Reviewerly/exHarmony huggingface dataset:
A large-scale comparison of human-written versus ChatGPT-generated essays

Abstract ChatGPT and similar generative AI models have attracted hundreds of millions of users and have become part of the public discourse. Many believe that such models will disrupt society and lead to significant changes in the education system and information generation. So far, this belief is based on either colloquial evidence or benchmarks from the owners of the models—both lack scientific rigor. We systematically assess the quality of AI-generated content through a large-scale study comparing human-written versus ChatGPT-generated argumentative student essays. We use essays that were rated by a large number of human experts (teachers). We augment the analysis by considering a set of linguistic characteristics of the generated essays. Our results demonstrate that ChatGPT generates essays that are rated higher regarding quality than human-written essays. The writing style of the AI models exhibits linguistic characteristics that are different from those of the human-written essays. Since the technology is readily available, we believe that educators must act immediately. We must re-invent homework and develop teaching concepts that utilize these AI models in the same way as math utilizes the calculator: teach the general concepts first and then use AI tools to free up time for other learning objectives.
Deep Learning of Appearance Models for Online Object Tracking

This paper introduces a deep learning based approach for vision based single target tracking. We address this problem by proposing a network architecture which takes the input video frames and directly computes the tracking score for any candidate target location by estimating the probability distributions of the positive and negative examples. An online fine-tuning step is carried out at every frame to learn the appearance of the target. The tracker has been tested on the standard tracking benchmark and the results indicate that the proposed solution achieves state-of-the-art tracking results.
Overcoming Blind Spots in the Real World: Leveraging Complementary Abilities for Joint Execution

Simulators are being increasingly used to train agents before deploying them in real-world environments. While training in simulation provides a cost-effective way to learn, poorly modeled aspects of the simulator can lead to costly mistakes, or blind spots. While humans can help guide an agent towards identifying these error regions, humans themselves have blind spots and noise in execution. We study how learning about blind spots of both can be used to manage hand-off decisions when humans and agents jointly act in the real-world in which neither of them are trained or evaluated fully. The formulation assumes that agent blind spots result from representational limitations in the simulation world, which leads the agent to ignore important features that are relevant for acting in the open world. Our approach for blind spot discovery combines experiences collected in simulation with limited human demonstrations. The first step applies imitation learning to demonstration data to identify important features that the human is using but that the agent is missing. The second step uses noisy labels extracted from action mismatches between the agent and the human across simulation and demonstration data to train blind spot models. We show through experiments on two domains that our approach is able to learn a succinct representation that accurately captures blind spot regions and avoids dangerous errors in the real world through transfer of control between the agent and the human.
Open quantum dynamics induced by light scalar fields

We consider the impact of a weakly coupled environment comprising a light scalar field on the open dynamics of a quantum probe field, resulting in a master equation for the probe field that features corrections to the coherent dynamics, as well as decoherence and momentum diffusion. The light scalar is assumed to couple to matter either through a nonminimal coupling to gravity or, equivalently, through a Higgs portal. Motivated by applications to experiments such as atom interferometry, we assume that the probe field can be initialized, by means of external driving, in a state that is not an eigenstate of the light scalar-field--probe system, and we derive the master equation for single-particle matrix elements of the reduced density operator of a toy model. We comment on the possibilities for experimental detection and the related challenges, and highlight possible pathways for further improvements. This derivation of the master equation requires techniques of nonequilibrium quantum field theory, including the Feynman-Vernon influence functional and thermo field dynamics, used to motivate a method of Lehmann-Symanzik-Zimmermann-like reduction. In order to obtain cutoff-independent results for the probe-field dynamics, we find that it is necessary to use a time-dependent renormalization procedure. Specifically, we show that non-Markovian effects following a quench, namely the violation of time-translational invariance due to finite-time effects, lead to a time-dependent modulation of the usual vacuum counterterms.
Cross-Language End-to-End Speech Recognition Research Based on Transfer Learning for the Low-Resource Tujia Language

To rescue and preserve an endangered language, this paper studied an end-to-end speech recognition model based on sample transfer learning for the low-resource Tujia language. From the perspective of the Tujia language international phonetic alphabet (IPA) label layer, using Chinese corpus as an extension of the Tujia language can effectively solve the problem of an insufficient corpus in the Tujia language, constructing a cross-language corpus and an IPA dictionary that is unified between the Chinese and Tujia languages. The convolutional neural network (CNN) and bi-directional long short-term memory (BiLSTM) network were used to extract the cross-language acoustic features and train shared hidden layer weights for the Tujia language and Chinese phonetic corpus. In addition, the automatic speech recognition function of the Tujia language was realized using the end-to-end method that consists of symmetric encoding and decoding. Furthermore, transfer learning was used to establish the model of the cross-language end-to-end Tujia language recognition system. The experimental results showed that the recognition error rate of the proposed model is 46.19%, which is 2.11% lower than the that of the model that only used the Tujia language data for training. Therefore, this approach is feasible and effective.
Assisting Individuals with Autism and Cognitive Disorders: An Augmented Reality-Based Framework

Individuals with autism require systematic assistance while dealing with the surrounding environment and its objects. The project aim is to develop a framework that could be of substantial help to people with autism and cognitive disorders. The framework is based on common mobile devices and freely available Augmented Reality (AR) applications. The Augmented Reality used in our approach is marker-based AR that employs a camera and a visual marker to trigger and present media content on the mobile device’s screen. The developed framework allows parents and teachers to easily create educational augmented environments for children with autism and cognitive disorders by populating a real-world space with visual markers of favorite cartoonish helpers that can evoke helpful AR content and embed it in the real-world environment. The paper analyzes and discusses the use of the proposed framework from conceptual and technological points of view.
A smartphone game to prevent HIV among young Kenyans: Household dynamics of gameplay in a feasibility study

Objective: mHealth interventions often favour individual-level effects. This is particularly problematic in contexts where social support and shifts in social norms are critical to sustained behaviour change. Mobile digital games represent a promising health education strategy for youth, including in low-resource settings. We sought to better understand the interpersonal and social interactions that can be elicited by digital games for health. Design: We piloted Tumaini, a smartphone game rooted in interactive narrative designed to prevent HIV among young Africans (aged 11–14), in a randomised controlled feasibility study and analysed reports of the household dynamics surrounding gameplay. Following a 16-day intervention period, phone gameplay log files were downloaded, and intervention arm participants ( n = 30) completed a gameplay experience survey; eight focus group discussions were held, four with intervention arm participants ( n = 27) and four with their parents ( n = 22). Setting: This study took place in Kisumu, Kenya, in Spring 2017. Method: Descriptive statistics were computed from survey responses and log files. Focus group transcripts were labelled, analysed thematically and compared demographically using MAXQDA software. Results: Data from log files, surveys and focus groups indicate that the game generated considerable interaction and dialogue with parents, siblings and friends and served as a catalyst for children to act as advocates for healthful decisions about sex, both within the family and beyond. The game showed a high level of acceptability with parents. Conclusion: Serious digital games using a smartphone platform can generate considerable household interaction. Games can model and facilitate these exchanges, maximising multi-level effects. An additional app for parents could reinforce these effects.
Graph-Based Image Segmentation Using Dynamic Trees

Image segmentation methods have been actively investigated, being the graph-based approaches among the most popular for object delineation from seed nodes. In this context, one can design segmentation methods by distinct choices of the image graph and connectivity function—i.e., a function that measures how strongly connected are seed and node through a given path. The framework is known as Image Foresting Transform (IFT) and it can define by seed competition each object as one optimum-path forest rooted in its internal seeds. In this work, we extend the general IFT algorithm to extract object information as the trees evolve from the seed set and use that information to estimate arc weights, positively affecting the connectivity function, during segmentation. The new framework is named Dynamic IFT (DynIFT) and it can make object delineation more effective by exploiting color, texture, and shape information from those dynamic trees. In comparison with other graph-based approaches from the state-of-the-art, the experimental results on natural images show that DynIFT-based object delineation methods can be significantly more accurate.
A feature adaptive image watermarking framework based on Phase Congruency and Symmetric Key Cryptography

In this paper, a Phase Congruency based digital color image watermarking algorithm is proposed which provides a higher degree of robustness against attacks and excellent imperceptibility. Here, Phase Congruency has been used to detect the local feature regions of the host image and then the watermark has been infused into it using a new technique called ‘Adaptive α-β Blending’. An accurate Human Visual System modeling has been incorporated via Lifting Wavelet Transform to take the full advantage of perceptual watermarking. The coefficients of the α-β blending are selected adaptively based on the Phase Congruency feature map of the host image. Furthermore, the watermark is secured with a cryptographic algorithm called Arnold’s Cat Map to prohibit further eavesdropping. From rigorous testing, results indicate that our approach is robust against various geometric, non-geometric and combined attacks while maintaining a sublime imperceptibility.
Virtual reality for animal navigation with camera-based optical flow tracking

Virtual reality combined with a spherical treadmill is used across species for studying neural circuits underlying navigation and learning. We developed an optical flow-based method for tracking treadmill ball motion in real time using a single high-resolution camera. Tracking accuracy and timing were determined using calibration data. Ball tracking was performed at 500 Hz and integrated with an open source game engine for virtual reality projection. The projection was updated at 120 Hz with a latency with respect to ball motion of 30 ± 8 ms. The system was tested for behavior with fruit flies. The application and source code are available at https://github.com/ivan-vishniakou/neural-circuits-vr. Optical flow-based tracking of treadmill motion is typically achieved using optical mice. The camera-based optical flow tracking system developed here is based on off-the-shelf components and offers control over the image acquisition and processing parameters. This results in flexibility with respect to tracking conditions – such as ball surface texture, lighting conditions, or ball size – as well as camera alignment and calibration. A fast system for rotational ball motion tracking suitable for virtual reality behavior with fruit flies was developed and characterized.
Security-enhanced optical voice encryption in various domains and comparative analysis

Optical voice encryption based on digital holography (DH), which uses the double random phase encoding (DRPE) in the Fourier transform domain, has been proposed [Opt. Lett.42, 4619 (2017)OPLEDP0146-959210.1364/OL.42.004619]. In the present work, we propose optical voice encryption in other optical domains such as fractional Fourier, Fresnel, and gyrator transform, which convert input information into different mixed space-frequency domains. We also analyze the recording conditions of human voice and some security aspects of the scheme. An optical setup based on off-axis DH is used to record the time-series of holograms, and these holograms can visualize the sound wave propagation. Then, the different encryption domains in double random phase encoding (DRPE) are used to encrypt voice information with enhanced security. With the numerical reconstruction and decryption procedure of DRPE with all correct keys and correct orders of optical domains, the original voice information can be retrieved. The extension of voice encryption in different domains will enhance the security level and analysis of recording, and security aspects will explore the possibilities and challenges of the optical voice encryption scheme. We present simulation results for encryption of an experimentally recorded human voice in various domains.
Evaluating associative classification algorithms for Big Data

Associative Classification, a combination of two important and different fields (classification and association rule mining), aims at building accurate and interpretable classifiers by means of association rules. A major problem in this field is that existing proposals do not scale well when Big Data are considered. In this regard, the aim of this work is to propose adaptations of well-known associative classification algorithms (CBA and CPAR) by considering different Big Data platforms (Spark and Flink). An experimental study has been performed on 40 datasets (30 classical datasets and 10 Big Data datasets). Classical data have been used to find which algorithms perform better sequentially. Big Data dataset have been used to prove the scalability of Big Data proposals. Results have been analyzed by means of non-parametric tests. Results proved that CBA-Spark and CBA-Flink obtained interpretable classifiers but it was more time consuming than CPAR-Spark or CPAR-Flink. In this study, it was demonstrated that the proposals were able to run on Big Data (file sizes up to 200 GBytes). The analysis of different quality metrics revealed that no statistical difference can be found for these two approaches. Finally, three different metrics (speed-up, scale-up and size-up) have also been analyzed to demonstrate that the proposals scale really well on Big Data. The experimental study has revealed that sequential algorithms cannot be used on large quantities of data and approaches such as CBA-Spark, CBA-Flink, CPAR-Spark or CPAR-Flink are required. CBA has proved to be very useful when the main goal is to obtain highly interpretable results. However, when the runtime has to be minimized CPAR should be used. No statistical difference could be found between the two proposals in terms of quality of the results except for the interpretability of the final classifiers, CBA being statistically better than CPAR.
Blockchain Usage for Government-Issued Electronic IDs: A Survey

Government IDs are traditionally plastic cards or paper-based passport booklets, sometimes with machine-readable contactless chips. Even though advanced implementations with cryptographic capabilities and online interfaces have been introduced, adoption and usage for online identification remain low. At the same time, there is a recognized need for trusted identities on the Internet, in Know-Your-Customer and Anti-Money-Laundering processes, and also in emerging blockchain-based, decentralized ecosystems. The contribution of this paper is a thorough analysis of the opportunities and state-of-the-art at the intersection of blockchain technology and government-issued electronical identity documents (eIDs), including existing implementations and pilots.
Methodology of High Accuracy, Sensitivity and Specificity in the Counts of Erythrocytes and Leukocytes in Blood Smear Images

The evaluation of human blood is an important diagnostic method for the detection of diseases. The analysis of the erythrocytes contained in the blood contributes to the detection of anemia and leukemia, whereas the leukocyte analysis allows the diagnosis of inflammation and/or infections. The blood is analyzed through of the complete blood count test (CBC), which is dependent on automated and/or manual methodologies. The dependence of medical areas on new technologies leads the present study to the goal of developing an image segmentation algorithm that meets the criteria of efficiency and reliability for detection and counting of blood cells. The algorithm was developed through the Matlab software, being the image processing methodology based on the union of the Watershed transform and Morphological Operations, originating the WT-MO methodology. For the simulations, 30 blood smear images containing erythrocytes and leukocytes were used in a non-pathological state. The results showed that the WT-MO methodology presents high sensitivity (99%), specificity (96%) and accuracy (98,3%) when compared with the manual methodology. Therefore, the WT-MO methodology is an accurate, reliable and low-cost technique and can be applied as a third more accessible methodology to perform of the complete blood count test (CBC) in populations of underdeveloped and developing countries.
Automatic Detection of Compensatory Movement Patterns by a Pressure Distribution Mattress Using Machine Learning Methods: A Pilot Study

For stroke patients with hemiplegia, reaching with the paretic arm is often associated with compensatory movements due to limited active arm movement and a loss of interjoint coordination.Detecting common compensatory movement patterns, such as excessive trunk displacement and scapular elevation, is critical for improving the motor function of the paretic arm.Existing compensatory movement pattern detection methods, including sensor-based systems and camera-based systems, suffer from object obstruction and require complex setups.In this paper, a compensatory movement pattern detection system using a pressure distribution mattress is presented.This method is a novel approach to detect compensations and has observed advantages; it is simple, unobtrusive and low cost.Fifteen healthy participants with no motor impairments performed three reaching tasks (back-and-forth, side-to-side, and up-and-down reaching) in a normal pattern and in compensatory movement patterns (trunk rotation and lean-forward, and scapular elevation).Pressure distribution data of all motions were recorded and processed to generate a group of features (average sensor values, the lateral center of pressure, longitudinal center of pressure, the ratio of left-side to right-side pressure, and the ratio of front-side to back-side pressure) reflecting the information of each predefined pattern.Four machine learning methods were implemented to detect compensatory movement patterns and showed good reliability and precision.Both k-nearest neighbor (kNN) and support vector machine (SVM) classifiers have achieved an excellent classification performance (F1-score = 0.934) in detecting compensation during all reaching tasks.For the multiclass classification of compensatory movement patterns, the SVM classifier exhibited a good classification performance for trunk lean-forward (F1-score = 0.933), scapular elevation (F1-score = 0.881), and trunk rotation (F1-score = 0.854) and outperformed previous reports.The study results provide initial evidence of a pressure distribution mattress for detecting compensatory movement patterns.Future work needs to test the approach on stroke survivors to verified the feasibility and validity.
Incremental Learning from Scratch for Task-Oriented Dialogue Systems

Clarifying user needs is essential for existing task-oriented dialogue systems.However, in real-world applications, developers can never guarantee that all possible user demands are taken into account in the design phase.Consequently, existing systems will break down when encountering unconsidered user needs.To address this problem, we propose a novel incremental learning framework to design task-oriented dialogue systems, or for short Incremental Dialogue System (IDS), without pre-defining the exhaustive list of user needs.Specifically, we introduce an uncertainty estimation module to evaluate the confidence of giving correct responses.If there is high confidence, IDS will provide responses to users.Otherwise, humans will be involved in the dialogue process, and IDS can learn from human intervention through an online learning module.To evaluate our method, we propose a new dataset which simulates unanticipated user needs in the deployment stage.Experiments show that IDS is robust to unconsidered user actions, and can update itself online by smartly selecting only the most effective training data, and hence attains better performance with less annotation cost. 1
A Wrapped Normal Distribution on Hyperbolic Space for Gradient-Based Learning

Hyperbolic space is a geometry that is known to be well-suited for representation learning of data with an underlying hierarchical structure. In this paper, we present a novel hyperbolic distribution called \textit{pseudo-hyperbolic Gaussian}, a Gaussian-like distribution on hyperbolic space whose density can be evaluated analytically and differentiated with respect to the parameters. Our distribution enables the gradient-based learning of the probabilistic models on hyperbolic space that could never have been considered before. Also, we can sample from this hyperbolic probability distribution without resorting to auxiliary means like rejection sampling. As applications of our distribution, we develop a hyperbolic-analog of variational autoencoder and a method of probabilistic word embedding on hyperbolic space. We demonstrate the efficacy of our distribution on various datasets including MNIST, Atari 2600 Breakout, and WordNet.