Reviewerly/exHarmony huggingface dataset:
Ordinal Historical Dependence in Graphical Event Models with Tree Representations

Graphical event models are representations that capture process independence between different types of events in multivariate temporal point processes. The literature consists of various parametric models and approaches to learn them from multivariate event stream data. Since these models are interpretable, they are often able to provide beneficial insights about event dynamics. In this paper, we show how to compactly model the situation where the order of occurrences of an event’s causes in some recent historical time interval impacts its occurrence rate; this sort of historical dependence is common in several real-world applications. To overcome the practical challenge of parameter explosion due to the number of potential orders that is super-exponential in the number of parents, we introduce a novel graphical event model based on a parametric tree representation for capturing ordinal historical dependence. We present an approach to learn such a model from data, demonstrating that the proposed model fits several real-world datasets better than relevant baselines. We also showcase the potential advantages of such a model to an analyst during the process of knowledge discovery.
Multi-Scale Tiny Region Gesture Recognition Towards 3D Object Manipulation In Industrial Design

This paper proposed a smart 3D virtual object manipulation by gesture recognition with deep network training for multiply scalar tiny region targets. We introduce the famous YOLOv5 based improved and enhanced target detection deep networks, and provide effective and high efficiency gesture recognition method towards 3D object manipulation. Meanwhile, combined with the adaptive anchor frame calculation and target recognition accelerator, our method can capture the tiny gesture region and for multi-scale of image detection. The method can be efficiently applied to the virtual control with different hand gestures. The experiment results are high efficiency and accuracy. It has high application value in the field of industrial design based on gesture manipulation.
EPPics: Enhanced Personalised Picture Stories

EPPics is a project that focuses on the concept of creating personalised stories to foster pre-reading skills in preschool children. The children, together with their teachers and parents, are engaged as co-designers in a collaborative design approach. After describing the theoretical foundation of my work, I will detail the steps that I have taken so far: a preliminary user study conducted in a preschool in Lugano and a series of interviews to create child personas. I will then outline the next steps in my research: the design of a interactive storytelling app for children, and the design of a conversational agent to help parents read with their children.
Fusion Algorithm for Foggy Image Enhancement Based on Transmittance Weight Factor

Fusion algorithm for foggy image enhancement based on transmittance weight factor is proposed. Firstly, the rough transmittance image of foggy image is obtained by the dark channel prior dehazing algorithm, and then the fine transmittance image is obtained by the guided filter. The dehazing image is obtained by the dark channel prior dehazing method based on the guided filter, and the enhanced image is obtained by histogram equalization based on the HSI color model. Finally, the weight factor is constructed by the fine transmittance, the above dehazing image and enhanced image are processed by weighted fusion. The algorithm gives full play to the respective advantages of two kinds of algorithms in foggy image processing, and significantly improves the fog removal and enhancement effect of foggy image.
Microblog-oriented Multi-scale CNN Multi-label Sentiment Classification Model

With the rapid development of the Internet, social networks have become an important platform for people to express their opinions. Through the sentiment analysis of the text, the user's emotional tendency can be captured in time. Multi-label sentiment classification is one of the difficult issues. Therefore, the paper proposes a multi-label sentiment classification model for short texts of Weibo based on multiscale CNN. Firstly, aiming at the insufficiency of multi-label emotional corpus, we give a method to construct a multi-label emotional corpus based on emotional seed words, which uses synonym forest and TF-IDF weights to automatically label the corpus. Then a multi-label sentiment analysis model based on multi-scale CNN is proposed, which uses different convolution scales to extract features of text. Finally we found that the accuracy of the method has been significantly improved.
Watermarking Techniques For Royalty Accounts In Content Management Websites For Iot Image Association

Utilizing IoT associations has been trending recently. They have been used in numerous fields of life, comprising protected and subtle segments like the healthcare and military. Images that are used across IoT platforms infringe copyright strategies and rescind the authenticity of the images taken with hard work by a human. Thus, the objective of this study is to provide IoT association of images (pictures/photos) to pages across content management websites with unique watermarks to account for Royalty to the person/association owning the camera. This model indulges in emerging and registering a distinctive number that exclusively ties up the human and the camera which is getting used in such a way that the photo taken by the person will leave a unique identifier or mark which will make the image copyrighted and uploads to cloud for direct usage on any pages in IoT so that direct revenue of the copyrighted photograph goes to the person who clicked the photo. Our results presented the watermarking method to account for royalty in content management websites for IoT association of images contrary to the work's existing result. Our result assumed that with the watermarking method, royalty can be accounted to the rightful owner of the image during the IoT-associated image.
Weak learning convex sets under normal distributions

This paper addresses the following natural question: can efficient algorithms weakly learn convex sets under normal distributions? Strong learnability of convex sets under normal distributions is well understood, with near-matching upper and lower bounds given by Klivans et al (2008), but prior to the current work nothing seems to have been known about weak learning. We essentially answer this question, giving near-matching algorithms and lower bounds.For our positive result, we give a poly(n)-time algorithm that can weakly learn the class of convex sets to advantage $\Omega(1/\sqrt{n})$ using only random examples drawn from the background Gaussian distribution. Our algorithm and analysis are based on a new “density increment” result for convexsets, which we prove using tools from isoperimetry.We also give an information-theoretic lower bound showing that $O(\log(n)/\sqrt{n})$ advantage is best possible even for algorithms that are allowed to make poly(n) many membership queries.
Comprehensive model of safe educational spaces and lifelong learning for educators: regional approach

This article considers approaches to the strategies and tactics of the methodological support for modern teachers’ activity in the frameworks of the National Project on Education as seen and presented via innovation humanitarian practices in the current professional community of the Regional Destination (Novosibirsk, Russia). Of particular theoretical and practical significance is the identification of the degree of objectivity and safety of the rationale of Tutoring within a network of consulting centers to provide methodological support for assessing the quality of professional skills and knowledge of educators of the Lifelong Learning stage. The empirical array is the regional centers’ activity in the aspect of functional literacy of the participants as the main message of theory, practice and provisions of the comprehensive axiological model of the regional information and education spaces. The conclusion is made on inconsistency of personalized teaching and learning as well as on management of continuing education.
The Search Studies Group at Hamburg University of Applied Sciences

We present an overview of the work of the Search Studies research group, focusing on commercial search engines from a user perspective. This encompasses studying what users of these search engines get to see on the result pages, how users interact with search engines, and the effect both have on knowledge acquisition in society. Our research combines search engine data analysis, by collecting and analysing data from commercial search engines (data science), with understanding information-seeking behaviour through conducting user studies in different settings (information science), ranging from large, representative online surveys to behavioural studies in the lab employing, amongst others, eye-tracking.
Energy-Based Imitation Learning

We tackle a common scenario in imitation learning (IL), where agents try to recover the optimal policy from expert demonstrations without further access to the expert or environment reward signals. Except the simple Behavior Cloning (BC) that adopts supervised learning followed by the problem of compounding error, previous solutions like inverse reinforcement learning (IRL) and recent generative adversarial methods involve a bi-level or alternating optimization for updating the reward function and the policy, suffering from high computational cost and training instability. Inspired by recent progress in energy-based model (EBM), in this paper, we propose a simplified IL framework named Energy-Based Imitation Learning (EBIL). Instead of updating the reward and policy iteratively, EBIL breaks out of the traditional IRL paradigm by a simple and flexible two-stage solution: first estimating the expert energy as the surrogate reward function through score matching, then utilizing such a reward for learning the policy by reinforcement learning algorithms. EBIL combines the idea of both EBM and occupancy measure matching, and via theoretic analysis we reveal that EBIL and Max-Entropy IRL (MaxEnt IRL) approaches are two sides of the same coin, and thus EBIL could be an alternative of adversarial IRL methods. Extensive experiments on qualitative and quantitative evaluations indicate that EBIL is able to recover meaningful and interpretative reward signals while achieving effective and comparable performance against existing algorithms on IL benchmarks.
“We will Reduce Taxes” - Identifying Election Pledges with Language Models

In an election campaign, political parties pledge to implement various projects-should they be elected.But do they follow through?To track election pledges from parties' election manifestos, we need to distinguish between pledges and general statements.In this paper, we use election manifestos of Swedish and Indian political parties to learn neural models that distinguish actual pledges from generic political positions.Since pledges might vary by election year and party, we implement a Multi-Task Learning (MTL) setup, predicting election year and manifesto's party as auxiliary tasks.Pledges can also span several sentences, so we use hierarchical models that incorporate contextual information.Lastly, we evaluate the models in a Zero-Shot Learning (ZSL) framework across countries and languages.Our results indicate that year and party have predictive power even in ZSL, while context introduces some noise.We finally discuss the linguistic features of pledges.
Error-Aware Policy Learning: Zero-Shot Generalization in Partially Observable Dynamic Environments

Simulation provides a safe and efficient way to generate useful data for learning complex robotic tasks.However, matching simulation and real-world dynamics can be quite challenging, especially for systems that have a large number of unobserved or unmeasurable parameters, which may lie in the robot dynamics itself or in the environment with which the robot interacts.We introduce a novel approach to tackle such a sim-to-real problem by developing policies capable of adapting to new environments, in a zero-shot manner.Key to our approach is an error-aware policy (EAP) that is explicitly made aware of the effect of unobservable factors during training.An EAP takes as input the predicted future state error in the target environment, which is provided by an error-prediction function, simultaneously trained with the EAP.We validate our approach on an assistive walking device trained to help the human user recover from external pushes.We show that a trained EAP for a hip-torque assistive device can be transferred to different human agents with unseen biomechanical characteristics.In addition, we show that our method can be applied to other standard RL control tasks.
Ontology-Enriched Query Answering on Relational Databases

We develop a flexible, open-source framework for query answering on relational databases by adopting methods and techniques from the Semantic Web community and the data exchange community, and we apply this framework to a medical use case. We first deploy module-extraction techniques to derive a concise and relevant sub-ontology from an external reference ontology. We then use the chase procedure from the data exchange community to materialize a universal solution that can be subsequently used to answer queries on an enterprise medical database. Along the way, we identify a new class of well-behaved acyclic EL-ontologies extended with role hierarchies, suitably restricted functional roles, and domain/range restrictions, which cover our use case. We show that such ontologies are C-stratified, which implies that the chase procedure terminates in polynomial time. We provide a detailed overview of our real-life application in the medical domain and demonstrate the benefits of this approach, such as discovering additional answers and formulating new queries.
More than just Frequency? Demasking Unsupervised Hypernymy Prediction Methods

This paper presents a comparison of unsupervised methods of hypernymy prediction (i.e., to predict which word in a pair of words such as fish-cod is the hypernym and which the hyponym).Most importantly, we demonstrate across datasets for English and for German that the predictions of three methods (Weeds-Prec, invCL, SLQS Row) strongly overlap and are highly correlated with frequency-based predictions.In contrast, the second-order method SLQS shows an overall lower accuracy but makes correct predictions where the others go wrong.Our study once more confirms the general need to check the frequency bias of a computational method in order to identify frequency-(un)related effects.
Exploring Word Usage Change with Continuously Evolving Embeddings

The usage of individual words can change over time, for example, when words experience a semantic shift.As text datasets generally comprise documents that were collected over a longer period of time, examining word usage changes in a corpus can often reveal interesting patterns.In this paper, we introduce a simple and intuitive way to track word usage changes via continuously evolving embeddings, computed as a weighted running average of transformer-based contextualized embeddings.We demonstrate our approach on a corpus of recent New York Times article snippets and provide code for an easy to use web app to conveniently explore semantic shifts with interactive plots.
Soft Computing Techniques in Social Sciences: The Recent Developments

The paradigm of soft computing encompasses mathematical techniques in solving the complex real-life problems which are capable of handling uncertainty and partial truth. As a matter of fact, soft computing techniques do not adhere to strict mathematical and technical dimensions, and as such, human mind is given importance to. Because of its adaptive nature, soft computing technique is pre-dominant in social sciences, too, as it renders human knowledge in the form of ‘cognition, recognition and learning’. Since soft computing techniques are indifferent towards strict mathematical formulation, social science arena of recent times focuses on the usage of it, as the characteristics of the human brain to recognise and perceive can be deduced through it. Behavioural approach can be studied through probabilistic reasoning, and diverse solutions can be provided for one complex problem. The present study is an attempt towards analysing the emergence of soft computing techniques in the field of social sciences, which has the ability to structure hazy problems which are fuzzy in nature amidst the environment of uncertainty.
End-to-End Task-Oriented Dialog Modeling With Semi-Structured Knowledge Management

Current task-oriented dialog (TOD) systems mostly manage structured knowledge (e.g. databases and tables) to guide the goal-oriented conversations. However, they fall short of handling dialogs which also involve unstructured knowledge (e.g. reviews and documents). In this paper, we formulate a task of modeling TOD grounded on a fusion of structured and unstructured knowledge. To address this task, we propose a TOD system with semi-structured knowledge management, SeKnow, which extends the belief state to manage knowledge with both structured and unstructured contents. Furthermore, we introduce two implementations of SeKnow based on a non-pretrained sequence-to-sequence model and a pretrained language model, respectively. Both implementations use the end-to-end manner to jointly optimize dialog modeling grounded on structured and unstructured knowledge. We conduct experiments on a modified version of MultiWOZ 2.1 dataset, Mod-MultiWOZ 2.1, where dialogs are processed to involve semi-structured knowledge. Experimental results show that SeKnow has strong performances in both end-to-end dialog and intermediate knowledge management, compared to existing TOD systems and their extensions with pipeline knowledge management schemes.
Blockchain Is Unbreakable? Think Again

Blockchain technology has tremendous advantages that many organizations are eagerly looking to exploit in sectors as diverse as shipping, real estate and diamonds. The advantages are so compelling and hyped that I sometimes say the easiest way to make money with a startup is to put “blockchain” in the company name, such as when the shares of the British investment enterprise On-line Plc surged 394% in direct response to the company’s new name “On-line Blockchain PLC.”
Window Loss for Bone Fracture Detection and Localization in X-ray Images with Point-based Annotation

Object detection methods are widely adopted for computer-aided diagnosis using medical images. Anomalous findings are usually treated as objects that are described by bounding boxes. Yet, many pathological findings, e.g., bone fractures, cannot be clearly defined by bounding boxes, owing to considerable instance, shape and boundary ambiguities. This makes bounding box annotations, and their associated losses, highly ill-suited. In this work, we propose a new bone fracture detection method for X-ray images, based on a labor effective and flexible annotation scheme suitable for abnormal findings with no clear object-level spatial extents or boundaries. Our method employs a simple, intuitive, and informative point-based annotation protocol to mark localized pathology information. To address the uncertainty in the fracture scales annotated via point(s), we convert the annotations into pixel-wise supervision that uses lower and upper bounds with positive, negative, and uncertain regions. A novel Window Loss is subsequently proposed to only penalize the predictions outside of the uncertain regions. Our method has been extensively evaluated on 4410 pelvic X-ray images of unique patients. Experiments demonstrate that our method outperforms previous state-of-the-art image classification and object detection baselines by healthy margins, with an AUROC of 0.983 and FROC score of 89.6%.
ACMo: Angle-Calibrated Moment Methods for Stochastic Optimization

Stochastic gradient descent (SGD) is a widely used method for its outstanding generalization ability and simplicity. Adaptive gradient methods have been proposed to further accelerate the optimization process. In this paper, we revisit existing adaptive gradient optimization methods with a new interpretation. Such new perspective leads to a refreshed understanding of the roles of second moments in stochastic optimization. Based on this, we propose Angle-Calibration Moment method (ACMo), a novel stochastic optimization method. It enjoys the benefits of second moments with only first moment updates. Theoretical analysis shows that ACMo is able to achieve the same convergence rate as mainstream adaptive methods. Experiments on a variety of CV and NLP tasks demonstrate that ACMo has a comparable convergence to state-of-the-art Adam-type optimizers, and even a better generalization performance in most cases. The code is available at https://github.com/Xunpeng746/ACMo.
Dilemas y desafíos de la universidad desde el cruce de visiones entre docentes y estudiantes

When voices are heard about the dilemmas and challenges facing universities, they come mainly from politicians, managers, academics, businesspeople and representatives of civil society. But rarely experiences and opinions of students are made visible. In this article we set out to bring young students into the conversation, starting with the question: how do young university students situate themselves and think of themselves in an institution that, in general terms, does not take their needs and expectations into account? An institution that imagines them as consumers of knowledge and skills that, in many cases, acts as a seller of diplomas and that does not consider who they are, how they learn and how they approach their present future. To this end, after giving an account of what allows us to think about the current situation of the University, we cross-reference it with what the young people say in the meetings linked to the research project TRAY-AP.