Reviewerly/exHarmony huggingface dataset:
Let there be a clock on the beach: Reducing Object Hallucination in Image Captioning

Explaining an image with missing or non-existent objects is known as object bias (hallucination) in image captioning. This behaviour is quite common in the state-of-the-art captioning models which is not desirable by humans. To decrease the object hallucination in captioning, we propose three simple yet efficient training augmentation method for sentences which requires no new training data or increase in the model size. By extensive analysis, we show that the proposed methods can significantly diminish our models&#x2019; object bias on hallucination metrics. Moreover, we experimentally demonstrate that our methods decrease the dependency on the visual features. All of our code, configuration files and model weights are available online<sup>1</sup>.
Towards Finding Longer Proofs

We present a reinforcement learning (RL) based guidance system for automated theorem proving geared towards Finding Longer Proofs (FLoP). Unlike most learning based approaches, we focus on generalising from very little training data and achieving near complete confidence. We use several simple, structured datasets with very long proofs to show that FLoP can successfully generalise a single training proof to a large class of related problems. On these benchmarks, FLoP is competitive with strong theorem provers despite using very limited search, due to its ability to solve problems that are prohibitively long for other systems.
Assessing the Success and Impact of Hoare’s Logic

chapter Share on Assessing the Success and Impact of Hoare’s Logic Authors: Krzysztof R. Apt Search about this author , Ernst-Rüdiger Olderog Search about this author Authors Info & Claims Theories of Programming: The Life and Works of Tony HoareOctober 2021 Pages 41–76https://doi.org/10.1145/3477355.3477359Online:02 October 2021Publication History 0citation7DownloadsMetricsTotal Citations0Total Downloads7Last 12 Months7Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
A Monocular Vision Obstacle Avoidance Method Applied to Indoor Tracking Robot

The overall safety of a building can be effectively evaluated through regular inspection of the indoor walls by unmanned ground vehicles (UGVs). However, when the UGV performs line patrol inspections according to the specified path, it is easy to be affected by obstacles. This paper presents an obstacle avoidance strategy for unmanned ground vehicles in indoor environments. The proposed method is based on monocular vision. Through the obtained environmental information in front of the unmanned vehicle, the obstacle orientation is determined, and the moving direction and speed of the mobile robot are determined based on the neural network output and confidence. This paper also innovatively adopts the method of collecting indoor environment images based on camera array and realizes the automatic classification of data sets by arranging cameras with different directions and focal lengths. In the training of a transfer neural network, aiming at the problem that it is difficult to set the learning rate factor of the new layer, the improved bat algorithm is used to find the optimal learning rate factor on a small sample data set. The simulation results show that the accuracy can reach 94.84%. Single-frame evaluation and continuous obstacle avoidance evaluation are used to verify the effectiveness of the obstacle avoidance algorithm. The experimental results show that an unmanned wheeled robot with a bionic transfer-convolution neural network as the control command output can realize autonomous obstacle avoidance in complex indoor scenes.
In-situ identification and recognition of multi-hand gestures using optimized deep residual network

The real-time perception of hand gestures in a deprived environment is a demanding machine vision task. The hand recognition operations are more strenuous with different illumination conditions and varying backgrounds. Robust recognition and classification are the vital steps to support effective human-machine interaction (HMI), virtual reality, etc. In this paper, the real-time hand action recognition is performed by using an optimized Deep Residual Network model. It incorporates a RetinaNet model for hand detection and a Depthwise Separable Convolutional (DSC) layer for precise hand gesture recognition. The proposed model overcomes the class imbalance problems encountered by the conventional single-stage hand action recognition algorithms. The integrated DSC layer reduces the computational parameters and enhances the recognition speed. The model utilizes a ResNet-101 CNN architecture as a Feature extractor. The model is trained and evaluated on the MITI-HD dataset and compared with the benchmark datasets (NUSHP-II, Senz-3D). The network achieved a higher Precision and Recall value for an IoU value of 0.5. It is realized that the RetinaNet-DSC model using ResNet-101 backbone network obtained higher Precision (99.21 %for AP0.5, 96.80%for AP0.75) for MITI-HD Dataset. Higher performance metrics are obtained for a value of γ= 2 and α= 0.25. The SGD with a momentum optimizer outperformed the other optimizers (Adam, RMSprop) for the datasets considered in the studies. The prediction time of the optimized deep residual network is 82 ms.
Separation, Composition, or Hybrid? – Comparing Collaborative 3D Object Manipulation Techniques for Handheld Augmented Reality

Augmented Reality (AR) supported collaboration is a popular topic in HCI research. Previous work has shown the benefits of collaborative 3D object manipulation and identified two possibilities: Either separate or compose users’ inputs. However, their experimental comparison using handheld AR displays is still missing. We, therefore, conducted an experiment in which we tasked 24 dyads with collaboratively positioning virtual objects in handheld AR using three manipulation techniques: 1) Separation – performing only different manipulation tasks (i. e., translation or rotation) simultaneously, 2) Composition – performing only the same manipulation tasks simultaneously and combining individual inputs using a merge policy, and 3) Hybrid – performing any manipulation tasks simultaneously, enabling dynamic transitions between Separation and Composition. While all techniques were similarly effective, Composition was least efficient, with higher subjective workload and worse user experience. Preferences were polarized between clear work division (Separation) and freedom of action (Hybrid). Based on our findings, we offer research and design implications.
A Cross-Domain Authentication Optimization Scheme between Heterogeneous IoT Applications

With the continuous enrichment of the Internet of Things (IoT) applications, the demand for value exchange and collaborative control between heterogeneous IoT applications is increasing. However, the user management space varies depending on the IoT application, where the security domain stands as an example. It is one of the key technologies of data sharing between heterogeneous IoT organizations to cross the boundary of the security domain and verify the identity and authority of users in other security domains. Aiming at the slow speed of authentication protocol authority authentication during cross-domain access and without considering the actual cross-domain situation, the same cryptographic system parameters are used for all communication nodes in a cross-domain environment. This article proposes a heterogeneous Internet of Things data access authority authentication scheme between applications. Based on certificate-less public key cryptography and smart contract technology, a certificate-less cross-domain authentication scheme that supports parameter differentiation is designed and implemented. The theoretical and empirical analyses, comparing the communication volume, identity signature, and verification calculation cost, validated that the method proposed improves the cross-domain identity authorization authentication ability and supports the use of differentiated cryptographic system parameters among different IoT applications.
An automatic arabic text summarization system based on genetic algorithms

Text summarization is the creation of compressed version of a given document that covers important information from original document. The aim of text summarization is to reduce the original text into a shorter text which represents significant content of the original text. There are two approaches for automatic text summarization: extractive and abstractive. Extractive summarization consists to select the most significant sentences on the original text. Abstractive summarization consists to compose novel sentences coherent with the original text. In this paper, we present an extractive based single document approach for Arabic text summarization system using Genetic Algorithms.
Imperceptible–Visible Watermarking to Information Security Tasks in Color Imaging

Digital image watermarking algorithms have been designed for intellectual property, copyright protection, medical data management, and other related fields; furthermore, in real-world applications such as official documents, banknotes, etc., they are used to deliver additional information about the documents’ authenticity. In this context, the imperceptible–visible watermarking (IVW) algorithm has been designed as a digital reproduction of the real-world watermarks. This paper presents a new improved IVW algorithm for copyright protection that can deliver additional information to the image content. The proposed algorithm is divided into two stages: in the embedding stage, a human visual system-based strategy is used to embed an owner logotype or a 2D quick response (QR) code as a watermark into a color image, maintaining a high watermark imperceptibility and low image-quality degradation. In the exhibition, a new histogram binarization function approach is introduced to exhibit any watermark with enough quality to be recognized or decoded by any application, which is focused on reading QR codes. The experimental results show that the proposed algorithm can embed one or more watermark patterns, maintaining the high imperceptibility and visual quality of the embedded and the exhibited watermark. The performance evaluation shows that the method overcomes several drawbacks reported in previous algorithms, including geometric and image processing attacks such as JPEG and JPEG2000.
Analysis of situ elemental concentration log data for lithology and mineralogy exploration— A case study

Metamorphic rocks are diverse with more compositions, structures, and textures that are complex. Rock type identification and prediction from metamorphic rocks using well log data are difficult tasks. This study shows the use of cross plot technique, Pearson correlation, and factor analysis in metamorphic rocks interpretation using borehole geochemical data from the 4390–5089 m interval depth of the Chinese Continental Scientific Drilling Main hole. Lithological identification abilities, correlation between geochemical and geophysical logs, and build a factor model which link in situ chemical element to minerals were studied. The results show that Potassium and Thorium logs are the most discriminating logs in metamorphic rocks. Pearson correlation shows that Potassium and Thorium are the largest contributors to the gamma ray responses. Factor analysis results show a 2 factor model-where factor 1 (amphibole mineral) and factor 2 (K-feldspar mineral) described 76.261% of the variation in log responses. These statistical methods can be a very helpful tool in helping the task of geoscientists in the context of research drillings.
Objects as Cameras: Estimating High-Frequency Illumination from Shadows

We recover high-frequency information encoded in the shadows cast by an object to estimate a hemispherical photograph from the viewpoint of the object, effectively turning objects into cameras. Estimating environment maps is useful for advanced image editing tasks such as relighting, object insertion or removal, and material parameter estimation. Because the problem is ill-posed, recent works in illumination recovery have tackled the problem of low- frequency lighting for object insertion, rely upon specular surface materials, or make use of data-driven methods that are susceptible to hallucination without physically plausible constraints. We incorporate an optimization scheme to update scene parameters that could enable practical capture of real-world scenes. Furthermore, we develop a methodology for evaluating expected recovery performance for different types and shapes of objects.
Improving Video Instance Segmentation by Light-weight Temporal Uncertainty Estimates

Instance segmentation with neural networks is an essential task in environment perception. In many works, it has been observed that neural networks can predict false positive instances with high confidence values and true positives with low ones. Thus, it is important to accurately model the uncertainties of neural networks in order to prevent safety issues and foster interpretability. In applications such as automated driving, the reliability of neural networks is of highest interest. In this paper, we present a time-dynamic approach to model uncertainties of instance segmentation networks and apply this to the detection of false positives as well as the estimation of prediction quality. The availability of image sequences in online applications allows for tracking instances over multiple frames. Based on an instances history of shape and uncertainty information, we construct temporal instance-wise aggregated metrics. The latter are used as input to post-processing models that estimate the prediction quality in terms of instance-wise intersection over union. The proposed method only requires a readily trained neural network (that may operate on single frames) and video sequence input. In our experiments, we further demonstrate the use of the proposed method by replacing the traditional score value from object detection and thereby improving the overall performance of the instance segmentation network.
Planning with Incomplete Information in Quantified Answer Set Programming

Abstract We present a general approach to planning with incomplete information in Answer Set Programming (ASP). More precisely, we consider the problems of conformant and conditional planning with sensing actions and assumptions. We represent planning problems using a simple formalism where logic programs describe the transition function between states, the initial states and the goal states. For solving planning problems, we use Quantified Answer Set Programming (QASP), an extension of ASP with existential and universal quantifiers over atoms that is analogous to Quantified Boolean Formulas (QBFs). We define the language of quantified logic programs and use it to represent the solutions different variants of conformant and conditional planning. On the practical side, we present a translation-based QASP solver that converts quantified logic programs into QBFs and then executes a QBF solver, and we evaluate experimentally the approach on conformant and conditional planning benchmarks.
Low Energy Interleaved Chaotic Secure Image Coding Scheme for Visual Sensor Networks Using Pascal’s Triangle Transform

The resource-constrained camera integrated Visual Sensor Networks (VSN) have conquered numerous visual aided services from visual surveillance to habitat monitoring.VSN is capable of sensing, processing and communicating visual data wirelessly.These networks are built with inexpensive low power sensor motes with a lightweight processor, limited storage, and bandwidth.The huge amount of redundancy present in the images makes the processing and communication consume more energy than expected.The number of bits must be reduced using energy-efficient compression techniques for efficient transmission.Low computational energy and communication energy are always favored for an increased lifetime of the wireless sensor network.The highly sensitive and self-descriptive nature of images makes security in VSN even more critical.In this work, we propose an energy-efficient low bitrate secured image coder for resource-constrained VSN.Light weight design protocols are highly required in secured image transmission over VSN.Through this communication, we also propose a novel chaotic map using Pascal's triangle.The system follows a unique interleaved fashion of compression and encryption process to consume less computational resources.A series of tests were carried out to validate the secured image coder's ruggedness and its suitability in VSN.The performance and the strength of the low bitrate secured image coder are tested with compression efficiency and cryptanalysis tests.Simulations were carried out in Atmel's ATmega128 processor for energy consumption analysis.The energy consumed by the proposed system for compression, encryption and transmission of an image of size 512 x 512 is 109.364mJ(milli Joules), which is only 4.57 % of the energy consumed by raw image transmission.In addition, the system is implemented in real time image sensor platform based on Arduino Due board integrated with OV7670 camera module for real time verification and the experimental results validated the suitability of the indicated scheme in real time VSN.
Automated extraction of information from Polish resume documents in the IT recruitment process

The problem of automated information retrieval and semantic analysis becomes more and more popular due to the difficulty of searching and analyzing large text datasets. Also, the recruitment process implies searching vast amounts of usually unstructured text files and makes human analysis challenging or even impossible to perform. This research presents a hybrid approach to automated information retrieval for the recruitment process in the IT industry. The proposed model implements a multi-module system in terms of low resource language dictionaries and complex linguistic dependencies in Polish. The experimental results confirmed our proposed solution. The keyword recognition for different resume sections was 60%-160% higher than for separate common tools.
Where Did the Web Archive Go?

To perform a longitudinal investigation of web archives and detecting variations and changes replaying individual archived pages, or mementos, we created a sample of 16,627 mementos from 17 public web archives. Over the course of our 14-month study (November, 2017–January, 2019), we found that four web archives changed their base URIs and did not leave a machine-readable method of locating their new base URIs, necessitating manual rediscovery. Of the 1,981 mementos in our sample from these four web archives, 537 were impacted: 517 mementos were rediscovered but with changes in their time of archiving (or Memento-Datetime), HTTP status code, or the string comprising their original URI (or URI-R), and 20 of the mementos could not be found at all.
Motion Target Detection and Recognition Based on YOLOv4 Algorithm

Abstract With the application of artificial intelligence more and more widely, the target detection of artificial intelligence “eyes” is becoming more and more important, which can give the machine the ability to detect and recognize image content in the environment. In this paper, YOLOv4 deep learning motion target detection algorithm is used to achieve the positioning and recognition of motion targets, to identify and mark the location and type of objects contained in the image, to achieve the detection of pictures, videos and camera realtime image detection, so that the machine has the most basic visual ability. Using YOLOv4 algorithm, the speed requirement of real-time detection can be realized with certain precision, and the model mAP value reached 69.48%.
Sequential movement pattern-mining (SMP) in field-based team-sport: A framework for quantifying spatiotemporal data and improve training specificity?

Athlete external load is typically quantified as volumes or discretised threshold values using distance, speed and time. A framework accounting for the movement sequences of athletes has previously been proposed using radio frequency data. This study developed a framework to identify sequential movement sequences using GPS-derived spatiotemporal data in team-sports and establish its stability. Thirteen rugby league players during one match were analysed to demonstrate the application of the framework. The framework (Sequential Movement Pattern-mining [SMP]) applies techniques to analyse i) geospatial data (i.e., decimal degree latitude and longitude), ii) determine players turning angles, iii) improve movement descriptor assignment, thus improving movement unit formation and iv) improve the classification and identification of players' frequent SMP. The SMP framework allows for sub-sequences of movement units to be condensed, removing repeated elements, which offers a novel technique for the quantification of similarities or dis-similarities between players and playing positions. The SMP framework provides a robust and stable method that allows, for the first time the analysis of GPS-derived data and identifies the frequent SMP of field-based team-sport athletes. The application of the SMP framework in practice could optimise the outcomes of training of field-based team-sport athletes by improving training specificity.
Video Question Answering Using Language-Guided Deep Compressed-Domain Video Feature

Video Question Answering (Video QA) aims to give an answer to the question through semantic reasoning between visual and linguistic information. Recently, handling large amounts of multi-modal video and language information of a video is considered important in the industry. However, the current video QA models use deep features, suffered from significant computational complexity and insufficient representation capability both in training and testing. Existing features are extracted using pre-trained networks after all the frames are decoded, which is not always suitable for video QA tasks. In this paper, we develop a novel deep neural network to provide video QA features obtained from coded video bit-stream to reduce the complexity. The proposed network includes several dedicated deep modules to both the video QA and the video compression system, which is the first attempt at the video QA task. The proposed network is predominantly model-agnostic. It is integrated into the state-of-the-art networks for improved performance without any computationally expensive motion-related deep models. The experimental results demonstrate that the proposed network outperforms the previous studies at lower complexity. https://github.com/Nayoung-Kim-ICP/VQAC