Reviewerly/exHarmony huggingface dataset:
No free lunch in linguistics or machine learning: Response to Pater

Pater’s (2019) target article proposes that neural networks will provide theories of learning that generative grammar lacks. We argue that his enthusiasm is premature since the biases of neural networks are largely unknown, and he disregards decades of work on machine learning and learnability. Learning biases form a two-way street: all learners have biases, and those biases constrain the space of learnable grammars in mathematically measurable ways. Analytical methods from the related fields of computational learning theory and grammatical inference allow one to study language learning, neural networks, and linguistics at an appropriate level of abstraction. The only way to satisfy our hunger and to make progress on the science of language learning is to confront these core issues directly
Algorithm for Automatic Detection of Ambiguities from Software Requirements

Software Systems are built by the Software engineers and must ensure that software requirement document (SRS) should be specific. Natural Language is the main representation of Software requirement specification document, because it is the most flexible and easiest way for clients or customers to express their software requirements [2]. However being stated in natural language, software requirement specification document may lead to ambiguities [28]. The main goal of presented work to automatically detection of the different types of ambiguities like Lexical, Syntactic, Syntax and Pragmatic. Then an algorithm is proposed to early detection the different types of ambiguities from software requirement document. Part of Speech (POS) technique and regular expression is used to detect each type of ambiguities. An algorithm presented in this paper have two main goals (1) Automatic detection of different types of ambiguities. (2) Count the total number of each types of ambiguities found and evaluate the percentage of ambiguous and non- ambiguous statements detected from software requirement document. The suggested algorithm can absolutely support the analyst in identifying different kinds of ambiguities in Software requirements specification (SRS) document.
Primer on Intellectual Property Law

Chapter 3 provides a short overview of IP law for those not familiar with it. It introduces the major concepts of patent law, copyright law, trademark law, and design patent law, focusing on internationally agreed upon frameworks and treaties such as the Berne Convention, the Paris Convention, and TRIPS. It also walks readers through fundamental concepts like territoriality, validity, direct and indirect infringement, and remedies.
Mutually reinforcing motion-pose framework for pose invariant action recognition

Action recognition from videos has many potential applications. However, there are many unresolved challenges, such as pose-invariant recognition, robustness to occlusion and others. In this paper, we propose to combine motion of body parts and pose hypothesis generation validated with specific canonical poses observed in a novel mutually reinforcing framework to achieve pose-invariant action recognition. To capture the temporal dynamics of an action, we introduce temporal stick features computed using the stick poses obtained. The combination of pose-invariant kinematic features from motion, pose hypothesis and temporal stick features are used for action recognition, thus forming a mutually reinforcing framework that repeats until the action recognition result converges. The proposed mutual reinforcement framework is capable of handling changes in posture of the person, occlusion and partial view-invariance. We perform experiments on several benchmark datasets which showed the performance of the proposed algorithm and its ability to handle pose variation and occlusion.
Cost of cybercrime jumps 24%

Cybercrime in the UK cost its victims £34.6m in the half-year from April to September 2018 – a jump of 24% over the previous six months, according to figures from the City of London Police, which is the national lead force for fraud.
Scenario development

Key methods discussed in this chapterDouble uncertainty matrix, Mānoa, scenario archetypes, La Prospective, causal layered analysis Connections to other chaptersScenario development connects to various other methods, but in particular to futures analysis (Chapter 10) as it is also regarded as a futures analysis method.It warrants a separate chapter in addition to the other futures analysis methods (described in Chapter 10) because of its importance and widespread use.Scenario development also connects to facilitated dialogues (Chapter 9), serious games (Chapter 12), participatory modelling and planning (Chapter 13), dynamical systems modelling (Chapter 26) and agent-based modelling (Chapter 28).
Towards combining the triple helix concept with competence-based approach of educational management theory

The purpose of this study is to find ways of combining the concept of the triple helix of university-industry-government relationships with competence-based approach of university management theory. The methodological framework is based on a number of theoretical provisions, which integration allowed to substantiate the concept of improving mechanisms of master students' research competencies formation in universities. Correlation analysis with the use of datasets characterising the effectiveness of the mechanisms of research competencies formation was carried out. The approach to improving master students' research competencies, presented in this research, is in line with the world and national trends of development in higher education sphere, which creates assumptions for the shift to the new quality of training competitive staff for regional innovation systems. The obtained research results are of interest to the regional development authorities, because they may be incorporated into management practices to improve the efficiency and competitiveness of regional innovation systems.
Search Engine Optimization with A/B Testing

The search engine optimization, or in short- SEO, is the largest speck of digital marketing, often the hardest thing to learn. Since there are so many individual components that make the large picture of what SEO is, people assume one can never fully learn SEO. While this can be considered true, due to the constant changes in the world of digital marketing, breaking SEO down to its core elements, and understanding them all individually, will help user better grasp the overall concept. In this paper we will learn the basics of search engine optimization and page rankings and this report will explain user how to start doing SEO with Google and provide user the basic SEO framework. Search engine optimization (SEO) is a methodology of strategies, techniques and tactics used to increase the amount of visitors to a website by obtaining a high-ranking placement in the search results page of a search engine (SERP) — including Google, Bing, Yahoo and other search engines.
SCRAPING OF SOCIAL MEDIA DATA USING PYTHON-3 AND PERFORMING DATA ANALYTICS USING MICROSOFT POWER BI.

The manifestation of humanity is driven by fulfillment of desires. These desires are satiated by the society and its resources. But after the advent of social media the societal boundaries have shrunken but desires haven’t, hence the desires are now fulfilled through social media. The aforementioned phenomenon was recognized by the business plutocrats very early and have started to satisfy human desires using social media as a tool. But before satisfying the desires, the businesses needs to identify the specific desires of an individual. The identification of specific desires/needs will help the marketing agencies to develop user specific marketing strategies. These desires are explicitly available through the expressions of sentiments in the social media. The sentiment analysis can provide an insight to the desires of an individual. These patterns and insights helps the businesses to market their product to the right person. The sentiments and expressions can be captured using the scraping technique. The aforesaid points highlight’s the course of study followed by this paper and it is to perform data analytics of the social media data scraped using python.
Auditing Deployed Software Licenses on Cloud using a Secure Loopback Protocol

The rapidly growing cloud-centric focuses among organizations and enterprises have thrown into sharp relief the pressing need to progressively upgrade security within a cloud infrastructure. While a significant portion of threat perception emanates from malicious software that can infiltrate a cloud computing infrastructure, the risk of unlicensed software deployments in infrastructure-as-a-service model can expose an affected cloud service provider to a host of legal and statutory issues. The task of managing software licenses and conducting license validation checks can be an intense activity. The onus of tracking and managing licenses of software that have been deployed on an infrastructure-as-a-service cloud model is on both cloud service provider as well as its customers. However, the business of effectively managing software licenses is contingent upon the ease and use of an effective license auditing management tool to which auditors can connect through a secure conduit. In this paper, we propose a model that would allow cloud service providers to securely track software deployments and tally corresponding usage of appropriate licenses within an infrastructure-as-a-service model. Using the proposed model a cloud service provider can match all software licenses that have been deployed by a customer within the IaaS cloud space allotted. Further, a license auditor can securely connect to cloud service provider’s console and generate reports on license usage and software deployments from any modern browser. At the core of our model, lies a secure loopback connection framework that prevents access to any other web site once a secure connection to the primary console for monitoring and tracking software deployments has been established.
Accounting information systems in the blockchain era

In this paper we analyse the advantages that the application of blockchain technology brings to accounting information systems (AIS), but also highlight the potential issues with its use. We examine the use of blockchain against the background of the historical evolution of AIS and explain the operational fit of this technology in AIS. We then analyse the pros and cons of the highly probable use of blockchain technology in AIS. For this purpose, we review the relevant contributions on this subject in the accounting academic literature to date and classify them into four categories on the basis of their focus: governance, transparency and trust; continuous audit; smart contracts; and roles of accountants and auditors. We also analyse the early reactions of the accounting industry and regulators to this new technological environment.
Rule authoring for vehicle configuration management: an experimental study on graph-based representations

The purpose of this research is to determine whether the implementation of a visual rule authoring mechanism would increase the accuracy and/or consistency of the rules being implemented by conducting a user study. The researchers hypothesised that using the visual method for implementing rules would result in a higher degree of accuracy and consistency among the participants. However, the results showed a decrease in the response accuracy for implementing all of the rules in the system. This is likely due in part to the graphical representation method requiring more training than the standard text-based rule implementation method. The most significant limitation in this study was the difference in familiarity of the participants with their respective rule implementation systems. As a result of this limitation, an additional study was conducted in which a third group of participants were given the same questions, but were provided the visualisations with an extended (five-minute) training period. The results from the third group closely matched the results from the control group that used the spreadsheet-based method. Another major limitation is that both groups of participants implemented the changes by hand rather than through the use of a graphical user interface (GUI) software module, which is how the graphical method could be conducted in a real environment.
External validation of clinical prediction models using big datasets from e-health records or IPD meta-analysis: opportunities and challenges

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Improving the performance of computer-aided diagnosis systems using semi-supervised learning: a survey and analysis

The healthcare sector generates important amount of medical data on a daily basis, several machine learning (ML) methods have been developed and studied in order to usefully exploit this substantial sum of information generated colossally, in a wide range practical data mining applications. Yet, an essential key when it comes to developing a competent computer-aided diagnosis (CAD) system is the supervision of data, made by expert annotators; a labelling process considered as a challenging task; as it is both very time consuming and expensive. This survey paper provides the influence of semi-supervised learning framework as it addresses the scarcity of the supervised data for the development of computer-aided diagnosis systems. The methods used and results obtained are discussed and key findings are highlighted. Further, in the light of this review some directions for future research are given; we present a proposed approach using a semi-supervised technique as a core for the learning of a case-based reasoning (CBR) system in CAD context.
Research on collaborative distance English teaching system based on mobile cloud computing

Aiming at the problems of low success rate and poor robustness of traditional distance English teaching system, this paper proposes a new collaborative distance English teaching system based on mobile cloud computing. The hardware of the system adopts USB-to-JTAG device to download files and adopts reverse principle to upload English teaching resources. The four-wire signal is purified and denoised by phase-locked loop. When the system fails, reset circuit is used to deal with it in time. The system software adopts the structure of the Yangtze River basin, establishes the virtual teaching place and video voice teaching method, and realises the remote synchronous teaching. The experimental results show that the success rate and robustness of distance cooperative English teaching system based on mobile cloud computing are 98.2% and 94.5%, which are significantly higher than traditional methods. This method has good reliability and is more suitable for English teaching system.
Context-Aware Approach for Restaurant Recommender Systems

This chapter addresses the issue of how to effectively use users' historical data in restaurant recommender systems, as opposed to systems, such as FindMe, that only rely on online operations. Towards that end, the authors propose a bias-based SVD method as the underlying recommendation algorithm and test it against the traditional item-based collaborative filtering method on the Entrée restaurant dataset. The results are promising as the obtained root-mean-square-error (RMSE) values reach 0.58 for the SVD and 0.62 for the item-based system. Researchers can extend the transformation from user behaviors to ratings in more application domains other than the restaurant one.
Wearable and non-invasive assistive technologies

In this chapter, we have reviewed the state-of-the-art wearable assistive technologies (AT) for people with severe physical disabilities. ATs play an important role in the lives of these individuals by allowing them to be more independent. More specifically, we have described the concept, architecture, and evaluation of a tongue-operated wireless and wearable AT, known as the Tongue Drive System (TDS), which can infer user's intentions by detecting their volitional tongue movements using an array of magnetic sensors and a small magnetic tracer secured near the tip of the tongue. We have also presented the latest multimodal version of the TDS (mTDS), which incorporates tongue commands, head tracking, and speech recognition into a lightweight wearable headset, to provide its end user with a more effective, intuitive, and reliable computer access tool that can be used in a wide variety of daily living tasks. Clinical trials by individuals with high-level spinal cord injuries (SCIs) have demonstrated that TDS can effectively substitute some of the hands and fingers functions in controlling computer mouse cursor and driving powered wheelchairs with comparable or exceeding performance to some of the existing devices in the market.
Effectiveness Evaluation System and Evaluation Method For Anti-birds Damage Technical Transformation Projects of Overhead Lines

Implementation of technical reform measures for anti-birds damage of overhead lines can effectively reduce the occurrence of power grid accidents, which is an important means to ensure the safety and stability of power grid operation and improve the level of equipment operation. Firstly, this paper sorts out the mechanism of bird damage on overhead lines and common protective measures. Then, an evaluation index system is put forward from four aspects: bird tripping, bird damage outage, damage of bird pecking equipment and reducing unit investment loss. The weight index is introduced into AHP-TOPSIS which is a new method to evaluate the effects of the technical transformation project comprehensively.
Digital Image Forensics Based on CFA Interpolation Feature and Gaussian Mixture Model

According to the characteristics of the color filter array interpolation in a camera, an image splicing forgery detection algorithm based on bi-cubic interpolation and Gaussian mixture model is proposed. The authors make the assumption that the image is acquired using a color filter array, and that tampering removes the artifacts due to a demosaicing algorithm. This article extracts the image features based on the variance of the prediction error and create image feature likelihood map to detect and locate the image tampered areas. The experimental results show that the proposed method can detect and locate the splicing tampering areas precisely. Compared with bi-linear interpolation, this method can reduce the prediction error and improve the detection accuracy.
Unsupervised Training of Automatic Dialogue Systems for Customer Support

Automated dialog systems for customer support have recently become a popular area of research in the field of natural language processing. The traditional (supervised) approach for training the dialog model for customer support includes: (1) building a tree of topics (states), (2) finding query examples for each state, (3) training the classifier. This is an expensive, labor-intensive process. In contrast, we trained our dialog model in an unsupervised way, avoiding the need for labeled corpora. Our dialog model is retrieval-based, and its architecture incorporates a siamese network similar to the Dual Encoder. Similar to other retrieval-based dialog models, our model attempts to find the best response out of provided ones for the current dialog history. The research we performed provided the opportunity to develop a customer service solution - software that acts as an intelligent assistant, suggesting a list of relevant prompts to the operator in real time.
Predictive Analysis and Research Of Python Usage Rate Based on Polynomial Regression Model

Nowadays, more and more people will choose Python to help them accomplish some things, in order to better predict the proportion of Python usage. This paper proposes a polynomial regression analysis model. First, crawl the historical usage data of the python language from the official website; then clean the analysis, use a scatter plot to visualize the relationship between tags and features; then use the training set data to train the polynomial regression Model; Finally, the generalization ability of the model is tested through the test set. After many experiments, it can be known that when the highest number is 9 times, the entire training set score is 0.912862, and the test set score is 0.886600, which achieves a better fitting effect and has a certain practical value, which can be used for popularization.
EDKSAP : Efficient Double-Key Stealth Address Protocol in Blockchain

To achieve user identities protection in transaction, the stealth address is introduced. However, double-key stealth address protocol (DKSAP) requires the receiver to continuously calculate and determine whether it is the real receiver of the transaction until it detected a transaction that matches. In this process, the receiver needs to perform many time-consuming elliptic curve scalar multiplication operations, which limits the application of DKSAP in low-performance devices, such as mobile terminals, IoT devices, etc. This paper proposes an efficient double-key stealth address protocol based on bilinear mapping, EDKSAP. Senders calculate the transaction temporary output address and receivers perform the verification calculation using bilinear mapping, which achieves the purpose of improving computing performance. Finally, we verify the feasibility and applicability of the protocol through experiments. In addition, the experimental results show that the computational performance of EDKSAP is significantly improved compared to DKSAP.
Analytical Study on Bug Triaging Practices

Software bugs are inevitable and fixing these bugs is a difficult and time consuming task. Bug report assignment is the activity of designating a developer who makes source code changes in order to fix the bug. Many bug assignment techniques have been proposed in the existing studies. These studies use different datasets, varied input and evaluation parameters to validate their work. This diversification in bug triaging results in perplexity among researchers. Hence, this paper organizes the work performed in bug triaging in a structured manner. This paper aims to present current state of the art to provide a structured consolidation of bug triaging approaches. The paper has identified six research questions under five dimensions to address the various aspects of bug triaging. 60 articles from 36 venues have been reviewed and categorized in order to organize and substructure existing work in the field of bug report assignment. This study will help researchers to wisely decide the weapons for bug triaging. Also, it will act as a ready reference for the bug triaging practitioners.