Reviewerly/exHarmony huggingface dataset:
HAI-GEN 2021: 2nd Workshop on Human-AI Co-Creation with Generative Models

Recent advances in generative AI have resulted in a rapid and dramatic increase to the fidelity of created artifacts, from realistic-looking images of faces [10] to antimicrobial peptide sequences that treat diseases [5] to faked videos of prominent business leaders [4, 11]. We believe that people skilled within their creative domain can realize great benefits by incorporating generative models into their own work: as a source of inspiration, as a tool for manipulation, or as a creative partner. Our workshop will bring together researchers and practitioners from both the HCI and AI disciplines to explore and better understand the opportunities and challenges in building, using, and evaluating human-AI co-creative systems.
Hand Gesture Identification and Recognition using Modern Deep Learning Algorithms

In this paper, we proposed an approach towards the real-time hand gesture recognition using the Gaussian Mixture-based Background / Foreground Segmentation Algorithm. We proposed a method for feature extraction by using measurements on joints of the extracted skeletons. The proposed algorithm will build a background subtract model to get the foreground image. We applied Gaussian blur to the foreground image and threshold for binary images. The contour hull and convexity are used to build a 3D image of the hand gesture recognition. We constructed a dataset and defined the gestures. We trained them by gesture classifiers by some assumptions such that those can be easily understood. Experimental results proved the effectiveness and potential of our modern deep learning approach.
PNNCP- Parallel Nearest Neighbor Classification and Prediction for Big Data Application Based on Apache Spark and Machine Learning

Right by and by the Colossal Information applications, for case, social orchestrating, helpful human administrations, agribusiness, keeping cash, stock show, direction, Facebook and so forward are making the data with especially tall speed. Volume and Speed of the Immense data plays a fundamental bit interior the execution of Colossal data applications. Execution of the Colossal data application can be affected by distinctive parameters. Quickly watch, capacity and precision are the a significant parcel of the triumphant parameters which impact the by and gigantic execution of any Huge data applications. Due the energize and underhanded affiliation of the qualities of 7Vs of Colossal data, each Colossal Information affiliations expect the tall execution.Tall execution is the foremost obvious test within the display advancing condition. In this paper we propose the parallel course of action way to bargain with speedup the explore for closest neighbor center. k-NN classifier is the preeminent basic and comprehensively utilized method for gathering. In this paper we apply a parallelism thought to k-NN for looking the another closest neighbor. This neighbor center will be utilized for putting lost and execution of the remarkable data streams. This classifier unequivocally overhaul and coordinate of the out of date data streams. We are utilizing the Apache Begin and scattered estimation space affiliation for snappier evaluation.
Cyber-Situational Crime Prevention and the Breadth of Cybercrimes among Higher Education Institutions

Academic institutions house enormous amounts of critical information from social security numbers of students to proprietary research data. Thus, maintaining up to date cybersecurity practices to protect academic institutions’ information and facilities against cyber-perpetrators has become a top priority. The purpose of this study is to assess common cybersecurity measures through a situational crime prevention (SCP) theoretical framework. Using a national data set of academic institutions in the United States, this study investigates the link between common cybersecurity measures, crime prevention activities, and cybercrimes. By focusing on the conceptualization of cybersecurity measures as SCP techniques, this study also offers the SCP approach as a framework by which universities can seek to reduce incidents of cybercrime through the design, maintenance, and use of the built environment in the digital realm. Implications for theory, research and practice are discussed.
Mixed Sessions

Abstract Session types describe patterns of interaction on communicating channels. Traditional session types include a form of choice whereby servers offer a collection of options, of which each client picks exactly one. This sort of choice constitutes a particular case of separated choice: offering on one side, selecting on the other. We introduce mixed choices in the context of session types and argue that they increase the flexibility of program development at the same time that they reduce the number of synchronisation primitives to exactly one. We present a type system incorporating subtyping and prove preservation and absence of runtime errors for well-typed processes. We further show that classical (conventional) sessions can be faithfully and tightly embedded in mixed choices. Finally, we discuss algorithmic type checking and a runtime system built on top of a conventional (choice-less) message-passing architecture.
A movie recommendation model combining time information and probability matrix factorisation

A deep analysis and discussion of matrix factorisation technologies are given in this paper taking into account the defects of traditional collaborative filtering recommendation algorithms.In addition, we provide an analysis of the effects of feature vector dimensions on the recommendation quality and efficiency of a probability matrix factorisation (PMF) algorithm.A PMF algorithm will lead to inaccurate recommendations if it does not consider possible dynamic changes in a user's interest over time.Accordingly, a TPMF model, a PMF algorithm integrated with time information, is proposed in this article.Its feasibility and effectiveness are empirically verified using movie recommendation datasets, and higher prediction accuracy is confirmed compared to existing recommendation algorithms.
Ada-Ranker

A large-scale recommender system usually consists of recall and ranking modules. The goal of ranking modules (aka rankers) is to elaborately discriminate users' preference on item candidates proposed by recall modules. With the success of deep learning techniques in various domains, we have witnessed the mainstream rankers evolve from traditional models to deep neural models. However, the way that we design and use rankers remains unchanged: offline training the model, freezing the parameters, and deploying it for online serving. Actually, the candidate items are determined by specific user requests, in which underlying distributions (e.g., the proportion of items for different categories, the proportion of popular or new items) are highly different from one another in a production environment. The classical parameter-frozen inference manner cannot adapt to dynamic serving circumstances, making rankers' performance compromised.
Single-Image Camera Calibration for Furniture Layout Using Natural-Marker-Based Augmented Reality

We propose an augmented-reality-based method for arranging furniture using natural markers extracted from the edges of the walls of rooms. The proposed method extracts natural markers and estimates the camera parameters from single images of rooms using deep neural networks. Experimental results show that in all the measurements, the superimposition error of the proposed method was lower than that of general marker-based methods that use practical-sized markers.
LOREN: Logic-Regularized Reasoning for Interpretable Fact Verification

Given a natural language statement, how to verify its veracity against a large-scale textual knowledge source like Wikipedia? Most existing neural models make predictions without giving clues about which part of a false claim goes wrong. In this paper, we propose LOREN, an approach for interpretable fact verification. We decompose the verification of the whole claim at phrase-level, where the veracity of the phrases serves as explanations and can be aggregated into the final verdict according to logical rules. The key insight of LOREN is to represent claim phrase veracity as three-valued latent variables, which are regularized by aggregation logical rules. The final claim verification is based on all latent variables. Thus, LOREN enjoys the additional benefit of interpretability --- it is easy to explain how it reaches certain results with claim phrase veracity. Experiments on a public fact verification benchmark show that LOREN is competitive against previous approaches while enjoying the merit of faithful and accurate interpretability. The resources of LOREN are available at: https://github.com/jiangjiechen/LOREN.
A Boundary Consistency-Aware Multitask Learning Framework for Joint Activity Segmentation and Recognition With Wearable Sensors

With the development of industrial and sensing technology, sensor-based activity recognition has become a promising technology for informatics applications. However, in a typical activity recognition procedure, sensory data segmentation, usually considered a preprocess with sliding windows, rarely has been investigated and significantly affected the recognition performance. In this article, we propose a novel deep-learning method to jointly segment and recognize activities with wearable sensors. Our contributions are three-fold: First, we introduce a multistage temporal convolutional network for sample-level activity prediction to overcome the multiclass windows problem. Second, for alleviating oversegmentation errors, our model forms a multitask learning framework with a boundary prediction module to adjust the entire model’s gradients. Third, we innovatively propose a boundary consistency loss to enforce the consistency of the activity and boundary prediction. Our method shows impressive performance on three public datasets, especially achieving 16% improvement over very recently advanced competing methods with class-average F1-score on the Hospital dataset. The code of this work will be open source on <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/xspc/Segmentation-Sensor-based-HAR</uri> .
Direct parsing to sentiment graphs

This paper demonstrates how a graph-based semantic parser can be applied to the task of structured sentiment analysis, directly predicting sentiment graphs from text.We advance the state of the art on 4 out of 5 standard benchmark sets.We release the source code, models and predictions. 1
Variational Graph Autoencoding as Cheap Supervision for AMR Coreference Resolution

Coreference resolution over semantic graphs like AMRs aims to group the graph nodes that represent the same entity.This is a crucial step for making document-level formal semantic representations.With annotated data on AMR coreference resolution, deep learning approaches have recently shown great potential for this task, yet they are usually data hungry and annotating data is costly.We propose a general pretraining method using variational graph autoencoder (VGAE) for AMR coreference resolution, which can leverage any general AMR corpus and even automatically parsed AMR data.Experiments on benchmarks show that the pretraining approach achieves performance gains of up to 6% absolute F1 points.Moreover, our model significantly improves on the previous state-of-theart model by up to 11% F1 points.
ETO

Recently, deep neural networks (DNNs) have achieved great success in various applications, where low inference latency is important. Existing solutions either manually tune the kernel library or utilize search-based compilation to reduce the operator latency. However, manual tuning requires significant engineering effort, and the huge search space makes the search cost of the search-based compilation unaffordable in some situations. In this work, we propose ETO, a framework for speeding up DNN operator optimization based on reusing the information of performant tensor programs. Specifically, ETO defines conditions for the information reuse between two operators. For operators satisfying the conditions, based on the performant tensor program information of one operator, ETO uses a reuse-based tuner to significantly prune the search space of the other one, and keeps optimization effectiveness at the same time. In this way, for a set of operators, ETO first determines the information reuse relationships among them to reduce the total search time needed, and then tunes the operators either by the backend compiler or by the reuse-based tuner accordingly. ETO further increases the reuse opportunities among the operators by injecting extra operators as bridges between two operators which do not satisfy the reuse conditions. Compared with various existing methods, the experiments show that ETO is effective and efficient in optimizing DNN operators.
Disentangled Contrastive Learning for Learning Robust Textual Representations

Although the self-supervised pre-training of transformer models has resulted in the revolutionizing of natural language processing (NLP) applications and the achievement of state-of-the-art results with regard to various benchmarks, this process is still vulnerable to small and imperceptible permutations originating from legitimate inputs. Intuitively, the representations should be similar in the feature space with subtle input permutations, while large variations occur with different meanings. This motivates us to investigate the learning of robust textual representation in a contrastive manner. However, it is non-trivial to obtain opposing semantic instances for textual samples. In this study, we propose a disentangled contrastive learning method that separately optimizes the uniformity and alignment of representations without negative sampling. Specifically, we introduce the concept of momentum representation consistency to align features and leverage power normalization while conforming the uniformity. Our experimental results for the NLP benchmarks demonstrate that our approach can obtain better results compared with the baselines, as well as achieve promising improvements with invariance tests and adversarial attacks. The code is available in https://github.com/zxlzr/DCL.
Poster Abstract: Realistic Multiuser, Multimodal (IMU, Acoustic) HAR Data Generation through Single User Data Augmentation

Multiuser activity recognition has been the core of different context-aware services. However, the development of such services is often plagued by the dearth of multiuser datasets. This paper presents a strategy for generating synthetic multiuser datasets by augmenting existing real-life datasets. The described strategy exploits pre-cise time synchronization and well-known audio augmentation approaches to generate a multimodal activity recognition dataset with locomotive and acoustic signatures.
An In-depth Study of Java Deserialization Remote-Code Execution Exploits and Vulnerabilities

Nowadays, an increasing number of applications uses deserialization. This technique, based on rebuilding the instance of objects from serialized byte streams, can be dangerous since it can open the application to attacks such as remote code execution (RCE) if the data to deserialize is originating from an untrusted source. Deserialization vulnerabilities are so critical that they are in OWASP's list of top 10 security risks for web applications. This is mainly caused by faults in the development process of applications and by flaws in their dependencies, i.e., flaws in the libraries used by these applications. No previous work has studied deserialization attacks in-depth: How are they performed? How are weaknesses introduced and patched? And for how long are vulnerabilities present in the codebase? To yield a deeper understanding of this important kind of vulnerability, we perform two main analyses: one on attack gadgets, i.e., exploitable pieces of code, present in Java libraries, and one on vulnerabilities present in Java applications. For the first analysis, we conduct an exploratory large-scale study by running 256515 experiments in which we vary the versions of libraries for each of the 19 publicly available exploits. Such attacks rely on a combination of gadgets present in one or multiple Java libraries. A gadget is a method which is using objects or fields that can be attacker-controlled. Our goal is to precisely identify library versions containing gadgets and to understand how gadgets have been introduced and how they have been patched. We observe that the modification of one innocent-looking detail in a class -- such as making it public -- can already introduce a gadget. Furthermore, we noticed that among the studied libraries, 37.5% are not patched, leaving gadgets available for future attacks. For the second analysis, we manually analyze 104 deserialization vulnerabilities CVEs to understand how vulnerabilities are introduced and patched in real-life Java applications. Results indicate that the vulnerabilities are not always completely patched or that a workaround solution is proposed. With a workaround solution, applications are still vulnerable since the code itself is unchanged.
Real-Time Robust Receding Horizon Planning Using Hamilton–Jacobi Reachability Analysis

Safety guarantee prior to the deployment of robots can be difficult due to unexpected disturbances in runtime. This article presents a real-time receding-horizon robust trajectory planning algorithm for nonlinear closed-loop systems, which guarantees the safety of the system under unknown but bounded disturbances. We characterize the forward reachable sets (FRSs) of the system based on the Hamilton–Jacobi reachability analysis as a means for safety verification. For the online computation of the FRSs, we approximate nonlinear systems as LTV systems with linearization errors and compute ellipsoids that encompass the FRSs in continuous time. Using the proposed ellipsoidal approximation of the FRSs, we formulate a computationally tractable robust planning problem that can be solved online. Consequently, the proposed method enables real-time replanning of a reference trajectory with safety guarantees even when the system encounters unexpected disturbances in runtime. The flight experiment of obstacle avoidance in a windy environment validates the proposed robust planning algorithm.
Operation system features and cloud services for lecturer work

The work proposes a conception of setup and use of teacher’s or lecturer’s workspace using common software and hardware products. The research object is a system built by using operating system capabilities in conjunction with office suite and public cloud service, as a foundation for teacher’s digital workspace. Research is made on how to set up, scale, and operate such a system, by studying the experience of national and foreign scientists and teachers, and using our own experience in educational processes, and working with operating systems and cloud services. As a result, we got a system which is easy to set up, learn, and apply by teachers without significant experience working remote education systems, and could be used for initial learning of remote education principles. It could be used as an initial step before migrating to specialized remote education systems. In the future, the system itself could be improved by adding additional objects into the system and a higher integration level between objects and external subjects.
Cloud enabling educational platforms with corc

In this paper, it is shown how teaching platforms at educational institutions can utilize cloud platforms to scale a particular service, or gain access to compute instances with accelerator capability such as GPUs. Specifically at the University of Copenhagen (UCPH), it is demonstrated how the internal JupyterHub service, named Data Analysis Gateway (DAG), could utilize compute resources in the Oracle Cloud Infrastructure (OCI). This is achieved by utilizing the introduced Cloud Orchestrator (corc) framework, in conjunction with the novel JupyterHub spawner named MultipleSpawner. Through this combination, we are able to dynamically orchestrate, authenticate, configure, and access interactive Jupyter Notebooks in the OCI with user defined hardware capabilities. These capabilities include settings such as the minimum amount of CPU cores, memory and GPUs the particular orchestrated resources must have. This enables teachers and students at educational institutions such as UCPH to gain easy access to the required capabilities for a particular course. In addition, we lay out how this groundwork, will enable us to establish a Grid of Clouds between multiple trusted institutions. This enables the exchange of surplus computational resources that could be employed across their organisational boundaries.
Simulation of intellectual system for evaluation of multilevel test tasks on the basis of fuzzy logic

The article describes the stages of modeling an intelligent system for evaluating multilevel test tasks based on fuzzy logic in the MATLAB application package, namely the Fuzzy Logic Toolbox. The analysis of existing approaches to fuzzy assessment of test methods, their advantages and disadvantages is given. The considered methods for assessing students are presented in the general case by two methods: using fuzzy sets and corresponding membership functions; fuzzy estimation method and generalized fuzzy estimation method. In the present work, the Sugeno production model is used as the closest to the natural language. This closeness allows for closer interaction with a subject area expert and build well-understood, easily interpreted inference systems. The structure of a fuzzy system, functions and mechanisms of model building are described. The system is presented in the form of a block diagram of fuzzy logical nodes and consists of four input variables, corresponding to the levels of knowledge assimilation and one initial one. The surface of the response of a fuzzy system reflects the dependence of the final grade on the level of difficulty of the task and the degree of correctness of the task. The structure and functions of the fuzzy system are indicated. The modeled in this way intelligent system for assessing multilevel test tasks based on fuzzy logic makes it possible to take into account the fuzzy characteristics of the test: the level of difficulty of the task, which can be assessed as “easy”, “average", “above average”, “difficult”; the degree of correctness of the task, which can be assessed as “correct”, “partially correct”, “rather correct”, “incorrect”; time allotted for the execution of a test task or test, which can be assessed as “short”, “medium”, “long”, “very long”; the percentage of correctly completed tasks, which can be assessed as “small”, “medium”, “large”, “very large”; the final mark for the test, which can be assessed as “poor”, “satisfactory”, “good”, “excellent”, which are included in the assessment. This approach ensures the maximum consideration of answers to questions of all levels of complexity by formulating a base of inference rules and selection of weighting coefficients when deriving the final estimate. The robustness of the system is achieved by using Gaussian membership functions. The testing of the controller on the test sample brings the functional suitability of the developed model.