Reviewerly/exHarmony huggingface dataset:
MLSNet: Resource-Efficient Adaptive Inference with Multi-Level Segmentation Networks

In this paper, we propose a multi-level convolutional network for semantic segmentation. The advantage of our network is that it allows us to adaptively control the balance between the classification accuracy and the inference speed, depending on the limited computational resource and the complexity of a given task. Our network realizes such adaptive mechanism by introducing a hierarchically-connected decoder with multilevel classifiers. The low-level classifier is used for inference when the application prioritizes low computational cost over accuracy, and the high-level classifier is utilized when more accurate prediction is required. This switching can be automatically performed by specifying only a threshold for classification performance. Besides, to boost the lower-level classifiers, we incorporate a knowledge distillation mechanism into our network. The entire network is trained in an end-to-end fashion. Experiments on semantic segmentation datasets demonstrate that our model overperforms the conventional approaches.
Extending Local Search in Geometric Semantic Genetic Programming

In this paper we continue the investigation of the effect of local search in geometric semantic genetic programming (GSGP), with the introduction of a new general local search operator that can be easily customized. We show that it is able to obtain results on par with the current best-performing GSGP with local search and, in most cases, better than standard GSGP.
The role of tessellation intersection in staggered quantum walks

The staggered quantum walk (SQW) model is defined by partitioning the graph into cliques, which are called polygons. We analyze the role that the size of the polygon intersection plays on the dynamics of SQWs on graphs. We introduce two processes (intersection reduction and intersection expansion), that change the number of vertices in some intersection of polygons, and we compare the behavior of the SQW on the reduced or expanded graph in relation to the SQW on the original graph. We describe how the eigenvectors and eigenvalues of the evolution operators relate to each other. This processes can help to establish the equivalence between SQWs on different graphs and to simplify the analysis of SQWs. We also show an example of a SQW on a graph that is not included in Szegedy’s model, but which is equivalent to an instance of Szegedy’s model after applying the intersection reduction.
NMF-Based Comprehensive Latent Factor Learning with Multiview Da

Multiview representations reveal the latent information of the data from different perspectives, consistency and complementarity. Unlike most multiview learning approaches, which focus only one perspective, in this paper, we propose a novel unsupervised multiview learning algorithm, called comprehensive latent factor learning (CLFL), which jointly exploits both consistent and complementary information among multiple views. CLFL adopts a non-negative matrix factorization based formulation to learn the latent factors. It learns the weights of different views automatically which makes the representation more accurate. Experiment results on a synthetic and several real datasets demonstrate the effectiveness of our approach.
List.MID: A MIDI-Based Benchmark for Evaluating RDF Lists

Linked lists represent a countable number of ordered values, and are among the most important abstract data types in computer science. With the advent of RDF as a highly expressive knowledge representation language for the Web, various implementations for RDF lists have been proposed. Yet, there is no benchmark so far dedicated to evaluate the performance of triple stores and SPARQL query engines on dealing with ordered linked data. Moreover, essential tasks for evaluating RDF lists, like generating datasets containing RDF lists of various sizes, or generating the same RDF list using different modelling choices, are cumbersome and unprincipled. In this paper, we propose List.MID, a systematic benchmark for evaluating systems serving RDF lists. List.MID consists of a dataset generator, which creates RDF list data in various models and of different sizes; and a set of SPARQL queries. The RDF list data is coherently generated from a large, community-curated base collection of Web MIDI files, rich in lists of musical events of arbitrary length. We describe the List.MID benchmark, and discuss its impact and adoption, reusability, design, and availability.
IoT Data Management System for Rapid Development of Machine Learning Models

Capturing and managing the data needed to build effective machine learning models for custom IoT environments requires a great deal of effort. The amount of data generated from IoT devices is abundant, but tools to find datasets appropriate for the desired models are lacking. This paper presents a data capture system and data management catalog with solutions addressing the challenges of curating IoT data applied to purpose-built machine learning deployments.
Recoloring Image For Color Vision Deficiency By GANS

Compared to normal people who can recognize all colors in 3-D color space, people with Color Vision Deficiency (CVD) just could recognize colors in 2-D color space or only in 1-D color space. Therefore, color version deficient people cannot distinguish some colors under certain circumstances. In this paper, a new GAN-based architecture is constructed to improve the color identifiability for color vision deficient people. The improved framework is based on BicycleGAN [1]. First, a CVD simulation module is introduced and integrated into the BicycleGAN [1], allowing us to inspect the recoloring progress from the perspective of people with CVD. Second, a color-to-gray module is added to eliminate the distortion. Third, Gaussian Mixture Model (GMM) [2] and KullbackLeibler (KL) divergence [3], [4] calculation model are inserted as priori information of the conditional GANs [5] to guide the direction of recoloring. Experimental results show that our proposed architecture achieves impressive performance.
Towards Industrial Assistance Systems: Experiences of Applying Multi-sensor Fusion in Harsh Environments

With the availability of miniaturized low cost sensors and the general availability and easy applicability of algorithms for activity recognition, we investigate how various sensors can be deployed in a harsh environment, the industrial shop-floor. We review related work and provide an in-depth review of our own experiences were sensors wer used to enable recognition of activity, task progress and also mental and cognitive states of assembly workers. The recognition process is based on stationary (RGBD cameras, stereo vision depth sensors) and wearable devices (IMUs, GSR, ECG, mobile eye tracker). We describe in detail the used sensors, the challenges of fusing the data from these various sources together in real-time and how to interpret that data semantically.
A Systematic Review of the Third Party Auditing in Cloud Security: Security Analysis, Computation Overhead and Performance Evaluation

Cloud storage offers a considerable efficiency and security to the user's data and provide high flexibility to the user. The hackers make attempt of several attacks to steal the data that increase the concern of data security in cloud. The Third Party Auditing (TPA) method is introduced to check the data integrity. There are several TPA methods developed to improve the privacy and efficiency of the data integrity checking method. Various methods involved in TPA, have been analyzed in this review in terms of function, security and overall performance. Merkel Hash Tree (MHT) method provides efficiency and security in checking the integrity of data. The computational overhead of the proof verify is also analyzed in this review. The communication cost of the most TPA methods observed as low and there is a need of improvement in security of the public auditing.
Research On 3d Interactive Model Selection And Customization Of Ceramic Products Based On Big Data Cloud Service Platform

In the new economic era led by big data, enterprises face many consumers with various product requirements. It is important for enterprises to timely meet these personalized product needs to improve their market competitiveness. In this paper, to show how to satisfy users' personalized product customization needs through the Internet and big data technologies, a ceramic product online interactive design system based on bigdata cloud service platform is implemented by adopting Web3D technology and storing a mass of various 3d digital models of ceramic products designed beforehand in the cloud server for designing users' personalized ceramic products. The procedure of online designing a ceramic product using this system includes choosing ceramic product 3d model in cloud server and calling the cloud decoration pattern resources or independent online design decorative pattern. In order to protect the intellectual property rights of users' design works, the platform provides privacy protection mechanism for users' personal information and works' information by setting three kinds of design works' sharing modes: free sharing, non-sharing and paid sharing. With the growth of 3d model data of ceramic products, the platform can also provide online 3d displaying services of ceramic product. The real application of this system demonstrate it can efficiently satisfy users' personalized product customization needs without disclose users' privacy information.
Explicit and Implicit Discourse Relations in the Prague Discourse Treebank

Coherence of a text is provided by various language means, including discourse connectives (coordinating and subordinating conjunctions, adverbs etc.). However, semantic relations between text segments can be deduced without an explicit discourse connective, too (the so called implicit discourse relations, cf. He missed his train. 0 He had to take a taxi.). In our paper, we introduce a corpus of Czech annotated for implicit discourse relations (Enriched Discourse Annotation of Prague Discourse Treebank Subset 1.0) and we analyze some of the factors influencing the explicitness/implicitness of discourse relations, such as the text genre, semantic type of the discourse relation and the presence of negation in discourse arguments.
Incentive-Based Ledger Protocols for Solving Machine Learning Tasks and Optimization Problems via Competitions

We propose incentive-based protocols that use competitions and public ledgers to solve optimization problems. We introduce Proof-of-Accumulated-Work (PoAW): miners compete in costumer-submitted jobs and accumulate recorded work on which they are later remunerated. These new competitions replace the standard hash puzzle-based competitions. A competition is managed by a dynamically-created small masternode network (dTMN) of invested miners, which improves scalability as we do not need the entire network to manage the competition. Using a careful design of incentives, our system preserves security, avoids attacks, and offers new markets to the miners. Finally, we illustrate how the new protocols can be used for implementing machine learning competitions.
Managing emotion online

I apply emotion work to a case study of reader responses on the blog, Young House Love, owned by Sherry and John Petersik. The author performed a qualitative analysis of 1070 comments, examining readers' collective emotion work. After learning of the bloggers' hiatus, YHL readers responded using interpersonal and personal emotion management techniques. Comments exhibit deep acting and surface acting strategies directed primarily at the bloggers and their perceived emotional state. Positive sentiment emerged among comments as readers shared personal stories of connection, influence, expressions of support, and accounts. Furthermore, readers' comments reveal feeling rules guiding the YHL bloggers and readers. Thus, discursive efforts to influence the bloggers' emotions are conceptually linked to readers' attempts to rescue and repair the bloggers' public persona on YHL. This analysis confirms the relevance of Goffman's (1959) and Hochschild's (1979) work online and explores blogs as a tool for managing emotion. I also discuss the importance of the comments section in the co-creation of a blog.
Image Decomposition and Classification Through a Generative Model

We demonstrate in this paper that a generative model can be designed to perform classification tasks under challenging settings, including adversarial attacks and input distribution shifts. Specifically, we propose a conditional variational autoencoder that learns both the decomposition of inputs and the distributions of the resulting components. During test, we jointly optimize the latent variables of the generator and the relaxed component labels to find the best match between the given input and the output of the generator. The model demonstrates promising performance at recognizing overlapping components from the multiMNIST dataset, and novel component combinations from a traffic sign dataset. Experiments also show that the proposed model achieves high robustness on MNIST and NORB datasets, in particular for high-strength gradient attacks and non-gradient attacks.
Vehicle Re-Identification by Multi-Grain Learni

Vehicle re-identification (re-ID) is to identify the same vehicle captured by different cameras with non-overlapping views, which plays an important role in intelligent transportation system and traffic safety. Compared with face recognition and person re-ID tasks, it is difficult to train an effective vehicle re-ID model since different vehicles of the same vehicle model, such as Mercedes-Benz C300, may exhibit strong inter-class similarity. To handle this difficulty, we propose a multi-grain ranking loss with the auxiliary of vehicle model, which models the vehicle re-ID task as two sub-tasks with different granularities including matching vehicles in the same vehicle model and different vehicle models. In particular, we infer the vehicle model labels online for the unlabeled training samples by clustering. The experimental results on two benchmarks demonstrate the proposed method can achieve state-of-the-art performance.
Utilization of Prediction Data for Prospective Decision Customers Insurance Using the Classification Method of C.45 and Naive Bayes Algorithms

Prospective Insurance Customers are one or several who buy physical products with various variables used in their determination such as price, quality, quantity of the products, places and services that can be provided based on results to achieve the desired results. Customers are differentiated into individual customers or group customers who buy physical products with various variables used in their determination such as price, quality, quantity of the product, place and service that can be provided based on results to achieve the desired results. Data mining is is based on the process of creating a patterned pattern in the future and for the information provided by the data provided by the data. The research we conducted uses the C45 Algorithm and the Naive Bayes Algorithm using the Sex, Marital, Age, Region, Job and Description Indicators. This research uses Rapidminner tools as a medium to test the data being carried out.
Service Recommendation Based on User Dynamic Preference Extraction and Prediction

User preferences are dynamically changing due to a variety of factors. Therefore, how to effectively extract the user's current preferences and accurately predict subsequent user preferences is one of the keys to improving service recommendation. This paper proposes a deep learning-based user preference prediction method, which combines user dynamic preference extraction and preference prediction. Firstly, the time-series LDA model is used to extract the user dynamic preference, then the LSTM neural network method is used to predict the user's preference of next time slice based on the user dynamic preference sequence. Finally, services are recommended to users based on predicted preference. The experimental results on real-world dataset show that the prediction performance of Time-aware-gate LSTM is slightly better than traditional LSTM, and the recommendation performance of the recommendation algorithm that takes preference prediction into account is better than those without preference prediction.
FAMULUS: Interactive Annotation and Feedback Generation for Teaching Diagnostic Reasoning

Our proposed system FAMULUS helps students learn to diagnose based on automatic feedback in virtual patient simulations, and it supports instructors in labeling training data.Diagnosing is an exceptionally difficult skill to obtain but vital for many different professions (e.g., medical doctors, teachers).Previous case simulation systems are limited to multiple-choice questions and thus cannot give constructive individualized feedback on a student's diagnostic reasoning process.Given initially only limited data, we leverage a (replaceable) NLP model to both support experts in their further data annotation with automatic suggestions, and we provide automatic feedback for students.We argue that because the central model consistently improves, our interactive approach encourages both students and instructors to recurrently use the tool, and thus accelerate the speed of data creation and annotation.We show results from two user studies on diagnostic reasoning in medicine and teacher education and outline how our system can be extended to further use cases.
Regularized Anderson Acceleration for Off-Policy Deep Reinforcement Learning

Model-free deep reinforcement learning (RL) algorithms have been widely used for a range of complex control tasks. However, slow convergence and sample inefficiency remain challenging problems in RL, especially when handling continuous and high-dimensional state spaces. To tackle this problem, we propose a general acceleration method for model-free, off-policy deep RL algorithms by drawing the idea underlying regularized Anderson acceleration (RAA), which is an effective approach to accelerating the solving of fixed point problems with perturbations. Specifically, we first explain how policy iteration can be applied directly with Anderson acceleration. Then we extend RAA to the case of deep RL by introducing a regularization term to control the impact of perturbation induced by function approximation errors. We further propose two strategies, i.e., progressive update and adaptive restart, to enhance the performance. The effectiveness of our method is evaluated on a variety of benchmark tasks, including Atari 2600 and MuJoCo. Experimental results show that our approach substantially improves both the learning speed and final performance of state-of-the-art deep RL algorithms.
PPSB: An Open and Flexible Platform for Privacy-Preserving Safe Browsing

Safe Browsing (SB) is an important security feature in modern web browsers to help detect new unsafe websites. Although useful, recent studies have pointed out that the widely adopted SB services, such as Google Safe Browsing and Microsoft SmartScreen, can raise privacy concerns since users' browsing history might be subject to unauthorized leakage to service providers. In this paper, we present a Privacy-Preserving Safe Browsing (PPSB) platform. It bridges the browser that uses the service and the third-party blacklist providers who provide unsafe URLs, with the guaranteed privacy of users and blacklist providers. Particularly, in PPSB, the actual URL to be checked, as well as its associated hashes or hash prefixes, never leave the browser in cleartext. This protects the user's browsing history from being directly leaked or indirectly inferred. Moreover, these lists of unsafe URLs, the most valuable asset for the blacklist providers, are always encrypted and kept private within our platform. Extensive evaluations using real datasets (with over 1 million unsafe URLs) demonstrate that our prototype can function as intended without sacrificing normal user experience, and block unsafe URLs at the millisecond level. All resources, including Chrome extension, Docker image, and source code, are available for public use.
Usability Evaluation the Prague Geoportal : Comparison of Methods

Usability is an important quality characteristic of software products. It is focused directly on users, namely on the quality of user interface. There have been proposed many various usability evaluation and testing methods. The methods differ from many points of view, e.g. used tools, involved participants, obtained results, stage of the system development life cycle, etc. The paper describes a case study which uses combination of usability user testing, survey and heuristic evaluation to evaluate usability of the Prague Geoportal. Case study shows that very similar results are obtained by the methods. Average time necessary for usability evaluation and testing planning, testing itself and results assessment is different. Different is a level of detail of obtained results. Combinations of different methods is useful because it brings different points of view.
5D Video Stabilization through Sensor Vision Fusion

We propose a novel 5D video stabilization solution based on sensor vision fusion. Traditional gyroscope based video stabilization approaches only stabilize the 3D rotation of a camera. They often suffer in scenes with highly dynamic translational movements. In this paper, we model the residual camera motion after 3D rotation based stabilization as residual 2D translation in the image plane, which can be estimated using visual information. The proposed 5D stabilization is achieved by carefully combining sensor based 3D rotation stabilization and vision based residual 2D translation stabilization. The 5D stabilization is prototyped on a smartphone for real time recording. We demonstrate its advantages over two state-of-the-art methods. The 5D stabilization can also be used to solve the challenging foreground object stabilization problem.
Stream Processing With Concept Drift for Event Identification in Sensors Enabled IoT Environment

A streaming IoT environment like health monitoring system is characterized by continuous generation of data at a high rate. This stream of data can neither be stored on the nodes nor transmitted to the sink due to resource constraints. This demands continuous perusal of the data stream at nodes for in-situ event detection. In this WSN scenario, existing schemes that rely on inference based on stored data on the nodes or the sink become in-efficacious. In this paper, temporal and spatial correlation in such data streams is exploited to effectively reduce the use of these resources in decision making process. We propose an All In One Stream Processing (AIOSP) scheme which employs sampling using temporal correlation, load shedding using spatial correlation and approximation using drift and diffusion for efficient decision making process in data streaming environment where a large amount of data is continually generated by the sensor nodes. The performance of proposed scheme is evaluated through simulations over varied sample size and is found to ensure 85% to 95% accuracy with small sample size.