Reviewerly/exHarmony huggingface dataset:
CommAID: Visual Analytics for Communication Analysis through Interactive Dynamics Modeling

Communication consists of both meta-information as well as content. Currently, the automated analysis of such data often focuses either on the network aspects via social network analysis or on the content, utilizing methods from text-mining. However, the first category of approaches does not leverage the rich content information, while the latter ignores the conversation environment and the temporal evolution, as evident in the meta-information. In contradiction to communication research, which stresses the importance of a holistic approach, both aspects are rarely applied simultaneously, and consequently, their combination has not yet received enough attention in automated analysis systems. In this work, we aim to address this challenge by discussing the difficulties and design decisions of such a path as well as contribute CommAID, a blueprint for a holistic strategy to communication analysis. It features an integrated visual analytics design to analyze communication networks through dynamics modeling, semantic pattern retrieval, and a user-adaptable and problem-specific machine learning-based retrieval system. An interactive multi-level matrix-based visualization facilitates a focused analysis of both network and content using inline visuals supporting cross-checks and reducing context switches. We evaluate our approach in both a case study and through formative evaluation with eight law enforcement experts using a real-world communication corpus. Results show that our solution surpasses existing techniques in terms of integration level and applicability. With this contribution, we aim to pave the path for a more holistic approach to communication analysis.
Recursive Tree-Structured Self-Attention for Answer Sentence Selection

Syntactic structure is an important component of natural language text.Recent topperforming models in Answer Sentence Selection (AS2) use self-attention and transfer learning, but not syntactic structure.Tree structures have shown strong performance in tasks with sentence pair input like semantic relatedness.We investigate whether tree structures can boost performance in AS2.We introduce the Tree Aggregation Transformer: a novel recursive, tree-structured self-attention model for AS2.The recursive nature of our model is able to represent all levels of syntactic parse trees with only one additional self-attention layer.Without transfer learning, we establish a new state of the art on the popular TrecQA and WikiQA benchmark datasets.Additionally, we evaluate our method on four Community Question Answering datasets, and find that tree-structured representations have limitations with noisy user-generated text.We conduct probing experiments to evaluate how our models leverage tree structures across datasets.Our findings show that the ability of treestructured models to successfully absorb syntactic information is strongly correlated with a higher performance in AS2.
Convolutional neural network with group theory and random selection particle swarm optimizer for enhancing cancer image classification

<abstract> As an epitome of deep learning, convolutional neural network (CNN) has shown its advantages in solving many real-world problems. Successful CNN applications on medical prognosis and diagnosis have been achieved in recent years. Their common goal is to recognize the insights from the subtle details from medical images by building a suitable CNN model with maximum accuracy and minimum error. The CNN performance is extremely sensitive to the parameter tuning for any given network structure. To approach this concern, a novel self-tuning CNN model is proposed with a significant characteristic of having a metaheuristic-based optimizer. The most optimal set of parameters is often found via our proposed method, namely group theory and random selection-based particle swarm optimization (GTRS-PSO). The insights of symmetric essentials of model structure and parameter correlation are extracted, followed by the hierarchical partitioning of parameter space, and four operators on those partitions are designed for moving neighborhoods and formulating the swarm topology accordingly. The parameters are updated by a random selection strategy at each interval of partitions during the search process. Preliminary experiments over two radiology image datasets: breast cancer and lung cancer, are conducted for a comprehensive comparison of GTRS-PSO versus other optimization algorithms. The results show that CNN with GTRS-PSO optimizer can achieve the best performance for cancer image classifications, especially when there are symmetric components inside the data properties and model structures. </abstract>
Aspect-oriented requirement analysis based on Formal method

Abstract Aspect-Oriented Programming (AOP) can effectively solve the code-tangling and code-scattering which are caused by crosscutting attributes, However, much work is presented on the programming and the implementation phase, and few works have addressed the early model stages. In this paper, we do research on the requirements engineering phase of software by using AOP technology. First in the requirements analysis phase, we identified the functional requirements and non-functional requirements by the separation of concerns, then to realize the functional requirements with components and the non-functional requirements with aspects. Then we represent the components and aspects by combining with the formal language Aspect-Z which have characteristics of accurate description; As actions accompany with conflicts while two or more aspects effect on the same joint point synchronously, we propose a method to solve by defining aspects priority level.
Single Image Dehazing Based on Weighted Variational Regularized Model

Image dehazing is of great significance in computer vision and other fields. The performance of dehazing mainly relies on the precise computation of transmission map. However, the computation of the existing transmission map still does not work well in the sky area and is easily influenced by noise. Hence, the dark channel prior (DCP) and luminance model are used to estimate the coarse transmission in this work, which can deal with the problem of transmission estimation in the sky area. Then a novel weighted variational regularization model is proposed to refine the transmission. Specifically, the proposed model can simultaneously refine the transmittance and restore clear images, yielding a haze-free image. More importantly, the proposed model can preserve the important image details and suppress image noise in the dehazing process. In addition, a new Gaussian Adaptive Weighted function is defined to smooth the contextual areas while preserving the depth discontinuity edges. Experiments on real-world and synthetic images illustrate that our method has a rival advantage with the state-of-art algorithms in different hazy environments.
DESIGN FOR ONLINE TEACHING AND LEARNING IN TNE CONTEXT OF DIGITAL EDUCATION

We live in the XXI century, the century of new technologies. ICT has had a huge positive affect upon the way in which education has evolved and become far easier for the world to access. Education cannot continue any more to work by old methods based on class-lesson system, where the teacher stands before the class and presents the material the students are to learn. To improve the process of teaching and learning new methods based on ICT should be used. The importance of using online learning in the context of digital education for the development of knowledge, skills and competences in the contemporary educational process is reported in the article. Great attention is focused on the basic concepts of distance learning and mixed learning. It is proposed a model of planning online teaching and learning activities and recommendations at the level of the teaching staff.
Modeling High-order Interactions across Multi-interests for Micro-video Reommendation (Student Abstract)

Personalized recommendation system has become pervasive in various video platform.Many effective methods have been proposed, but most of them didn’t capture the user’s multilevel interest trait and dependencies between their viewed micro-videos well. To solve these problems, we propose a Self-over-Co Attention module to enhance user’s interest representation. In particular, we ﬁrst use co-attention to model correlation patterns across different levels and then use self attention to modelcorrelation patterns within a speciﬁc level. Experimental results on ﬁltered public datasets verify that our presented module is useful.
Optimal Order Pick-and-Place of Objects in Cluttered Scene by a Mobile Manipulator

In this letter, we present a fast method for autonomously planing manipulation tasks for mobile manipulators. The planner defines an optimal order to perform pick-and-place operations for taking objects from a cluttered scene to specific deposit areas considering both, manipulator and mobile base motion. Our method first examines the grasping feasibility of the objects with an inverse reachability map. Then, it defines all the placing locations for the objects and analyses the corresponding preconditions to reach them. Finally, it defines a sequence for rearranging the objects that minimize the execution time. We take advantage of the environment's underlying combinatorial structure to define the shortest path. In this work, we consider both the monotone case, where each object may be moved at most once, and the non-monotone cases. An experimental evaluation on the Human Support Robot (HSR) shows the effectiveness of our solutions and the scalability of our method as the number of objects increases for both cases. Tests on monotone problem instances with 20 objects show the proposed method can save up to 17% base traveling time when comparing to baseline methods at the cost of less than 9 seconds planning time. In a test on a simple non-monotone instance, the proposed method further reduces the total execution time by 25% by minimizing the total number of actions in a few seconds of planning time. The characteristic speed in re-planning makes our method suitable for online usage.
CLIP2Video: Mastering Video-Text Retrieval via Image CLIP

We present CLIP2Video network to transfer the image-language pre-training model to video-text retrieval in an end-to-end manner. Leading approaches in the domain of video-and-language learning try to distill the spatio-temporal video features and multi-modal interaction between videos and languages from a large-scale video-text dataset. Different from them, we leverage pretrained image-language model, simplify it as a two-stage framework with co-learning of image-text and enhancing temporal relations between video frames and video-text respectively, make it able to train on comparatively small datasets. Specifically, based on the spatial semantics captured by Contrastive Language-Image Pretraining (CLIP) model, our model involves a Temporal Difference Block to capture motions at fine temporal video frames, and a Temporal Alignment Block to re-align the tokens of video clips and phrases and enhance the multi-modal correlation. We conduct thorough ablation studies, and achieve state-of-the-art performance on major text-to-video and video-to-text retrieval benchmarks, including new records of retrieval accuracy on MSR-VTT, MSVD and VATEX.
Revealing Hidden Preconditions and Effects of Compound HTN Planning Tasks – A Complexity Analysis

In Hierarchical Task Network (HTN) planning, compound tasks need to be refined into executable (primitive) action sequences. In contrast to their primitive counterparts, compound tasks do not specify preconditions or effects. Thus, their implications on the states in which they are applied are not explicitly known: they are "hidden" in and depending on the decomposition structure. We formalize several kinds of preconditions and effects that can be inferred for compound tasks in totally ordered HTN domains. As relevant special case we introduce a problem relaxation which admits reasoning about preconditions and effects in polynomial time. We provide procedures for doing so, thereby extending previous work, which could only deal with acyclic models. We prove our procedures to be correct and complete for any totally ordered input domain. These results are embedded into an encompassing complexity analysis of the inference of preconditions and effects of compound tasks, an investigation that has not been made so far.
Comparison of WebSocket and HTTP protocol performance

The aim of the author of this article is analyze the performance of WebSocket and HTTP protocol and their comparison. For this purpose, was used equipment working in a local network consisting of server, two client computers, switch and self-created research web application. Using a test application was measured time of data transfer between clients and server as well server and clients. The tests included transmission 100-character texts in specified number of copies considering speed of hardware (laptops) and software (web browsers). Additionally, was investigated the impact of overhead and TLS encryption to performance. The obtained results have illustrated in the form of charts, discussed and appropriate conclusions drawn.
Pushing Paraphrase Away from Original Sentence: A Multi-Round Paraphrase Generation Approach

In recent years, neural paraphrase generation based on Seq2Seq has achieved superior performance, however, the generated paraphrase still has the problem of lack of diversity.In this paper, we focus on improving the diversity between the generated paraphrase and the original sentence, i.e., making generated paraphrase different from the original sentence as much as possible.We propose BTmPG (Back-Translation guided multi-round Paraphrase Generation), which leverages multi-round paraphrase generation to improve diversity and employs back-translation to preserve semantic information.We evaluate BTmPG on two benchmark datasets.Both automatic and human evaluation show BTmPG can improve the diversity of paraphrase while preserving the semantics of the original sentence.
Level generation for angry birds with sequential VAE and latent variable evolution

Video game level generation based on machine learning (ML), in particular, deep generative models, has attracted attention as a technique to automate level generation. However, applications of existing ML-based level generations are mostly limited to tile-based level representation. When ML techniques are applied to game domains with non-tile-based level representation, such as Angry Birds, where objects in a level are specified by real-valued parameters, ML often fails to generate playable levels. In this study, we develop a deep-generative-model-based level generation for the game domain of Angry Birds. To overcome these drawbacks, we propose a sequential encoding of a level and process it as text data, whereas existing approaches employ a tile-based encoding and process it as an image. Experiments show that the proposed level generator drastically improves the stability and diversity of generated levels compared with existing approaches. We apply latent variable evolution with the proposed generator to control the feature of a generated level computed through an AI agent's play, while keeping the level stable and natural.
Improving Automated Evaluation of Open Domain Dialog via Diverse Reference Augmentation

Multiple different responses are often plausible for a given open domain dialog context.Prior work has shown the importance of having multiple valid reference responses for meaningful and robust automated evaluations.In such cases, common practice has been to collect more human written references.However, such collection can be expensive, time consuming, and not easily scalable.Instead, we propose a novel technique for automatically expanding a human generated reference to a set of candidate references.We fetch plausible references from knowledge sources, and adapt them so that they are more fluent in context of the dialog instance in question.More specifically, we use (1) a commonsense knowledge base to elicit a large number of plausible reactions given the dialog history (2) relevant instances retrieved from dialog corpus, using similar past as well as future contexts.We demonstrate that our automatically expanded reference sets lead to large improvements in correlations of automated metrics with human ratings of system outputs for DailyDialog dataset. 1
A Light Field FDL-HSIFT Feature in Scale-Disparity Space

Many computer vision applications rely on feature matching, hence the need for computationally efficient and robust 4D light field (LF) feature detectors and descriptors for applications using this imaging modality. In this paper, we propose a novel LF feature extraction method in the scale-disparity space, based on a Fourier disparity layer representation. The proposed feature extraction takes advantage of both the Harris feature detector and SIFT descriptor, and is shown to yield more accurate feature matching, compared with the LiFF light field feature with low computational complexity. In order to evaluate the feature matching performance with the proposed descriptor, we generated synthetic LF datasets with ground truth matching points. Experimental results with synthetic and real datasets show that, our solution outperforms existing methods in terms of both feature detection robustness and feature matching accuracy.
In Factuality: Efficient Integration of Relevant Facts for Visual Question Answering

Visual Question Answering (VQA) methods aim at leveraging visual input to answer questions that may require complex reasoning over entities.Current models are trained on labelled data that may be insufficient to learn complex knowledge representations.In this paper, we propose a new method to enhance the reasoning capabilities of a multi-modal pretrained model (Vision+Language BERT) by integrating facts extracted from an external knowledge base.Evaluation on the KVQA dataset benchmark demonstrates that our method outperforms competitive baselines by 19%, achieving new state-of-the-art results.We also perform an extensive analysis highlighting the limitations of our best performing model through an ablation study.
Semi-Supervised Knowledge Amalgamation for Sequence Classification

Sequence classification is essential for domains from medical diagnosis to online advertising. In these settings, data are typically proprietary, and annotations are expensive to acquire. Often times, so few annotations are available that training a robust model from scratch is impractical. Recently, knowledge amalgamation (KA) has emerged as a promising strategy for training models without this hard-to-come-by labeled training dataset. To achieve this, KA methods combine the knowledge of multiple pre-trained teacher models (trained on different classification tasks and proprietary datasets) into one student model that becomes an expert on the union of all teachers’ classes. However, we demonstrate that the state-of-the-art solutions fail in the presence of overconfident teachers, which make confident but incorrect predictions for instances from classes upon which they were not trained. Additionally, to-date no work has explored KA for sequence models. Therefore, we propose and then solve the open problem of semi-supervised KA for sequence classification (SKA). Our SKA approach first learns to estimate how trustworthy each teacher is for a given instance, then rescales the predicted probabilities from all teachers to supervise a student model. Our solution overcomes overconfident teachers through careful use of a very small amount of labeled instances. We demonstrate that this approach beats eight state-of-the-art alternatives on four real-world datasets by on average 15% in accuracy with as little as 2% of training data being annotated.
HGAN: Hyperbolic Generative Adversarial Network

Recently, Hyperbolic Spaces in the context of Non-Euclidean Deep Learning have gained popularity because of their ability to represent hierarchical data.We propose that it is possible to take advantage of the hierarchical characteristic present in the images by using hyperbolic neural networks in a GAN architecture.In this study, different configurations using fully connected hyperbolic layers in the GAN, WGAN, CGAN, and the mapping network of the StyleGAN2 are tested in what we call the HGAN, HWGAN, HCGAN, and HStyleGAN, respectively.Furthermore, we test multiple values of curvature and introduce an exponential way to train it.The results are measured using the Inception Score (IS) and the Fréchet Inception Distance (FID) over the MNIST dataset and with FID over CIFAR-10.Depending on the configuration and space curvature, better results are achieved for each proposed hyperbolic version than their euclidean counterpart.
Safety-Aware Integration of Hardware-Assisted Program Tracing in Mixed-Criticality Systems for Security Monitoring

In Mixed-Criticality Systems (MCS), low-critical applications have a larger attack surface compared to high-critical applications. Even though MCS isolate criticality domains of execution by design, a threat affecting a low-critical service can alter the execution of a high-critical task: e.g by degrading the system availability or user-experience. Such attack on low-critical tasks can even introduce an entry point for propagating the attack further to high-critical tasks. In this context, detecting such threats in applications during runtime is a major issue for MCS security. Control-Flow Integrity (CFI) monitoring is a common security technique for runtime threat detection in program execution: using a predefined policy, it identifies unauthorized control-flow (CF) transitions as malicious activity. Recent research has introduced the use of hardware-assisted CF tracing for CFI monitoring into industrial real-time systems, since such implementation does not require instrumentation of the monitored program. However, CF tracing brings a high performance overhead for monitoring, which depends on the monitored program execution path (the more executed CF transitions, the higher the overhead). On one hand to integrate the security service in a MCS, we must consider the worst-case monitoring overhead. On the other hand, this assumption is highly pessimistic for a practical deployment. We propose a first safety-aware method to integrate hardware-assisted CF based security monitoring with ARM CoreSight into a MCS, and metrics to evaluate the trade-off between performance impact and security monitoring coverage. Our security framework combines a predictable CF transition level monitoring with trace collection that can be used for CFI checking, together with an anomaly detection service to monitor the full program execution. We validate our approach on an industrial MCS platform with ARM CoreSight support, using a set of programs from TACLeBench benchmark.