Reviewerly/exHarmony huggingface dataset:
Practical Guidelines for Intent Recognition

Intent recognition models, which match a written or spoken input's class in order to guide an interaction, are an essential part of modern voice user interfaces, chatbots, and social robots. However, getting enough data to train these models can be very expensive and challenging, especially when designing novel applications such as real-world human-robot interactions. In this work, we first investigate how much training data is needed for high performance in an intent classification task. We train and evaluate BiLSTM and BERT models on various subsets of the ATIS and Snips datasets. We find that only 25 training examples per intent are required for our BERT model to achieve 94% intent accuracy compared to 98% with the entire datasets, challenging the belief that large amounts of labeled data are required for high performance in intent recognition. We apply this knowledge to train models for a real-world HRI application, character strength recognition during a positive psychology interaction with a social robot, and evaluate against the Character Strength dataset collected in our previous HRI study. Our real-world HRI application results also confirm that our model can produce 76% intent accuracy with 25 examples per intent compared to 80% with 100 examples. In a real-world scenario, the difference is only one additional error per 25 classifications. Finally, we investigate the limitations of our minimal data models and offer suggestions on developing high quality datasets. We conclude with practical guidelines for training BERT intent recognition models with minimal training data and make our code and evaluation framework available for others to replicate our results and easily develop models for their own applications.
Toward an Objective Measure of Developers’ Cognitive Activities

Understanding how developers carry out different computer science activities with objective measures can help to improve productivity and guide the use and development of supporting tools in software engineering. In this article, we present two controlled experiments involving 112 students to explore multiple computing activities (code comprehension, code review, and data structure manipulations) using three different objective measures including neuroimaging (functional near-infrared spectroscopy (fNIRS) and functional magnetic resonance imaging (fMRI)) and eye tracking. By examining code review and prose review using fMRI, we find that the neural representations of programming languages vs. natural languages are distinct. We can classify which task a participant is undertaking based solely on brain activity, and those task distinctions are modulated by expertise. We leverage insights from the psychological notion of spatial ability to decode the neural representations of several fundamental data structures and their manipulations using fMRI, fNIRS, and eye tracking. We examine list, array, tree, and mental rotation tasks and find that data structure and spatial operations use the same focal regions of the brain but to different degrees: they are related but distinct neural tasks. We demonstrate best practices and describe the implication and tradeoffs between fMRI, fNIRS, eye tracking, and self-reporting for software engineering research.
THE LOGIC OF THE EVIDENTIAL CONDITIONAL

Abstract In some recent works, Crupi and Iacona proposed an analysis of ‘if’ based on Chrysippus’ idea that a conditional holds whenever the negation of its consequent is incompatible with its antecedent. This paper presents a sound and complete system of conditional logic that accommodates their analysis. The soundness and completeness proofs that will be provided rely on a general method elaborated by Raidl, which applies to a wide range of systems of conditional logic.
Hierarchical pathology screening for cervical abnormality

Cervical smear screening is an imaging-based cancer detection tool, which is of pivotal importance for the early-stage diagnosis. A computer-aided screening system can automatically find out if the scanned whole-slide images (WSI) with cervical cells are classified as "abnormal" or "normal", and then alert pathologists. It can significantly reduce the workload for human experts, and is therefore highly demanded in clinical practice. Most of the screening methods are based on automatic cervical cell detection and classification, but the accuracy is generally limited due to the high variation of cell appearance and lacking context information from the surroundings. Here we propose a novel and hierarchical framework for automatic cervical smear screening aiming at the robust performance of case-level diagnosis and finding suspected "abnormal" cells. Our framework consists of three stages. We commence by extracting a large number of pathology images from the scanned WSIs, and implementing abnormal cell detection to each pathology image. Then, we feed the detected "abnormal" cells with corresponding confidence into our novel classification model for a comprehensive analysis of the extracted pathology images. Finally, we summarize the classification outputs of all extracted images, and determine the overall screening result for the target case. Experiments show that our three-stage hierarchical method can effectively suppress the errors from cell-level detection, and provide an effective and robust way for cervical abnormality screening.
A Novel Mixed Reality Solution Based on Learning Environment for Sutures in Minor Surgery

Minor Surgery Sutures is a fundamental skill for healthcare professionals. However, in the educational field, the practice of suturing is sometimes limited and reduced, with more theoretical than practical study. In order to facilitate learning, our goal is to develop an immersive and interactive educational tool that complements theoretical study, called Suture MR. This application could enhance suture procedural skills in the fields of nursing and medicine. Applying Mixed Reality techniques, we generate a 3D model of an arm with a full-scale wound. Realistically, the user will simulate the suture movements as part of the learning process. The application has surgical clamps and a needle holder that are virtually visualized in the user’s hands, allowing gestures and movements faithful to the real ones. In this article, we want to demonstrate the usability of our environment and the feasibility of using Mixed Reality learning experiences in clinical practical training as a complement to theoretical training. The results of the study reveal a greater perception of learning and the willingness of students to use this methodology.
Spatial Consistency Constrained GAN for Human Motion Transfer

In this paper, we propose a new GAN-based framework to implement video-based human motion transfer, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , transferring the motions from the source person to the target one with the help of pose information. Human motion transfer involves large scaled spatial deformations from pose to body image and emphasizes spatial consistency of details. However, GAN is not suitable for the region-unaligned task due to the global adversarial loss does not focus on the spatial details. Therefore, we design a two-stage Spatial Consistency Constrained GAN architecture to generate realistic target person images. Within the model, we first generate a segment map to align the regions of different body parts with a given pose in stage-I and then concatenate the pose and the segment map as condition to generate a target person image in stage-II, so that the deformation problem is avoided. Furthermore, to improve the spatial detail consistency, we propose the shape consistency loss for the segment map generation to make the model pay more attention to the shape of each body part. We also propose a pose consistency loss for the target person image generation to enforce the generated images to contain similar enough poses to the input ones. The synthesized images with clear shape and sharp details demonstrate the effectiveness of the proposed method.
Analysis of Characteristics of Tennis Singles Matches Based on 5G and Data Mining Technology

The level of technical and tactical decision-making in a tennis game has a very important impact on the outcome of the game. How to discover the characteristics and rules of the game from a large amount of technical and tactical data, how to overcome the shortcomings of traditional statistical methods, and how to provide a scientific basis for correct decision-making are a top priority. Based on 5G and association analysis data mining theory, we established a data mining model for tennis technical offensive tactics and association rules and conducted specific case studies. It can calculate the maximization and distribution rate of certain technologies, also distinguish between the athlete’s gain and loss rate and the spatial position on the track, and use artificial statistical methods to cause errors and subjective participation. This solution provides objective and scientific decision support for this problem and is used in the decision-making of the landing point in tennis match technology and tactics. Experimental simulation shows that the data mining technology analysis system used for regional tennis matches is more concise, efficient, and accurate than traditional movie analysis methods.
The Geriatric Acute and Post-Acute Fall Prevention Intervention (GAPcare) II to Assess the Use of the Apple Watch in Older Emergency Department Patients With Falls: Protocol for a Mixed Methods Study

Background Falls are a common problem among older adults that lead to injury, emergency department (ED) visits, and institutionalization. The Apple Watch can detect falls and alert caregivers and clinicians that help is needed; the device could also be used to objectively collect data on gait, fitness, and falls as part of clinical trials. However, little is known about the ease of use of this technology among older adult ED patients, a population at high risk of recurrent falls. Objective The goal of this study—the Geriatric Acute and Post-Acute Fall Prevention Intervention (GAPcare) II—is to examine the feasibility, acceptability, and usability of the Apple Watch Series 4 paired with the iPhone and our research app Rhode Island FitTest (RIFitTest) among older adult ED patients seeking care for falls. Methods We will conduct field-testing with older adult ED patients (n=25) who sustained a fall and their caregivers (n=5) to determine whether they can use the Apple Watch, iPhone, and app either (1) continuously or (2) periodically, with or without telephone assistance from the research staff, to assess gait, fitness, and/or falls over time. During the initial encounter, participants will receive training in the Apple Watch, iPhone, and our research app. They will receive an illustrated training manual and a number to call if they have questions about the research protocol or device usage. Participants will complete surveys and cognitive and motor assessments on the app during the study period. At the conclusion of the study, we will solicit participant feedback through semistructured interviews. Qualitative data will be summarized using framework matrix analyses. Sensor and survey response data will be analyzed using descriptive statistics. Results Recruitment began in December 2019 and was on pause from April 2020 until September 2020 due to the COVID-19 pandemic. Study recruitment will continue until 30 participants are enrolled. This study has been approved by the Rhode Island Hospital Institutional Review Board (approval 1400781-16). Conclusions GAPcare II will provide insights into the feasibility, acceptability, and usability of the Apple Watch, iPhone, and the RIFitTest app in the population most likely to benefit from the technology: older adults at high risk of recurrent falls. In the future, wearables could be used as part of fall prevention interventions to prevent injury before it occurs. Trial Registration ClinicalTrials.gov NCT04304495; https://clinicaltrials.gov/ct2/show/NCT04304495 International Registered Report Identifier (IRRID) DERR1-10.2196/24455
Using Mobile Augmented Reality for Teaching 3D Transformations

Learning computer graphics requires a diverse range of skills, such as mathematics, programming, problem-solving and 3D reasoning skills. One fundamental area students struggle with is three-dimensional (3D) transformations, partially due to a lack of visuospatial skills.
Hybridization of ring theory-based evolutionary algorithm and particle swarm optimization to solve class imbalance problem

Abstract Many real-life datasets are imbalanced in nature, which implies that the number of samples present in one class (minority class) is exceptionally less compared to the number of samples found in the other class (majority class). Hence, if we directly fit these datasets to a standard classifier for training, then it often overlooks the minority class samples while estimating class separating hyperplane(s) and as a result of that it missclassifies the minority class samples. To solve this problem, over the years, many researchers have followed different approaches. However the selection of the true representative samples from the majority class is still considered as an open research problem. A better solution for this problem would be helpful in many applications like fraud detection, disease prediction and text classification. Also, the recent studies show that it needs not only analyzing disproportion between classes, but also other difficulties rooted in the nature of different data and thereby it needs more flexible, self-adaptable, computationally efficient and real-time method for selection of majority class samples without loosing much of important data from it. Keeping this fact in mind, we have proposed a hybrid model constituting Particle Swarm Optimization (PSO), a popular swarm intelligence-based meta-heuristic algorithm, and Ring Theory (RT)-based Evolutionary Algorithm (RTEA), a recently proposed physics-based meta-heuristic algorithm. We have named the algorithm as RT-based PSO or in short RTPSO. RTPSO can select the most representative samples from the majority class as it takes advantage of the efficient exploration and the exploitation phases of its parent algorithms for strengthening the search process. We have used AdaBoost classifier to observe the final classification results of our model. The effectiveness of our proposed method has been evaluated on 15 standard real-life datasets having low to extreme imbalance ratio. The performance of the RTPSO has been compared with PSO, RTEA and other standard undersampling methods. The obtained results demonstrate the superiority of RTPSO over state-of-the-art class imbalance problem-solvers considered here for comparison. The source code of this work is available in https://github.com/Sayansurya/RTPSO_Class_imbalance .
Landmark Breaker: Obstructing DeepFake By Disturbing Landmark Extraction

The recent development of Deep Neural Networks (DNN) has significantly increased the realism of AI-synthesized faces, with the most notable examples being the DeepFakes. The DeepFake technology can synthesize a face of target subject from a face of another subject, while retains the same face attributes. With the rapidly increased social media portals (Facebook, Instagram, etc), these realistic fake faces rapidly spread though the Internet, causing a broad negative impact to the society. In this paper, we describe Landmark Breaker, the first dedicated method to disrupt facial landmark extraction, and apply it to the obstruction of the generation of DeepFake videos. Our motivation is that disrupting the facial landmark extraction can affect the alignment of input face so as to degrade the DeepFake quality. Our method is achieved using adversarial perturbations. Compared to the detection methods that only work after DeepFake generation, Landmark Breaker goes one step ahead to prevent DeepFake generation. The experiments are conducted on three state-of-the-art facial landmark extractors using the recent Celeb-DF dataset.
Encryption method and security analysis of medical images based on stream cipher enhanced logical mapping

BACKGROUND: Medical image security has been paid more attention in the medical field. OBJECTIVE: In order to achieve a higher security level of medical image encryption, this paper proposes a stream cipher enhanced logic mapping encryption method. METHODS: According to the theory of stream cipher, this method uses Chebyshev map to form encryption key. A series of coding operations are used to set the initial value before image chaos processing. Combining with logical mapping, the original image information is encrypted by chaos from X and Y dimensions. RESULTS: The experimental results show that the NPCR value of the encrypted image is 0.9874 after the blood cells are encrypted. CONCLUSIONS: All four evaluation methods fully prove that this method has excellent encryption performance.
Towards Fair Deep Anomaly Detection

Anomaly detection aims to find instances that are considered unusual and is a fundamental problem of data science. Recently, deep anomaly detection methods were shown to achieve superior results particularly in complex data such as images. Our work focuses on deep one-class classification for anomaly detection which learns a mapping only from the normal samples. However, the non-linear transformation performed by deep learning can potentially find patterns associated with social bias. The challenge with adding fairness to deep anomaly detection is to ensure both making fair and correct anomaly predictions simultaneously. In this paper, we propose a new architecture for the fair anomaly detection approach (Deep Fair SVDD) and train it using an adversarial network to de-correlate the relationships between the sensitive attributes and the learned representations. This differs from how fairness is typically added namely as a regularizer or a constraint. Further, we propose two effective fairness measures and empirically demonstrate that existing deep anomaly detection methods are unfair. We show that our proposed approach can remove the unfairness largely with minimal loss on the anomaly detection performance. Lastly, we conduct an in-depth analysis to show the strength and limitations of our proposed model, including parameter analysis, feature visualization, and run-time analysis.
T-RACKs: A Faster Recovery Mechanism for TCP in Data Center Networks

Cloud interactive data-driven applications generate swarms of small TCP flows that compete for the small buffer space in data-center switches.Such applications require a short flow completion time (FCT) to perform their jobs effectively.However, TCP is oblivious to the composite nature of application data and artificially inflates the FCT of such flows by several orders of magnitude.This is due to TCP's Internet-centric design that fixes the retransmission timeout (RTO) to be at least hundreds of milliseconds.To better understand this problem, in this paper, we use empirical measurements in a small testbed to study, at a microscopic level, the effects of various types of packet losses on TCP's performance.In particular, we single out packet losses that impact the tail end of small flows, as well as bursty losses, that span a significant fraction of the small congestion window of TCP flows in data-centers, to show a non-negligible effect on the FCT.Based on this, we propose the so-called, timely-retransmitted ACKs (or T-RACKs), a simple loss recovery mechanism to conceal the drawbacks of the long RTO even in the presence of heavy packet losses.Interestingly enough, T-RACKS achieves this transparently to TCP itself as it does not require any change to TCP in the tenant's virtual machine (VM).T-RACKs can be implemented as a software shim layer in the hypervisor between the VMs and server's NIC or in hardware as a networking function in a SmartNIC.Simulation and real testbed results show that T-RACKs achieves remarkable performance improvements.
Using Actors and Use Cases for Software Size Estimation

Software size estimation represents a complex task, which is based on data analysis or on an algorithmic estimation approach. Software size estimation is a nontrivial task, which is important for software project planning and management. In this paper, a new method called Actors and Use Cases Size Estimation is proposed. The new method is based on the number of actors and use cases only. The method is based on stepwise regression and led to a very significant reduction in errors when estimating the size of software systems compared to Use Case Points-based methods. The proposed method is independent of Use Case Points, which allows the elimination of the effect of the inaccurate determination of Use Case Points components, because such components are not used in the proposed method.
A Novel Double Pressure Sensors-Based Monitoring and Alarming System for Fall Detection

Considering non-wearable devices or systems, two technologies, camera-based and sensor-based, are frequently investigated and implemented. Most people who concern very seriously about their privacy choose the sensor-based one. Previously, a pressure sensor-based system called “NEF” was developed to monitor on-bed movements and send out alarming notifications to end-users when undesired events occurred. The system used only one pressure sensor that was placed on the bed or mattress. On-bed positions and patterns were classified with high accuracy. However, the fall event is actually very dynamic. Movements outside the bed shall be included. In this paper, a novel double pressure sensors-based system was proposed. Applying different machine learning techniques, random forest yielded the best fall detection model with 100% accuracy. Further experiments in multiple centers will be investigated.
A Novel Image Encryption Algorithm Based on the Delayed Maps and Permutation-Confusion-Diffusion Architecture

To improve the dynamical behaviors of 1D chaotic maps, a new linear-delay-modulation method (LDM) is proposed. Derived from the Sine map, a delayed Sine map (DSM) is proposed based on the LDM. Then, we substitute the Sine map in the SIMM system with DSM and obtained a delayed SIMM system (DSIMM). Its chaotic performance is analyzed through the phase diagram, Lyapunov exponent spectrum, and complexity. The results show that the delayed chaotic map can generate more complex dynamical behaviors and more random sequences. Hence, we apply the two delayed systems to a novel image encryption algorithm with the permutation-confusion-diffusion architecture. Firstly, to permutate the pixel of the image efficiently, the plain-image is scrambled by using a multilayer of the nonlinear index. Secondly, the image is confused by using the chaotic matrix generated with two chaotic sequences, and then, the ciphertext is transformed into a 1D sequence. Finally, to improve the plaintext sensitivity and facilitate key management, we enhance the sensitivity by applying a novel diffusion algorithm instead of using plaintext-related keystream. The diffusion equation contains the sum of undiffused pixels and the operation of cyclic bit-shift. Simulation results for the gray image illustrate the effectiveness of the proposed encryption algorithm.