Reviewerly/exHarmony huggingface dataset:
Hierarchical Virtual Bitmaps for Spread Estimation in Traffic Measurement

This paper introduces a hierarchical traffic model for spread measurement of network traffic flows. The hierarchical model, which aggregates lower level flows into higher-level flows in a hierarchical structure, will allow us to measure network traffic at different granularities at once to support diverse traffic analysis from a grand view to fine-grained details. The spread of a flow is the number of distinct elements (under measurement) in the flow, where the flow label (that identifies packets belonging to the flow) and the elements (which are defined based on application need) can be found in packet headers or payload. Traditional flow spread estimators are designed without hierarchical traffic modeling in mind, and incur high overhead when they are applied to each level of the traffic hierarchy. In this paper, we propose a new Hierarchical Virtual bitmap Estimator (HVE) that performs simultaneous multi-level traffic measurement, at the same cost of a traditional estimator, without degrading measurement accuracy. We implement the proposed solution and perform experiments based on real traffic traces. The experimental results demonstrate that HVE improves measurement throughput by 43% to 155%, thanks to the reduction of perpacket processing overhead. For small to medium flows, its measurement accuracy is largely similar to traditional estimators that work at one level at a time. For large aggregate and base flows, its accuracy is better, with up to 97% smaller error in our experiments.
Investigating People’s Sentiment from Twitter Data for Smart Cities: A Survey

The usage of World Wide Web (WWW) and speed of internet has facilitated information consumers to become information producers via social media. Rapid growths of social media in recent years instigate to realize city officials that it can be a great platform to understand their citizens. To provide better services to citizens, the authorities are trying to move closer to them. Twitter sentiment analysis can be a one of the powerful tools to analyze valuable reflection of public perception. This paper is focused on Twitter sentiment analysis for smart cities. Effective feature engineering of twitter text is one of the key aspects for Twitter Sentiment Analysis along with different analysis approaches. These variables are analyzed and some related work is included for discussion. Several characteristics of tweets such as the size of tweets and resulting compact, novel language with Twitter specific communication elements, a strong sentiment class imbalance, and stream-based tweet generation complicate the analysis and challenge Twitter Sentiment Analysis approaches, resulting in generally poor sentiment classification performance. This paper also review methods devised to address these challenges.
Fair Classification with Noisy Protected Attributes: A Framework with Provable Guarantees

We present an optimization framework for learning a fair classifier in the presence of noisy perturbations in the protected attributes. Compared to prior work, our framework can be employed with a very general class of linear and linear-fractional fairness constraints, can handle multiple, non-binary protected attributes, and outputs a classifier that comes with provable guarantees on both accuracy and fairness. Empirically, we show that our framework can be used to attain either statistical rate or false positive rate fairness guarantees with a minimal loss in accuracy, even when the noise is large, in two real-world datasets.
Just How Toxic is Data Poisoning? A Unified Benchmark for Backdoor and Data Poisoning Attacks

Data poisoning and backdoor attacks manipulate training data in order to cause models to fail during inference. A recent survey of industry practitioners found that data poisoning is the number one concern among threats ranging from model stealing to adversarial attacks. However, we find that the impressive performance evaluations from data poisoning attacks are, in large part, artifacts of inconsistent experimental design. Moreover, we find that existing poisoning methods have been tested in contrived scenarios, and many fail in more realistic settings. In order to promote fair comparison in future work, we develop standardized benchmarks for data poisoning and backdoor attacks.
"I Won the Election!": An Empirical Analysis of Soft Moderation Interventions on Twitter

Over the past few years, there is a heated debate and serious public concerns regarding online content moderation, censorship, and the principle of free speech on the Web. To ease these concerns, social media platforms like Twitter and Facebook refined their content moderation systems to support soft moderation interventions. Soft moderation interventions refer to warning labels attached to potentially questionable or harmful content to inform other users about the content and its nature while the content remains accessible, hence alleviating concerns related to censorship and free speech. In this work, we perform one of the first empirical studies on soft moderation interventions on Twitter. Using a mixed-methods approach, we study the users who share tweets with warning labels on Twitter and their political leaning, the engagement that these tweets receive, and how users interact with tweets that have warning labels. Among other things, we find that 72% of the tweets with warning labels are shared by Republicans, while only 11% are shared by Democrats.By analyzing content engagement, we find that tweets with warning labels had more engagement compared to tweets without warning labels. Also, we qualitatively analyze how users interact with content that has warning labels finding that the most popular interactions are related to further debunking false claims, mocking the author or content of the disputed tweet, and further reinforcing or resharing false claims. Finally, we describe concrete examples of inconsistencies, such as warning labels that are incorrectly added or warning labels that are not added on tweets despite sharing questionable and potentially harmful information.
Gradual Semantics for Weighted Bipolar SETAFs

Gradual semantics are now well-studied in the computational argumentation literature. In this paper, we argue that gradual semantics that can handle both bipolarity (e.g. attacks and supports) and sets of attacking arguments (i.e. several arguments together attacking an argument) might be useful in some contexts. We define the formal framework and properties for such semantics. We proceed by adapting, studying and implementing three well-known semantics from the bipolar gradual literature to this new framework.
Cautious Bayesian Optimization for Efficient and Scalable Policy Search.

Sample efficiency is one of the key factors when applying policy search to real-world problems. In recent years, Bayesian Optimization (BO) has become prominent in the field of robotics due to its sample efficiency and little prior knowledge needed. However, one drawback of BO is its poor performance on high-dimensional search spaces as it focuses on global search. In the policy search setting, local optimization is typically sufficient as initial policies are often available, e.g., via meta-learning, kinesthetic demonstrations or sim-to-real approaches. In this paper, we propose to constrain the policy search space to a sublevel-set of the Bayesian surrogate model's predictive uncertainty. This simple yet effective way of constraining the policy update enables BO to scale to high-dimensional spaces (>100) as well as reduces the risk of damaging the system. We demonstrate the effectiveness of our approach on a wide range of problems, including a motor skills task, adapting deep RL agents to new reward signals and a sim-to-real task for an inverted pendulum system.
Dynamics under Uncertainty: Modeling Simulation and Complexity

This issue contains the successful invited submissions [...]
What Did You Refer to? Evaluating Co-References in Dialogue

Existing neural end-to-end dialogue models have limitations on exactly interpreting the linguistic structures, such as ellipsis, anaphor and co-reference, etc., in dialogue history context.Therefore, it is hard to determine whether the dialogue models truly understand a dialogue or not, only depending on the coherence evaluation of their generated responses.To address these issues, in this paper, we proposed to directly measure the capability of dialogue models on understanding the entity-oriented structures via question answering and construct a new benchmark dataset, DEQA, including large-scale English and Chinese humanhuman dialogues.Experiments carried on representative dialogue models show that these models all face challenges on the proposed dialogue understanding task.The DEQA dataset will release for research use.
Scheduled Dialog Policy Learning: An Automatic Curriculum Learning Framework for Task-oriented Dialog System

In reinforcement learning (RL) based taskoriented dialogue systems, users act as the environment and the agent learns the policy by interacting with users.However, due to the subjectivity of different users, the complexity of user-generated training conversations varies greatly, which leads to different difficulties for the agent to learn.Therefore, it is necessary for modeling dialogue complexity and make a reasonable learning schedule for efficiently training the agent.Towards that, we propose Scheduled Dialog Policy Learning, an automatic curriculum learning framework for jointing curriculum learning and policy optimization in the task-oriented dialog system.To our best knowledge, it is the first RL framework that improves dialogue policy learning by scheduling its learning process.Specifically, we introduce an automatic measurement to evaluate the dialogue complexity, and based on this automatic measurement, we train the dialog agent from easy dialogues to complex ones.Experiments demonstrate that our approach can be applied to the task-oriented dialogue policy learning and outperforms the previous state-of-the-art model, which increases 9.6% and 10.0% in the accuracy on the dialog success rate, respectively on the MultiWoz and Movie-Ticket Booking datasets.
Evaluating Reading Activities Of Intermediate Grade-1 (English For Iraq Textbook) For In The Light Of Higher-Order Thinking Skills

The  basic goal of this research paper is to evaluate the reading activities in the light of higher order thinking skills (HOTS) of English for Iraq textbook Grade -1in order to discover to what degree all the reading activities in the SB and AB correspond the suggested criteria. To fulfill this aim, the researcher utilized the descriptive approach. As a research tool , the researcher used content analysis card by constructing a checklist. The researcher constructed the checklist from books, papers, the related literature, and several previous studies. Consequently, the results were as follows: the analyzing skill got 51.92% (SB 58.44% and AB 33.33%). Concerning the evaluating skill, it got 41.35% (SB  32.47%and AB 66.67%). While the creating skill got 6.73% in the reading activities of the SB.So, HOTS were not well distributed in the student's book as well as the activity book.
Fully Convolutional Lightweight Pyramid Network for Vehicle Detection in Aerial Images

Vehicle detection in aerial images has a wide range of applications, and the majority of vehicle detection methods use the bounding-box approach for localization. But the bounding-box approach usually yields low precision and recall rates, especially under the dense vehicle density situations where the close spatial proximity of the vehicles confuses the bounding-box detectors. This letter proposes a Fully Convolutional Lightweight Pyramid Network (FCLPN) to detect vehicles in visible-spectrum aerial images. Unlike the bounding-box approach, FCLPN performs pixel-level localization and classification. FCLPN trained on the DLR-3K dataset is directly tested on DLR-3K, VEDAI, COWC, and VAID datasets to validate its generalization strength. Experimental results show that FCLPN performs better than state-of-the-art methods in aerial vehicle detection in terms of Precision, F1 score, and mAP.
Pedagogical Strategies for Reflection in Project-based HCI Education with End Users

As HCI pedagogy research grows, so too does an emerging set of evidence-based teaching and curricular recommendations. Yet, few studies have implemented and examined these recommendations in the classroom. In this paper, we present a Research Through Design investigation of a studio-based HCI course, which was revised based on HCI education literature. Drawing on reflection surveys, video recordings of student-led user sessions, final project artifacts, and student interviews, we explore how students responded to key educational changes, the strategies that supported and hindered their reflective practices, and how reflection afforded new student insights. Our findings highlight the utility of video-based reflection exercises to support student learning in designing and running user sessions, the importance of multi-faceted reflection prompts, and how students noticed moments of inclusion and exclusion by attending to users' non-verbal cues. Additionally, we empirically demonstrate the importance of implementing and studying HCI education research recommendations in the classroom.
Specific Features Of Self-Realization In Students With Different Levels Of Self-Control

The relevance of this study is due to the importance of studying personal factors that determine the patterns of individual self-realization.One of these factors is self-control that in our opinion largely defines the selfrealization of an individual.The purpose of the study was to identify the specific features of self-realization in students with different levels of self-control.To achieve this goal, an empirical study was conducted with the participation of 140 students aged 17-25.As diagnostic tools, the following methods were selected: the test "Self-control abilities" by N. M. Peysakhov; "Questionnaire of self-realization" by S. I. Kudinova.In the process of analyzing empirical data, the subjects were divided into five groups (according to the level of development of self-control abilities), where specific differences in the parameters of their selfrealization were studied.The analysis revealed that the most favorable self-control level in terms of selfrealization is above average.Students with this level of self-control are active and optimistic; in the process of self-realization, they have equally high social-corporate and subjective-personal goals of self-realization; egocentric and sociocentric motives are equally present.The most unfavorable for self-realization were low and high levels of self-control, as these groups of subjects revealed the predominance of passivity over activity, of egocentric self-realization motivations over sociocentric ones, low level of constructive selfrealization along with social and personal barriers.
Dynamic to Static Lidar Scan Reconstruction Using Adversarially Trained Auto Encoder

Accurate reconstruction of static environments from LiDAR scans of scenes containing dynamic objects, which we refer to as Dynamic to Static Translation (DST), is an important area of research in Autonomous Navigation. This problem has been recently explored for visual SLAM, but to the best of our knowledge no work has been attempted to address DST for LiDAR scans. The problem is of critical importance due to wide-spread adoption of LiDAR in Autonomous Vehicles. We show that state-of the art methods developed for the visual domain when adapted for LiDAR scans perform poorly. We develop DSLR, a deep generative model which learns a mapping between dynamic scan to its static counterpart through an adversarially trained autoencoder. Our model yields the first solution for DST on LiDAR that generates static scans without using explicit segmentation labels. DSLR cannot always be applied to real world data due to lack of paired dynamic-static scans. Using Unsupervised Domain Adaptation, we propose DSLR-UDA for transfer to real world data and experimentally show that this performs well in real world settings. Additionally, if segmentation information is available, we extend DSLR to DSLR-Seg to further improve the reconstruction quality. DSLR gives the state of the art performance on simulated and real-world datasets and also shows at least 4× improvement. We show that DSLR, unlike the existing baselines, is a practically viable model with its reconstruction quality within the tolerable limits for tasks pertaining to autonomous navigation like SLAM in dynamic environments.
Open source and open hardware mobile robot for developing applications in education and research

Nowadays, additive manufacturing, rapid prototyping and assembly modules represent a market that has invaded the entire world, especially in developing countries where traditional manufacturing is more restricted. In robotics, it is pertinent to think that modular construction is essential, due to the complexity of geometry in each of the pieces and their manufacture. Taking into account the globalization of information and the worldwide reproduction of databases, facilitating access to CAD files to be reproduced in 3D printing promotes the easy construction of archived mechanical designs. A robotic architecture becomes a complex assembly by having multiple operating systems. The sensorics, mechanics, electronics and programming that it requires for navigation, collaboration, development, operation and even industrial manufacturing means that more and more elaborate embedded systems are used. In this work, a mobile robotics architecture was developed with a sensory system that allows free movement and navigation in closed loop inverse kinematics. This kind of robot uses navigation algorithms to take a trajectory in collaborative closed environments, that is, closed industrial environments where obstacles are normally immovable and corridors to move narrow, in addition to having mobile obstacles like humans.
A Motion Capture System for Hand Movement Recognition

One of the most frequently-used body regions in daily activities is the upper limbs, and many of the work-related musculoskeletal disorders occur in this area, mainly the hands. We highlight the importance of studying hand movements executed at work, and how they affect workers’ health and productivity. Data were collected from a hand-motion capture system conformed by six inertial measurement units and six resistive force sensors from hand and fingers movements. Two common hand movements were analyzed using wrist flexion-extension with a small (−15° to 15°) and medium (<−15° and >15°) range of motion and flexion-extension movement with the hand pronated-supinated. Data were classified by traditional methods. A more complex movement involving a 3-finger spherical grip was also recorded. It was found that the lectures from the six inertial sensors and the six force resistive sensors showed a pattern that facilitates the recognition of basic and more complex movements (flexion-extension and spheric handgrip) through visual analysis of the plotted data, even at different ranges of motion.
Improving Lexically Constrained Neural Machine Translation with Source-Conditioned Masked Span Prediction

Accurate terminology translation is crucial for ensuring the practicality and reliability of neural machine translation (NMT) systems.To address this, lexically constrained NMT explores various methods to ensure pre-specified words and phrases appear in the translation output.However, in many cases, those methods are studied on general domain corpora, where the terms are mostly uni-and bi-grams (>98%).In this paper, we instead tackle a more challenging setup consisting of domainspecific corpora with much longer n-gram and highly specialized terms.Inspired by the recent success of masked span prediction models, we propose a simple and effective training strategy that achieves consistent improvements on both terminology and sentence-level translation for three domain-specific corpora in two language pairs.
A NOVEL BLOCKCHAIN FRAMEWORK FOR DIGITAL LEARNING

Blockchain is a structure of data blocks arranged in sequential order, including reliability, data sharing, decentralization, security and, extensively used in a smart contract, digital currency, credit encryption, and other fields.Currently, there is a rapid increase in blockchain technology applications, offering opportunities to deal with the limitation in the online education field, e.g., the shortage of a unified e-learning assessment standard, the complication of e-learning assessment, and the concern of digital education certificates.In this paper, a secured blockchain system proposes building an adaptable and safe data delivery facility that interacts with current educational data.The proposed framework improves data security and eliminates trust concerns among users and, between other institutions accessing services and applications.
Многозадачность образовательного процесса в условиях цифровизации высшей школы

Innovative processes in the technological field, such as computerization, informatization, digitalization, involve lecturers and students in virtual interaction, that consequences are difficult to predict. Models of universities, means, forms and methods of teaching are evolving. The formation of a digital educational environment is the most important trend in the modernization of higher education, and digital technologies are becoming an integral part of the educational process. Distance learning enters the phase of active development, and the principles of learning  continuity and individualization are implemented within its framework. The introduction of digital technologies in the educational process led to a change in the basic role of the teacher - from a source of knowledge to a mentor, a moderator of the educational process. At the same time, digital literacy is becoming one of the key factors of competitiveness in the information society. All these changes, which are characterized by a fast and ubiquitous spread, require a timely assessment in terms of their impact on the educational process and its subjects in the context of digitalization of higher education. The research carried out brings closer understanding and further work in this direction.
Differentially Private k-Means via Exponential Mechanism and Max Cover

We introduce a new (ϵₚ, δₚ)-differentially private algorithm for the k-means clustering problem. Given a dataset in Euclidean space, the k-means clustering problem requires one to find k points in that space such that the sum of squares of Euclidean distances between each data point and its closest respective point among the k returned is minimised. Although there exist privacy-preserving methods with good theoretical guarantees to solve this problem, in practice it is seen that it is the additive error which dictates the practical performance of these methods. By reducing the problem to a sequence of instances of maximum coverage on a grid, we are able to derive a new method that achieves lower additive error than previous works. For input datasets with cardinality n and diameter Δ, our algorithm has an O(Δ² (k log² n log(1/δₚ)/ϵₚ + k √(d log(1/δₚ))/ϵₚ)) additive error whilst maintaining constant multiplicative error. We conclude with some experiments and find an improvement over previously implemented work for this problem.