Reviewerly/exHarmony huggingface dataset:
On desynchronised multivariate algorithms of El Gamal type for stable semigroups of affine Cremona group

Families of stable cyclic groups of nonlinear polynomial transformations of affine spaces Kn over general commutative ring K of increasing with n order can be used in the key exchange protocols and related to them El Gamal multivariate cryptosystems. To use high degree of noncommutativity of affine Cremona group correspondents have to modify multivariate El Gamal algorithm via the usage of conjugations for two polynomials of kind gk and g−1 given by key holder (Alice) or giving them as elements of different transformation groups. The idea of hidden tame homomorphism and comlexity of decomposition of polynomial transwormation into word of elements of Cremona semigroup can be used. We suggest usage of new explicit constructions of infinite families of large stable subsemigroups of affine Cremona group of bounded degree as instruments of multivariate key exchange protocols. Recent results on generation of families of stable transformations of small degree and density via technique of symbolic walks on algebraic graphs are observed. Some of them used for the implementation of schemes as above with feasible computational complexity. We consider an example of a new implemented quadratic multivariate cryptosystem based on the above mentioned ideas.
Personal Context Recognition via Skeptical Learning

In personal context recognition many solutions rely on supervised learning that uses sensor data collected from the users' mobile devices. However, the recognition performance is significantly affected by the annotations’ quality. The problem lies in the fact that the annotator in such scenarios is usually the user herself which is not an expert and thus provides a significant amount of incorrect labels, while existing solutions can only tolerate a small fraction of mislabels. Our solution is Skeptical Learning, a framework for interactive machine learning where the machine uses all its available knowledge to check the correctness of its own and the user labeling. This allows to have a uniform confidence measure to be used when a contradiction arises that applies to both the annotator and the machine. The criteria of success is an improvement of the final recognition accuracy with respect to traditional supervised approaches.
Increasing protected data accessibility for age-related cataract research using a semi-automated honest broker

Ophthalmology researchers are becoming increasingly reliant on protected data sets to find new trends and enhance patient care. However, there is an inherent lack of trust in the current healthcare community ecosystem between the data custodians (i.e., health care organizations and hospitals) and data consumers (i.e., researchers and clinicians). This typically results in a manual governance approach that causes slow data accessibility for researchers due to concerns such as ensuring auditability for any authorization of data consumers, and assurance to ensure compliance with health data security standards. In this paper, we address this issue of long-drawn data accessibility by proposing a semi-automated “honest broker” framework that can be implemented in an online health application. The framework establishes trust between the data consumers and the custodians by:&#x0D; 1. improving the eiciency in compliance checking for data consumer requests using a risk assessment technique;&#x0D; 2. incorporating auditability for consumers to access protected data by including a custodian-in-the-loop only when essential; and&#x0D; 3. increasing the speed of large-volume data actions (such as view, copy, modify, and delete) using a popular common data model.&#x0D; Via an ophthalmology case study involving an age-related cataract research use case in a community cloud testbed, we demonstrate how our solution approach can be implemented in practice to improve timely data access and secure computation of protected data for ultimately achieving data-driven eye health insights.
Type Sequence Preserving Heterogeneous Information Network Embedding

Lacking in sequence preserving mechanism, existing heterogeneous information network (HIN) embedding discards the essential type sequence information during embedding. We propose a Type Sequence Preserving HIN Embedding model (SeqHINE) which expands the HIN embedding to sequence level. SeqHINE incorporates the type sequence information via type-aware GRU and preserves representative sequence information by decay function. Abundant experiments show that SeqHINE can outperform state-of-the-art even with 50% less labeled data.
Categorization of Factors Affecting Classification Algorithms Selection

A lot of classification algorithms are available in the area of data mining for solving the same kind of problem with a little guidance for recommending the most appropriate algorithm to use which gives best results for the dataset at hand.As a way of optimizing the chances of recommending the most appropriate classification algorithm for a dataset, this paper focuses on the different factors considered by data miners and researchers in different studies when selecting the classification algorithms that will yield desired knowledge for the dataset at hand.The paper divided the factors affecting classification algorithms recommendation into business and technical factors.The technical factors proposed are measurable and can be exploited by recommendation software tools.
Research on Facial Expression Recognition Algorithm Based on Convolutional Neural Network

A network model for facial expression recognition is designed and named DI-FERNet in this paper. The network uses depth-wise separable convolution, dilated convolution and residual module to build the network structure. This paper uses MTCNN to perform face alignment processing on the pictures in the dataset. A large number of experiments are carried out on the selected expression datasets KDEF and RAF. The test accuracy on KDEF is 97.2% and on the RAF is 77.1%.
Find a Reasonable Ending for Stories: Does Logic Relation Help the Story Cloze Test?

Natural language understanding is a challenging problem that covers a wide range of tasks. While previous methods generally train each task separately, we consider combining the cross-task features to enhance the task performance. In this paper, we incorporate the logic information with the help of the Natural Language Inference (NLI) task to the Story Cloze Test (SCT). Previous work on SCT considered various semantic information, such as sentiment and topic, but lack the logic information between sentences which is an essential element of stories. Thus we propose to extract the logic information during the course of the story to improve the understanding of the whole story. The logic information is modeled with the help of the NLI task. Experimental results prove the strength of the logic information.
Domain Uncertainty Based On Information Theory for Cross-Modal Hash Retrieval

Cross-modal hash retrieval has received considerable interest in the area of deep learning. Here hash codes of data of different modalities are learned where pair-wise loss functions control feature similarity in a shared embedding space. In this paper we improve on feature similarity by using Shannon's information entropy with respect to the modality information that is left in learning superior hash codes. We introduce a novel network for predicting the domain from the learned features while the protagonist network uses a loss function based on Shannon's information entropy to learn to maximize the domain uncertainty and therefore the information content. Additionally, according to the number of common labels between each similar image-text pair, we define a multi-level similarity matrix as supervisory information, which constrains all similar pairs with different weights. We show with extensive experiments that our novel approach to domain uncertainty leads to a cross-modal hash retrieval that outperforms the state-of-the-art.
The Effect of Subject Lines on Open Rates of Email Marketing Messages

This article investigates the effects of email subject lines on the open rate of email marketing messages. Based on previous literature, three different subject lines with distinctive elements of copywriting were developed: a (1) personal, (2) emotion eliciting and (3) short subject line. The open rates of emails with these subject lines were compared to the open rates of emails with a fourth subject line without these copywriting elements, the control group. An A/B test was conducted where an email marketing message sent to a total of 1,409,963 customers of bol.com. Recipients received an email with one out of four possible subject lines. Results show that all emails with subject lines including a distinctive element of copywriting had higher open rates compared to those with a subject line that belonged to the control group. The control group had an open rate of 24.3%, the personal subject line 25.0%, the short subject line 26.9% and the emotion inducing subject line 26.1%. The differences are statistically significant and substantive for bol.com. In conclusion, adjusting a subject line by influencing its content in terms of length, emotional triggers and personalization can all independently lead to a higher open rate of email marketing messages.
Augmenting App Review with App Changelogs: An Approach for App Review Classification

Recent research on the automatic classification of app reviews either focused on grouping app reviews into categories relevant to software evolution, or employed app reviews as the only research data to improve app reviews classification.Although it was reported that app review classification can benefit from supplementing user reviews with the data from other sources, only a few studies employed app changelogs for this purpose.This paper explores how to augment app reviews with changelogs to improve the accuracy and performance of classifying functional and non-functional requirements in app reviews.Specifically, we propose AUG-AC as an approach to extract feature words from app changelogs and construct the augments for app reviews.Next, we designed a series of experiments to evaluate our approach, varying in the length of AC-based augments for app reviews.The results show that AUG-AC outperforms the existing method by using app changelogs as a source of data next to app reviews.
A Multi-Criteria Ranking Algorithm Based on the VIKOR Method for Meta-Search Engines

Ranking of web pages is one of the most important parts of search engines and is, in fact, a process that through it the quality of a page is estimated by the search engine. In this study, a ranking algorithm based on VIKOR multi-criteria decision-making method for Meta-Search Engines (MSEs) was proposed. In this research, the considered MSE first will receive the suggested pages associated with the search term from eight search engines including, Teoma, Google, Yahoo!, AlltheWeb, AltaVista, Wisenut, ODP, MSN. The results, at most 10 first pages are selected from each search engine and creates the initial dataset contains 80 web pages. The proposed parser is then executed on these pages and the eight criteria including the rank of web page in the related search engine, access time, number of repetitions of search terms, positions of search term at the webpage, numbers of media at the webpage, the number of imports in the webpage, the number of incoming links, and the number of outgoing links are extracted from these web pages. Finally, by using the VIKOR method and these extracted criteria, web pages will rank and 10 top results will be provided for the user. To implement the proposed method, JAVA and MATLAB languages are used. In the experiments, the proposed method is implemented for a query and its ranking results have been compared in terms of accuracy with three famous search engine including Google, Yahoo, and MSN. The results of comparisons show that the proposed method offers higher accuracy.
Uncertain Greenhouse Gas Implication in Waste Heat Utilization – A Case Study with a Data Center

Waste heat utilization is shown to have the potential to decrease greenhouse gas emissions globally.The purpose of this case study is to illustrate how the utilization of waste heat to decrease municipal boundary greenhouse gas emissions may increase such emissions within wider boundaries.The case study assesses the utilization of waste heat generated by a data center.In this paper, we analyze the implications within Scopes 1-3 of the Greenhouse Gas Protocol together with attributional and consequential life cycle assessment principals.Only Scope 1 showed negative greenhouse gas emission implications.In order to achieve negative Scope 2 emissions, approximately half of the waste heat would need to be utilized, which is the purpose of further site development.In order for negative Scope 3 emission implications, electricity production changes are needed or local municipal replaceable greenhouse gas emissions would need to be much higher.
An Efficient Approximation for Nakagami-<i>m</i>Quantile Function Based on Generalized Opposition-Based Quantum Salp Swarm Algorithm

With the further research in communication systems, especially in wireless communication systems, a statistical model called Nakagami- m distribution appears to have better performance than other distributions, including Rice and Rayleigh, in explaining received faded envelopes. Therefore, the Nakagami- m quantile function plays an important role in numerical calculations and theoretical analyses for wireless communication systems. However, it is quite difficult to operate numerical calculations and theoretical analyses because Nakagami- m quantile function has no exact closed-form expression. In order to obtain the closed-form expression that is able to fit the curve of Nakagami- m quantile function as well as possible, we adopt the method of curve fitting in this paper. An efficient expression for approximating the Nakagami- m quantile function is proposed first and then a novel heuristic optimization algorithm—generalized opposition-based quantum salp swarm algorithm (GO-QSSA)—which contains quantum computation, intelligence inspired by salp swarm and generalized opposition-based learning strategy in quantum space, to compute the coefficients of the proposed expression. Meanwhile, we compare GO-QSSA with three swarm intelligence algorithms: artificial bee colony algorithm (ABC), particle swarm optimization algorithm (PSO), and salp swarm algorithm (SSA). The comparing simulation results reveal that GO-QSSA owns faster convergence speed than PSO, ABC, and SSA. Moreover, GO-QSSA is capable of computing more accurately than traditional algorithms. In addition, the simulation results show that compared with existing curve-fitting-based methods, the proposed expression decreases the fitting error by roughly one order of magnitude in most cases and even higher in some cases. Our approximation is proved to be simple and efficient.
Research on Complete Coverage Path Planning for Unmanned Surface Vessel

Abstract In order to monitor the water quality in real time, all monitoring points should be covered by Unmanned Surface Vessel (USV). This paper takes the USV as the research object and proposes an improved genetic algorithm to solve the complete coverage path planning (CCPP) problem to ensure that the USV can cover all monitoring points, and minimize the path length, the rate of repeat grid and running time of the algorithm. The feasibility of this solution model is verified by simulation on the platform of Visual Studio.
Sequence-Aware Recommendation with Long-Term and Short-Term Attention Memory Networks

Next item recommendation is an important yet challenging task in real-world applications such as E-commerce. Since people often carry out a series of online shopping activities, in order to predict what a user may purchase next, it is essential to model the user's general taste as well as the sequential correlation between purchases. Existing models combine these two factors directly without considering the dynamic changes of a user's long-term and short-term preferences. Meanwhile, when a purchase session contains multiple items, not all of them have the same impact on the next item to purchase. To address these limitations, we propose a model that introduces hierarchical attention to dynamically balance between general taste (long-term preference) and sequential behavior (short-term preference). To weight individual items in the same session, we design a neural memory network with attention mechanism to learn the dynamic weights. Our model can adapt the embedding of each session as well as the embedding of long-term and short-term preferences. Extensive experiments on three real-world datasets show that our model significantly outperforms state-of-the-art methods based on commonly used evaluation metrics.
Composable and Finite Computational Security of Quantum Message Transmission

Recent research in quantum cryptography has led to the development of schemes that encrypt and authenticate quantum messages with computational security. The security definitions used so far in the literature are asymptotic, game-based, and not known to be composable. We show how to define finite, composable, computational security for secure quantum message transmission. The new definitions do not involve any games or oracles, they are directly operational: a scheme is secure if it transforms an insecure channel and a shared key into an ideal secure channel from Alice to Bob, i.e., one which only allows Eve to block messages and learn their size, but not change them or read them. By modifying the ideal channel to provide Eve with more or less capabilities, one gets an array of different security notions. By design these transformations are composable, resulting in composable security. Crucially, the new definitions are finite. Security does not rely on the asymptotic hardness of a computational problem. Instead, one proves a finite reduction: if an adversary can distinguish the constructed (real) channel from the ideal one (for some fixed security parameters), then she can solve a finite instance of some computational problem. Such a finite statement is needed to make security claims about concrete implementations. We then prove that (slightly modified versions of) protocols proposed in the literature satisfy these composable definitions. And finally, we study the relations between some game-based definitions and our composable ones. In particular, we look at notions of quantum authenticated encryption and $$\mathsf{QCCA2}$$, and show that they suffer from the same issues as their classical counterparts: they exclude certain protocols which are arguably secure.
THE INFLUENCE OF WORK LIFE BALANCE AND ORGANIZATIONAL PRIDE ON JOB SATISFACTION AND ITS IMPACT ON ORGANIZATIONAL CITIZENSHIP BEHAVIOR IN FIVE AND FOUR STAR HOTELS EMPLOYEE

This study aims to analyze the influence of work life balance on organizational citizenship behavior through organizational pride and job satisfaction.This research was conducted on 209 five-star and four-star hotel employees in Malang City with sampling techniques using proportionate stratified random sampling then analyzed using Generalized Structured Component Analysis (GeSCA).The results of this study indicate that work life balance has a positive and significant direct effect on organizational pride and job satisfaction.Organizational pride and job satisfaction also positively and significantly influence organizational citizenship behavior.
Predicting reliability by severity and priority of defects

Quality of software systems is continuing to be an important investigation of software systems. Assessing and predicting quality attributes of object-oriented design are performed by using software metrics, knowing that a good internal structure of software system influences in a great extent its external quality attributes.
Combining VSM and BTM to Improve Requirements Trace Links Generation

Trace links between software artifacts provide available traceability information and in-depth insights for different stakeholders.Unfortunately, establishing trace links is a fallible, tedious, and labor-intensive task.To alleviate these problems, many Information Retrieval (IR) methods, such as Vector Space Model (VSM), Latent Semantic Indexing (LSI) and their variants, have been proposed to establish trace links automatically.In recent years, short-text artifacts (or even lack of documentation) become a new trend as more and more software systems are developed abiding by agile methodologies.It makes the effects of traditional IR-based trace links generation methods even worse.In this paper, Biterm Topic Model (BTM), which is good at dealing with short text, is introduced to solve the problem.A hybrid method combining VSM and BTM is proposed to generate requirements trace links.The empirical experiments conducted on three real and frequently-used datasets indicate that the hybrid method can achieve better performance, and the results can reach the "acceptable level" directly.
Informatian Retrieval for Popular Words in Bahasa Translation of Al Quran and Hadith Bukhori Using Enhance Confix Stripping (ECS) Stemming

This paper discusses other ways that can be used to obtain information about information seeking for popular words in Qur'an and Hadith language translations.Popular words like the example of searching for the word "corruption" will be very difficult to find in translations of the Qur'an and Hadith.Information about popular words in the translation of the Qur'an and Hadith is needed so as to facilitate the search.In the research carried out was to obtain information about popular words first and then find the word synonym.This study uses Information Retrieval Technique which is a tokenizing process, stopword removal and stemming.The stemming method used in this study is ECS (Enhance Confix Stripping).Information retrieval is used to display some of the Qur'an and hadith that relate to the keywords searched for according to certain criteria.The dataset used in this study comes from the translation of the Ministry of Religion of the Republic of Indonesia.
Application and Prospect of Integrated Energy Interoperability Management System Based on Blockchain

In order to effectively promote energy clean production and near consumption, improve the comprehensive energy utilization, economy and stability of the energy system in the whole region, in order to achieve energy conservation and environmental protection, this paper proposes a blockchain integrated energy system framework. Here, the two-way interaction problem between the user and the load aggregator's demand response is introduced, which exists in the process of interoperability of the integrated energy management system. The application model of blockchain technology for integrated energy interaction is analyzed and discussed to solve the interoperability problem in integrated energy systems.