widely accepted representations like character trigram frequencies actually represent an author's writing style, in contrast to more domain-specific style components or even topic. We address this shortcoming for the first time in a novel experimental setup of fixed authors but swapped domains between training and testing. With this setup, we reveal that approaches using character trigram features are highly susceptible to favor domain information when applied without attention to domains, suffering drops of up to 55.4 percentage points in classification accuracy under domain swapping. We further propose a new remedy based on domain-adversarial learning and compare it to ones from the literature based on heuristic rules. Both can work well, reducing accuracy losses under domain swapping to 3.6% and 3.9%, respectively.
Decentralized and permission-less green energy certificates with GECKO

Abstract A growing demand in sustainable energy harvested from renewable resources, such as wind or solar power, leads to new challenges in the electricity grid, which in future is also referred to as the smart grid . This also reflects in a more decentralized and diverse energy market. In such a market, prices do not only depend on production and demand, but also the source of energy production influences the price. In this paper, we present a decentralized and permission-less system for issuing, receiving and verifying Green Energy Certificates for kWh Ownership (GECKO) similar to the established Renewable Energy Certificates or Green Tags . These certificates allow to create a market for renewable energy. While the established system is designed for the wholesale market and does not allow for decentralized and permission-less verification, the proposed system is built on a blockchain-based approach and allows for the management and transfer of certificates. In our exemplary use case, Distribution System Operators (DSO) act as certification authority for privately or community owned power plants in regional energy markets. Customers can easily verify the integrity of such certificates without relying on a trusted third party or escrow service.
3D Liver and Tumor Segmentation with CNNs Based on Region and Distance Metrics

Liver and liver tumor segmentation based on abdomen computed tomography (CT) images is an essential step in computer-assisted clinical interventions. However, liver and tumor segmentation remains the difficult issue in the medical image processing field, which is ascribed to the anatomical complexity of the liver and the poor demarcation between the liver and other nearby organs on the image. The existing 3D automatic liver and tumor segmentation algorithms based on full convolutional networks, such as V-net, have utilized the loss functions on the basis of integration (summing) over a segmented region (like Dice or cross-entropy). Unfortunately, the number of foreground and background voxels is usually highly imbalanced in liver and tumor segmentation tasks. This greatly varies the value of regional loss between various segmentation classes, and affects the training stability and effect. In the present study, an improved V-net algorithm was applied for 3D liver and tumor segmentation based on region and distance metrics. The distance metric-based loss function utilized a distance metric of the contour (or shape) space rather than the area. The model was jointly trained by the original regional loss and the three distance-based loss functions (including Boundary (BD) loss, Hausdorff (HD) loss, and Signed Distance Map (SDM) loss) to solve the problem of the highly unbalanced liver and tumor segmentation. Besides, the algorithm was tested in two databases LiTS 2017 (Technical University of Munich, Munich, Germany, 2017) and 3D-IRCADb (Research Institute against Digestive Cancer, Strasbourg Cedex, France, 2009), and the results proved the effectiveness of improvement.
Repackaging LIS professionals and libraries for the fourth industrial revolution

Purpose The purpose of this paper is to discuss the need for African Library Schools to repackage their curriculum to be able to produce graduates that will match up with jobs demand in the fourth industrial revolution (4IR) era. The discussion argued for the review of African Library Schools, identified the technologies that drive the 4IRs which LIS graduates will be working with, if employed, and relevant skills needed for functionality in the era. Design/methodology/approach This viewpoint adopts discussion method with reference to some review of literature to argue the pints. Findings This viewpoint finds out that to be relevant in the 4IR, LIS graduate will have to possess the required skills relevant to function in the 4IR. The skills identified among other are information curation, in-depth research, digital scanning, preservation, cloud data expansion, collaboration, teaching and facilitation, analytical thinking and innovation, active learning and learning strategies. Originality/value This paper is an original idea from the author and has been published or submitted to any platform. This paper is original because it has been able to emphasise that African LIS schools should work closely with the employer to make sure their courses and curriculum are designed in such a way to teach employability skills. Moreover, this paper is important, as it made it clear that there is need to ensure removal of barriers to implementing better assessment and training and providing much-needed funding and resources to support the efforts. LIS graduates or those in training should also work to develop in-demand skills and learn to use tools that reveal their capabilities to their potential employers.
LP-SBA-XACML: Lightweight Semantics Based Scheme Enabling Intelligent Behavior-Aware Privacy for IoT

The broad applicability of Internet of Things (IoT) would truly enable the pervasiveness of smart devices for sensing data. In this context, achieving service personalization requires collecting sensitive data about users. That yields to privacy concerns due to the possibility of abusing the data through unauthorized access. Moreover, IoT devices have limited computing resources, making them difficult to perform heavy protection mechanisms. Despite several existing solutions for privacy protection, they were not designed to run on limited resources in large scale environment. In addition, existing access control solutions, including XACML, are heavy to run on resource constraint devices and lack behavior-based customization of user privacy where users have little to no control over their private data. In this regard, we address the aforementioned problems by proposing LP-SBA-XACML, which embeds an efficient and lightweight semantics-based scheme targeting user privacy and providing efficient policy evaluation. LP-SBA-XACML is a scalable and lightweight solution suitable for the IoT context while preserving the assumptions of XACML. Moreover, an intelligent model for real-time behavior/activity prediction is integrated to systematically customize user&#x2019;s privacy and services. Experiments conducted on synthetic and real-life scenarios demonstrate the feasibility and relevance of our proposed framework within a mobile IoT resource-constrained environment.
Diagrammatic approach for analytical non-Markovian time evolution: Fermi's two-atom problem and causality in waveguide quantum electrodynamics

Non-Markovian time-evolution of quantum systems is a challenging problem, often mitigated by employing numerical methods or making simplifying assumptions. In this work, we address this problem in waveguide QED by developing a diagrammatic approach, which performs fully analytical non-Markovian time evolution of single-photon states. By revisiting Fermi's two atom problem, we tackle the impeding question of whether rotating-wave approximation violates causality in single-photon waveguide QED. Afterward, we introduce and prove the \emph{no upper half-plane poles (no-UHP) theorem}, which connects the poles of scattering parameters to the causality principle. Finally, we visualize the time-delayed coherent quantum feedback mediated by the field, discuss the Markovian limit for microscopically separated qubits where short-distance causality violations occur and the emergence of collective decay rates in this limit. Our diagrammatic approach is the first method to perform exact and analytical non-Markovian time evolution of multi-emitter systems in waveguide QED.