Reviewerly/exHarmony huggingface dataset:
Manipulation of the Bitcoin market: an agent-based study

Abstract Fraudulent actions of a trader or a group of traders can cause substantial disturbance to the market, both directly influencing the price of an asset or indirectly by misinforming other market participants. Such behavior can be a source of systemic risk and increasing distrust for the market participants, consequences that call for viable countermeasures. Building on the foundations provided by the extant literature, this study aims to design an agent-based market model capable of reproducing the behavior of the Bitcoin market during the time of an alleged Bitcoin price manipulation that occurred between 2017 and early 2018. The model includes the mechanisms of a limit order book market and several agents associated with different trading strategies, including a fraudulent agent, initialized from empirical data and who performs market manipulation. The model is validated with respect to the Bitcoin price as well as the amount of Bitcoins obtained by the fraudulent agent and the traded volume. Simulation results provide a satisfactory fit to historical data. Several price dips and volume anomalies are explained by the actions of the fraudulent trader, completing the known body of evidence extracted from blockchain activity. The model suggests that the presence of the fraudulent agent was essential to obtain Bitcoin price development in the given time period; without this agent, it would have been very unlikely that the price had reached the heights as it did in late 2017. The insights gained from the model, especially the connection between liquidity and manipulation efficiency, unfold a discussion on how to prevent illicit behavior.
Grouped Pointwise Convolutions Reduce Parameters in Convolutional Neural Networks

In DCNNs, the number of parameters in pointwise convolutions rapidly grows due to the multiplication of the number of filters by the number of input channels that come from the previous layer. Our proposal makes pointwise convolutions parameter efficient via grouping filters into parallel branches or groups, where each branch processes a fraction of the input channels. However, by doing so, the learning capability of the DCNN is degraded. To avoid this effect, we suggest interleaving the output of filters from different branches at intermediate layers of consecutive pointwise convolutions. We applied our improvement to the EfficientNet, DenseNet-BC L100, MobileNet and MobileNet V3 Large architectures. We trained these architectures with the CIFAR-10, CIFAR-100, Cropped-PlantDoc and The Oxford-IIIT Pet datasets. When training from scratch, we obtained similar test accuracies to the original EfficientNet and MobileNet V3 Large architectures while saving up to 90% of the parameters and 63% of the flops.
SVAM: Saliency-guided Visual Attention Modeling by Autonomous Underwater Robot

This paper presents a holistic approach to saliencyguided visual attention modeling (SVAM) for use by autonomous underwater robots.Our proposed model, named SVAM-Net, integrates deep visual features at various scales and semantics for effective salient object detection (SOD) in natural underwater images.The SVAM-Net architecture is configured in a unique way to jointly accommodate bottom-up and top-down learning within two separate branches of the network while sharing the same encoding layers.We design dedicated spatial attention modules (SAMs) along these learning pathways to exploit the coarselevel and fine-level semantic features for SOD at four stages of abstractions.The bottom-up branch performs a rough yet reasonably accurate saliency estimation at a fast rate, whereas the deeper top-down branch incorporates a residual refinement module (RRM) that provides fine-grained localization of the salient objects.Extensive performance evaluation of SVAM-Net on benchmark datasets clearly demonstrates its effectiveness for underwater SOD.We also validate its generalization performance by several ocean trials' data that include test images of diverse underwater scenes and waterbodies, and also images with unseen natural objects.Moreover, we analyze its computational feasibility for robotic deployments and demonstrate its utility in several important use cases of visual attention modeling.
Quantum microgrid state estimation

This paper investigates the feasibility and efficiency of quantum-circuit-based algorithms for microgrid state estimation. Our new contributions include: (1) a general quantum state estimation (GQSE) formulation is devised for swing-bus-contained microgrids through the quantized Gaussian–Newton iteration, (2) a preconditioned quantum linear solver (PQLS) is developed for tackling the ill-conditioned GQSE with limited quantum resources, and (3) an enhanced quantum state estimation (EQSE) algorithm is further established for hierarchical-control-based microgrids with exogenous disturbances. Extensive case studies demonstrate the correctness of GQSE, PQLS and EQSE in two typical microgrids. The robustness and convergence performance of EQSE are also verified.
Privacy-Preserving Personalized Revenue Management

This paper examines how data-driven personalized decisions can be made while preserving consumer privacy. Our setting is one in which the firm chooses a personalized price based on each new customer’s vector of individual features; the true set of individual demand-generating parameters is unknown to the firm and so must be estimated from historical data. We extend the existing personalized pricing framework by requiring also that the firm’s pricing policy preserve consumer privacy, or (formally) that it be differentially private: an industry standard for privacy preservation. We develop privacy-preserving personalized pricing algorithms and show that they achieve near-optimal revenue by deriving theoretical (upper and lower) performance bounds. Our analyses further suggest that, if the firm possesses a sufficient amount of historical data, then it can achieve a certain level of differential privacy almost “for free.” That is, the revenue loss due to privacy preservation is of smaller order than that due to estimation. We confirm our theoretical findings in a series of numerical experiments based on synthetically generated and online auto lending (CPRM-12-001) data sets. Finally, motivated by practical considerations, we also extend our algorithms and findings to a variety of alternative settings, including multiproduct pricing with substitution effect, discrete feasible price set, categorical sensitive features, and personalized assortment optimization. This paper was accepted by Vishal Gaur, operations management. Funding: R. Momot acknowledges financial support from the HEC Paris Foundation and the Agence Nationale de la Recherche (French National Research Agency) “Investissements d’Avenir” [Grant LabEx Ecodec/ANR-11-LABX-0047] during the initial stages of this project. Supplemental Material: The data files and online appendices are available at https://doi.org/10.1287/mnsc.2023.4925 .
Explaining Toxic Text via Knowledge Enhanced Text Generation

Warning: This paper contains content that is offensive and may be upsetting.Biased or toxic speech can be harmful to various demographic groups.Therefore, it is not only important for models to detect these speech, but to also output explanations of why a given text is toxic.Previous literature has mostly focused on classifying and detecting toxic speech, and existing efforts on explaining stereotypes in toxic speech mainly use standard text generation approaches, resulting in generic and repetitive explanations.Building on these prior works, we introduce a novel knowledgeinformed encoder-decoder framework to utilize multiple knowledge sources to generate implications of biased text.Experiments show that our knowledge informed models outperform prior state-of-the-art models significantly, and can generate detailed explanations of stereotypes in toxic speech compared to baselines, both quantitatively and qualitatively.
SLOGAN: Handwriting Style Synthesis for Arbitrary-Length and Out-of-Vocabulary Text

Large amounts of labeled data are urgently required for the training of robust text recognizers. However, collecting handwriting data of diverse styles, along with an immense lexicon, is considerably expensive. Although data synthesis is a promising way to relieve data hunger, two key issues of handwriting synthesis, namely, style representation and content embedding, remain unsolved. To this end, we propose a novel method that can synthesize parameterized and controllable handwriting S tyles for arbitrary-Length and O ut-of-vocabulary text based on a G enerative A dversarial N etwork (GAN), termed SLOGAN. Specifically, we propose a style bank to parameterize specific handwriting styles as latent vectors, which are input to a generator as style priors to achieve the corresponding handwritten styles. The training of the style bank requires only writer identification of the source images, rather than attribute annotations. Moreover, we embed the text content by providing an easily obtainable printed style image, so that the diversity of the content can be flexibly achieved by changing the input printed image. Finally, the generator is guided by dual discriminators to handle both the handwriting characteristics that appear as separated characters and in a series of cursive joins. Our method can synthesize words that are not included in the training vocabulary and with various new styles. Extensive experiments have shown that high-quality text images with great style diversity and rich vocabulary can be synthesized using our method, thereby enhancing the robustness of the recognizer.
Sentiment Analysis of Algerian Dialect Using Machine Learning and Deep Learning with Word2vec

In this paper, we deal with the issue of sentiment analysis on dialectal comments extracted from social media. These comments concern the Algerian spoken language, written in Arabic and/or Latin characters, which could be either Modern Standard Arabic, French or local dialect. This complexity gives rise to a large number of text processing issues. The contributions of this work are fourfold. First, we build an Algerian dialect sentiment dataset of 11760 comments collecting from diverse social media platforms. Second, we also create Skip-Gram and CBOW model by word2vec from a corpus containing 466424 comments, these latter are used for enhancing the sentiment dataset by semantically similar words. Third, we propose an adapted preprocessing step set to deal with dialectal texts. Finally, we implement and conduct different machine learning classifiers (SVM, Naive Bayes via its three variants (Bernoulli NB, Gaussian NB and Multinomial NB)) and two deep learning architectures (CNN, RNN) to evaluate and compare the dataset in original version, in a transcribed to Latin character version and then in a semantically-enhanced version by word2vec models . Experiments reach performances of sentiment classifiers applied on "dataset transcribed to Latin characters" of accuracies = (MNB:84.21%, CNN:64.11%) and on "transcribed dataset and enhanced by word2vec models" of accuracies = (SVM:83.70%, RNN:65.21%).
Ubiquitous Verification in Centralized Ledger Database

Verifiability is the backbone of most ledger systems to realize credible authentication. However, existing permissioned blockchains and centralized ledger databases lack rigorous verifiability to authenticate all facts (i.e., what-when-who validation). Besides, they suffer from high verification cost to a continually growing immutable storage. In this paper, we introduce verification principles behind LedgerDB, a centralized ledger database that achieves both strong external auditability and fast verification. We coin a novel concept called Dasein Verification that composes of three validation factors what-when-who to formalize ledger auditing. Regarding what, LedgerDB devises fam (fractal accumulating model) to accelerate existence verification, and CM-Tree for efficient lineage verification. Veri-fiable data mutations are also supported. For when, we discuss attacks on existing time pegging protocols that compromise the authenticity of timestamps, and propose a time notary protocol to resolve those threats. Evaluations show that fam and CM- Tree significantly outperform traditional approaches. Compared to Hyperledger Fabric, LedgerDB achieves 23x higher verification throughput with 500 x lower latency in notarization applications, and 3 x higher throughput with 300 x lower latency in lineage tracking applications. As a public-cloud ledger service, the end-to-end verification latencies of LedgerDB are on average 50 x and 1000x lower than that of QLDB in the above applications, respectively.
BDS/IMU Integrated Auto-Navigation System of Orchard Spraying Robot

To improve the accuracy and reliability of orchard spraying robots, an integrated navigation system was developed, consisting of a real-time kinematic positioning-Beidou satellite navigation system (RTK-BDS) receiver, an inertial measurement unit (IMU), a navigation controller, and servo motors. Using the loose coupling combination method, an error Kalman filter algorithm based on the measurement of position and heading angle is implemented to correct the error of the inertial measurement unit in real time. Combining the kinematics model and the pure pursuit model of the spraying robot, a path-tracking control algorithm is proposed. Path planning was conducted according to the terrain characteristics of orchards. Field experiments were carried out on a spraying robot to evaluate the proposed auto-navigation system. The results showed that when the spraying robot was static, the positioning performances of BDS alone and that of the BDS/IMU combined system were similar, the positioning error was less than 1.5 cm, and the heading angle errors were within 0.3°; when the spraying robot moving alone to a straight line at the speed of 0.4 m/s, the position error of the navigation system only using BDS was less than 5.29 cm, the heading angle error was within 3°, while the position error of BDS/IMU integrated navigation system was less than 2.49 cm, and the heading angle error was within 2°. The accuracy of BDS/IMU integrated navigation system is significantly improved. When the orchard spraying robot was moving at the speed of 0.4 m/s, the maximum offset error was lower than 10.77 cm, the average offset error was not higher than 3.55 cm, and the root mean square error (RMSE) of the lateral deviation was 1.19 cm. The results showed that the proposed auto-navigation system could make the spraying robot track the pre-set path smoothly and stably.
Parity Deformed Tavis-Cummings Model: Entanglement, Parameter Estimation and Statistical Properties

In this paper, we introduce the parity extension of the harmonic oscillator systems to develop the generalized Tavis-Cummings model (T-CM) based on a specific deformation of the Heisenberg algebra. We present a quantum scheme of a two-qubit system (TQS) interacting with a quantized field that is initially prepared in parity deformed coherent states (PDCSs). The dynamical features of the considered system are explored in the presence of parity deformed parameter (PDP) and time-dependent coupling (t-dc). In particular, we examine the amount of the entanglement formed in the qubit–field and qubit–qubit states. We find that the maximal amount of the entanglement may be occurred periodically during the time evolution. Finally, we investigate the influence of PDP on the Fisher information and the photon statistics of the deformed field with respect to the main parameters of the system.
VocabulARy: Learning Vocabulary in AR Supported by Keyword Visualisations

Learning vocabulary in a primary or secondary language is enhanced when we encounter words in context. This context can be afforded by the place or activity we are engaged with. Existing learning environments include formal learning, mnemonics, flashcards, use of a dictionary or thesaurus, all leading to practice with new words in context. In this work, we propose an enhancement to the language learning process by providing the user with words and learning tools in context, with VocabulARy. VocabulARy visually annotates objects in AR, in the user's surroundings, with the corresponding English (first language) and Japanese (second language) words to enhance the language learning process. In addition to the written and audio description of each word, we also present the user with a keyword and its visualisation to enhance memory retention. We evaluate our prototype by comparing it to an alternate AR system that does not show an additional visualisation of the keyword, and, also, we compare it to two non-AR systems on a tablet, one with and one without visualising the keyword. Our results indicate that AR outperforms the tablet system regarding immediate recall, mental effort and task-completion time. Additionally, the visualisation approach scored significantly higher than showing only the written keyword with respect to immediate and delayed recall and learning efficiency, mental effort and task-completion time.
A New Type-3 Fuzzy Predictive Approach for Mobile Robots

Wheeled mobile robots are widely used for their excellent mobility and high maneuverability. The intelligentization of mobile robots to perform autonomous movement is one of the favorite topics in the robotic field. This paper proposes a new intelligent path-following scheme for mobile robots. A secure path is designed by the chaotic systems and an unknown switching mechanism. The proposed controller is constructed by the type-3 (T3) fuzzy logic systems (FLSs) and a predictive compensator. The T3-FLSs are taught to obtain good accuracy. In addition, the stability is mathematically investigated and guaranteed by the designed compensator. The simulations show that the case-study robot follows the planned secure path well and resists uncertainties.
A framework for privacy preserving digital trace data collection through data donation

A potentially powerful method of social-scientific data collection and investigation has been created by an unexpected institution: the law. Article 15 of the EU’s 2018 General Data Protection Regulation (GDPR) mandates that individuals have electronic access to a copy of their personal data, and all major digital platforms now comply with this law by providing users with “data download packages” (DDPs). Through voluntary donation of DDPs, all data collected by public and private entities during the course of citizens’ digital life can be obtained and analyzed to answer social-scientific questions – with consent. Thus, consented DDPs open the way for vast new research opportunities. However, while this entirely new method of data collection will undoubtedly gain popularity in the coming years, it also comes with its own questions of representativeness and measurement quality, which are often evaluated systematically by means of an error framework. Therefore, in this paper we provide a blueprint for digital trace data collection using DDPs, and devise a “total error framework” for such projects. Our error framework for digital trace data collection through data donation is intended to facilitate high quality social-scientific investigations using DDPs while critically reflecting its unique methodological challenges and sources of error. In addition, we provide a quality control checklist to guide researchers in leveraging the vast opportunities afforded by this new mode of investigation.
Creating, Using, Misusing, and Detecting Deep Fakes

Synthetic media—so-called deep fakes—have captured the imagination of some and struck fear in others. Although they vary in their form and creation, deep fakes refer to text, image, audio, or video that has been automatically synthesized by a machine-learning system. Deep fakes are the latest in a long line of techniques used to manipulate reality, yet their introduction poses new opportunities and risks due to the democratized access to what would have historically been the purview of Hollywood-style studios. This review describes how synthetic media is created, how it is being used and misused, and if (and how) it can be perceptually and forensically distinguished from reality.
<i>SliceTeller</i> : A Data Slice-Driven Approach for Machine Learning Model Validation

Real-world machine learning applications need to be thoroughly evaluated to meet critical product requirements for model release, to ensure fairness for different groups or individuals, and to achieve a consistent performance in various scenarios. For example, in autonomous driving, an object classification model should achieve high detection rates under different conditions of weather, distance, etc. Similarly, in the financial setting, credit-scoring models must not discriminate against minority groups. These conditions or groups are called as “ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Data Slices</i> ”. In product MLOps cycles, product developers must identify such critical data slices and adapt models to mitigate data slice problems. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Discovering</i> where models fail, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">understanding</i> why they fail, and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mitigating</i> these problems, are therefore essential tasks in the MLOps life-cycle. In this paper, we present <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>SliceTeller</i></b> , a novel tool that allows users to debug, compare and improve machine learning models driven by <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">critical</i> data slices. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SliceTeller</i> automatically discovers problematic slices in the data, helps the user understand why models fail. More importantly, we present an efficient algorithm, <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>SliceBoosting</i></b> , to estimate trade-offs when prioritizing the optimization over certain slices. Furthermore, our system empowers model developers to compare and analyze different model versions during model iterations, allowing them to choose the model version best suitable for their applications. We evaluate our system with three use cases, including two real-world use cases of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">product development</i> , to demonstrate the power of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SliceTeller</i> in the debugging and improvement of product-quality ML models.