Reviewerly/exHarmony huggingface dataset:
Visual Analysis of Shared Bicycle Data Based on User's Spatiotemporal Behavior Characteristics

With the continuous development of shared bicycles, large-scale spatiotemporal trajectory data has been generated and stored. Visual research and analysis of these data provide guidance and reference for bicycle scheduling, station planning, urban road planning. Moreover, what kind of visualization technology can efficiently mine the hidden information in data, which has attracted people's attention. Based on this research motivation, a visual analysis system of shared bicycle user's behavior was constructed, which started from time and space to construct spatiotemporal trajectory data and high-dimensional multivariate data visualization. Via the analysis of cycling routes and stations, the purpose is to use collaborative interaction between views to realize visual analysis of users' cycling activity patterns and influencing factors.
Optimum design of 3R manipulator using hybrid PSOGSA algorithm

Larger workspace volume is one of the most important objectives in the optimum design of robot manipulator. In the present study, the workspace volume of a 3R manipulator has been maximised. The nonlinear constrained optimisation problem has been solved using the particle swarm optimisation (PSO) and a hybrid PSO and gravitational search algorithm (GSA). The proposed algorithm combines the search method of PSO and GSA with enhanced exploration ability to achieve higher workspace volume as compared to the established available results. Further, the total void cross section area has been estimated and a quantitative analysis of the results has been made to identify the key influencing kinematic parameters and prioritise the constraints. Applications of the hybrid algorithm on two industrial manipulators are presented as case studies. An important implication of this article is that the proposed hybrid algorithm provides encouraging result in terms of objective function values and CPU time.
Reinforced Natural Language Inference for Distantly Supervised Relation Classification

Distant supervision (DS) has the advantage of automatically annotate large amounts of data and has been widely used for relation classification. Despite its efficiency, it often suffers from the label noise problem, which would impair the performance of relation classification. Recently, there are two ways to solve the label noise problem. The first way is to use multi-instance learning to consider the noises of instances, but they do not perform well for sentence-level prediction. The second way is to use reinforcement learning or adversarial learning to directly find noisy label instances but with high computational overhead and poor performance. In this paper, we propose to use the natural language inference (NLI) model to evaluate the quality of the instances directly, and select the high-quality instances as refined training data for sentence-level relation classification. Due to the lack of high-quality supervised data, we use reinforcement learning to train the NLI model. Experimental results on two human re-annotated NYT datasets show the effectiveness and efficiency of our method at the sentence-level relation classification. The source code of this paper can be found in https://github.com/xubodhu/RLRC.
A general evaluation method of university curriculum summative text based on optimized BERT model

At present, many curriculum evaluations in universities are based on students' summative texts of courses. However, because the evaluation methods are based on Teachers' subjective judgment and time-consuming, they are unstable and inefficient. In order to solve this problem, this paper proposes a text classification model based on optimized BERT (bidirectional encoder representations from transformers) for summative text evaluation of university courses. In this model, the deep dynamic representation of word vector is extracted by using the BERT pre-trained language model, and then the summative text analysis is carried out by using the Mean-Max-pooling method. Finally, the text classification is completed by using the sigmoid function and threshold fine-tuned. The experimental result shows that the AUC (Area Under Curve) of this model is 12.5% higher than that of the RNN(Recurrent Neural Network) and CNN(Convolutional Neural Network) model, and the model is effective.
A method of abstracting short text for consulting clients

Text abstracting is widely used in search engine, text database, information filtering. In the text extraction method, the mathematical model is usually established first and extracted by the model, but this method is not effective in solving nonlinear problems. Support vector machine (SVM), based on statistical learning theory, converts a certain amount of text into vectorized training text data for extraction. This paper adopts support vector machine (SVM) to study text extraction in telecommunication field, and we obtained good results.
Effects of Lossy Image Compression on Medical Image Registration Accuracy

Lossy Image compression has been effective in reducing medical image size, but it affects registration accuracy. In this study, the effects of lossy compression on 2D and 3D medical image registration accuracy is investigated via Target Registration Error (TRE). Mean Square Error (MSE) and Mutual Information (MS) were considered as similarity image metrics for Rigid 3D transform Registration. Results has indicated that MS performs well at lossless compression, but it did not perform as well on lossy compression.
µ-Planner: A Robot Path Planning Approach Based on Language Measure of Unsupervised Automata

This paper proposes a robot path planner based on language measure, m-planner. Workspace is discretized in a occupancy grid map and we model the system by considering how events, associated to robot’s motions, take it to different cells (discrete positions). The calculated language measure values corresponds to a gradient, which the robot can use reach its destination by choosing events that take it to states with higher measure values. Concepts of Lapace’s equation and harmonic functions are used to prove that our method guarantees both the existence and monotonicity of language measure. The proposed method is simple and computationally inexpensive and guarantees existence of path from any co-accessible state to the destination. Experiments considering different scenarios have been performed to validate and compare m-planner with similar methods.
The German NetzDG as Role Model or Cautionary Tale? Implications for the Debate on Social Media Liability

What can be done against discrimination, bullying, insults, and the spread of dangerous fake news on social media platforms? While platforms in the United States enjoy broad discretion on how to approach that issue, there are both legal and political debates regarding social media regulation. Germany, by contrast, advances the opposite approach: requiring social media providers to block or remove illegal content. The Netzwerkdurchsetzungsgesetz (“NetzDG,” “Network Enforcement Act,” the “Act”) of 2017 outlines a specific procedure for implementing such a claim. The Act is the first of its kind in the western democratic states. Other countries have invoked or discussed whether to follow the German example, which could make NetzDG a pioneer in its strategy of combating hate speech and fake news. This Article is intended to explain the background, mode of operation, and reception of the NetzDG. Furthermore, this Article will attempt to clear up misunderstandings and discuss current developments around this Act. A main purpose of this Article is to examine whether the Act is a suitable prototype for the United States Congress to introduce regarding platform liability and to determine which alternatives are available at hand. To that end, the Article evaluates the constitutional leeway for a regulation of social media. The Article concludes that Congress could establish principles and mechanisms similar to the NetzDG which, despite its room for improvement, is better than its reputation. Data and recent judgments indicate that the debate surrounding this system, however, was based on exaggerated assumptions and misunderstandings. Therefore, it is hopeful that the United States averts the defected discussion surrounding the NetzDG and draws positive and negative lessons from it.
SIGCHI Lifetime Practice Award Talk: Usability Barriers and How to Overcome Them

How long is a lifetime of practice in human-computer interaction? In my case, it has been 42 years, almost all of that in IBM Research (with a couple of sabbatical years in the then new Watson Group). Having queued up for access to DEC and Prime machines during my PhD work in the mid 70s, I was positively thrilled when, upon arriving at the T.J. Watson Research Center in 1978, I found a dedicated 3277 “green screen” terminal on my desk attached to a 370 mainframe. Hot-rodded with an onboard character buffer and double speed cursor circuitry, I could fly through writing and coding, personally experiencing the productivity benefits of sub-second response time [1].
NASA: A Noise-Adaptive and Structure-Aware Learning Framework for Image Deblurring

Image deblurring is a classical low-level visual processing task, which aims to recover a potentially noise-free sharp image from the blurred image. Existing prior-based and learning-based methods usually need to manually set some vital auxiliary components (e.g., noise level). It brings about extremely weak adaptability and flexibility. To settle this issue, we develop a Noise-Adaptive Structure-Aware learning framework (NASA) to achieve fully intelligent manufacturing. Concretely, by introducing a new task-assisted module, we define a novel robust image deblurring model derived from a MAP-based energy function. Consequently, we establish the NASA which consists of three basic modules including the task-assisted, fidelity-term, and regularization-term modules, to solve our designed model. The task-assisted module generates the noise-adaptive and structure-aware maps, which are fed to the other two modules. By end-to-end training our NASA, we successfully avoid the cumbersome manually parameters-adjustment process. Quantitative and qualitative experiments demonstrate our superiority compared to the state-of-the-art methods, both in visual effect and numerical scores. A series of ablation study also verify the effectiveness and necessity of our designed mechanism.
Object Segmentation Tracking from Generic Video Cues

We propose a light-weight variational framework for online tracking of object segmentations in videos based on optical flow and image boundaries. While high-end computer vision methods on this task rely on sequence specific training of dedicated CNN architectures, we show the potential of a variational model, based on generic video information from motion and color. Such cues are usually required for tasks such as robot navigation or grasp estimation. We leverage them directly for video object segmentation and thus provide accurate segmentations at potentially very low extra cost. Our simple method can provide competitive results compared to the costly CNN-based methods with parameter tuning. Furthermore, we show that our approach can be combined with state-of-the-art CNN-based segmentations in order to improve over their respective results. We evaluate our method on the datasets DAVIS16,17 and SegTrack v2.
Euro CBDC: A Regime to Enhance the Next Generation EU Fund

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Analyzing the Correlation Between Tweets and Sales for Product Brands

In recent years, Social Networking Services (SNS) have become widespread, and anyone can freely spread information via this Consumer Generated Media (CGM).The Social Media Data generated in such a way contains a great deal of opinions and values of consumers, and active utilization of this data is being considered. In this paper, in order to develop a method to obtain more informative information about consumer purchasing behavior, we targeted the carbonated drink brand "Product Brand A", and analyzed the correlation between tweets including a product brand's name and sales to capture the correlation; then we identified and excluded information(noise) other than information about consumer purchasing behavior included in the tweets information for a more detailed correlation analysis. In addition, by applying the analysis of the qualitative aspects of tweets(emotion evaluation analysis) to actual data and analyzing them, we suggested that further possibility of data utilization.
Numerical Study of the Process of Unsteady Filtration of a Fluid in Interacting Porous Pressure Layers

A review of the fundamental studies conducted in 2010 - 2020 is given in the article to develop a mathematical model related to the fluid and gas filtration processes in porous media. To conduct a comprehensive study of the process of unsteady filtration of fluid in multi-layer porous pressure media and to make a management decision, a mathematical model described by a system of partial differential equations with corresponding initial and boundary conditions and a conservative numerical algorithm were developed. On the basis of the developed software of the problem posed, computational experiments were conducted on a computer; the calculation results were presented in the form of tables and graphical objects. The schemes of location and capacity of vertical drainage wells to protect irrigated and non-irrigated areas from flooding were proposed on the basis of the developed software. Using the proposed mathematical tool, it is possible to obtain the prognostic groundwater levels for any area for the required period of time, considering a number of factors, for example, the formation heterogeneity in plan, the gradient of the permeability barrier, and other hydrogeological, hydro-technical, and natural conditions; to calculate the capacity and optimal drilling pattern of vertical drainage wells to protect the territory and to develop oil and gas fields.
Handwritten Indic Digit Recognition Using Deep Hybrid Capsule Network

Indian subcontinent is a birthplace of multilingual people where documents such as job application form, passport, number plate identification and so forth are written in different languages. These languages may be in the form of different Indic digits in a single page. So, building a generic recognizer that is capable of recognizing handwritten Indic digits written by diverse writers is needed. Also, numerous works have been done on non-Indic numerals particularly, in case of Roman, but, in case of Indic digits, the research is limited. Moreover, most of the research focuses only on MNIST datasets or with only single datasets, either because of time restraints or because the model is tailored to a specific task. In this work, a hybrid model is developed to recognize all available Indic handwritten digit images using the existing benchmark datasets. The proposed method bridges the automatically learnt features of capsule network with handcrafted bag of feature (BoF) extraction method. Along the way, we analyze (1) the successes and (2) explore whether this method will perform well on more difficult conditions, i.e., noise, color, affine transformations, intra-class variation and natural scenes. Experimental results confirm that the developed method gives better accuracy in comparison with capsule network.
Weak Supervision Network Embedding for Constrained Graph Learning

Constrained learning, a weakly supervised learning task, aims to incorporate domain constraints to learn models without requiring labels for each instance. Because weak supervision knowledge is useful and easy to obtain, constrained learning outperforms unsupervised learning in performance and is preferable than supervised learning in terms of labeling costs. To date, constrained learning, especially constrained clustering, has been extensively studied, but was primarily focused on data in the Euclidean space. In this paper, we propose a weak supervision network embedding (WSNE) for constrained learning of graphs. Because no label is available for individual nodes, we propose a new loss function to quantify the constraint-based loss, and integrate this loss in a graph convolutional neural network (GCN) and variational graph auto-encoder (VGAE) combined framework to jointly model graph structures and node attributes. The joint optimization allows WSNE to learn embedding not only preserving network topology and content, but also satisfying the constraints. Experiments show that WSNE outperforms baselines for constrained graph learning tasks, including constrained graph clustering and constrained graph classification.
A Secure E-Cheque bank system using Elliptic Curve

The Cheque based banking transactions are widely used all over the world, Cheque Forgery One of the problems that banks confrontation, An E-Cheque is the solution, To implementation security on the Cheque to protect the E-Cheque from Forgery, the Elliptic Curve Cryptography is used to achieving both authentication, integrity and confidentiality on the Cheque data, by creating public key and private key for the bank and the customers of the bank, an android application has been created, it can be used by the customer of the bank to write Cheque, the Cheque data is signed using private key of the customer and encrypt using bank public key, the bank server has been created using java servlet, uses the bank private key to decrypt the Cheque data, and verifies the signature using the customer public key.
Best Next-Viewpoint Recommendation by Selecting Minimum Pose Ambiguity for Category-Level Object Pose Estimation

Object manipulation is one of the essential tasks for a home helper robot, especially in helping a disabled person to complete everyday tasks. For handling various objects in a category, accurate pose estimation of the target objects is required. Since the pose of an object is often ambiguous from an observation, it is important to select a good nextviewpoint to make a better pose estimation. This paper introduces a metric of the object pose ambiguity based on the entropy of the pose estimation result. By using the metric, a best next-viewpoint recommendation method is proposed for accurate category-level object pose estimation. Evaluation is performed with synthetic object images of objects in five categories. It shows the proposed methods is applicable to various kind of object categories.
COMMUNITY COLLEGE CORNER: ACM CCECC activities

column Free AccessCOMMUNITY COLLEGE CORNER: ACM CCECC activities Share on Author: Cara Tang Portland Community College Portland Community CollegeView Profile Authors Info & Claims ACM InroadsVolume 12Issue 2June 2021 pp 14–16https://doi.org/10.1145/3462646Online:10 May 2021Publication History 0citation112DownloadsMetricsTotal Citations0Total Downloads112Last 12 Months112Last 6 weeks3 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteView all FormatsPDF
Improvement of Difficulty Parameter Equating base on Ability Parameter Distribution Adjustment

In automated test construction, one of the remaining issues is an inaccurate estimation of exam item parameters from different examinee ability distribution in linear equating. This work proposes a method to adjust the examinee's ability distribution to prevent carrying on error rates from the previous calibration to affect later procedures. By making the examinee's ability distribution in the minor' group to be similar to the examinee's ability distribution in the major distribution, the few errors are generated. From experiments, the proposed methods yield better results than those of baseline in terms of Root Mean Square Error in all test combinations.
Communication Chain in the Internet of Things with Spread-out Electronic Device System Abstraction

Communication is the act of transferring information between entities. Existing architectural communication approaches provide solutions for data transfer from the sender to the receiver. While those solutions are targeting different domains, we can find a lot of similarities in their architectures. In this paper, we follow the interpersonal communication process principles with its communication components for defining a whole communication chain that combines the sensor actuator and data transfer items in one concept, pointing to the person to person and device to device communication similarities. We come with an approach of a methodology for the Internet of Things abstracted by the Spread-out Electronic Device concept. We consider the whole IoT as a single electronic device with cut-out and broken wires and replaced with communication chains.
AugIR Meets GestureCards: A Digital Sketching Environment for Gesture-Based Applications

Abstract To gain a common understanding of an application’s layouts, dialogs and interaction flows, development teams often sketch user interface (UI). Nowadays, they must also define multi-touch gestures, but tools for sketching UIs often lack support for custom gestures and typically just integrate a basic predefined gesture set, which might not suffice to specifically tailor the interaction to the desired use cases. Furthermore, sketching can be enhanced with digital means, but it remains unclear whether digital sketching is actually beneficial when designing gesture-based applications. We extended the AugIR, a digital sketching environment, with GestureCards, a hybrid gesture notation, to allow software engineers to define custom gestures when sketching UIs. We evaluated our approach in a user study contrasting digital and analog sketching of gesture-based UIs.
Annotation uncertainty in the context of grammatical change

Abstract This paper elaborates on the notion of uncertainty in the context of annotation in large text corpora, specifically focusing on (but not limited to) historical languages. Such uncertainty might be due to inherent properties of the language, for example, linguistic ambiguity and overlapping categories of linguistic description, but could also be caused by a lack of annotation expertise. By examining annotation uncertainty in more detail, we identify the sources, deepen our understanding of the nature and different types of uncertainty encountered in daily annotation practice, and discuss practical implications of our theoretical findings. This paper can be seen as an attempt to reconcile the perspectives of the main scientific disciplines involved in corpus projects, linguistics and computer science, to develop a unified view and to highlight the potential synergies between these disciplines.