Reviewerly/exHarmony huggingface dataset:
Improvement and Application of Deep Belief Network Based on Sparrow Search Algorithm

Deep Belief Network (DBN) is one of the most popular network structures in the field of Deep Learning (DL). It consists of multiple Restricted Boltzmann Machines (RBMs) and an output layer, which gives it a strong feature extraction and non-linear mapping capability through layer-by-layer feature extraction and backward fine-tuning of the BP network. However, it is mainly affected by the number of neuron nodes in the hidden layer. Too few neurons will cause the DBN model to suffer from underfitting problems, and conversely too many neurons will also cause the DBN model to suffer from overfitting, so the performance of the DBN model is difficult to be satisfied directly, leading to some limitations in its application. Thus, this paper proposes an improved DBN model based on the sparrow search algorithm, which can overcome the problem of difficult neuron selection and find the optimal number of neural nodes per layer of the neural network through the sparrow search algorithm, greatly reducing the human debugging time and achieving excellent diagnosis performance. A comparison of the Western Reserve dataset shows that the method proposed in this paper has higher accuracy and more stable model training than the traditional DBN model.
The Evaluation Study of the Deep Learning Model Transformer in Speech Translation

Neural machine translation (NMT) employs the prevailing deep learning techniques to build a single deep neural network (DNN) that directly maps the input speech utterances of one language to the corresponding texts of the other language. Compared with the conventional statistical machine translation, which separately optimizes each component model (such as acoustic models and language models) in series, NMT can learn the used DNN to directly maximize the overall translation performance. In particular, a novel encoder-decoder DNN structure termed transformer, which Google develops, has been applied in NMT and revealed outstanding translation performance. In this study, we investigate and evaluate the Transformer-based speech translation algorithm by varying the model settings in the training process of the used Transformer. The experiments follow a tutorial script provided in the Tensorflow forum, which are conducted on the TED talk dataset to translate Portuguese to English, which consists of 50,000 utterances for training, 1,100 utterances for validation, and 2,000 utterances for testing. The baseline system, which sets the encoding dimension as 128, the number of encoder/decoder layers as 4, the dropout rate as 0.1 and the negative exponent as −1.5, gives rise to 68.01% in translation accuracy. While the encoding dimension is increased to be 512, the translation accuracy can be promoted to be 76.02%. Also, changing the number of layers to be 2, the dropout rate to be 0.01 and the negative exponent to be 1 can achieve 70.98%, 80.97% and 75.40% in translation accuracy, respectively. The experimental results indicate that we can further improve the translation performance of the transformer by properly arranging the underlying hyper-parameters.
Semantic web based platform for the harmonization of teacher education curricula

This paper describes a developed semi-automatic software platform for the harmonization of the informatics curricula at all levels of education. The applied algorithms for matching ontologies are described in detail, as well as the principle of mapping informatics curricula to ontological models. The model of the selected informatics teacher education curriculum from the Republic of Serbia was created and compared to the model of the reference informatics teacher education curriculum using a software platform. The analysis of the results includes a comparison with the data obtained for other possible pairs of the created input ontological models (the secondary school ACM K12 model and the reference model, the secondary school model and the model of the selected curriculum). The research presented in this paper indicates that it is necessary to consider the improvement of teacher education curriculum as well as the application of new matching techniques.
Reply to “Comment on ‘Which Earthquake Accounts Matter?’ by Susan E. Hough and Stacey S. Martin” by David J. Wald

Abstract We thank David Wald (Wald, 2021; henceforth, W21) for his interest in our recent article (Hough and Martin, 2021; henceforth, HM21). Although different perspectives are vital in science, we are concerned that W21 misrepresents HM21 as an oblique criticism of the U.S. Geological Survey “Did You Feel It?” (DYFI) system, calling for HM21 to be retracted. Readers who are interested in the issues raised by HM21 and the statements made by us therein are referred to that article. In this brief reply, we respond to specific accusations made by W21 and return to the focus of HM21, calling attention to the extent to which macroseismic data sets and inferences drawn from them can be shaped by a lack of representation among individuals whose observations are available to science. HM21 never questioned the benefits of the community science DYFI project to science. HM21 noted, however, and we reiterate here, that community science also potentially benefits the community. Whether or not it matters for science, if participation in community science projects is unrepresentative across socioeconomic groups, it underscores the need for the scientific community to be proactive in its efforts to reach out to groups that have been underserved by current outreach and education programs. We appreciate this opportunity to continue the important conversation about representation.
Functional ANOVA based on empirical characteristic functionals

Functional two-sample tests based on empirical characteristic functionals are studied. We consider a test statistic of Cramér–von Mises type with integration over a preselected family of probability measures, say Q, leading to a computationally feasible and powerful test statistic. Small sample properties of the resulting two- and k-sample functional tests are investigated in a simulation study. In particular, we compare the resulting tests to previously proposed tests of equality of mean functions and covariance operators and we show that a proper choice of the probability measure Q gives very good power in detecting shift and scale alternatives.
Augmented Reality Textbook: A Classroom Quasi-Experimental Study

Augmented Reality (AR) has evolved considerably over the last ten years, having been implemented in several areas, including education. Multiple AR systems have been developed and tested in the laboratory dissociated from the reality of the classroom and without considering the pedagogical material used by students and teachers. We developed an AR prototype based on a natural sciences sixth-grade textbook. This prototype allows the use of the traditional textbook without changes, admitting the creation of new layers of virtual information. We describe the comparative analysis of using the augmented textbook versus the textbook without AR. This research was developed with a quasi-experimental study with 50 students in an elementary school. The results show that the AR textbook prototype can have a positive impact on the perception of understanding, the perception of innovation, satisfaction, emotional induction, and student learning. These results, also allow us to generate guidelines that could help to design future augmented textbooks.
Stubbifier: debloating dynamic server-side JavaScript applications

JavaScript is an increasingly popular language for server-side development, thanks in part to the Node.js runtime environment and its vast ecosystem of modules. With the Node.js package manager npm, users are able to easily include external modules as dependencies in their projects. However, npm installs modules with all of their functionality, even if only a fraction is needed, which causes an undue increase in code size. Eliminating this unused functionality from distributions is desirable, but the sound analysis required to find unused code is difficult due to JavaScript's extreme dynamicity. We present a fully automatic technique that identifies unused code by constructing static or dynamic call graphs from the application's tests, and replacing code deemed unreachable with either file- or function-level stubs. Due to JavaScript's highly dynamic nature, call graph construction may suffer from unsoundness, i.e., code identified as unused may in fact be reachable. To handle such cases, if a stub is called, it will fetch and execute the original code on-demand to preserve the application's behavior. The technique also provides an optional guarded execution mode to guard application against injection vulnerabilities in untested code that resulted from stub expansion. This technique is implemented in an open source tool called Stubbifier, designed to help package developers to produce a minimal production distribution. Stubbifier supports the ECMAScript 2019 standard. In an empirical evaluation on 15 Node.js applications and 75 clients of these applications, Stubbifier reduced application size by 56% on average while incurring only minor performance overhead. The evaluation also shows that Stubbifier's guarded execution mode is capable of preventing several known injection vulnerabilities that are manifested in stubbed-out code. Finally, Stubbifier can work alongside bundlers, popular JavaScript tools for bundling an application with its dependencies. For the considered subject applications, we measured an average size reduction of 37% in bundled distributions.
The Advanced Encryption Standard: 20 Years Later

The 20th anniversary of the standardization of the Advanced Encryption Standard (AES), the workhorse of cryptographic algorithms, takes place on 26 November 2021. In this column, we recount the history of the AES and its predecessor, the Data Encryption Standard.
Cargo Drone Airspace Integration in Very Low Level Altitude

Current initiatives to integrate UAS into airspace mostly target small or medium-sized drones. However, it is foreseeable that there will be a demand for cargo UAS with a payload in the range of about 1000 kg. It is proving to be a challenge to integrate these larger drones into a national airspace. The concept of choice for this problem is to use the EASA Specific Category. In this category it is possible that the reliability of the vehicles is not maximised by increasingly complex system components, but that the drones are guided on special trajectories so that any accident that occurs does not result in fatalities and only financial losses. The advantage is that the concept can be realized with existing technology and only moderate effort for new equipment on board and on ground. However, this poses some further challenges to air traffic procedures. In Germany and Europe in general, the airspace is densely occupied, especially near airports. UAS missions should disturb this existing structure as little as possible. An integration concept must therefore ensure that the future air cargo system can strategically or at least tactically avoid any potential risk, regardless of whether it is a conurbation, an industrial infrastructure or other aircraft. We present an integration concept for airspace structure, communication infrastructure and information management. Based on publicly available data on population and ground infrastructure, we present exemplary calculations that show the concept’s feasibility.
Distributed Participatory Design: The challenges of designing with physically disabled musicians during a global pandemic

The global COVID-19 pandemic has been an extraordinary situation. Social distancing has impacted the vast majority of people, reorganising society, physically separating us from friends, family and colleagues. Collectively we found ourselves in a distributed state, reliant upon digital technologies to maintain social and professional connections. Some activities can translate unabated to a digital medium, with benefits, such as the convenience inherent in many online shopping and banking services. Other activities, particularly those which are socially engaged, including inclusive music-making or design, may need to be re-framed and re-thought due to the absence of in-person contact. In Northern Ireland, the Performance Without Barriers (PwB) research group works with disabled artists from the Drake Music Project Northern Ireland (DMNI) to identify ways in which technology can remove access barriers to music-making. Since disabled people are experts in their unique lived experience of disability, they must be involved in the design process, an approach known as participatory design . At the end of 2020, many of us are still adjusting to the new normal , only beginning to understand the impact of distributed digital living. In this article, we examine how the socially engaged work of PwB has been affected, changed and adapted during the pandemic throughout 2019 to 2020, expanding ideas of distributed creativity to the notion of distributed design. The authors formalise the concept of socially engaged distributed participatory design, an approach that classifies PwB’s current research activities in the area of accessible music technology design and improvised musicking. Consideration is given to the impact the notion of ‘distribution’ has on degrees of participation.
On the relation between architectural smells and source code changes

Abstract Although architectural smells are one of the most studied type of architectural technical debt, their impact on maintenance effort has not been thoroughly investigated. Studying this impact would help to understand how much technical debt interest is being paid due to the existence of architecture smells and how this interest can be calculated. This work is a first attempt to address this issue by investigating the relation between architecture smells and source code changes. Specifically, we study whether the frequency and size of changes are correlated with the presence of a selected set of architectural smells. We detect architectural smells using the Arcan tool, which detects architectural smells by building a dependency graph of the system analyzed and then looking for the typical structures of the architectural smells. The findings, based on a case study of 31 open‐source Java systems, show that 87% of the analyzed commits present more changes in artifacts with at least one smell, and the likelihood of changing increases with the number of smells. Moreover, there is also evidence to confirm that change frequency increases after the introduction of a smell and that the size of changes is also larger in smelly artifacts. These findings hold true especially in Medium–Large and Large artifacts.
MixBERT for Image-Ad Relevance Scoring in Advertising

For a good advertising effect, images in the ad should be highly relevant with the ad title. The images in an ad are normally selected from the gallery based on their relevance scores with the ad's title. To ensure the selected images are relevant with the title, a reliable text-image matching model is necessary. The state-of-the-art text- image matching model, cross-modal BERT, only understands the visual content in the image, which is sub-optimal when the image description is available. In this work, we present MixBERT, an adimage relevance scoring model. It models the ad-image relevance by matching the ad title with the image description and visual content. MixBERT adopts a two-stream architecture. It adaptively selects the useful information from noisy image description and suppresses the noise impeding effective matching. To effectively describe the details in visual content of the image, a set of local convolutional features is used as the initial representation of the image. Moreover, to enhance the perceptual capability of our model in key entities which are important to advertising, we upgrade masked language modeling in vanilla BERT to masked key entity modeling. Offline and online experiments demonstrate its effectiveness.
Free-Space Optical Neural Network Based on Optical Nonlinearity and Pooling Operations

Despite various optical realizations of convolutional neural networks (CNNs), optical implementation of nonlinear activation functions and pooling operations are still challenging problems. In this regard, this paper proposes an optical saturable absorption nonlinearity and its atomic-level model, as well as two various optical pooling operations, namely optical average pooling and optical motion pooling, by means of 4f optical correlators. Proposing these optical building blocks not only speed up the neural networks due to negligible optical processing latency, but also facilitate the concatenation of optical convolutional layers with no optoelectrical conversions in-between, as the significant bottlenecks of implementing photonic CNNs. Furthermore, the proposed optical motion pooling layer increases the translation invariance property of CNNs, avoiding the inclusion of all corresponding translated images for the training procedure, and hence, increases the training speed of the neural network. The classification accuracy of the proposed optical convolutional layer is evaluated as the first layer of a customized version of AlexNet architecture, named as OP-AlexNet, for classification of Kaggle Cats and Dog challenge, CIFAR-10, and MNIST datasets, as 83.76%, 72.82%, and 99.25%, respectively, by using optical average pooling.
India’s research contribution to world LIS literature: a study 2011–20

Purpose This study aims to find out the India’s research contribution to world Library and Information Science (LIS) literature from 2011 to 2020. Attempt has been made to reflect both the number of research articles published by the Indian LIS researchers during the past decade and the percentage share of Indian LIS researchers to the world LIS literature. Attempt has also been made to reflect the 20 leading LIS researchers from India, 20 leading LIS journals in which Indian LIS researchers mostly prefer to publish their research results and leading LIS research institutions and more. Design/methodology/approach The scope of the study is limited to library and information research undertaken across the world in general and India in particular during the past decade, viz., 2011–20. To undertake the present study, data were retrieved from SCOPUS data source. The data were retrieved keeping in view the objectives of the study; accordingly, key words were framed and used to search the relevant data. Findings Overall 77,659 research articles were published in the subject discipline of LIS across the world during the past decade, viz., 2011 through 2020. During the period of study, LIS researchers from India contributed 3,562 research articles constituting 4.59% of the total global LIS research output. The USA emerged the leading LIS research country with 33.03% research contribution, while India emerged as the fifth largest LIS research country in the world. A total of 12.85% research articles from India were contributed by 20 leading researchers. Only two LIS research journals from India were found indexed in the SCOPUS, in which 18.10% research articles were published by Indian LIS researchers during the period of study. More than 80% research results produced by Indian LIS researchers were published in foreign LIS research journals, mostly published from the USA and the UK. A good lot of Indian LIS researchers prefer to publish their research results in open access research journals. Library professionals, teachers and scholars from University of Delhi and University of Kashmir were found more active in LIS research in India. Research limitations/implications India is one of the world’s popular LIS academic, research and practicing country and has more responsibility toward world LIS research, whereby India, apart from fulfilling its own LIS research requirements, has to meet out the global LIS research demand. Thereon, if it lacks on research front, which otherwise acts as a backbone in the survival of a subject discipline, the survival of subject discipline may become difficult. Social implications India is one of the most progressive LIS research countries in the world and is about to play a far greater role in propagating newer and modern library practices and services in the years to come. Originality/value Quantification of research output at regional level or country level is very common among LIS professionals; accordingly, the existing study adds to existing world LIS literature about the India’s contribution to world LIS research during the past decade.
Gait-Free Planning for Hexapod Walking Robot

This paper presents a gait-free motion planning approach for quasi-static walking of hexapod walking robots on terrains with limited available footholds. The proposed approach avoids using a prescribed gait pattern allowing an arbitrary sequence of leg swings. Furthermore, it is allowed that some legs do not need to be placed on the terrain for an extended duration. The proposed method is based on a decomposition of the motion planning into: (i) finding a candidate sequence of stances and intermediate configurations representing plausible steps using a graph-search; and (ii) connecting the intermediate configurations by feasible paths satisfying the motion constraints of the walking robot. The individual one-step paths are determined using a Bézier curve-based parametrization that seems to be sufficient for the relatively simple paths of a single step, and the low-capacity parametrization yields natural-looking motion.
Questgram [Qg]: Toward a Mixed-Initiative Quest Generation Tool

Quests are a core element in many games, especially role-playing and adventure games, where quests drive the gameplay and story, engage the player in the game's narrative, and in most cases, act as a bridge between different game elements. The automatic generation of quests and objectives is an interesting challenge since this can extend the lifetime of games such as in Skyrim, or can help create unique experiences such as in AI Dungeon. This work presents Questgram [Qg], a mixed-initiative prototype tool for creating quests using grammars combined in a mixed-initiative level design tool. We evaluated our tool quantitatively by assessing the generated quests and qualitatively through a small user study. Human designers evaluated the system by creating quests manually, automatically, and through mixed-initiative. Our results show the Questgram's potential, which creates diverse, valid, and interesting quests using quest patterns. Likewise, it helps engage designers in the quest design process, fosters their creativity by inspiring them, and enhance the level generation facet of the Evolutionary Dungeon Designer with steps towards intertwining both level and quest design.
Stochastic Gradient Boosting Model for Twitter Spam Detection

In today's world of connectivity there is a huge amount of data than we could imagine. The number of network users are increasing day by day and there are large number of social networks which keeps the users connected all the time. These social networks give the complete independence to the user to post the data either political, commercial or entertainment value. Some data may be sensitive and have a greater impact on the society as a result. The trustworthiness of data is important when it comes to public social networking sites like facebook and twitter. Due to the large user base and its openness there is a huge possibility to spread spam messages in this network. Spam detection is a technique to identify and mark data as a false data value. There are lot of machine learning approaches proposed to detect spam in social networks. The efficiency of any spam detection algorithm is determined by its cost factor and accuracy. Aiming to improve the detection of spam in the social networks this study proposes using statistical based features that are modelled through the supervised boosting approach called Stochastic gradient boosting to evaluate the twitter data sets in the English language. The performance of the proposed model is evaluated using simulation results.