Reviewerly/exHarmony huggingface dataset:
How to choose the best QKD network technology: three different satellite based scenarios compared

Quantum cryptography promises unconditionally secure communication. Today, there exists a vast array of different QKD protocols that claim to offer security. However, looking in more detail, many subtleties lead to different security levels, or in the worst-case to no security at all. Here, we introduce the most crucial QKD security properties, ranging from different attack schemes to actual implementation security considerations. We present three different QKD use-cases with different network topologies: one trusted-node-based scenario (BB84 decoy) and two trusted-node-free (entanglement-based BBM92 and twin-field QKD). Using our, in cooperation with the European Space Agency (ESA), in-house developed simulations package, we simulate all relevant performance parameters, including the expected finite secret-key-rate. Furthermore, we assess all simulated QKD protocols’ applicability to satellite-based QKD networks and identify essential technologies. Interestingly, not the single-photon detection modules are the key drivers in terms of secure-key-rate performance, but the optical sending and receiving telescopes.
Enhancing Software Quality Assurance by Using Knowledge Discovery and Bug Prediction Techniques

A defect or bug in the software program is the instance in a software system that does not satisfy a requirement for functionality as stated in the standard specifications or as per the end-user requirements which may not be specified but are reasonable. Most bugs result from defects and failures that exist either in the architecture of a program or its source code or in modules and operating systems used by such programs. The earlier methods are not optimal in the accuracy and performance parameters which can be escalated using advanced statistical and machine learning approaches. Our proposed technique is based on code analytics using feature extraction and key evaluation using statistical approaches for predictive analytics. The projected method is quite capable in conditions of assorted parameters and evaluation factors after multiple simulation attempts. We obtained enhanced results toward the software quality assurance by 85.02% higher than currently available techniques in various areas of software quality assurance domain by use of statistical analysis, which is 11.03% higher than recent models.
The availability of mineral resources

The wealth of nations is based on various resources provided by nature, and an enormous amount of these are found in the Earth’s crust. However, these materials are not homogeneously distributed in the Earth’s crust, with a relatively small part concentrated in enriched deposits: the ores. A material is only a resource if the form and amount of that material in the Earth’s crust make extraction profitable. In this chapter, we show that there is a relation between the average concentration of an element in the Earth’s crust and the extractable amount of that element. We compare different approaches for the determination of the extractable amounts of 36 elements and groups of elements in the Earth’s crust. On this basis, we come to a list of ultimately available resources of the considered elements.
Efficient Construction of Nonlinear Models over Normalized Data

Machine Learning (ML) applications are proliferating in the enterprise. Relational data which are prevalent in enterprise applications are typically normalized; as a result, data has to be denormalized via primary/foreign-key joins to be provided as input to ML algorithms. In this paper, we study the implementation of popular nonlinear ML models, Gaussian Mixture Models (GMM) and Neural Networks (NN), over normalized data addressing both cases of binary and multiway joins over normalized relations. For the case of GMM, we show how it is possible to decompose computation in a systematic way both for binary joins and for multi-way joins to construct mixture models. We demonstrate that by factoring the computation, one can conduct the training of the models much faster compared to other applicable approaches, without any loss in accuracy. For the case of NN, we propose algorithms to train the network taking normalized data as the input. Similarly, we present algorithms that can conduct the training of the network in a factorized way and offer performance advantages. The redundancy introduced by denormalization can be exploited for certain types of activation functions. However, we demonstrate that attempting to explore this redundancy is helpful up to a certain point; exploring redundancy at higher layers of the network will always result in increased costs and is not recommended. We present the results of a thorough experimental evaluation, varying several parameters of the input relations involved and demonstrate that our proposals for the training of GMM and NN yield drastic performance improvements typically starting at 100%, which become increasingly higher as parameters of the underlying data vary, without any loss in accuracy.
Cross-Domain Transfer of Generative Explanations Using Text-to-Text Models

Deep learning models based on the Transformers architecture have achieved impressive state-of-the-art results and even surpassed human-level performance across various natural language processing tasks. However, these models remain opaque and hard to explain due to their vast complexity and size. This limits adoption in highly-regulated domains like medicine and finance, and often there is a lack of trust from non-expert end-users. In this paper, we show that by teaching a model to generate explanations alongside its predictions on a large annotated dataset, we can transfer this capability to a low-resource task in another domain. Our proposed three-step training procedure improves explanation quality by up to 7% and avoids sacrificing classification performance on the downstream task, while at the same time reducing the need for human annotations.
Interactivity, Fairness and Explanations in Recommendations

More and more aspects of our everyday lives are influenced by automated decisions made by systems that statistically analyze traces of our activities. It is thus natural to question whether such systems are trustworthy, particularly given the opaqueness and complexity of their internal workings. In this paper, we present our ongoing work towards a framework that aims to increase trust in machine-generated recommendations by combining ideas from three separate recent research directions, namely explainability, fairness and user interactive visualization. The goal is to enable different stakeholders, with potentially varying levels of background and diverse needs, to query, understand, and fix sources of distrust.
Research on analysis of desert crossing problem based on Dijkstra model

Abstract This article analyzes the optimal solution of the game desert nuggets in different situations. Taking into account the weather, the amount of resources (water and food), the travel conditions and the number of people in the same group, more funds are reserved for reaching the end. According to the requirements of different backgrounds, various models and solutions are proposed. Based on the Dijkstra model and using methods such as greedy algorithm and game theory, run under Linux terminal to find the optimal strategy.
Ceramic image retrieval method based on HSH-SIFT fusion features

The SIFT (Scale Invariant Feature Transform) feature is a descriptor of the key points in the gray-gradient spatial. It cannot distinguish samples with the same SIFT features and the varied color distributions. The paper proposes a novel feature expression, named HSH-SIFT, which firstly extracts the SIFT feature of Value channel in HSV image, secondly calculates the hue-saturation histogram (abbrev. HSH) of the neighborhood of the key point and flatten it to be a 96-D vector, finally attaches the HSH vector to the tail of the corresponding SIFT feature vector to construct a 224-D fusion feature vector. Secondly, it proposes a fusion match strategy to improve the precision of content-based image retrieval, it fuses k-D tree, NNDR strategy, unified perspective transformation, and symmetric test to filter mismatched feature points. Experiments have shown that it significantly effects the precision of ceramic image retrieval and has been applied to ceramic image retrieval application.
Research and Design of Big Data Relevance Analysis System for Land Development Industry Chain

In the land development industry chain, there are a variety of data, such as land transaction data, building sales data, developer data, etc. These data are relatively scattered, difficult to aggregate and share, unable to play the hidden value of the data. This paper presents an improved algorithm for Chinese address segmentation, and based on this algorithm, the entity linking algorithm of building and land is proposed, which correlates a large number of discrete building data with land data, and finally, the entity link algorithm is applied to the big data association analysis system as the service of the association analysis subsystem, and the analysis results are visualized through the client and server. The results show that the system can correlate a large number of isolated building and land, effectively correlate and integrate discrete data, and has good data analysis ability, which provides a strong support for enterprises and users to make decisions.
Movie genres and beyond: An analytical survey of classification techniques

In today's world with a plethora of options available on movie content, it is very important to have models which could classify these large contents based on their genres. Movies are the most widely entertaining source all over the world. Every person has his own taste of movies, some feel action movies are better, some would say romantic. So the online platforms must have the collection of movies according to a particular category i.e. based on genres. This paper has proposed an extensive and exhaustive survey for various models and techniques, which could be applied to solve the challenges involved in performing the genre based classification. This paper summarizes and compares all those techniques by classifying them into four broad domains -through text mining, poster detection, audio and video features of the movies. These domains included various classifying techniques: Bi-directional LSTM, gram layer, scene categorization and CNN respectively. With such an extensive amount of work going in the field in the last not many years, this review paper will help specialists in the field of movie genre classification.
Let’s Summarize Scientific Documents! A Clustering-Based Approach via Citation Context

Scientific documents are getting published at expanding rates and create challenges for the researchers to keep themselves up to date with the new developments. Scientific document summarization solves this problem by providing summaries of essential facts and findings. We propose a novel extractive summarization technique for generating a summary of scientific documents after considering the citation context. The proposed method extracts the scientific document’s relevant sentences with respect to citation text in semantic space by utilizing the word mover’s distance (WMD); further, it clusters the extracted sentences. Moreover, it assigns a rank to cluster of sentences based on different aspects like similarity with the title of the paper, position of the sentence, length of the sentence, and maximum marginal relevance. Finally, sentences are selected from different clusters based on their ranks to form the summary. We conduct our experiments on CL-SciSumm 2016 and CL-SciSumm 2017 data sets. The obtained results are compared with the state-of-the-art techniques. Evaluation results show that our method outperforms others in terms of ROUGE-2, ROUGE-3, and ROUGE-SU4 scores.
A useful tool for constructing linear codes

We introduce and discuss an elementary tool from representation theory of finite groups for constructing linear codes invariant under a given permutation group G. The tool gives theoretical insight as well as a recipe for computations of generator matrices and weight distributions. In some interesting cases a classification of code vectors under the action of G can be obtained. As an explicit example a class of binary codes is studied extensively which is closely related to the class of binary codes associated to triangular graphs. A second explicit application is related to the action of the Mathieu simple group M24 on the set of octads giving many binary codes of length 759 with interesting properties. We also obtain new alternative proofs for several other theorems and construct several new codes invariant under various subgroups of the Conway simple group Co1.
An Efficient Minimal Text Segmentation Method for URL Domain Names

Text segmentation of the URL domain name is a straightforward and convenient method to analyze users’ online behaviors and is crucial to determine their areas of interest. However, the performance of popular word segmentation tools is relatively low due to the unique structure of the website domain name (such as extremely short lengths, irregular names, and no contextual relationship). To address this issue, this paper proposes an efficient minimal text segmentation (EMTS) method for URL domain names to achieve efficient adaptive text mining. We first designed a targeted hierarchical task model to reduce noise interference in minimal texts. We then presented a novel method of integrating conflict game into the two-directional maximum matching algorithm, which can make the words with higher weight and greater probability to be selected, thereby enhancing the accuracy of recognition. Next, Chinese Pinyin and English mapping were embedded in the word segmentation rules. Besides, we incorporated a correction factor that considers the text length into the F1-score to optimize the performance evaluation of text segmentation. The experimental results show that the EMTS yielded around 20 percentage points improvement with other word segmentation tools in terms of accuracy and topic extraction, providing high-quality data for the subsequent text analysis.
SequalsK—A Bidirectional Swift-Kotlin-Transpiler

Developing two separate versions of an app for iOS and Android causes considerable efforts. Therefore, a lot of cross-platform development frameworks are available that are able to produce apps for both platforms out of a single code base. However, there are tradeoffs that are connected with these frameworks, in particular, a high tool dependency. Therefore, we propose to stick with native development but to take advantage of the shrinking gap between both platforms due to the fact that the current programming languages, Swift for iOS and Kotlin for Android, are considerably more alike than their respective predecessors. Relating to the model-view- controller design pattern, we propose to automatically transpile the model part (which includes the business logic) of the app in a bidirectional fashion, i.e., from Kotlin to Swift and vice versa. This way, iOS experts can concentrate on optimizing the user interface (view- and controller-part) for iOS, and Android experts can do the same for Android, but all developers can jointly work on the shared model part: each developer can read, correct, and enhance the source code of the model using their own preferred programming language; the resulting version is then transpiled to an equivalent version in the other language.We present a working prototype of a transpiler-which we call SequalsK-that supports the majority of the important constructs of both languages and is able to generate syntactically and semantically correct Kotlin code out of Swift code and vice versa.In a case study we show that the model part of a board game app can be transpiled in both directions without any limitations. Starting with a working Swift version, the Android version can be derived with little manual effort: the automatically transpiled model part forms 86 percent of the resulting source code.
China–U.S. cyber-crisis management

In the past decade, cyber security issues have led to multiple conflicts between China and the United States, resulting in significant risks and threats to the cooperation of the two countries in cyberspace. Despite early successes and failures, cyber-crisis management is still in its infancy. Challenges, such as the misreading of cyberspace strategic intentions and the ideologization of technology competition, are very real. In the future, the two sides need to work together to seek consensus on the basic principles of crisis management, emphasize the important role of academic exchanges, and take confidence-building measures to lay the foundation for cooperation in cyber-crisis management.
Analysis of Sybil Attacks in Online Social Networks Using SyPy

Sybil attacks in online social networks have crucial security implications. The problem of pernicious activities in these networks, such as Sybil attacks and use of bogus identities, can severely affect the social activities in which users interact. Hence, these types of spiteful accounts must be observed and deactivated. In this paper, we simulate an online social network with an honest region and a Sybil region. This paper mainly focuses on the various parameters in the online social network and scrutinizing the impact of these parameters on Sybil attack. It is equally important to detect the most vulnerable nodes in the network to avert them from malicious users. This paper requires extensive knowledge about graphs and its properties are probability triads, clustering coefficient, degree distribution, etc.
Design of a Big Data Driven Internal Quality Assurance System for the Extracurricular Activities

Currently, the construction of the internal quality assurance system for the extracurricular activities is drawing increasing attention from the academic and educational circles. By analyzing the predicaments, the connections between different elements and value congruence of the internal quality assurance for the extracurricular activities, this paper explores the design of a big data driven closed-loop multi-agent internal quality assurance (MQA) system with universal applicability for the extracurricular activities teaching, thereby providing a whole-new possibility for the university management to have a better grasp of the learning behaviors, the adaptability of teaching models, the reasonableness of teaching processes and the effectiveness of teaching activities under the MQA system.
Storytelling in instant messenger communication. Sequencing a story without turn-taking

Everyday storytelling in private instant messenger communication is surprisingly understudied although it is to be considered a linguistic activity of high importance for the everyday life of the interlocutors in these days. This paper undertakes exploratory analyses of stories typed under the media conditions of instant messaging and outlines the spectrum of adaptation strategies that accompany the transposition from face-to-face to instant messengers. The analyses here focus especially on the interactional techniques of sequencing a story. This focus is due to specific media differences between face-to-face and instant messenger communication which have as one result that the latter lacks the temporal dynamics of turn-taking. The study’s corpus comprises 40 instances of storytelling. The findings show a broad spectrum of possible techniques of telling a story under the specific media conditions of messenger communication: On the one end of the spectrum, one can find techniques clearly borrowed from face-to-face conversation and, on the other end, techniques that exploit the affordances of instant messaging paying special regard to its advantages.
Using Unsupervised Learning for Data-driven Procurement Demand Aggregation

Procurement is an essential operation of every organization regardless of its size or domain. As such, aggregating the demands could lead to better value-for-money due to: (1) lower bulk prices; (2) larger vendor tendering; (3) lower shipping and handling fees; and (4) reduced legal and administration overheads. This paper describes our experience in developing an AI solution for demand aggregation and deploying it in A*STAR, a large governmental research organization in Singapore with procurement expenditure to the scale of hundreds of millions of dollars annually. We formulate the demand aggregation problem using a bipartite graph model depicting the relationship between procured items and target vendors, and show that identifying maximal edge bicliques within that graph would reveal potential demand aggregation patterns. We propose an unsupervised learning methodology for efficiently mining such bicliques using a novel Monte Carlo subspace clustering approach. Based on this, a proof-of-concept prototype was developed and tested with the end users during 2017, and later trialed and iteratively refined, before being rolled out in 2019. The final performance was 71% of past cases transformed into bulk tenders correctly detected by the engine; for new opportunities pointed out by the engine 81% were deemed useful for potential bulk tender contracts in the future. Additionally, per each valid pattern identified, the engine achieved 100% precision (all aggregated purchase orders were correct), and 79% recall (the engine correctly identified 79% of orders that should have been put into the bulk tenders). Overall, the cost savings from the true positive contracts spotted so far are estimated to be S$7 million annually.
FaultEx: Explaining operational changes in terms of design variables in CPS control code

Complexities arising from interaction between multiple inter-connected components in an autonomous cyber-physical system can potentially result in differences in intended and observed operations of a cyber-physical system. To perform fault analysis of a CPS, it is imperative to explain this discrepancy in terms of the components of the cyber-physical system's control code. This paper proposes FaultEx framework that derives a hybrid system representation of the cyber-physical system operation in deployment from the observed input/output traces and matches it with a finite state machine based simplification of the CPS code. Discrepancy in system's operational behavior can then be explained in terms of call conditions as binary or unary operations on input and output variables and status of function call arguments. Usage of FaultEx on two different heavy vehicle braking system emulation platform shows that it can identify new modes and timed transitions in CPS code.
At the Frontiers of Art and IoT: the IoTgo Toolkit as a Probe for Artists

Smart things, such as smart watches and smart-door bells, are becoming part of our daily life. Artists are usually inexperienced of how smart things are designed. Experiencing the design of smart things can trigger their imagination and help them produce novel artwork. This paper reports on workshops with five artists, without prior experience of smart-thing design. At the core of the workshops were the ideation tools of IoTgo, which is a smart-thing design toolkit. The first workshop served to use and co-create parts of the ideation tools of IoTgo for and with artists. The second workshop enabled artists to use the tools so as to ideate novel smart-artwork things, and engage again in the evolution of IoTgo. Results of the workshops are reflected over, and useful lessons are distilled in relation to design toolkits for artists.