Reviewerly/exHarmony huggingface dataset:
Open Knowledge Graph Link Prediction with Segmented Embedding

Open Knowledge Graph (OpenKG) link prediction is important for using OpenKGs in applications such as question answering and text comprehension. The noun phrases (NPs) and relation phrases in OpenKGs are not canonicalized, making OpenKG link prediction highly challenging. Existing methods addressing this problem infuse canonicalization information into knowledge graph embedding models. However, they still fail to fully exploit the semantics of NPs. First, two different NPs, even referring to the same entity, can carry different versions of information, which has been ignored by previous methods. Second, neighborhood information of NPs in OpenKGs has not been utilized, which contains abundant information for link prediction. Based on these observations, we propose the OpenKG Segmented Embedding (OKGSE) method. Specifically, to fully capture the dissimilarity of NPs belonging to the same cluster, we learn separate parts of embedding for both the NP cluster and NP. Meanwhile, we exploit neighborhood information by integrating graph context into the semantic matching score function. Extensive experiments across four benchmarks show that OKGSE can achieve state-of-the-art performance as well as effectively capture the unique semantics of each NP.
Effect of Requirements Analyst Experience on Elicitation Effectiveness: A Family of Quasi-Experiments

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Context.</i> In software engineering there is a widespread assumption that experience improves requirements analyst effectiveness, although empirical studies demonstrate the opposite. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Aim.</i> Determine whether experience (interviews, eliciting, development, professional) influences requirements elicitation using interviews. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Method.</i> We ran 12 quasi-experiments recruiting 124 subjects in which we measured analyst effectiveness as the number of items (i.e., concepts, rules, processes) correctly elicited. The experimental task was to elicit requirements using the open interview technique followed by the consolidation of the elicited information in domains with which the analysts were and were not familiar. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Results.</i> In unfamiliar domains, interview experience, requirements experience, development experience, and professional experience does not have any relationship with analyst effectiveness. In familiar domains, effectiveness varies depending on the type of experience. Interview experience has a positive effect, whereas professional experience has a moderate negative effect. Requirements experience appears to have a moderately positive effect; however, the statistical power of the analysis is insufficient to be able to confirm this point. Development experience has no effect. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Conclusion.</i> Experience impacts analyst effectiveness differently depending on the problem domain type (familiar, unfamiliar). Generally, experience does not account for all the observed variability in effectiveness, so there are other influential factors.
Machine Learning, Artificial Intelligence and the Future of Work: Impact on HR, Learning and Development Professionals

The world of work is evolving and the COVID- 19 pandemic has accelerated this change. AI&ML is here to stay, and HR professionals must be able to compete in this increasingly technology-dependent workplace where new skills and roles are gaining prominence. In this research we explore how technology is changing the way organisations operate and the effect this is having on employees and the challenges and opportunities that HR face. This chapter is based on key themes that emerged from 3 case studies derived from semi-structured interviews with senior HR practitioners and gives an oversight of current experience and perceptions of the future use of technology from these professionals. The chapter also informs potential knowledge and skills development for delivery to HR practitioners.
Sonar-to-RGB Image Translation for Diver Monitoring in Poor Visibility Environments

This work evaluates a method for generating visuallike images from sonar images using Generative Adversarial Networks (GANs), for the purpose of monitoring technical divers working in low visibility environments. The general goal is to enhance the interpretability of sonar images in order to assist emergency operators that monitor the safety of the divers. To train the models, sonar and visual data were collected over the course of three trials from two different sites, an indoor pool and a lake. We evaluate and compare two different generative models namely, a modified version of pix2pix and vid2vid. Results show that it is possible to recover visual information from sonar data when the camera image is highly disturbed.
VEA: An FPGA-Based Voxel Encoding Accelerator for 3D Object Detection with LiDAR

Voxel-based 3D object detection methods have been applied in various applications such as autonomous driving, robot navigation, and Augmented Reality. However, the sparse and unstructured characteristics of the point cloud and voxels prevent high-performance voxel encoding and usually require generalized platforms, such as CPUs. In this paper, an FPGAbased Voxel Encoding Accelerator (VEA) is proposed, which contains a generalized voxel generator and a feature extender. The generalized voxel generator decouples the point storage and voxel information storage, leading to high-speed voxelization and low memory consumption. The feature extender can efficiently extract the geometric information of the voxels and extend the features of the points. Based on the proposed VEA, an FPGA-based 3D object detection accelerator is implemented, and experimental results show that the proposed VEA can outperform prior studies by 19× faster in voxelization and 1.3×~ 9.6× faster in object detection.
Visual Pursuit Control based on Gaussian Processes with Switched Motion Trajectories*

This paper considers a scenario of pursuing a moving target that may switch behaviors due to external factors in a dynamic environment by motion estimation using visual sensors. First, we present an improved Visual Motion Observer with switched Gaussian Process models for an extended class of target motion profiles. We then propose a pursuit control law with an online method to estimate the switching behavior of the target by the GP model uncertainty. Next, we prove ultimate boundedness of the control and estimation errors for the switch in target behavior with high probability. Finally, a Digital Twin simulation demonstrates the effectiveness of the proposed switching estimation and control law to prove applicability to real world scenarios.
Cognitive Function Tools/Robot Design for Elderly Using Image-Processing

With increasing age, humans who have entered old age (elderly) will experience a decline in cognitive function. Decreased cognitive function can cause a decrease in human ability in terms of attention / sensitivity in seeing an object. If the level of human sensitivity when looking at objects decreases, it will be difficult to distinguish objects that have almost the same shape. Therefore, this study aims to make a robot as a media to help the elderly who have visual cognitive function weaknesses to be able to choose and pick up objects. To select objects, the robot is equipped with an object detection system in the form of YOLO which applies the CNN algorithm.
IMPROVING STUDENTS' WRITING OF REPORT TEXTS USING ISO TEXT MODELING STRATEGY

The teaching of writing in Indonesia is aimed at developing writing communicative competence and spoken as well. Furthermore, the coverage of teaching writing is that the students are expected to be competent in creating functional short texts, monolog texts, and essay texts in the forms of genre-based texts. Due to the importance and lack of writing skills of students, this study was performed to solve the students’ problems in writing and encourage students to actively participate in the teaching-learning process through ISO Text Modeling strategy. The findings of the research revealed that ISO Text Modeling strategy was effective to improve the students’ writing skills, especially sorting, selecting, and organizing information skills, and activate students to participate in the teaching-learning process of writing.&#x0D; Key Words: ISO Text Modeling strategy, writing skills
Ensemble Distillation Based Adaptive Quantization for Supporting Federated Learning in Wireless Networks

Federated learning (FL) has become a promising technique for developing intelligent wireless networks. In traditional FL paradigms, local models are usually required to be homogeneous for aggregation. However, due to heterogeneous models coming with wireless sysTem heterogeneity, it is preferable for user equipments (UEs) to undertake appropriate amount of computing and/or data transmission work based on sysTem constraints. Meanwhile, considerable communication costs are incurred by model training, when a large number of UEs participate in FL and/or the transmitted models are large. Therefore, resource-efficient training schemes for heterogeneous models are essential for enabling FL-based intelligent wireless networks. In this paper, we propose an adaptive quantization scheme based on ensemble distillation (AQeD), to facilitate heterogeneous model training. We first partition and group the participating UEs into clusters, where the local models in specific clusters are homogeneous with different quantization levels. Then we propose an augmented loss function by jointly considering ensemble distillation loss, quantization levels and wireless resources constraints. In AQeD, model aggregations are performed at two levels: model aggregation for individual clusters and distillation loss aggregation for cluster ensembles. Numerical results show that the AQeD scheme can significantly reduce communication costs and training time in comparison with some state-of-the-art solutions.
Mask-Guided Font Style Transfer in Natural Scene

Font style transfer in natural scene is a challenging task, because it is necessary to maintain the content, color char-acteristics and background information of the font in the original scene. In this paper, we propose a mask-based method to change the font style in natural scene pictures. We design a two-stage method to complete this task. Firstly, the font part in the image is extracted through the image segmentation network for font and generate corresponding mask, at the same time, the feature information of the surrounding part of the font is reserved for the later font background fusion part. Next, the font style is converted through the generated mask to realize the transformation from one font to another. We have proved the effectiveness of our method through a series of experiments, and our method can be applied to tasks such as augmented reality.
A competitive analysis of information security investment: The role of hacker attacks

The role of hacker attacks on information security investment and firm profits is fundamental in information security economics, which, however, has not been fully examined yet. This paper builds a duopolistic horizontal differentiation model to study this issue and yields a series of interesting findings. First, we find that each firm may benefit from an increase of the probability of hacker attacks when it gains more from security differentiation and suffers less from investment competition. Second, when market competition becomes fiercer, each firm invests less in information security to maintain security differentiation. Third, each firm may benefit from an increase of consumer security loss because the resulting security investment can lead to a more desirable security differentiation. Finally, an increase of cost coefficient of security investment may be profitable to each firm in spite of an increase of security expenditure. This is because an increase of cost coefficient may facilitate security differentiation through discouraging security investment. Additionally, the model is extended to analytically and numerically check the robustness of the main results.
Blockchain-Based Self-Sovereign Identity System with Attribute-Based Issuance

With the rapid development of blockchain applications, digital identity management systems have started being deployed on decentralized networks. However, the inherent transparency of blockchain technology poses a challenge to privacy-conscious applications. To address this challenge, we adopt a DDH-based oblivious transfer and trust execution environment (TEE) to hide users’ private attributes. Furthermore, we propose a concrete system that includes transferring users’ attributes from a legacy server for verifying and issuing on the blockchain. In verifying protocol, we apply TEE in confidential smart contracts that execute the verification logic privately. Users can control their data and freely compose their identities using verified attributes. We also leverage smart contracts to record the status of attributes to achieve batch revocation of identities. Security analysis and comparison demonstrate that our system achieves privacy protection and is more user-centric in revocation than existing blockchain-based identity systems.
Combined greedy algorithm on construction binary de Bruijn sequence

In this paper we construct one type of de bruijn sequence. First, we modify the prefer-one and prefer-zero algorithm to build one cycle. The other cycles are constructed by modified pure circulating register. Finally, we use cycle joining method to get the full length of the sequence.
Three-Dimensional Copy of a Three-Dimensional Copy: Database of Estampages of Proto-Bulgarian Inscriptions

This article highlights the experience of creating a digital scientific database in the St. Petersburg Branch of the Archive of the Russian Academy of Sciences, which contains information about the collection of estampages of the Russian Archaeological Institute in Constantinople—the only Russian scientific institution that worked outside the Russian Empire in 1894–1914. One of the results of the archaeological excursions of the Institute was a collection of prints from inscriptions, including estampages from Proto-Bulgarian inscriptions of the ninth century; these estampages were made in 1899–1900. An estampage itself is a kind of 3d-copy of the original inscription on the stone, which performs the functions of an insurance and user copy from a stone document; it allows you to save information from the stone even if the monument itself is lost or destroyed. However, the preservation should be ensured also for estampages. Estampages, as paper documents, are also susceptible to destruction. The St. Petersburg archive has developed a unique technique for the restoration of prints, and also implemented a program to create their digital database, which included not only information about the type, condition and content of the document, but also its 3d copy, that allows to carry out all the research work that can be carried out with original estampage, excluding, perhaps, only organoleptic sensations.
Evaluating the Impact of Cryptographic Algorithms on Network Performance

Cryptographic algorithms enable secure data communication over public insecure networks. Though they enhance network security, complex cryptographic operations consume substantial amounts of computing resources, introducing significant network overhead costs. This study aims to find the cryptographic algorithm that can efficiently utilize network resources. The study evaluates three cryptographic algorithms with different file formats on varying numbers of node densities. The NS-3 simulator was used to measure latency, data throughput, end-to-end delay, packet delivery ratio, and packet loss of files in text, image, and audio formats. The results find AES as better than DES and 3DES for a large number of node densities for the three file formats in terms of latency, data throughput, end-to-end delay, and packet delivery ratio. However, DES has the lowest packet loss as AES records the highest packet loss. The findings provide researchers avenues for further research and the practitioners the choice of suitable algorithms based on the overhead performance.
Automation of milky mushroom cultivation and its prospects

Milky Mushrooms are tactful towards their growth environment, The parameters required for creating a healthy growth environment for cultivation of milky mushrooms mainly involve Temperature, Humidity, Soil moisture, Lower light intensity, good air quality. Over the time mushroom production has been affected severely even after keeping track of the mushroom farm by manual inspection throughout days, the owners of the milky mushroom farm still face sudden risk as they fail to identify the drastic change in temperature, humidity, soil moisture level, light intensity, Carbon di oxide level or air quality level, unable to supply the required resources at appropriate time, threat of rodents, unfortunately due to the misunderstandings and assumptions the manual inspections has failed multiple times as the manual inspection isn't consistent nor accurate. In this project, we try to find the convenient solution that could help the Milky Mushroom cultivators by reducing the risk of loss and also simplifies the hard challenges faced due to the need of regular farm visit, this project is the study and design of a monitoring system that has an ability to measure accurate temperature, humidity, soil moisture level, light intensity, air quality and facilitating the supply of resources like water and pesticide at accurate time, also takes care of the farm safety by rodent inspection from large distance. Taking the help of Internet of Things in monitoring the Milky mushroom makes things better and easier by achieving the most suitable environment for Milky mushroom cultivation at appropriate time and this can increase the yield at minimal cost and less efforts.
Less-Last Number Hyperparameter Algorithm for MCMC on Online Scheme: In Updating Hyperparameter Online Gaussian Process

The exciting topic on the Gaussian process is hyper-parameter estimation. The simple and powerful method for the hyper-parameter estimation is Markov Chain Monte Carlo (MCMC). In the internet era, we need to consider the influence of new data coming to hyper-parameter of the Gaussian Process in online manner, updating is needed. Meanwhile, the main problem of running hyper-parameter estimation using MCMC is time-computation. The large size of covariance matrices on the Gaussian process has an expensive time-computing if should be combined to MCMC. We proposed the Less-Last Number Hyper-parameter (LLNH) algorithm for less time computing MCMC. The idea is merging two Markov Chain Monte Carlo (MCMC) sub-posterior algorithm iteratively. The first sub-posterior results recent hyper-parameter estimation. Meanwhile, the second sub-posterior run MCMC for <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$m$</tex> last data points including new data coming iteratively, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$m$</tex> -online sub-data ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$m$</tex> « data size), using the recent hyper-parameter estimation. The merging is to estimates new hyper-parameter estimation. Technically, MCMC runs m-data size on every iteration for less time-computing. Moreover, MCMC is started by the recent estimation to represent the MCMC involving left data. The algorithm is an efficient method for the hyper-parameter estimation of Gaussian process regression on iterative real-time data. It is applicable to the online scheme. The result showed that the LLNH algorithm performs well on hyper-parameter estimation and has less time-computation comparing to the offline MCMC.
Object-Scale Adaptive Optical Flow Estimation Network

Current deep learning methods for optical flow estimation often use spatial feature pyramids to extract image features. To get the correlation between images, they directly compute the cost volume of the obtained image features. In this process, fine object details tend to be ignored. To solve this fundamental problem, an object-scale adaptive optical flow estimation network is proposed, in which multi-scale features are selectively extracted and exploited using our developed feature selectable block (FSB). As a result, we can obtain the multi-scale receptive fields of objects at different scales in the image. To consolidate all image features generated from all scales, a new cost volume generation scheme called multi-scale cost volume generation block (MCVGB) is further proposed to aggregate information from different scales. Extensive experiments conducted on the Sintel and KITTI2015 datasets show that our proposed method can capture fine details of different scale objects with high accuracy and thus deliver superior performance over a number of state-of-the-art methods.
Data Augmentation via Partial Nonlinear Registration for Brain-Age Prediction

Data augmentation techniques that improve the classification and segmentation of natural scenes often do not transfer well to brain imaging data. The conceptually most plausible augmentation technique for biological tissue, elastic deformation, works well on microscopic tissue but is limited on macroscopic structures like the brain, as the majority of mathematically possible elastic deformations of the human brain are anatomically implausible. Here, we characterize the subspace of anatomically plausible deformations for a participant's brain image by nonlinearly registering the image to the brain images of several reference participants. Using the resulting warp fields for data augmentation outperformed both random elastic deformations and the non-augmented baseline in age prediction from T1 brain images.
Study of Speech Recognition System Based on Transformer and Connectionist Temporal Classification Models for Low Resource Language

Sequence-to-sequence methods have been extensively used in end-to-end (E2E) speech processing for recognition, translation, and synthesis work. In speech recognition, the Transformer model, which supports parallel computation and has intrinsic attention, is frequently used nowadays. This technology's primary aspects are its quick learning efficiency and absence of sequential operation, unlike Deep Neural Networks (DNN). This study concentrated on Transformer, an emergent sequential model that excels in applications for natural language processing (NLP) and neural machine translation (NMT) applications. To create a framework for the automated recognition of spoken Hindi utterances, an end-to-end and Transformer based model to understand the phenomenon classification was considered. Hindi is one of several agglutinative languages, and there isn't much information available for speech/voice recognition algorithms. According to several research, the Transformer approach enhances the performance of the system for languages with limited resources. As per the analyses done by us, it was found that the Hindi-based speech recognition system performed better when Transformers were used along with the Connectionist Temporal Classification (CTC) models altogether. Further, when a language model was included, the Word Error Rate (WER) on a clean dataset was at its lowest i.e., 3.2%.