Reviewerly/exHarmony huggingface dataset:
Three-dimensional measurement of fringe projection based on the camera response function of the polarization system

Fringe projection technology is often used for three-dimensional (3D) measurement, but it is difficult to measure highlight surfaces. Polarization systems are usually used to remove highlight surfaces. Polarizing filters can be used to eliminate the highlights of the image, but they may also cause the image to be too dark and affect the measurement accuracy. Otherwise, to ensure measurement accuracy, the complexity of the operation of the polarization system will be increased. A method of polarization-based camera intensity response function for 3D measurement is proposed. The intensity response function of the camera under the polarization system is established. It can avoid the complicated polarized bidirectional reflectance distribution function model and directly and quantitatively calculate the required angle between the transmission axes of the two polarizing filters. Then it is combined with the image fusion algorithm to generate the optimal fringe pattern. Experimental results demonstrate that this method significantly eliminates the effects of highlights in the image. The fuzzy transition area between the black and white fringes is effectively reduced, and the edge information of the fringes is correctly restored. Moreover, the high signal-to-noise ratio and contrast of the image are retained when the polarization filters are added.
DA-SACOT: Domain adaptive-segmentation guided attention for correlation based object tracking

Object tracking relies on a recursive search technique around the previous target location, concurrently learning the target appearance in each frame. A failure in any frame causes a drift from its optimal target path. Thus, obtaining highly confident search regions is essential in each frame. Motivated by the strong localization property of segmented object masks, the proposed method introduces instance segmentation as an attention mechanism in object tracking framework. The core contribution of this paper is threefold: (i) a region proposal module (RPM) based on instance segmentation to focus on search proposals having a high probability of being the target, (ii) a target localization module (TLM) to localize the final target using a correlation filter and (iii) a domain adaptation technique in both RPM and TLM modules to incorporate target specific knowledge and strong discrimination ability. Extensive experimental evaluation on three benchmark datasets demonstrate a significant average gain of 2.47% in precision, 2.55% in AUC score and 2.15% in overlap score in comparison with recent competing trackers.
Back to Prior Knowledge: Joint Event Causality Extraction via Convolutional Semantic Infusion

Joint event and causality extraction is a challenging yet essential task in information retrieval and data mining. Recently, pre-trained language models (e.g., BERT) yield state-of-the-art results and dominate in a variety of NLP tasks. However, these models are incapable of imposing external knowledge in domain-specific extraction. Considering the prior knowledge of frequent n-grams that represent cause/effect events may benefit both event and causality extraction, in this paper, we propose convolutional knowledge infusion for frequent n-grams with different windows of length within a joint extraction framework. Knowledge infusion during convolutional filter initialization does not only help the model capture both intra-event (i.e., features in an event cluster) and inter-event (i.e., associations across event clusters) features but also boost training convergence. Experimental results on the benchmark datasets show that our model significantly outperforms the strong BERT+CSNN baseline.
Ethical Design of Conversational Agents: Towards Principles for a Value-Sensitive Design

Conversational Agents (CAs) have become a new paradigm for human-computer interaction. Despite the potential benefits, there are ethical challenges to the widespread use of these agents that may inhibit their use for individual and social goals. However, besides a multitude of behavioral and design-oriented studies on CAs, a distinct ethical perspective falls rather short in the current literature. In this paper, we present the first steps of our design science research project on principles for a value-sensitive design of CAs. Based on theoretical insights from 87 papers and eleven user interviews, we propose preliminary requirements and design principles for a value-sensitive design of CAs. Moreover, we evaluate the preliminary principles with an expert-based evaluation. The evaluation confirms that an ethical approach for design CAs might be promising for certain scenarios.
It’s the End of the Gold Standard as We Know It

Supervised machine learning, in particular in Natural Language Processing, is based on the creation of high-quality gold standard datasets for training and benchmarking. The de-facto standard annotation methodologies work well for traditionally relevant tasks in Computational Linguistics. However, critical issues are surfacing when applying old techniques to the study of highly subjective phenomena such as irony and sarcasm, or abusive and offensive language. This paper calls for a paradigm shift, away from monolithic, majority-aggregated gold standards, and towards an inclusive framework that preserves the personal opinions and culturally-driven perspectives of the annotators. New training sets and supervised machine learning techniques will have to be adapted in order to create fair, inclusive, and ultimately more informed models of subjective semantic and pragmatic phenomena. The arguments are backed by a synthetic experiment showing the lack of correlation between the difficulty of an annotation task, its degree of subjectivity, and the quality of the predictions of a supervised classifier trained on the resulting data. A further experiment on real data highlights the beneficial impact of the proposed methodologies in terms of explainability of perspective-aware hate speech detection.
Permission-Based Verification of Red-Black Trees and Their Merging

This paper presents a verification case study, focussing on red-black trees. In particular, we verify a parallel algorithm for merging red-black trees, which uses lists as intermediate representations and which an industrial partner uses to efficiently manage tables of IP addresses. To verify the algorithm, we use the tool VERCORS, which uses permission-based separation logic as its logical foundation. Thus, we first needed a suitable specification of the data structure, using that logic. This specification relies on the magic wand operator (a.k.a. separating implication), which is a connective often neglected when discussing separation logic. This paper describes that specification, as well as the verification of the parallel algorithm. It is an interesting case connecting the more academic endeavour of verifying a data structure with the practical one of verifying industrial code.
Colorectal Cancer Tissue Classification Using Semi-Supervised Hypergraph Convolutional Network

Colorectal Cancer (CRC) is a leading cause of death around the globe, and therefore, the analysis of tumor micro environment in the CRC WSIs is important for the early detection of CRC. Conventional visual inspection is very time consuming and the process can undergo inaccuracies because of the subject-level assessment. Deep learning has shown promising results in medical image analysis. However, these approaches require a lot of labeling images from medical experts. In this paper, we propose a semi-supervised algorithm for CRC tissue classification. We propose to employ the hypergraph neural network to classify seven different biologically meaningful CRC tissue types. Firstly, image deep features are extracted from input patches using the pre-trained VGG19 model. The hypergraph is then constructed whereby patch-level deep features represent the vertices of hypergraph and hyperedges are assigned using pair-wise euclidean distance. The edges, vertices, and their corresponding patch-level features are passed through a feed-forward neural network to perform tissue classification in a transductive manner. Experiments are performed on an independent CRC tissue classification dataset and compared with existing state-of-the-art methods. Our results reveal that the proposed algorithm outperforms existing methods by achieving an overall accuracy of 95.46% and AvTP of 94.42%.
Evolutionary Optimization of Graphs with GraphEA

Many practically relevant computing artifacts are forms of graphs, as, e.g., neural networks, mathematical expressions, finite automata. This great generality of the graph abstraction makes it desirable a way for searching in the space of graphs able to work effectively regardless of the graph form and application. In this paper, we propose GraphEA, a modular evolutionary algorithm (EA) for evolving graphs. GraphEA is modular in the sense that it can be tailored to any graph optimization task by providing components for specifying how to vary edges and nodes, and how to check the validity of a graph. We designed GraphEA by building on previous EAs dealing with particular kinds of graphs and included a speciation mechanism for the preservation of structural innovations and one for the gradual complexification of the solutions structure. To validate the generality of GraphEA, we applied it to 3 radically different tasks (regressions, in two flavors, text extraction from examples, evolution of controllers for soft robots) and compared its effectiveness against less general approaches. The results are promising and indicate some directions for further improvement of GraphEA.
Cb2Onto: OWL Ontology Learning Approach from Couchbase

Big Data are collections of large data sets of both unstructured and structured data characterized by volume, variety and velocity. These characteristics accentuate the heterogeneity and complexity of data, which exceeds the capacity of traditional systems to cope with it. Due to the aforementioned characteristics, it is crucial to have a unified conceptual view of the data, as well as, an efficient representation of knowledge for big data management. Since the ontology has the maturity we need to understand and provide the meaning to process big data. However, Construction of ontology by hand is an incredibly challenging and error-prone process. Learning Ontology from existing resource gives a reasonable alternative. Therefore, this paper proposes an approach to learn OWL ontology from data in Couchbase database by the application of six mapping rules, we use Ontop reasoner to evaluate the consistency of extracted ontology.
Normalization and outlier removal in class center-based firefly algorithm for missing value imputation

Abstract A missing value is one of the factors that often cause incomplete data in almost all studies, even those that are well-designed and controlled. It can also decrease a study’s statistical power or result in inaccurate estimations and conclusions. Hence, data normalization and missing value handling are considered the major problems in the data pre-processing stage, while classification algorithms are adopted to handle numerical features. In cases where the observed data contained outliers, the missing value estimated results are sometimes unreliable or even differ greatly from the true values. Therefore, this study aims to propose the combination of normalization and outlier removals before imputing missing values on the class center-based firefly algorithm method (ON + C3FA). Moreover, some standard imputation techniques like mean, a random value, regression, as well as multiple imputation, KNN imputation, and decision tree (DT)-based missing value imputation were utilized as a comparison of the proposed method. Experimental results on the sonar dataset showed normalization and outlier removals effect in the methods. According to the proposed method (ON + C3FA), AUC, accuracy, F1-Score, Precision, Recall, and AUC-PR had 0.972, 0.906, 0.906, 0.908, 0.906, 0.61 respectively. The result showed combining normalization and outlier removals in C3-FA (ON + C3FA) was an efficient technique for obtaining actual data in handling missing values, and it also outperformed the previous studies methods with r and RMSE values of 0.935 and 0.02. Meanwhile, the D ks value obtained from this technique was 0.04, which indicated that it could maintain the values or distribution accuracy.
RFCBF: Enhance the Performance and Stability of Fast Correlation-Based Filter

Feature selection is a preprocessing step that plays a crucial role in the domain of machine learning and data mining. Feature selection methods have been shown to be effective in removing redundant and irrelevant features, improving the learning algorithm’s prediction performance. Among the various methods of feature selection based on redundancy, the fast correlation-based filter (FCBF) is one of the most effective. In this paper, we developed a novel extension of FCBF, called resampling FCBF (RFCBF) that combines resampling technique to improve classification accuracy. We performed comprehensive experiments to compare the RFCBF with other state-of-the-art feature selection methods using three competitive classifiers (K-nearest neighbor, support vector machine, and logistic regression) on 12 publicly available datasets. The experimental results show that the RFCBF algorithm yields significantly better results than previous state-of-the-art methods in terms of classification accuracy and runtime.
An Improved SSD for small target detection

SSD is one of heuristic one-stage target detection approaches. Although it has got impressive results in general target detection, it still struggles in small-size object detection and precise location. In this paper, we proposed an improved SSD which forces on the small-size target detection. We include a shallow and high resolution feature into the hierarchical detection feature which are used for prediction. Then, we fuse the detection features (including the shallow and high resolution one) as a feature pyramid through some convolution layers and unsample operations to pass information from deep features to the shallow ones, aiming to enrich the semantic information of the shallow features. To make the network easier to converge, we add a L2 normalization to the bottom detection feature of the feature pyramid to make a norm balance between each pyramid feature. The experimental results on the VEDAI dataset show that the proposed method has obtained impressive progress than the original SSD for the small targets detection.
CARAF: Crypto Agility Risk Assessment Framework

Abstract Crypto agility refers to the ability of an entity to replace existing crypto primitives, algorithms, or protocols with a new alternative quickly, inexpensively, with no or acceptable risk exposure. These changes may be driven by regulatory action, advances in computing, or newly discovered vulnerabilities. Yet everyday operational needs may put crypto agility considerations on the back burner when deploying technology, designing processes, or developing products/services. Consequently, changes are often performed in an ad hoc manner. Transition from one crypto solution to another can then take a long time and expose organizations to unnecessary security risk. This paper presents a framework to analyze and evaluate the risk that results from the lack of crypto agility. The proposed framework can be used by organizations to determine an appropriate mitigation strategy commensurate with their risk tolerance. We demonstrate the application of this framework with a case study of quantum computing and related threats to cryptography in the context of TLS for Internet of Things.
Computing Nonlinear Polynomial Functions on FPGA-Class PLD Arrays

This paper estimates the quantities of the resources of FPGA-class PLD generators of Boolean functions, D-triggers, and input/output units that are required for the distributed computing of nonlinear polynomial functions over a Galois field of a certain power in a preset number of variables. Computations were performed considering the usage factor of the relevant PLD/FPGA resources, as well as the number of the variables for the above functions. We also calculated the PLD/FPGA array size required for computing the predefined polynomial function over a Galois field on both existing and potential distributed computing systems.
DSIC: Dynamic Sample-Individualized Connector for Multi-Scale Object Detection

Although object detection has reached a milestone recently, the scale variation is still the key challenge. Integrating multilevel features is presented to alleviate the problems, like Feature Pyramid Network (FPN) and its improvements. However, the specifically designed architectures and fixed data flow paths of these methods are not flexible for feature fusion, especially when fed with various samples. To overcome the limitations, we propose a Dynamic Sample-Individualized Connector (DSIC) for multi-scale object detection, which dynamically adjusts network connections to fit different samples. In particular, DSIC consists of two components: Intra-scale Selection Gate (ISG) and Cross-scale Selection Gate (CSG). With the help of the presented gate operator, ISG adaptively extracts proper multi-level features from backbone as the inputs of feature integration. CSG automatically activates informative data flow paths based on the extracted multi-level features. These two components are both plug-and-play and can be embedded in any backbone. Experimental results demonstrate that the proposed method outperforms the state-of-the- arts.
Classifier aided training for semantic segmentation

Semantic segmentation is a prominent problem in scene understanding expressed as a dense labeling task with deep learning models being one of the main methods to solve it. Traditional training algorithms for semantic segmentation models produce less than satisfactory results when not combined with post-processing techniques such as CRFs. In this paper, we propose a method to train segmentation models using an approach which utilizes classification information in the training process of the segmentation network. Our method employs the use of classification network that detects the presence of classes in the segmented output. These class scores are then used to train the segmentation model. This method is motivated by the fact that by conditioning the training of the segmentation model with these scores, higher order features can be captured. Our experiments show significantly improved performance of the segmentation model on the CamVid and CityScapes datasets with no additional post processing.
Low level position control for 4-DOF arm robot using fuzzy logic controller and 2-DOF PID controller

The various types of applications for arm robots have made it one of the most important topics within the field. An important step to be considered is the application of a suitable control algorithm that takes into account the complex nonlinear dynamics. However, nonlinear control methods can still face difficulties due to the complexity of implementation. In this paper, four degrees of freedom (4-DOF) robot arm model is considered. Simple linear control methods are proposed to be used in the control of the angle of robotjoints. For this purpose, the fuzzy logic controller (FLC) and the two degree of freedom PID (2-DOF PID) controller are applied to control the joint angles of robot arm and in order to improve the response speed and reduce overshoot. The system simulation was performed using the MATLAB/Simulink software package. Simulation results show that the FLC has a faster response with a better rise time compared to the 2-DOF PID controller.
The limits of pan privacy and shuffle privacy for learning and estimation

There has been a recent wave of interest in intermediate trust models for differential privacy that eliminate the need for a fully trusted central data collector, but overcome the limitations of local differential privacy. This interest has led to the introduction of the shuffle model (Cheu et al., EUROCRYPT 2019; Erlingsson et al., SODA 2019) and revisiting the pan-private model (Dwork et al., ITCS 2010). The message of this line of work is that, for a variety of low-dimensional problems—such as counts, means, and histograms—these intermediate models offer nearly as much power as central differential privacy. However, there has been considerably less success using these models for high-dimensional learning and estimation problems.
Simulation analysis of a 3.37 MW PV system using bifacial modules in desert environment

Abstract This study is conducted to investigate the performance of bifacial solar PV with high albedo in desert regions. High amount of radiation falls on the Arabian Peninsula where there is an average amount of 2200 kWh/m 2 annually and needs to be utilize as much as possible to get higher amount of energy. A 3.37 MW system is simulated by PVsyst software using two different scenarios which the first is without an axis tracker while the other is with a dual axis tracker to track the sunlight from all directions. When the tracking system is not included the yearly energy production set to be 6684 MWh/year where the system is simulated at a fixed mounting at a single direction facing the south at an optimum tilt angle of 25?. On the other hand, when the tracking system is set to be dual trackers at a minimum tilt angle of 0? and maximum tilt angle of 80? vertically, and minimum azimuth angle of -120 and maximum azimuth angle of -120? horizontally. There annual energy production is set to be 8897 MWh which has a gain of 25% using bifacial solar module with tracking system which can add a value to the solar PV projects and decrease the levelized cost of energy (LCOE).
A Priority-Based Multicast Flow Scheduling Method for a Collaborative Edge Storage Datacenter Network

Edge storage, as a supplement to cloud storage, reduces latency by providing services in a timely and efficient manner near the source. In a collaborative edge storage datacenter network (CESN), not only does the edge storage datacenter (ESDC) that is closest to the user provide services, but multiple ESDCs work together to provide better services. In this collaborative work mechanism, different application session requests create large persistent multicast flows with diverse performance requirements. Existing multicast scheduling methods such as unicast shortest path (USP) and static single tree (SST) do not consider flow characteristics or performance requirements. In this paper, we first modeled the multicast flow scheduling problem in a CESN. The model is based on different types of flows with diverse network requirements. Then, we tailored a multicast flow scheduling method based on multiple-attribute decision-making and a genetic algorithm (MDGA). MDGA selects appropriate multicast routing paths for flows in a CESN by considering the requested flow types and network status. The experimental results show that the proposed MDGA method can balance network loads and reduce the average transmission delay for high-priority flows better than USP and SST.