Reviewerly/exHarmony huggingface dataset:
Biomarker data visualisation for decision making in clinical trials

To understand how visualization of biomarker data is used for decision making in clinical trials, and identify problems with and suggest improvements to this process. We carried out semi-structured interviews with 18 professionals involved in various aspects of developing or using visualizations of biomarker data for decision making in clinical trials. We used an inductive thematic analysis to identify implicit and explicit ideas within the data captured from the interviews. We identified 6 primary themes, including: how visualizations were used in clinical trials; the importance of having a clear understanding of the underlying data; the purpose or use of the visualization, and the properties of the visualizations themselves. The results show that participants’ ‘trust’ in the visualization depends on access to the underlying data, and that there is currently no standard or straightforward way to support this access. Incorporating information about data provenance into biomarker-related visualizations used for decision making in clinical trials may increase users’ trust, and therefore facilitate the decision making process.
Rapid Calibration Method for 3D Laser Scanner

Background: The 3D laser scanner is a non-contact active-sensing system, which has a number of applications. Many patents have been filed on the technologies for calibrating 3D laser scanner. A precise calibration method is important for measuring the accuracy of the 3D laser scanner. The system model contains three categories of parameters to be calibrated which include the camera intrinsic parameters, distortion coefficients and the light plane parameters. Typically, the calibration process is completed in two steps. Based on Zhang’s method, the calibration of the camera intrinsic parameters and distortion coefficients can be performed. Then, 3D feature points on the light plane should precisely be formed and extracted. Finally, the points are used to calculate the light plane parameters. Methods: In this paper, a rapid calibration method is presented. Without any high precision auxiliary device, only one coplanar reference target is used. By using a group of captured images of the coplanar reference target placed in the field of view arbitrarily, calibration can be performed in one step. Based on the constraint from the planes formed by the target in different directions and the camera imaging model, a large amount of 3D points on the light plane can easily be obtained. The light plane equation in the camera coordinates system can be gathered by executing plane fitting to the 3D points. Results: During the experimental process, the developed 3D laser scanner was calibrated by the proposed method. Then, the measuring accuracy of the system was verified with known distance in vertical direction of 1mm with sequential shifting motion generated by precision translation stage. The average value of the measured distances was found to be 1.010mm. The standard deviation was 0.008mm. Conclusion: Experimental results prove that the proposed calibration method is simple and reliable.
Digital equity for marginalised and displaced peoples

ABSTRACT Upholding human rights – in particular for marginalised and displaced peoples – depends on exercising rights in data, information and records. In considering these information rights we face the challenge of designing ethical, adaptive, and inclusive information systems and technologies that support the actualisation of human rights, social justice, and Indigenous and First Nations sovereignty. Too often systems designed for an uncritical and generalised “happy centre” fail those on the margins, in all of their individualised complexity.The purpose of this panel will be to introduce an international initiative aimed at addressing this challenge, and to explore a developing research agenda for tackling the broad range of socio‐technical issues that comprise this wicked problem. It is hoped that this panel will galvanise the community towards action and engagement with this programme or its goals.
Data Replication Optimization Using Simulated Annealing

Data replication is ubiquitous in a large organization where multiple IT systems need to share information for their operation. This function is usually fulfilled by an enterprise replicating software that is dependent on the configuration that the IT administrator sets. The setup specifies the tables and routes, but it may not be optimum to meet the workload, leading to replication’s lag and bottlenecks. This paper proposes an approach to solving the configuration optimization problem for the data replication software with the simulated-annealing based heuristic. Empirical results show that the configuration setting enables the replicating software to perform at least 5 times better than the baseline configuration set achieved by this approach.
Towards A Knowledge Base for Software Developers to Choose Suitable Traceability Techniques

Huge amount of techniques for creating, maintaining and/or recovering traceability among software development artifacts have been proposed. It is thus not easy for a potential user of such techniques to choose a technique suitable for his/her project because too many techniques exist and projects are different from each other. In this paper, we proposed a model for characterizing a traceability technique with respect to its users. The model can become a meta-model of the knowledge base for such users. The model is designed on the basis of the contents of existing technical papers so that we can easily describe model instances on the basis of technical papers. The model is represented in a feature model, and it has four mandatory features: source, destination, consequence and process. The user can at least understand a technique is unsuitable for him/her by referring such features. If a technique estimates the traceability relationships, the instance of its feature model may contain the quality metrics of its estimation such as precision and recall. It has also several optional features: assumptions, preprocess and tool. Although we sometimes cannot obtain such optional features from technical papers, they are so helpful for a user to decide a technique suitable for him/her. On the basis of the feature model, we described model instances of several techniques in technical papers. We also examined and discussed who the suitable user for each technique is.
Trajectory Planning and Tracking Control of Vehicle Obstacle Avoidance based on Optimization Control

For the problem of vehicle obstacle avoidance control, a steering obstacle avoidance method based on optimization idea is designed. In the research of obstacle avoidance path planning method, a design method with three-segment sinusoidal lateral acceleration is adopted, combining with various practical constraints of vehicle and safe distance, an optimization solution method of obstacle avoidance trajectory is presented, which takes obstacle avoidance time as the optimal objective. Based on the established two-degree-of-freedom vehicle dynamics model, a tracking controller with moving horizon idea is designed. Using the vehicle dynamics simulation software veDYNA, the scene of vehicle lane change and obstacle avoidance is constructed. The effectiveness and control performance of this method are illustrated by simulation.
Development of vocabulary learning application by using machine learning technique

Nowadays an educational mobile application has been widely accepted and opened new windows of opportunity to explore. With its flexibility and practicality, the mobile application can promote learning through playing with an interactive environment especially to the children. This paper describes the development of mobile learning to help children above 4 years old in learning English and Arabic language in a playful and fun way. The application is developed with a combination of Android Studio and the machine learning technique, TensorFlow object detection API in order to predict the output result. Developed application namely “LearnWithIman” has successfully been implemented and the results show the prediction of application is accurate based on the captured image with the list item. The inclusion of the user database for lesson tracking and new lesson will be added for improvement in the future.
Requirements Validation in the Information Systems Software Development: An Empirical Evaluation of Its Benefits for a Public Institution in Lima

Under Peruvian legislation, all information technology offices belonging to public institutions must meet ISO/IEC 12207 and, in the case of small organizations the international standard series ISO/IEC 29110 could be a good strategy to comply with the legislation. In this context, ISO/IEC 29110-5-1-2 establishes to apply software validation to requirements as a relevant activity; but it is an unusual practice during the software development of information systems in Peru. In this study, we assess the benefits of software validation in the context of a public institution technical unit. For this purpose, a case study was performed and a quasi-experimental design was applied on a group of projects. The quality indicators defined increased their values as a consequence of process improvement based on software validation. As a conclusion, applying software validation to requirements is beneficial and contributes to increase project quality.
Point the Point: Uyghur Morphological Segmentation Using PointerNetwork with GRU

Uyghur is an agglutinative language that has many morphemes. It is necessary for processing Uyghur to segment words into morphemes. This work is called morphological segmentation. Previous works treat morphological segmentation as a tagging task and classify each character as one of four classes, which are $$\{b,m,e,s\}$$ . However, these labels are not independent from each other, which makes the models easily overfitted. We propose a new method for the segmentation task. Instead of using these labels, we use only segmentation points for modeling. The model used in our method is more robust and easier to train than previous methods. Applying our model to Uyghur morphological segmentation, it achieves high accuracy and higher recall and f1 score than previous models.
Two Zaslavsky maps in pseudorandom byte generation

We present a novel, two Zaslavsky maps based pseudorandom byte formation function. We evaluated output data theoretically and tested by NIST and ENT statistical suites. The results of the cryptographic analysis show that the output bytes behave like an ideal random source.
Research opportunities on virtual reality and augmented reality: a survey

Augmented reality(AR) is a technology that covers virtual objects into a real environment with real objects for better observer's knowledge. Virtual Reality (VR) enables user to interact with a computer-simulated environment which is either a simulation of the real world or an imaginary world. VR and AR are the key to explore, and touch the past, present and the future. They are the basis of creating one's own world, one's own custom-built reality. It enables users to design a video game to have a virtual stroll around the universe, to practice their own dream house, to experience a walk on an alien planet. We can learn by experiencing the most threatening and difficult situations by playing safe. Very few really know the basic principles of VR & AR and their open problems. In this survey, we present the historical overview of Virtual Reality and Augmented Reality, characteristics, and types of VR and AR systems. The requirements and challenges of typical VR and AR systems are illustrated.
Modeling and optimization of a geometrical calibration procedure for stereoscopic video endoscopes

Stereoscopic video endoscopes are widely used for remote visual inspection and precise three-dimensional (3D) measurements in industrial and biomedical applications. The reconstruction of 3D points from the corresponding image points requires a geometrical calibration procedure, the accuracy of which affects the measurement uncertainty. We propose to perform an optimal choice of the calibration technique and the calibration target parameters using a computer simulation at the design stage. The effectiveness of this approach is demonstrated via the design of a self-developed miniature prism-based stereoscopic system. We simulated acquisition of calibration and measurement data using optical design software. The conventional calibration technique, requiring many positions of the flat target with arbitrary displacements and rotations, was compared with another one that uses the translation stage to provide pure translation of the target. We analyzed the impact of the translation uncertainty, the number of positions, the number of targets, and the uncertainty of image point coordinates on the uncertainty of calibration parameters and 3D measurements. We have shown that the second technique could provide acceptable measurement accuracy using only two images. The results of the computer simulation were confirmed experimentally using the prototype of the stereoscopic endoscope. The proposed approach may be used to optimize calibration techniques and reduce the cost of calibration equipment for various stereoscopic measurement systems.
Dynamic State Estimation of Distribution Network Base on Pseudo Measurement Modeling and UPF

The economic and reliable operation of distribution network relies on state variables information from state estimator. However, since the amount of distributed generation (DG) and electric vehicles connected to distribution network is steadily growing, the distribution network is full of randomness and volatility, which increases the difficulty of accurate state estimation. In this paper, a dynamic state estimation (DSE) algorithm based on unscented particle filter (UPF) is proposed. This algorithm uses the unscented Kalman filter (UKF) estimated results as the proposal distribution of particle filter (PF), and updates the latest measurements information in the process of generating predictive particles, which makes the distribution of particles much closer to the posterior probability distribution of true states. At the same time, a pseudo measurement model based on spiking neural network (SNN) is established, which outputs the pseudo measurement load at each sampling moment to enhance the accuracy of state estimation. The simulation results show that the proposed algorithm can effectively track the true value of distribution network states and is superior to the traditional DSE algorithm in estimation accuracy and noise robustness.
Analysis of Program Patches Nature and Searching for Unpatched Code Fragments

Software developers often copy and paste code within a project. Due to the possible existence of defects in the initial code fragment, this can lead to defects propagation across the project. Software changes in new version (patches) usually contain bug fixes, which can be used for detecting similar defects in a project. The purpose of this work is to develop method for analyzing the nature of patches between versions of executables and finding unpatched code fragments. At first, two versions of executables are compared for finding common and changed parts of code. Then, the method determines patches that can possibly be fixes of bugs. The final step is detection of unpatched code fragments. It is based on finding all clones of the buggy code fragments found in previous step which are not patched in the new version of the program. These fragments possibly contain defects. Developed tool allows to analyze programs of several architectures (x86, x86-64, arm, mips, powerpc). The experimental results show that the average percentage of true positive rate on the CoreBench test suite is 73%.
Europe's controversial digital copyright directive finalized

Considering the new liability risks for ISPs, search engines, and news aggregators under recent EU-wide mandatory rules.
Efficient web navigation prediction using hybrid models based on multiple evidence combinations

Modeling user(s) navigation sequences and predicting their preferences has been an interesting area of research. For Web Navigation Prediction (WNP) the Markov model(s) are predominantly used for analyzing and discovering user navigation patterns. One of the major issues with the Markov model is that it fails to predict for unclassified navigations. Presence of such navigations reduces the prediction power of the model. Deep machine learning models can be used to address unclassified navigations but their prediction ability deteriorates if training sessions are less in number. As Navigations have been modeled using N-Grams where the number of training sessions reduces at higher N-Grams. It might affect the performance of deep learning models. However, their prediction ability can be improvised by integrating it with the Markov model. This paper proposes three integrated models to minimize the unclassified navigations and to boost the overall prediction accuracy. Proposed hybrid models are formed by integrating All-Kth Markov Model with Deep Neural Network (DKM) and All-Kth Modified Markov Model with Shallow Neural Network and Deep Neural Network (SKMM and DKMM). The proposed models are evaluated on three standard datasets: CTI, BMS, and Wikispeedia. DKMM has obtained the best results in terms of improvement in prediction accuracy and reduction in unclassified navigations on higher N-grams. Prediction accuracy was improved up to 4.71, 6.2 and 7.67 in CTI, BMS and Wikispeedia dataset.
A Collaborative Support for Recommending References in Papers

Understanding citations to scientific publications is a task of vital importance in the academic world. This task can be supported by appropriate data structures and visualization mechanisms. One challenge is the amount of existing relationships and the difficulty of determining which of the references of a document are considered the most potentially relevant to it. In this paper, we propose a visual approach based on graphs to recommend important references. Also, we propose a procedure to build and update the citation graph in a collaborative way.
Future Directions in Image Quality

With the advancements made in the field of image processing and computer vision, the last few decades have seen an increase in studies focused on image quality assessment. While this has resulted in the introduction of different new metrics which some show high correlation with the perceptual judgement of the human observers there still exists a huge room for improvement. In this short paper which is prepared as a complement to the workshop on Future Directions in Image Quality at CIC 27 in Paris, France we aim to introduce future directions in the field and challenges facing ahead.
Kernel Graph Attention Network for Fact Verification

This paper presents Kernel Graph Attention Network (KGAT), which conductsmore fine-grained evidence selection and reasoning for the fact verificationtask. Given a claim and a set of potential supporting evidence sentences, KGATconstructs a graph attention network using the evidence sentences as its nodesand learns to verify the claim integrity using its edge kernels and nodekernels, where the edge kernels learn to propagate information across theevidence graph, and the node kernels learn to merge node level information tothe graph level. KGAT reaches a comparable performance (69.4%) on FEVER, alarge-scale benchmark for fact verification. Our experiments find that KGATthrives on verification scenarios where multiple evidence pieces are required.This advantage mainly comes from the sparse and fine-grained attentionmechanisms from our kernel technique.
Fusion Learning Model for Mobile Face Safe Detection and Facial Gesture Analysis

Face pose analysis has a very broad application prospect in the fields of public safety monitoring, human-computer interaction. Traditional deep learning methods are mostly based on public dataset training, and the robustness is poor in specific application scenarios. Secondly, most models need to crop the facial region during analysis, which is not only slow but also loses facial context in the natural environment. In response to these problems, this paper proposes a joint learning network model for Mobile Face Safe Detection and pose analysis. This method first proposes a cloud-service assisted semi-automated image annotation method. The image of the driver's pose in road traffic monitoring scenes is marked for, which provides additional training data for subsequent joint learning. Secondly, through the cascaded multi-task network, the problem of face pose analysis relying on Mobile Face Safe Detection is solved. At the same time, the fusion loss function, classified training data and Online Hard Example Mining (OHEM) training strategies are used to improve the robustness of the model in complex environments. In the end, the FDDB, AFLW and Prima data sets are used to verify the superiority of our model by comparing with other algorithms.
Obstacle Avoidance Algorithm for Cooperative Mobile Robots under Velocity and Curvature Constraints

This paper solves an obstacle avoidance problem for cooperative robots considering the velocity and curvature constraints. Existing approaches for cooperative robots based on potential flow can generate the smooth paths for obstacle avoidance, but they cannot consider the velocity and curvature constraints for smooth path. Optimization approach can handle such constraints, but they often require a heavy computational load. To solve these issues, this paper propose a simple approach based on curvature-constrained stream potential flow and streamline changing algorithm. By using this simple approach, a smooth path for cooperative robots can be generated in real-time. The simulation results show the comparison results of existing approach.
Assessing the Impact of a User-Item Collaborative Attack on Class of Users.

Collaborative Filtering (CF) models lie at the core of most recommendation systems due to their state-of-the-art accuracy. They are commonly adopted in e-commerce and online services for their impact on sales volume and/or diversity, and their impact on companies' outcome. However, CF models are only as good as the interaction data they work with. As these models rely on outside sources of information, counterfeit data such as user ratings or reviews can be injected by attackers to manipulate the underlying data and alter the impact of resulting recommendations, thus implementing a so-called shilling attack. While previous works have focused on evaluating shilling attack strategies from a global perspective paying particular attention to the effect of the size of attacks and attacker's knowledge, in this work we explore the effectiveness of shilling attacks under novel aspects. First, we investigate the effect of attack strategies crafted on a target user in order to push the recommendation of a low-ranking item to a higher position, referred to as user-item attack. Second, we evaluate the effectiveness of attacks in altering the impact of different CF models by contemplating the class of the target user, from the perspective of the richness of her profile (i.e., cold v.s. warm user). Finally, similar to previous work we contemplate the size of attack (i.e., the amount of fake profiles injected) in examining their success. The results of experiments on two widely used datasets in business and movie domains, namely Yelp and MovieLens, suggest that warm and cold users exhibit contrasting behaviors in datasets with different characteristics.