Reviewerly/exHarmony huggingface dataset:
Inception Parallel Attention Network for Small Object Detection in Remote Sensing Images

Small object detection in remote sensing images is a major challenge in the field of computer vision. Most previous methods detect small objects using a multiscale feature fusion approach with the same weights. However, experiments shows that the inter feature maps and the feature map in different scales have different contribution to the network. To further strengthen the effective weights, we proposed an inception parallel attention network (IPAN) that contains three main parallel modules i.e. a multiscale attention module, a contextual attention module, and a channel attention module to perform small object detection in remote sensing images. In addition, The network can extract not only rich multiscale, contextual features and the interdependencies of global features in different channels but also the long-range dependencies of the object to another based on the attention mechanism, which contributes to precise results of small object detections. Experimental results shows that the proposed algorithm significantly improves the detection accuracy especially in complex scenes and/or in the presence of occlusion.
Does our culture influence requirements engineering activities? Vietnamese practitioners’ view

Requirements Engineering (RE) activities require intensive communication among different stakeholders and an understanding of their behaviour in order to accurately identify requirements. As RE requires intensive communication, an individual’s culture might influence RE activities. Thus, cultural influences affecting RE activities might need to be considered to improve RE activities and prevent issues that might affect software project outcomes. In our work, we propose a framework for identifying cultural factors that could influence RE activities. In this paper, we present the preliminary results of a case study conducted with Vietnamese practitioners.
A Survey on Digital Image Steganography and Steganalysis

Currently, there is a dramatic development ion aspects such as the verbal exchange of data, as well as computer-related technology. This trend has attracted growing scholarly attention, with the aspects of data protection also linked to dramatic growth in the interest of many scholars. For more than a decade, improvements have been felt in certain notable fields. They include steganalysis and steganography. Indeed, these attributes are important because they lend support to and gain application in areas such as national security, intelligence, and commercial enterprise. The aim of the current study lies in the enhancement of the attributes of steganalysis and steganography relative to image analysis, from the perspective of the pixel block-based totally adaptive steganography. The study considers several multi-decision decomposition processes and standards governing the steganalysis of images. It is also worth highlighting that the study demonstrates that through the least large beat, the matching process via the steganalysis procedure is capable of and yields superior outcomes relative to message embedment in single pixels. Indeed, the factor of images’ texture distribution does not play a significant role in hampering the aforementioned ability, if any. Overall, it is concluded that compared to flat regions, the attribute of image texture exhibits more structural randomness. Hence, hidden image embedment is more secure when high textures are involved.
FatSeal: An Efficient Lattice-based Signature Algorithm

The lattice-based signature schemes are promising quantum-resistant replacements for classical signature schemes based on number theoretical hard problems. An important approach to construct lattice-based signature is utilizing the Fiat-Shamir transform and rejection sampling techniques. There are two Fiat-Shamir signatures among five lattice signature schemes submitted to the post-quantum project initiated by National Institute of Standards and Technology. One of them is called Dilithium, which is based on Module-Learning-With-Errors (MLWE) problem, it features on its simple design in the signing algorithm by using uniform sampling. The Dilithium is built on the generic lattices, to make the size of public key more compact, Dilithium adopts compression technique. On the other hand, schemes using NTRU lattices outperform schemes using generic lattices in efficiency and parameter sizes. This paper devotes to designing an efficient NTRU variant of Dilithium, by combining the advantage of NTRU and uniform rejection sampling, this scheme enjoys a concise structure and gains performance improvement over other lattice-based Fiat-Shamir signature without using extra compression techniques.
Computation of Absorption Coefficient for Pöschl–Teller Potential in Double Quantum Well Structure for Photodetector Applications

Absorption coefficient due to lowest quantum state of double-well-triple-barrier structure is analytically investigated for Pöschl–Teller potential configuration. Kane-type conduction band nonparabolicity of first order is considered for evaluation of eigenstates for realistic computation, and Lorentzian lineshape is taken into account. Tuning parameters of the Pöschl–Teller geometry is varied along with structural parameters and results are compared with earlier obtained rectangular, parabolic and Gaussian potentials. Corresponding blueshift/redshift is obtained for each specific case, and effect of applied field along the direction of quantum confinement is analyzed compared with the situation when field is absent. Comparative study speaks in favor of the present structure both in terms of higher value of absorption as well as lower linewidth spectrum (wavelength scale). Result speaks in favor of the configuration compared to the idealized potential well for photodetector and sensor applications.
Induced Suffix Sorting

Suffix sorting is one of the most important tasks in string processing. In the past two decades more than 20 suffix sorting algorithms have been proposed. To date, the fastest alternatives to compute the suffix array are based on the induced sorting technique. In this chapter, we present a brief history of suffix sorting, we review important induced sorting algorithms, and we detail algorithms SAIS (Nong et al., 2011) and SACA-K (Nong, 2013), which are the basis of the algorithms introduced in Chaps. 4, 6, and 5.
System Security Engineering and Feature‐based Product Line Engineering: A Productive Marriage

ABSTRACT Product Line Engineering (PLE) is a systems engineering discipline to engineer a product portfolio using variation management techniques to take advantage of the products’ similarities while managing their differences. It has well known cost, quality, and time to delivery improvements compared to single‐system development. Systems Security Engineering (SSE) is a discipline for engineering systems proactively and reactively mitigating vulnerabilities. Do these two engineering disciplines conflict with each other? Or are they compatible, even complementary? This article will discuss the relationship between PLE and SSE and how they work together. Specific topics include managing a product line factory of products in a secure way and implementing cyber resilient frameworks within a product line addressing cyber resiliency commonality and variability in product design, implementation, and migration over time.
Sentiment Analysis of Audio Diary

Natural language processing and sentiment analysis are a major field of research in this era. This paper explores the possibility of combining the presently available sentiment analysis algorithm in determining human emotions. This is done through diary case studies. The method is used on voice recordings of people over the period of one month. The result shows that presently available open-source sentiment analysis algorithms give satisfying results in the majority of cases but exceptional cases must be taken into consideration. Also, for effective sentiment analysis, we must consider the wide spectrum of human emotions and develop the algorithm accordingly. Spyder IDE is used for accessing IPython console to obtain results.
“Paper Wave”, “Paper Pressing” And “Paper Genocide” As Applicable To The Russian Education System

The article considers the metaphors of “paper wave,” “paper pressing” and “paper genocide” as reflecting the social realities of the Russian education system, which are nonetheless poorly understood in sociolinguistics and mostly tabooed within respectable Russian academia and top-management. The relevancy and applicability of these metaphors are substantiated as their criteria, social contexts, and basic connotations are specified. “Paper genocide” is analyzed in journalistic and academic contexts as a term that reproduces the most significant aspects of genocide but with a social and non-criminal meaning. “Paper genocide” helps draw attention to the most acute social and managerial problem, a deadlock within the contemporary Russian education system.
Taming Unstructured Sparsity on GPUs via Latency-Aware Optimization

Neural Networks (NNs) exhibit high redundancy in their parameters so that pruning methods can achieve high compression ratio without accuracy loss. However, the very high sparsity produced by unstructured pruning methods is difficult to be efficiently mapped onto Graphics Processing Units (GPUs) because of its decoding overhead and workload imbalance. With the introduction of Tensor Core, the latest GPUs achieve even higher throughput for the dense neural networks. This makes unstructured neural networks fail to outperform their dense counterparts because they are not currently supported by Tensor Core. To tackle this problem, prior work suggests structured pruning to improve the performance of sparse NNs on GPUs. However, such structured pruning methods have to sacrifice a significant part of sparsity to retain the model accuracy, which limits the speedup on the hardware. In this paper, we observe that the Tensor Core is also able to compute unstructured sparse NNs efficiently. To achieve this goal, we first propose ExTensor, a set of sparse Tensor Core instructions with a variable input matrix tile size. The variable tile size allows a matrix multiplication to be implemented by mixing different types of ExTensor instructions. We build a performance model to estimate the latency of an ExTensor instruction given an operand sparse weight matrix. Based on this model, we propose a heuristic algorithm to find the optimal sequence of the instructions for an ExTensor based kernel to achieve the best performance on the GPU. Experimental results demonstrate that our approach achieves 36% better performance than the state-of-the-art sparse Tensor Core design.
Projector-Based Augmented Reality System for Computer Assisted Orthopaedic Surgery

This paper presents a projector-based augmented reality (AR) system for Computer- Assisted Orthopaedic Surgery (CAOS). After calibration, our AR system allows for projection of not only the virtual model directly on the surface of the target organ to create an augmented reality but also important clinical information such as distance and angular deviations from a surgical plan, which are important for various computer-assisted surgical procedures such as trajectory drilling and fracture reduction. The feasibility and accuracy of the system is experimentally validated on a 3D printed phantom model with pyramid shape, a dry goat bone and an in vitro pig leg. An average projection distance error of 1.03±0.58mm and an average drill alignment error of 1.17±0.43°were found. The results demonstrate the efficacy of the proposed AR system.
Design and Construction of Traditional Fishing Boat in Jepara in the Context of Cultural Ecology: The Implication as Arts Learning Resources

The existence of a traditional fisherman community in Jepara is proven based on the system of equipment or technology of fishing vessels. As part that is familiar with the life of the fishing community, this phenomenon is very interesting as well as important to be studied more deeply in the context of the north coast Javanese sub-culture. Based on that, this study aims to explain: (1) the design of fishing boat construction as a representation of the technology system of traditional fishing communities in Jepara, (2) the ecological aspect of the north coast Javanese sub-culture includes the process of creating traditional fishing boat in Jepara communities, and (3) wisdom potential in traditional ship design as learning resources. This study uses a qualitative approach with case study design. The data is collected through interviews, observations and document reviews, and analyzed by using flow mode. The results of the study will provide an empirical explanation of the construction design and boat creation technology in the ecological perspective of the north coast Javanese sub-culture. Traditional ship design in Jepara is potential as learning resources to create the value of cooperation, openness,Â communication and religious.
Chinese poetry and couplet automatic generation based on self-attention and multi-task neural network model

Poetry and couplets, as a valuable part of human cultural heritage, carry traditional Chinese culture. Auto-generation couplet and poetry writing are challenges for NLP. This paper proposed a new multi-task neural network model for the automatic generation of poetry and couplets. The model used seq2seq encoding and decoding structure, which combined attention mechanism, self-attention mechanism and multi-task learning parameter sharing. The encoding part used two BiLSTM networks to learn the similar characteristics of ancient poems and couplets, one for encoding keywords and the other for encoding generated poems or couplet sentences. The decoding parameters were not shared. It consisted of two LSTM networks which decode the output of ancient poems and couplets, respectively, in order to preserve the different semantic and grammatical features of ancient poems and couplets. Poetry and couplets have many similar characteristics, and multi-task learning can learn more features through related tasks, making the model more generalized. Therefore, we used multi-task model to generate poems and couplets, which is significantly better than single-task model. Also our model introduced a self-attention mechanism to learn the dependency and internal structure of words in sentences. Finally, the effectiveness of the method was verified by automatic and manual evaluations.
Kajian Awal Motivasi Siswa Pada Penerapan Virtual Learning Dalam Pembelajaran Jarak Jauh

ABSTRACT In the field of education specifically in the use of instructional media is strongly influenced by technological developments in the field of informatics. The effectiveness of distance learning that applies the concept of virtual learning needs to be reviewed in its learning system. At present and in the future the learning system is predicted to be applied to the concept of virtual learning including biology subjects that can contain a variety of supporting learning media. In this case the internet is one of the utilization of technology that has been applied in several educational institutions. In improving the quality of virtual learning education has the potential to be used, in this case virtual learning has the potential to support students with other students and students with teachers, and learning materials that are not bound by time space. The purpose of this study is to determine the level of student learning motivation on the application of virtual learning in distance learning. This study is a qualitative study that describes the data obtained through the distribution of student motivation questionnaires and then collected, analyzed, and concluded based on study purposes. Keywords: Virtual Learning, Distance Learning
Methods for building a trusted environment in Unix operating systems based on the implementation of a digital watermark

As a problematic, this article discusses the construction of a trusted computing environment (TCE) based on the introduction of digital watermarks (DW) into the modules of the software product of a Unix-like operating / Linux system (Linux OS). One of the threats faced by an information security operator is the illegal use of a program or its components by unscrupulous competitors as part of "foreign" programs. Thus, we are talking about the joint use of the license key and the DW, which can act as a comprehensive solution for protecting the Linux OS. The above confirms the relevance of creating a methodology for building a trusted environment in Unix-like based on the implementation of a digital watermark. In this paper, the parameters of using the digital watermark, the admissible memory of Unix-like systems are considered.
ROSMI: A Multimodal Corpus for Map-based Instruction-Giving

We present the publicly-available Robot Open Street Map Instructions (ROSMI) corpus: a rich multimodal dataset of map and natural language instruction pairs that was collected via crowdsourcing. The goal of this corpus is to aid in the advancement of state-of-the-art visual-dialogue tasks, including reference resolution and robot-instruction understanding. The domain described here concerns robots and autonomous systems being used for inspection and emergency response. The ROSMI corpus is unique in that it captures interaction grounded in map-based visual stimuli that is both human-readable but also contains rich metadata that is needed to plan and deploy robots and autonomous systems, thus facilitating human-robot teaming.
Mapping Derivative Relationships from BIBFRAME 2.0 to RDA

The mapping from BIBFRAME 2.0 to Resource Description and Access (RDA) is studied focusing on core entities, inherent relationships, and derivative relationships. The proposed mapping rules are evaluated with two gold datasets. Findings indicate that 1) core entities, inherent and derivative relationships may be mapped to RDA, 2) the use of the bf:hasExpression property may cluster bf:Works with the same ideational content and enable their mapping to RDA Works with their Expressions, and 3) cataloging policies have a significant impact on the interoperability between RDA and BIBFRAME datasets. This work complements the investigation of semantic interoperability between the two models previously presented in this journal.
Author Correction: Automated design of a convolutional neural network with multi-scale filters for cost-efficient seismic data classification

An amendment to this paper has been published and can be accessed via a link at the top of the paper.
The effect of Six Sigma approach as a tool for strategic cost management on achieving competitive advantage: the case of Jordanian industrial public shareholding companies

The study aimed at investigating the effect of six sigma criteria on competitive advantage for Jordanian industrial public shareholding companies. Data was collected from a random sample using a questionnaire specially designed for this purpose. Multiple regression analysis was utilised to test the hypotheses using the stepwise approach. The study revealed that Six Sigma criteria as a whole significantly affect the competitive advantage; and each criterion was significant and explained a significant amount of the variance in a competitive advantage. The findings of the study also showed that Jordanian industrial companies apply Six Sigma criteria at an average level, and they are interested in the competitive advantage at an average level.
Artificial Intelligent Based Fall Detection System for Elderly People Using IoT

In general, one of the serious problems faced by elderly people is falling. Sometimes, this fall, not a little can cause death. if a person falls and does not get help within one hour, then the impact that must be borne will be felt up to 6 months later. Therefore, Fall detection for elderly people is a crucial problem which requires the development of modern technology that is easy and practical to use. Besides, the use of the devices do not limit and interfere with the activities of the elderly. This paper proposes a fall detection device which is able to monitor and inform all activities of the elderly people, especially some dominant events that have the impact of falling by utilizing IoT-based technology. It used two sensor to detect falling event including gyroscope and sound. The signals sent by the two sensors are then processed by the microprocessor using the fuzzy PSO algorithm to identify and distinguish between ordinary activities and falling events. PSO is used to optimized the membership functions of the fuzzy in order to improve fuzzy performance in identifying falling event. If the results state that a fall occurs, a notification will be sent to the medical officer at that place via a wi-fi network. To test the reliability of the device that have been made, two performance indices are measured, namely sensitivity and specificity. The experiment results showed that the sensitivity and the specificity of the device were 100% and 100%, respectively when it was located on the chest and the abdomen.
When Push Comes to Ads: Measuring the Rise of (Malicious) Push Advertising

The rapid growth of online advertising has fueled the growth of ad-blocking software, such as new ad-blocking and privacy-oriented browsers or browser extensions. In response, both ad publishers and ad networks are constantly trying to pursue new strategies to keep up their revenues. To this end, ad networks have started to leverage the Web Push technology enabled by modern web browsers. As web push notifications (WPNs) are relatively new, their role in ad delivery has not yet been studied in depth. Furthermore, it is unclear to what extent WPN ads are being abused for malvertising (i.e., to deliver malicious ads). In this paper, we aim to fill this gap. Specifically, we propose a system called PushAdMiner that is dedicated to (1) automatically registering for and collecting a large number of web-based push notifications from publisher websites, (2) finding WPN-based ads among these notifications, and (3) discovering malicious WPN-based ad campaigns.Using PushAdMiner, we collected and analyzed 21,541 WPN messages by visiting thousands of different websites. Among these, our system identified 572 WPN ad campaigns, for a total of 5,143 WPN-based ads that were pushed by a variety of ad networks. Furthermore, we found that 51% of all WPN ads we collected are malicious, and that traditional ad-blockers and URL filters were mostly unable to block them, thus leaving a significant abuse vector unchecked.
Accelerating Discovery Science with an Internet of FAIR Data and Services

Biomedicine has always been a fertile and challenging domain for computational discovery science. Indeed, the existence of millions of scientific articles, thousands of databases, and hundreds of ontologies offer exciting opportunities to mine our collective knowledge, were we not stymied by incompatible formats, incomplete and overlapping vocabularies, confusing licensing policies, and heterogeneous data access points.