Reviewerly/exHarmony huggingface dataset:
On the Intrinsic and Extrinsic Fairness Evaluation Metrics for Contextualized Language Representations

Multiple metrics have been introduced to measure fairness in various natural language processing tasks.These metrics can be roughly categorized into two categories: 1) extrinsic metrics for evaluating fairness in downstream applications and 2) intrinsic metrics for estimating fairness in upstream contextualized language representation models.In this paper, we conduct an extensive correlation study between intrinsic and extrinsic metrics across bias notions using 19 contextualized language models.We find that intrinsic and extrinsic metrics do not necessarily correlate in their original setting, even when correcting for metric misalignments, noise in evaluation datasets, and confounding factors such as experiment configuration for extrinsic metrics.
StyleCLIPDraw: Coupling Content and Style in Text-to-Drawing Translation

Generating images that fit a given text description using machine learning has improved greatly with the release of technologies such as the CLIP image-text encoder model; however, current methods lack artistic control of the style of image to be generated. We present an approach for generating styled drawings for a given text description where a user can specify a desired drawing style using a sample image. Inspired by a theory in art that style and content are generally inseparable during the creative process, we propose a coupled approach, known here as StyleCLIPDraw, whereby the drawing is generated by optimizing for style and content simultaneously throughout the process as opposed to applying style transfer after creating content in a sequence. Based on human evaluation, the styles of images generated by StyleCLIPDraw are strongly preferred to those by the sequential approach. Although the quality of content generation degrades for certain styles, overall considering both content and style, StyleCLIPDraw is found far more preferred, indicating the importance of style, look, and feel of machine generated images to people as well as indicating that style is coupled in the drawing process itself. Our code, a demonstration, and style evaluation data are publicly available.
Presupposition Triggers of Teacher Talk in Higher Education

The present study is aimed at investigating the way of presupposition triggers realized in the lecturer talk. This qualitative research is conducted in English class of the State Institute for Islamic Studies (IAIN) Salatiga by purposive sampling. The present study serves reading class recordings as the data. Reading class was chosen for the reason of similar process in making prediction on reading, i.e. schemata theory, with presumption on presupposition. It is equipped by observation and documentation in order to enhance confidence in the ensuing findings. The finding shows that presupposition triggers realized by the several categories; they are definite description, factive verb, implicative verb, change of state verb, iterative, verb of judging, temporal clause, comparison and contrast, non-restrictive relative clause, and question. The most frequent used category is definite description, with the simple structure of possessive construction and definite noun phrase. Another important result is the preference to use question as the next favored triggers.
Probabilistic shaped 128-APSK CV-QKD transmission system over optical fibres

In this Letter we present a discrete modulated, continuous variables quantum key distribution implementation using two probabilistically shaped, 128-symbol, amplitude and phase shift keying constellations. At Bob's detection side, a polarization diverse, true heterodyne receiver architecture is implemented for symbol recovery. We demonstrate experimentally that our system is capable of achieving security against collective attacks, while using accessible, telecom-grade material, and of functioning for an indefinitely long period of time at distances in excess of 185 km, in the asymptotic regime.
Spatial–Spectral Cross-Correlation Embedded Dual-Transfer Network for Object Tracking Using Hyperspectral Videos

Hyperspectral (HS) videos can describe objects at the material level due to their rich spectral bands, which are more conducive to object tracking compared with color videos. However, the existing HS object trackers cannot make good use of deep-learning models to mine their semantic information due to limited annotation data samples. Moreover, the high-dimensional characteristics of HS videos makes the training of a deep-learning model challenging. To address the above problems, this paper proposes a spatial–spectral cross-correlation embedded dual-transfer network (SSDT-Net). Specifically, first, we propose to use transfer learning to transfer the knowledge of traditional color videos to the HS tracking task and develop a dual-transfer strategy to gauge the similarity between the source and target domain. In addition, a spectral weighted fusion method is introduced to obtain the inputs of the Siamese network, and we propose a spatial–spectral cross-correlation module to better embed the spatial and material information between the two branches of the Siamese network for classification and regression. The experimental results demonstrate that, compared to the state of the art, the proposed SSDT-Net tracker offers more satisfactory performance based on a similar speed to the traditional color trackers.
Collision-free trajectory planning for robotic assembly of lightweight structures

This research presents a trajectory planning approach for robotic assembly of lightweight structures for COVID-19 healthcare facilities. The prefabricated building components of COVID-19 healthcare facilities have nonnegligible volume, where the crux of the scientific question lies in how to incorporate geometry-based collision checks in trajectory planning. This research developed an algorithm that refines the RRT* (Rapidly-exploring Random Tree-Star) algorithm to enable the detour of a planned trajectory based on the geometry of prefabricated components to prevent collisions. Testing of the approach reveals that it has satisfactory collision-avoiding and trajectory-smoothing performance, and is time- and labour-saving compared with the traditional human method. The satisfactory results highlight the practical implication of this research, where robots can replace human labour and contribute to the mitigation of COVID-19 spread on construction sites. The subsequent research will investigate the use of a collaborative robot to screw bolt connections after the components are assembled at locations.
DoF-NeRF: Depth-of-Field Meets Neural Radiance Fields

Neural Radiance Field (NeRF) and its variants have exhibited great success on representing 3D scenes and synthesizing photo-realistic novel views. However, they are generally based on the pinhole camera model and assume all-in-focus inputs. This limits their applicability as images captured from the real world often have finite depth-of-field (DoF). To mitigate this issue, we introduce DoF-NeRF, a novel neural rendering approach that can deal with shallow DoF inputs and can simulate DoF effect. In particular, it extends NeRF to simulate the aperture of lens following the principles of geometric optics. Such a physical guarantee allows DoF-NeRF to operate views with different focus configurations. Benefiting from explicit aperture modeling, DoF-NeRF also enables direct manipulation of DoF effect by adjusting virtual aperture and focus parameters. It is plug-and-play and can be inserted into NeRF-based frameworks. Experiments on synthetic and real-world datasets show that, DoF-NeRF not only performs comparably with NeRF in the all-in-focus setting, but also can synthesize all-in-focus novel views conditioned on shallow DoF inputs. An interesting application of DoF-NeRF to DoF rendering is also demonstrated. The source code will be made available at https://github.com/zijinwuzijin/DoF-NeRF.
DnS: Distill-and-Select for Efficient and Accurate Video Indexing and Retrieval

Abstract In this paper, we address the problem of high performance and computationally efficient content-based video retrieval in large-scale datasets. Current methods typically propose either: (i) fine-grained approaches employing spatio-temporal representations and similarity calculations, achieving high performance at a high computational cost or (ii) coarse-grained approaches representing/indexing videos as global vectors, where the spatio-temporal structure is lost, providing low performance but also having low computational cost. In this work, we propose a Knowledge Distillation framework, called Distill-and-Select (DnS), that starting from a well-performing fine-grained Teacher Network learns: (a) Student Networks at different retrieval performance and computational efficiency trade-offs and (b) a Selector Network that at test time rapidly directs samples to the appropriate student to maintain both high retrieval performance and high computational efficiency. We train several students with different architectures and arrive at different trade-offs of performance and efficiency, i.e., speed and storage requirements, including fine-grained students that store/index videos using binary representations. Importantly, the proposed scheme allows Knowledge Distillation in large, unlabelled datasets—this leads to good students. We evaluate DnS on five public datasets on three different video retrieval tasks and demonstrate (a) that our students achieve state-of-the-art performance in several cases and (b) that the DnS framework provides an excellent trade-off between retrieval performance, computational speed, and storage space. In specific configurations, the proposed method achieves similar mAP with the teacher but is 20 times faster and requires 240 times less storage space. The collected dataset and implementation are publicly available: https://github.com/mever-team/distill-and-select .
Breast cancer classification by a new approach to assessing deep neural network-based uncertainty quantification methods

Deep learning-based approaches have become widespread in medical fields and have achieved profound success in recent years. Nonetheless, most of these approaches cannot provide the certainty of their output numerically, and their output often has too many confidence levels. Therefore, they cannot be efficient and may even lead to irreparable damage. Accordingly, to tackle such a challenge, the Bayesian approximation and Ensemble learning techniques are deliberated as uncertainty quantification (UQ) methods. We apply and evaluate three UQ models regarding the classification of breast tumor tissue types. These methods are Mont Carlo-dropout (MCD), Bayesian Ensemble, and MCD Ensemble. Furthermore, to increase the classification's precision and eliminate the adverse effects of the smallness of the data collection in Wisconsin Diagnostic Breast Cancer (WDBC) used in the present research, transfer learning technique and pre-trained Convolutional Neural Network (DenseNet121) are considered. Three proposed models are compared based on their ability to quantify the reliability of classification according to novel performance metrics employed to assess estimated uncertainty. Our quantitative and qualitative examinations in the study demonstrate that models show high uncertainty in misclassifications, which is vital in determining the rate of medical diagnosis risks. As a result, by using these new assessment criteria, we intend to ascertain when can trust the output of the deep neural network. Moreover, in the examination, we witness more reliability in uncertainty quantification of the Bayesian Ensemble model.
Analysis of cyber security knowledge gaps based on cyber security body of knowledge

Due to the increasing number of cyber incidents and overwhelming skills shortage, it is required to evaluate the knowledge gap between cyber security education and industrial needs. As such, the objective of this study is to identify the knowledge gaps in cyber security graduates who join the cyber security workforce. We designed and performed an opinion survey by using the Cyber Security Knowledge Areas (KAs) specified in the Cyber Security Body of Knowledge (CyBOK) that comprises 19 KAs. Our data was gathered from practitioners who work in cyber security organizations. The knowledge gap was measured and evaluated by acknowledging the assumption for employing sequent data as nominal data and improved it by deploying chi-squared test. Analyses demonstrate that there is a gap that can be utilized to enhance the quality of education. According to acquired final results, three key KAs with the highest knowledge gap are Web and Mobile Security, Security Operations and Incident Management. Also, Cyber-Physical Systems (CPS), Software Lifecycles, and Vulnerabilities are the knowledge areas with largest difference in perception of importance between less and more experienced personnel. We discuss several suggestions to improve the cyber security curriculum in order to minimize the knowledge gaps. There is an expanding demand for executive cyber security personnel in industry. High-quality university education is required to improve the qualification of upcoming workforce. The capability and capacity of the national cyber security workforce is crucial for nations and security organizations. A wide range of skills, namely technical skills, implementation skills, management skills, and soft skills are required in new cyber security graduates. The use of each CyBOK KA in the industry was measured in response to the extent of learning in university environments. This is the first study conducted in this field, it is considered that this research can inspire the way for further researches.
Preliminary results of a survey on the use of self-adaptation in industry

Self-adaptation equips a software system with a feedback loop that automates tasks that otherwise need to be performed by operators. Such feedback loops have found their way to a variety of practical applications, one typical example is an elastic cloud. Yet, the state of the practice in self-adaptation is currently not clear. To get insights into the use of self-adaptation in practice, we are running a large-scale survey with industry. This paper reports preliminary results based on survey data that we obtained from 113 practitioners spread over 16 countries, 62 of them work with concrete self-adaptive systems. We highlight the main insights obtained so far: motivations for self-adaptation, concrete use cases, and difficulties encountered when applying self-adaptation in practice. We conclude the paper with outlining our plans for the remainder of the study.
Weighted Byzantine Fault Tolerance consensus algorithm for enhancing consortium blockchain efficiency and security

In blockchain, the consensus algorithm is a core component that governs the trust among the participants in the blockchain activities. However, the exiting consensus algorithms suffer from performance bottleneck such as low throughput, high delay, unstable performance, sustainability issues and vulnerability to targeted attacks. In this paper, we propose a new consortium blockchain consensus algorithm, referred to as Weighted Byzantine Fault Tolerance (WBFT) consensus algorithm that improves system throughput and consensus delay. We introduce a dynamic weighting mechanism for consensus nodes, which enhances the security of blockchain system by weakening the influence of malicious nodes and reduces the probability of malicious behavior. We validate the performance of WBFT experimentally and compare it against Practical Byzantine Fault Tolerance (PBFT) and Reputation-Based Byzantine Fault-Tolerance (RBFT) based approaches. The results show that WBFT substantially outperforms PBFT and RBFT in terms of system throughput, consensus delay and security.
Exploratory Analysis of Blockchain Platforms in Supply Chain Management

The spread of blockchain technology is gaining ground worldwide, including in the supply chain and logistics sector. Its proliferation is expected to transform supply chains. Academic research is needed to investigate the reasons for and barriers to adoption. The objective of this study is to explore the blockchain (BC) platform and its inputs as technological solutions. In addition, the application of blockchain technology in managing supply chain (SC) business processes like shipment tracking, authenticity, and identification is also a focus of research. This research was carried out in three ways to explore the issue. Expert interviews were used to develop a research framework for the comparative analysis of BC platforms to find out the benefits and the barriers of blockchain adoption. In association with the diffusion of the technology, a qualitative comparative analysis was applied to benchmark blockchain platform providers. We analysed the blockchain-supply chain market by component, provider, type, and the conditions of usage. As part of this research, the Forbes TOP 50 companies were analysed by business area, country of origin, application area, and benefits in order to see in which area they applied blockchain technology and what improvements they have achieved. The results revealed that blockchain use in supply chains of selected industries has outstanding benefits of transparency, trustworthiness, traceability, and cost efficiencies which give businesses an advantage in terms of implementation costs, technology needs, human resources, legal environments, volatile costs, and security. In the supply chain, Hyperledger Fabric and Ethereum are the most widely used blockchain platforms. For practical implication, the application and benefits of BC in SC were analysed, and the results indicate the traceability, sustainability-related, cost, and time-saving benefits.
DSTS: A hybrid optimal and deep learning for dynamic scalable task scheduling on container cloud environment

Abstract Containers have grown into the most dependable and lightweight virtualization platform for delivering cloud services, offering flexible sorting, portability, and scalability. In cloud container services, planner components play a critical role. This enhances cloud resource workloads and diversity performance while lowering costs. We present hybrid optimum and deep learning approach for dynamic scalable task scheduling (DSTS) in container cloud environment in this research. To expand containers virtual resources, we first offer a modified multi-swarm coyote optimization (MMCO) method, which improves customer service level agreements. Then, to assure priority-based scheduling, we create a modified pigeon-inspired optimization (MPIO) method for task clustering and a rapid adaptive feedback recurrent neural network (FARNN) for pre-virtual CPU allocation. Meanwhile, the task load monitoring system is built on a deep convolutional neural network (DCNN), which allows for dynamic priority-based scheduling. Finally, the presentation of the planned DSTS methodology will be estimated utilizing various test vectors, and the results will be associated to present state-of-the-art techniques.
Time series anomaly detection in power electronics signals with recurrent and ConvLSTM autoencoders

The anomalies in the high voltage converter modulator (HVCM) remain a major down time for the spallation neutron source facility, that delivers the most intense neutron beam in the world for scientific materials research. In this work, we propose neural network architectures based on Recurrent AutoEncoders (RAE) to detect anomalies ahead of time in the power signals coming from the HVCM. Bi-directional gated recurrent unit, bi-directional long-short term memory (LSTM), and convolutional LSTM (ConvLSTM) are developed, trained, and tested using real experimental signals from the HVCM module. The results show a good performance of the proposed RAE models, achieving precision up to 91%, recall up to 88%, false omission rate as low as 20% (i.e. 80% of the anomalies were detected), and area under the ROC curve up to 0.9. The three RAE models provide very comparable performance, with LSTM showing slightly better performance than GRU and ConvLSTM. The RAE models are benchmarked against other anomaly detection methods, including isolation forest, support vector machine, local outlier factor, feedforward and convolutional autoencoders, and others; showing a better performance. The results of this study demonstrate the promising potential of RAE in anomaly detection for real-world power systems, and for increasing the reliability of the HVCM modules in the spallation neutron source.
Deep Learning-Based Software Defect Prediction via Semantic Key Features of Source Code—Systematic Survey

Software defect prediction (SDP) methodology could enhance software’s reliability through predicting any suspicious defects in its source code. However, developing defect prediction models is a difficult task, as has been demonstrated recently. Several research techniques have been proposed over time to predict source code defects. However, most of the previous studies focus on conventional feature extraction and modeling. Such traditional methodologies often fail to find the contextual information of the source code files, which is necessary for building reliable prediction deep learning models. Alternatively, the semantic feature strategies of defect prediction have recently evolved and developed. Such strategies could automatically extract the contextual information from the source code files and use them to directly predict the suspicious defects. In this study, a comprehensive survey is conducted to systematically show recent software defect prediction techniques based on the source code’s key features. The most recent studies on this topic are critically reviewed through analyzing the semantic feature methods based on the source codes, the domain’s critical problems and challenges are described, and the recent and current progress in this domain are discussed. Such a comprehensive survey could enable research communities to identify the current challenges and future research directions. An in-depth literature review of 283 articles on software defect prediction and related work was performed, of which 90 are referenced.
Post-COVID-19 Transition in University Physics Courses: A Case of Study in a Mexican University

The extended confinement imposed by the COVID-19 pandemic lockdowns resulted in the imposition of online education for two years. Many students experienced their transitions to higher educative levels during this time, surely losing some academic learning as a consequence. On the other hand, this context could have promoted different types of competencies, until recently not explored, as a function of students’ personalities or academic profiles. Physics teaching is among those areas which have changed from its traditional methods during this period. The return to school during the ‘New Normal’ has resulted in certain concerns about students’ adaptability due to their possible lack of learning over this time. We analyse, in the current research, the transitions of three generations crossing several pandemic stages during their time participating in university physics courses during the first year, a common entry point for engineering programs. In addition, we analyse several academic traits as causal factors of academic success in order to understand how performance could be affected during online education and during the ‘New Normal’. The results highlight a general high level of adaptation for the most of the students, but still, some of them were affected in terms of the functioning of their learning styles or regarding their personality profiles. Notably, no meaningful losses were detected among the last transition; instead, several interesting aspects were found relating to academic profile appearing to have an effect on the students’ performance during the first transition to online education, and then during the second transition back to face-to-face education in the ‘New Normal’.