Reviewerly/exHarmony huggingface dataset:
Statistical model for reproducibility in ranking-based feature selection

The stability of feature subset selection algorithms has become crucial in real-world problems due to the need for consistent experimental results across different replicates. Specifically, in this paper, we analyze the reproducibility of ranking-based feature subset selection algorithms. When applied to data, this family of algorithms builds an ordering of variables in terms of a measure of relevance. In order to quantify the reproducibility of ranking-based feature subset selection algorithms, we propose a model that takes into account all the different sized subsets of top-ranked features. The model is fitted to data through the minimization of an error function related to the expected values of Kuncheva’s consistency index for those subsets. Once it is fitted, the model provides practical information about the feature subset selection algorithm analyzed, such as a measure of its expected reproducibility or its estimated area under the receiver operating characteristic curve regarding the identification of relevant features. We test our model empirically using both synthetic and a wide range of real data. The results show that our proposal can be used to analyze feature subset selection algorithms based on rankings in terms of their reproducibility and their performance.
Swipe&amp;Switch

Swipe-based methods for text entry by gaze allow users to swipe through the letters of a word by gaze, analogous to how they can swipe with a finger on a touchscreen keyboard. Two challenges for these methods are: (1) gaze paths do not possess clear start and end positions, and (2) it is difficult to design text editing features. We introduce Swipe&Switch, a text-entry interface that uses swiping and switching to improve gaze-based interaction. The interface contains three context regions, and detects the start/end of a gesture and emits text editing commands (e.g., word insertion, deletion) when a user switches focus between these regions. A user study showed that Swipe&Switch provides a better user experience and higher text entry rate over a baseline, EyeSwipe.
Evaluation of Tweets for Content Analysis Using Machine Learning Models

Now-a-days we are virtually residing in the digital networking era, and constantly sharing thoughts, opinions about things that are associated with us. As a result, social networking sites, especially Twitter, get flooded with tremendous information in the form of opinions. They are unstructured so disseminated, and a solid base has to be established such that they can be seen as useful knowledge about a specific issue. This work focuses on the evaluation of different models with respect to feature selected and dataset of user's feelings on twitter data using different pre-processing measures. The dataset is extracted from Kaggle and Twitter, pre-processing performed using NLTK, Scikit-learn and features selection, extraction done for a Bag of Words (BOW), Term Frequency (TF) and Inverse Document Frequency (IDF). For polarity identification and evaluation, five different Machine Learning (ML) algorithms were compared. The performance comparison of these algorithms in an attempt to decide which algorithm is ideally suited for the selected dataset in view of the recall, accuracy, Fl-score, and precision observed. As the evaluation results, an SVM classifier outperformed the other implemented models.
A survey on service composition languages

In recent years, service-oriented architecture (SOA) has been adopted by industry in developing enterprise systems. Web service composition has been one of the challenging topics in SOA. Numerous approaches have been proposed to tackle this problem. In industry big companies such as Amazon, Netflix, and Uber have developed their own web service composition languages and tools. In academia, on the other hand, there have also been attempts to resolve some of the complexities in web service composition. In this survey we identify and evaluate current prominent service composition languages, and discuss our key findings. After a scan of dozens of service composition systems, 14 systems that used a language-based approach were included in this study. We believe that our findings will help people from industry and academia to learn about some of the major active composition languages and get an overall idea about their commonalities and differences.
Quasi-independence models with rational maximum likelihood estimator

We classify the two-way quasi-independence models (independence models with structural zeros) that have rational maximum likelihood estimators, or MLEs. We give a necessary and sufficient condition on the bipartite graph associated to the model for the MLE to be rational. In this case, we give an explicit formula for the MLE in terms of combinatorial features of this graph. We also use the Horn uniformization to show that for general log-linear models M with rational MLE, any model obtained by restricting to a face of the cone of sufficient statistics of M also has rational MLE.
Fostering the Digital Competence of Schools: Piloting SELFIE in the Italian Education Context

This paper reports on interesting outcomes from the pilot initiative that deployed the SELFIE tool in a sample of 201 schools in Italy, involving a population of more than 31,000 school leaders, teachers and students. Developed by the European Commission's Joint Research Centre, SELFIE is based on the conceptual framework for Digitally-Competent Educational Organizations (DigCompOrg) and it aims at supporting schools to self-reflect on their digital capacity. Results show variance in the school leaders', teachers' and students' perception of their school's digital competence and different levels of use of digital technology. The paper also discusses the issue of the systemic approach needed to integrate, sustain and scale-up SELFIE in compulsory education, drawing some practical implications for other schools willing to adopt and adapt SELFIE to their local context.
Predictive visual control framework of mobile robot for solving occlusion

Human-following mobile robots have been widely used in industry and daily life. However, during the tracking procedure, the human body is usually blocked by various moving or fixed occlusions. Although researchers have proposed some methods, they usually cannot restore human movement. In order to address this issue, we present a novel predictive visual control framework based on image generation. It includes visual tracking module, visual servo control module and occlusion solution module. In the occlusion solution module, we propose a convolutional attention-based differential LSTM (CAD-LSTM) Network to learn and predict the human motion state. The network focuses on how to predict for a long time from the difference between the previous frame and the next frame, which makes the robot tracking more robust. To show the effectiveness of our framework and CAD-LSTM Network, we apply them to image generation and conduct experiments in datasets and simulation environments. Our experimental results demonstrate that the proposed method outperforms than other methods.
Keeper: An Online Synchronous Conversation Environment Informed by In-Person Facilitation Practices

Facilitation, or the craft of using specific tools, methods, and practices to influence the way groups gather and converse as they strive for a desired goal, requires the nuanced craft of a facilitator to guide multidimensional, complex conversations. In recent years, facilitators have adapted their craft to online contexts. However, online communication platforms are not designed with the unique needs of complex dialogue or knowledge of facilitators in mind, creating friction in complex online conversations. This poster is an early-stage exploration and evaluation of an online synchronous audio conversation platform, Keeper, designed and informed by facilitation practices to aid complex conversations online. In this paper, we define three initial goals of the platform: 1) set the tone for the conversation, 2) ease the flow of these online conversations, and 3) improve social presence online. After an initial controlled study with 107 participants across 30 conversations, we observe that tone and ease of synchronous conversations could be improved with the introduction of this tool, and patterns that suggest strong social presence in the tool.
Variational Connectionist Temporal Classification

Connectionist Temporal Classification (CTC) is a training criterion designed for sequence labelling problems where the alignment between the inputs and the target labels is unknown. One of the key steps is to add a blank symbol to the target vocabulary. However, CTC tends to output spiky distributions since it prefers to output blank symbol most of the time. These spiky distributions show inferior alignments and the non-blank symbols are not learned sufficiently. To remedy this, we propose variational CTC (Var-CTC) to enhance the learning of non-blank symbols. The proposed Var-CTC converts the output distribution of vanilla CTC with hierarchy distribution. It first learns the approximated posterior distribution of blank to determine whether to output a specific non-blank symbol or not. Then it learns the alignment between non-blank symbols and input sequence. Experiments on scene text recognition and offline handwritten text recognition show Var-CTC achieves better alignments. Besides, with the enhanced learning of non-blank symbols, the confidence scores of model outputs are more discriminative. Compared with the vanilla CTC, the proposed Var-CTC can improve the recall performance by a large margin when the models maintain the same level of precision.
quoras: A Python API for Quora Data Collection to Increase Multi-Language Social Science Research

Quora is a fast growing crowdsourced Q/A site that also creates online social networks and community practices among the users. Operating in several regional languages, it catalyzes more contextual discussions on local incidents and issues. To understand how language-specific social communities conduct Q/A-based discussions on online forums, we need to study Quora platform. As the first step to that, we need a data collection API. We introduce quoras, a Python API for collecting data from Quora. The API relies on Selenium, which is an open-source cross platform web automation framework. The API operates by creating custom HTTPS requests to Quora and parsing responses from it. It has the ability to perform many types of advanced searches that are otherwise only available on the Quora website, and not through any other existing APIs. The quoras API is released under an open-source MIT license and available along with the full API reference on GitHub. The latest stable release is also available on Python Package Index (PyPI).
Domain-Specific Customization of Schema.org Based on SHACL

Schema.org is a widely adopted vocabulary for semantic annotation of web resources. However, its generic nature makes it complicated for publishers to pick the right types and properties for a specific domain. In this paper, we propose an approach, a domain specification process that generates domain-specific patterns by applying operators implemented in SHACL syntax to the schema.org vocabulary. These patterns can support annotation generation and verification processes for specific domains. We provide tooling for the generation of such patterns and evaluate the usability of both domain-specific patterns and the tools with use cases in the tourism domain.
Evaluation of an InfiniBand Switch: Choose Latency or Bandwidth, but Not Both

Today's cloud datacenters feature a large number of concurrently executing applications with diverse intradatacenter latency and bandwidth requirements. To remove the network as a potential performance bottleneck, datacenter operators have begun deploying high-end HPC-grade networks, such as InfiniBand (IB), which offer fully offloaded network stacks, remote direct memory access (RDMA) capability, and non-discarding links. While known to provide both low latency and high bandwidth for a single application, it is not clear how well such networks accommodate a mix of latencyand bandwidth-sensitive traffic that is likely in a real-world deployment. As a step toward answering this question, we develop a performance measurement tool for RDMA-based networks, RPerf, that is capable of precisely measuring the IB switch performance without hardware support. Using RPerf, we benchmark a rack-scale IB cluster in isolated and mixedtraffic scenarios. Our key finding is that the evaluated switch can provide either low latency or high bandwidth, but not both simultaneously in a mixed-traffic scenario. We evaluate several options to improve the latency-bandwidth trade-off and demonstrate that none are ideal.
An Automatic Object Detection and Tracking Method Based on Video Surveillance

For intelligent video surveillance, this paper proposes an automatic object detection and tracking method. In proposed method, a cooperative working mechanism between the detector and the tracker is designed, which can initialize the position of the object automatically in the first frame and improves the detection accuracy. This method consists of three modules: detection, tracking and decision-making. The detection module is designed to quickly extract specified categories of objects. The tracking module is used to perform data association and processing based on the kernel correlation filters tracker (KCF). For the object occlusion problem, the decision module can make a judgment and outputs the result. The experimental results show that the proposed method has high real-time performance and robustness, and is suitable in long time video surveillance.
An Empirical Study of i18n Collateral Changes and Bugs in GUIs of Android apps

Mobile markets allow developers to easily distribute mobile apps worldwide and collect complaints and feature requests in the form of user reviews and star ratings. Therefore, internationalization (i18n) of apps is a highly desired feature, which is currently supported in mobile platforms by using resources flies with strings that can be internationalized manually. This manual translation can be a time consuming and error-prone task when the app is targeted for different languages and the amount of strings to be internationalized is large. Moreover, the lack of consideration of the impact of internationalized, strings can drive to collateral (i.e., unexpected) changes and bugs in the GUI layout of apps. In this paper, we present an empirical study on how i18n can impact the GUIs of Android apps. In particular, we investigated the changes, bugs and bad practices related to GUIs when strings of a given default language (i.e., English in this case) are translated to 7 different languages. To this, we created a source- codeless approach, ITDroid, for automatically (i) translating strings, and (ii) detecting bad practices and collateral changes introduced in the GUIs of Android apps after translation. ITDroid was used on a set of 31 Android apps and their translated versions. Then, we manually validated the i18n changes that introduced bugs into the GUIs of the translated apps. Based on these results, we present a taxonomy of i18n changes and bugs found along in the apps as well as implications of our findings for practitioners and researchers. Online appendix: https://thesoftwaredesignlab.github.io/ITDroid/.
Reframing AX with Critical Design: The Potentials and Limits of Algorithmic Experience as a Critical Design Concept

This paper investigates how Algorithmic Experience (AX) can be used as a conceptual tool to help make algorithms and experiences of them visible through systems design and how interactions might be designed in order to elicit conscious and critical examination. Can algorithmic systems be positioned as critical artifacts? If so, to what extent? The paper reflects on interactions between users and the Social Privilege Estimator– a social scoring system based on facial recognition and classification that was built as a critical design artifact in order to raise awareness of existing inequalities and adverse effects of facial recognition systems. We argue that critical design can and should be used for AX and human-algorithm interaction design in order to support algorithmic literacy and critical capacity of the users and we suggest some conceptual reframings of AX towards this goal.
Rotationally-Consistent Novel View Synthesis for Humans

Human novel view synthesis aims to synthesize target views of a human subject given input images taken from one or more reference viewpoints. Despite significant advances in model-free novel view synthesis, existing methods present two major limitations when applied to complex shapes like humans. First, these methods mainly focus on simple and symmetric objects, e.g., cars and chairs, limiting their performances to fine-grained and asymmetric shapes. Second, existing methods cannot guarantee visual consistency across different adjacent views of the same object. To solve these problems, we present in this paper a learning framework for the novel view synthesis of human subjects, which explicitly enforces consistency across different generated views of the subject. Specifically, we introduce a novel multi-view supervision and an explicit rotational loss during the learning process, enabling the model to preserve detailed body parts and to achieve consistency between adjacent synthesized views. To show the superior performance of our approach, we present qualitative and quantitative results on the Multi-View Human Action (MVHA) dataset we collected (consisting of 3D human models animated with different Mocap sequences and captured from 54 different viewpoints), the Pose-Varying Human Model (PVHM) dataset, and ShapeNet. The qualitative and quantitative results demonstrate that our approach outperforms the state-of-the-art baselines in both per-view synthesis quality, and in preserving rotational consistency and complex shapes (e.g. fine-grained details, challenging poses) across multiple adjacent views in a variety of scenarios, for both humans and rigid objects.
Removable camera based 2D digital image correlation for planar slender area quasi-static deformation measurement

As one of the non-contact full-field measurement techniques, digital image correlation (DIC) has attracted much attention and has gained rapid acceptance in various fields, owing to its well-known advantages of a simple optical setup and low environmental vulnerability. In practical applications, the effective resolution of image is reduced due to the large difference in the size of the specimen in different directions, which makes it impossible to make effective full-field measurement. To significantly improve DIC-based deformation measurement for planar slender specimen without losing resolution, a removable camera based DIC method is proposed in this study for quasi-static high-resolution deformation and strain measurement. An industrial camera was mounted on a stable sliding table structure composed of linear sliding guide rail and ball screw. Under the control of the program, the camera moved with the sliding table to capture parts of the specimen at different positions. A correlation based image stitching method and overlapped region minimization method were proposed and were applied to these images, and their corresponding displacement fields and strain fields was calculated based on DIC. To verify the feasibility and effectiveness of the proposed removable camera based DIC method, static error test and quasi-static three-point bending test were performed. The results indicate that compared with a single fixed camera based DIC, the strain resolution could be increased by approximately 5 times by stitching 5 images while ensuring the same displacement measurement accuracy by using the proposed method. It concluded that the use of the removable camera array has great potential to perform deformation measurements of slender area without losing resolution.
Happiness and Fear

In February 2016, Facebook launched Reactions, an interactive feature expanding the Like button to include five additional emotional responses: Love, Sadness, Anger, Wow, and Haha. In this article, we examine users’ feedback about this new feature and identify important design implications of this significant modification of Facebook's interface. We did this by applying theories of human emotion and emotion-specific influences on cognitive appraisals to conduct a heuristic evaluation of Facebook Reactions and a thematic content analysis of the 3,000 “top” comments posted by Facebook users on the official pre- and post-launch announcements about Reactions. Prior to launch, many users were concerned that the addition of a Dislike button would lead to abuse; thus, they favored the more nuanced design of Reactions. After launch, users were more positive about the feature as many of their misconceptions were clarified through actual use. Overall, we identified several design constraints of this new feature, including users’ inability to express conflicting emotions. We conclude the article by discussing the implications of our findings and the challenges around research and design for sociotechnical systems that involve complex human emotions.
Off-Policy Deep Reinforcement Learning Based on Steffensen Value Iteration

As an important machine learning method, deep reinforcement learning (DRL) has been rapidly developed in recent years and has achieved breakthrough results in many fields, such as video games, natural language processing, and robot control. However, due to the inherit trial-and-error learning mechanism of reinforcement learning and the time-consuming training of deep neural network itself, the convergence speed of DRL is very slow and consequently limits the real applications of DRL. In this article, aiming to improve the convergence speed of DRL, we proposed a novel Steffensen value iteration (SVI) method by applying the Steffensen iteration to the value function iteration of off-policy DRL from the perspective of fixed-point iteration. The proposed SVI is theoretically proved to be convergent and have a faster convergence speed than Bellman value iteration. The proposed SVI has versatility, which can be easily combined with existing off-policy RL algorithms. In this article, we proposed two speedy off-policy DRLs by combining SVI with DDQN and TD3, respectively, namely, SVI-DDQN and SVI-TD3. Experiments on several discrete-action and continuous-action tasks from the Atari 2600 and MuJoCo platforms demonstrated that our proposed SVI-based DRLs can achieve higher average reward in a shorter time than the comparative algorithm.