Reviewerly/exHarmony huggingface dataset:
Revealing the Demographic Attributes of the Authors from the Abstracts of Scientific Articles

This study presents multiple strategies to automatically reveal undisclosed demographic attributes of the authors in the double-blind submissions. From a limited amount of textual content of around 100-200 words excerpted from an abstract, this study aims to reveal the following pieces of information, i) the English language nativeness of the primary author, ii) the country of origin of the primary author, and iii) the gender of the primary author. We introduce an annotated dataset of over 5600 articles labeled with the native language, country of origin, and gender information of the primary authors. We employ classical machine learning (CML) algorithms with statistical n-gram features and transformer-based fine-tuned language models to determine various demographic attributes. We observe that transformer-based models yield slightly better performances for all three tasks. The transformer-based models achieve macro F1 scores close to 75% for identifying the English language nativeness of the primary authors. To determine the country of the non-native English authors, the fine-tuned transformer-based models obtain F1 scores of around 60% (10-class classification). For the gender prediction task, we attain F1 scores of 0.65 by the transformer-based models. The experimental results demonstrate that the fine-tuned language models and CML classifiers are capable of disclosing various author attributes with an acceptable level of accuracy that can undermine the blindness of the double-blind submission.
A computer that sketches along with you

Sketching is a widely used activity in creative processes. One can argue that we now have Artificial Intelligence (AI) capable of making better drawings and sketches than humans. Some computational drawing applications build upon human input, be it text, an input image, or even a hand-drawn path using the cursor. However, drawing along a present-day computer yields a very different experience from drawing collaboratively with another person. For one, the shared canvas is likely not a physical sheet of paper but rather a screen of some sort. Even with paper-like computer interfaces, the computer mainly draws back virtually, needing no material embodiment to make a drawing. My research investigates human collaboration with computers that draw, specifically focused on the act of sketching. I build exploratory physical systems that allow people to draw and sketch together with a computer. Through my studies, I investigate interaction design to allow for human-computer collaborative sketching. My initial studies with a first experimental platform show that the computers are social actors (CASA) paradigm also applies to non-anthropomorphic pen plotters when performing a Tic-Tac-Toe playing task.
Sensing Range Extension for Short-Baseline Stereo Camera Using Monocular Depth Estimation

This paper proposes a method to extend a sensing range of a short-baseline stereo camera (SBSC). The proposed method combines a stereo depth and a monocular depth estimated by a convolutional neural network-based monocular depth estimation (MDE). To combine a stereo depth and a monocular depth, the proposed method estimates a scale factor of a monocular depth using stereo depth-mono depth pairs and then combines the two depths. Another advantage of the proposed method is that the trained MDE model may be utilized for different environments without retraining. The performance of the proposed method is verified qualitatively and quantitatively using the directly collected and open datasets.
Terrorism, Automated Hosts, and COVID-19

The reign of terror ignited by 9/11 and the resulting War on Terror has accelerated the effects on social imaginary. Beyond the economic aftermaths, 9/11 recreated the conditions towards a monopoly of radicalized discourse, which was mainly oriented toward demonizing the “Non-Western Other.” Terrorism allowed the introduction of a conspiracy plot that marked some minority ethnicities as an enemy living within. At the bottom, these narratives punctuated that Muslim communities hate the society they inhabit. Such discourse changed forever the ways the “Other” is imagined. The chapter deals with HBO Saga Westworld, a futurist and dystopian world where robots (hosts) are subjected to torture, violence, and even sadist practices at the hands of wealthy tourists (guests). The formula in “Robots We Must Trust” is placed under the critical lens of scrutiny.
The Technical Dilemmas of Creative Design and Rapid Prototyping for Immersive Storytelling

In this paper, we present an overview of the problem by discussing the landscape of extended reality (XR) and attention guidance techniques considering the effects of current trends, cybersickness, presence and user performance. Then, we discuss designing and rapid prototyping for immersive storytelling as a case study on the applications of these techniques and their related challenges. In conclusion, we propose a research plan that addresses two interrelated themes, and potential research directions to ensure better ways to design and rapid prototype for attention guidance techniques in XR and, as a result, better XR experiences.
On the Mixture of Normal and Half-Normal Distributions

In this research, we studied the mixture of normal and half-normal distributions and introduced some properties for this mixture. In particular, we derived the mean, median, and mode of the mixture of normal and half-normal distributions. We also focused on exploring the Bayesian estimation of parameters of the mixture of normal and half-normal distributions by using different methods and then, using type-I censored sample units, presented a simulation study on the mixture of normal and half-normal distributions.
Identifying neutral reviews from unlabeled data: An exploratory study on user ratings and word-level polarity scores

The presence of the reviews containing mixed or contrasting opinions, also known as neutral reviews, is prevalent in user feedback data. By leveraging annotated data, supervised machine learning (ML) classifiers can learn implicit patterns to identify these neutral reviews. However, labeled data are barely available in most circumstances. When annotated data are unavailable, unsupervised approaches such as lexicon-based methods are employed that utilize word-level polarity scores with a set of rules. As a preliminary study for developing a sophisticated unsupervised framework for recognizing neutral reviews, here, we scrutinize the performances of the existing lexicon-based methods. When applied to four multi-domain review datasets, we observe that all of them perform poorly for identifying neutral reviews. We manually inspect the semantic attributes of a subset of neutral reviews classified wrong by these lexicon-based methods. The experimental results and manual analysis reveal that determining neutrality utilizing the lexical rule-based methods is often ineffective due to numerous reasons, such as user preferences on certain aspects, coverage of the sentiment lexicon, irregularly in the efficacy of aggregation rules, and the context-sensitive polarity of words. As a preliminary study, this analysis reveals traits of neutral reviews and limitations of existing approaches and provides insights to develop methods for neutral review identification from the unlabeled data.
Fairness Indicators for Systematic Assessments of Visual Feature Extractors

Does everyone equally benefit from computer vision systems? Answers to this question become more and more important as computer vision systems are deployed at large scale, and can spark major concerns when they exhibit vast performance discrepancies between people from various demographic and social backgrounds.
Towards a distributed SaaS management system in a multi-cloud environment

Nowadays, the multi-cloud environment is gaining a momentum since it provides its users with reduced costs, greater flexibility and elasticity, high availability and better fault-tolerance. Despite these advantages, these environments present many challenges, including service management and interoperability issues. Due to the diversity of cloud service providers, their heterogeneity and the exponential growth of offered services, user requirements cannot be adequately met. These problems, if not addressed effectively, can have a negative impact on the cloud environment as well as on the user experience. Thus, to answer the service management problem in multi-cloud environments, we propose in this paper a threefold contribution (i) a noval cloud federation architecture, (ii) a suitable service management system and (iii) a service publication algorithm in order to manage, store and retrieve efficiently cloud services within the federation. Our solution consists in combining several concepts including trust, clustering and , ontologies. Our aim is to automate the management process, maximise the profit and ensure a better user experience. Our experiments highlight the effectiveness of the proposed cloud federation architecture as well as the deployed management system in optimizing the storage space and answering effectively and rapidly users’ requests.
A Tensor‐based Regression Approach for Human Motion Prediction

Abstract Collaborative robotic systems will be a key enabling technology for current and future industrial applications. The main aspect of such applications is to guarantee safety for humans. To detect hazardous situations, current commercially available robotic systems rely on direct physical contact to the co‐working person. To further advance this technology, there are multiple efforts to develop predictive capabilities for such systems. Using motion tracking sensors and pose estimation systems combined with adequate predictive models, potential episodes of hazardous collisions between humans and robots can be predicted. Based on the provided predictive information, the robotic system can avoid physical contact by adjusting speed or position. A potential approach for such systems is to perform human motion prediction with machine learning methods like artificial neural networks (NNs). In our approach, the motion patterns of past seconds are used to predict future ones by applying a linear Tensor‐on‐Tensor Regression model, selected according to a similarity measure between motion sequences obtained by dynamic time warping (DTW). For test and validation of our proposed approach, industrial pseudo assembly tasks were recorded with a motion capture system, providing unique traceable Cartesian coordinates for each human joint. The prediction of repetitive human motions associated with assembly tasks, whose data vary significantly in length and have highly correlated variables, has been achieved in real time.
DiFi: A Go-as-You-Pay Wi-Fi Access System

As video streaming services become more popular, users desire high perceived video quality, which has placed more stringent requirements on the quality of connection. Existing issues of cellular networks encourage users to seek alternative connections such as public Wi-Fi networks; however, expectations of both users and owners of Wi-Fi networks are not sufficiently satisfied and various concerns are yet to be addressed by a better Wi-Fi access system. Based on a go-as-you-pay scheme, we design and implement DiFi, a per-user-based system with dynamic resource allocation and pricing. DiFi offers data burst that accommodates user requirements on the burstiness of traffic, in addition to bandwidth. It better caters to the various individual requirements of users, and better utilizes the limited network resources for the owners. We leverage the blockchain-based smart contract to address realistic concerns on decentralized control, privacy and trustiness and our implementation is compatible with existing Wi-Fi infrastructures.
A Generative Model for Evasion Attacks in Smart Grid

Adversarial machine learning (AML) studies how to fool a machine learning (ML) model with malicious inputs degrade the ML method’s performance. Within AML, evasion attacks are an attack category that involves manipulation of input data during the testing phase to induce a misclassification of the data input by the ML model. Such manipulated data inputs that are called, adversarial examples. In this paper, we propose a generative approach for crafting evasion attacks against three ML learning based security classifiers. The proof of concept application for the ML based security classifier is the classification of compromised smart meters launching false data injection. Our proposed solution is validated with a real smart metering dataset. We found degradation in compromised meter detection performance under our proposed generative evasion attack.
Machine Learning models for Customer Relationship Analysis to Improve Satisfaction Rate in Banking

The purpose of this research project was to analyze customer complaint data from financial institutions and identify areas of opportunity for these institutions to improve their customer satisfaction rate. In addition to pointing out areas for improvement, this paper also looks into similar research and tries to understand if themes found in this analysis are consistent with those done by other researchers. Banking is an essential piece to everyday life for all people across the world. Banks need to ensure that their products and processes are simple and accessible to all. Although banks have a monopoly on our financial needs their desire to retain existing customers and gain new ones drives the necessity of providing excellent and timely customer service.The study was conducted using a dataset of over two million customer complaint records and examining what were the top three financial institutions receiving complaints and which products received them. In addition, other aspects of complaints such as state of origination was also looked at. Analysis was done using machine learning, python, tableau and other tools to show the data points and their correlation. Understanding the top financial institutions methods of handling customer complaints, we are able to make recommendations for further product improvements to increase customer satisfaction. Concluding the research project is a list of challenges and opportunities for further research projects. In addition, there are recommendations for the financial institutions investigated in this project on how to move forward from analyzing customer complaint data.
Analysis and Evaluation of a EOD Robot Prototype

In this document, a prototype of an explosive deactivator robot called JVC 0.1 was analyzed, based on the minimum requirements of the UDEX- Arequipa and the international standards given by the ASTM and NIST organizations, then it was evaluated using the Failure Modes and Effects Analysis (FMEA) matrix. In this way, design parameters are proposed for a next version of this prototype. It was obtained that the robot is oversized in material, dimensions and motors, has a reduced working space for the manipulator with low drive speeds and an inadequate gripper. Therefore, a redesign is necessary.
Identifying Functional and Non-functional Software Requirements From User App Reviews

Mobile app developers are always looking for ways to use the reviews (provided by their app’s users) to improve their application (e.g., adding a new functionality in the app that a user mentioned in their review). Usually, there are thousands of user reviews that are available for each mobile app and isolating software requirements manually from such as big dataset can be difficult and time-consuming. The primary objective of the current research is to automate the process of extracting functional requirements and filtering out non-requirements from user app reviews to help app developers better meet the wants and needs of their users. This paper proposes and evaluates machine learning based models to identify and classify software requirements from both, formal Software Requirements Specifications (SRS) documents and Mobile App Reviews (written by users) using machine learning (ML) algorithms combined with natural language processing (NLP) techniques. Initial evaluation of our ML-based models show that they can help classify user app reviews and software requirements as Functional Requirements (FR), Non-Functional Requirements (NFR), or Non-Requirements (NR).
A new audio mixing paradigm: evaluation from professional practitioners’ perspectives

Commercial implications of the COVID-19 pandemic have challenged the creative industries to transform service delivery from inter-personal to online operations. Existing online music and audio production collaboration platforms and cloud storage cannot replicate intrinsic in-studio interactions between an audio engineer and clients in a professional post-production audio mixing environment. We developed the DAW Collaboration Framework that transitions audio practitioners to a real-time, internet-linked mixing environment. This paper documents our evaluation of the framework’s performance through real-world practitioners’ perspectives, having participated in professional audio mixing scenarios. Our evaluation attests to the facilitation of a new audio mixing paradigm, where remote participants work synchronously in a collaborative, professional, DAW-based mixing environment, similar to traditional in-studio settings. Every participant monitors high-resolution audio files while accessing complete synchronised mixing control of the shared DAW project. Identified benefits include time savings, increased productivity, and engaging, collaborative audio mixing. This paradigm empowers remote participants to contribute to audio mixing projects, promoting new global reach and business opportunities for audio engineers. We identify impediments to widespread adoption of this new paradigm, thereby posing future research directions. Ultimately, in a pandemic-impacted era of disruptions and travel restrictions, our framework presents a viable alternative to in-person, studio-based mixing.
Effect of knowledge management on innovative performance with the mediating effect of unlearning

Purpose The purpose of this study is to examine the effect of knowledge management (KM) on innovative performance with the mediating effect of unlearning (UNL) in Iran Water Resources Management Company. Design/methodology/approach In terms of data collection, this study has elements of library study and field study. To collect the data, a researcher-made questionnaire with three subscales of KM, UNL and innovative performance was used. The population of this study was 800 employees of Iran Water Resources Management Company, from whom a sample of 256 participants was selected through random sampling. Structural equation modeling was used to analyze the data and test the hypotheses through smart PLS software. Findings The results of data analysis showed that there is a significant relationship between KM and innovative performance, with UNL having a positive mediating effect. Research limitations/implications Recent research complements existing knowledge in the field of organizational KM. Practical implications This study provides a good view of the impact of using KM in improving innovative performance by applying the UNL process. Originality/value This study can be a beacon for decision-makers in the optimal use of KM in the organization. In the continuation of this study, managers can plan with special attention to KM processes, by removing barriers to organizational learning to achieve better innovative performance.
ArmSpy: Video-assisted PIN Inference Leveraging Keystroke-induced Arm Posture Changes

PIN inference attack leveraging keystroke-induced side-channel information poses a substantial threat to the security of people’s privacy and properties. Among various PIN inference attacks, video-assisted method provide more intuitive and robust side-channel information to infer PINs. But it usually requires there is no visual occlusion between the attacker and the victims or their hand gestures, making the attackers either easy to expose themselves or inapplicable to the scenarios such as ATM or POS terminals. In this paper, we present a novel and practical video-assisted PIN inference system, ArmSpy, which infers victim’s PIN by observing from behind the victims in a stealthy way. Specifically, ArmSpy explores the subtle keystroke-induced arm posture changes, including elbow bending angle changes and the spatial relationship between different arm joints, to infer the PIN entries. We develop the keystroke inference mechanism to detect the keystroke events and pinpoint the keystroke positions, and then accurately infer the PINs with the proposed inferred PIN coordination mechanism. Extensive experimental results demonstrate that ArmSpy can achieve over 67% average accuracy on inferring the PIN with 3 attempts and even over 80% for some victims, indicating the severity of the threat posed by ArmSpy.
Intelligent English Translation and Optimization Based on Big Data Model

With the development and popularization of communication technology, intelligent analysis using big data front-end in various fields is also skillful. In the study of English literature and short sentence translation, we should realize a fast and intelligent translation approach. Based on the analysis and research of big data, a set of online learning algorithms for English translation learning algorithms is compiled, which will greatly improve the efficiency and accuracy of translation. On the premise of online processing of big data English information, this paper shortens the processing time without affecting the translation accuracy. The radial function algorithm for big data reduces the complexity of big data, improves the computational efficiency of dealing with problems, and realizes the generalization of translation performance. The effective application of big data in the optimization practice of English translation will provide an effective theoretical way for accurate translation and realize intelligent model algorithm. The experimental results are summarized as follows: (1) with the increase of the number of translated texts, the translation time will also increase and the difficulty will increase. (2) The approximation value in radial basis function is used to evaluate the translation effect, and its error in 100 texts will decrease with the increase of literature, and its approximation state is consistent with the real state. (3) Among various translation techniques, conventional literal translation is still the usual method, and its feedback effect is also the best, which is the basic method of thinking according to data. (4) The evaluation of accuracy in intelligent calculation methods is different, from 100% accuracy under heterogeneous functions to 50% accuracy under unified algorithms, which shows that optimization methods need to be updated and improved in time.
Auter: Automatically Tuning Multi-layer Network Buffers in Long-Distance Shadowsocks Networks

To bypass network censorship, Shadowsocks is often deployed on long-distance transnational networks; however, such proxy networks are usually plagued by high latency, high packet loss rate, and unstable bandwidth. Most existing tuning solutions rely on hand-tuned heuristics, which cannot work well in the volatile Shadowsocks networks due to the labor intensive and time-consuming properties. In this paper, we propose Auter, which automatically tunes multi-layer buffer parameters with reinforcement learning (RL) to improve the performance of Shadowsocks in long-distance networks. The key insight behind Auter is that different network environments require different sizes of buffers to achieve sufficiently good performance. Hence, Auter continuously learns a tuning policy from volatile network states and dynamically alter sizes of multi-buffers for high network performance. We prototype Auter and evaluate its effectiveness under various real networks. Our experimental results show that Auter can effectively improve network performance, up to 40.5% throughput increase in real networks. Besides, we demonstrate that Auter outperforms all the existing tuning schemes.