Reviewerly/exHarmony huggingface dataset:
Better Safe Than Sorry: Constructing Byzantine-Robust Federated Learning with Synthesized Trust

Byzantine-robust federated learning empowers the central server to acquire high-end global models amidst a restrictive set of malicious clients. The general idea of existing learning methods requires the central server to statistically analyze all local parameter (gradient or weight) updates, and to delete suspicious ones. The drawback of these approaches is that they lack a root of trust that would allow us to identify which local parameter updates are suspicious, which means that malicious clients can still disrupt the global model. The machine learning community has recently proposed a new method, FLTrust (NDSS’2021), where the server achieves robust aggregation by using a tiny, uncontaminated dataset (denoted as the root dataset) to generate the root of trust; however, the global model’s accuracy will significantly decline if the root dataset greatly deviates from the client’s dataset. To address the above problems, we propose FLEST: a Federated LEarning with Synthesized Trust method. Our method considers that trust and anomaly detection methods can complementarily solve their respective problems; therefore, we designed a new robust aggregation rule with synthesized trust scores (STS). Specifically, we propose the trust synthesizing mechanism, which can aggregate trust scores (TS) and confidence scores (CS) into STS through a dynamic trust ratio γ, and we use STS as the weight for aggregating the local parameter updates. Our experimental results demonstrated that FLEST is capable of resisting existing attacks, even when the root dataset distribution significantly differs from the total dataset distribution: for example, the global model trained by FLEST is 41% more accurate than FLTrust for adaptive attacks using the mnist-0.5 dataset with the bias probability set to 0.8.
Modeling Supply Chain Firms’ Stock Prices in the Fertilizer Industry through Innovative Cryptocurrency Market Big Data

Due to the volatility of the markets and the ongoing crises (COVID-19, the Ukrainian war, etc.), investors are keen to exploit any potential chances to make profits. For this reason, the idea of harvesting data from cryptocurrency market users takes an innovative step. Potential investors in supply chain firms in the fertilizer industry need to know whether the observation of data originating from the cryptocurrency market is capable of explaining their stock price variation. The authors identify the innovative utilization of cryptocurrency markets’ user analytical data to model and predict the stock price of supply chain firms in the fertilizer industry stock price. The main aim of this research is to evaluate the contribution of cryptocurrency market big data as a predicting factor for the stock price of fertilizer market firms. Such a finding improves the knowledge and decision-making of potential investors in the fertilizer market. Moreover, this study seeks to highlight the benefits of utilizing cryptocurrency market big data for other financial purposes, apart from stock price prediction. The analytical data was derived from cryptocurrency websites and applications and was then processed through statistical analysis (correlation and linear regressions), Fuzzy Cognitive Maps (FCM), and Hybrid Modeling (HM) modeling. The hybrid model’s simulation showed that analytical data from the cryptocurrency markets tend to explain and predict the stock price of supply chain firms in the fertilizer industry. Such data refer to Bitcoin’s website organic keywords and traffic costs, as well as paid traffic costs from cryptocurrency trade websites/apps. A rise in Bitcoin and cryptocurrency trade websites’ organic and paid traffic costs tend to increase supply chain firms in the fertilizer industry’s stock prices, while Bitcoin’s website organic keywords variation decreases accordingly.
Entropy Fluctuation Formulas of Fermionic Gaussian States

We study the statistical behavior of quantum entanglement in bipartite systems over fermionic Gaussian states as measured by von Neumann entropy. The formulas of average von Neumann entropy with and without particle number constraints have been recently obtained, whereas the main results of this work are the exact yet explicit formulas of variances for both cases. For the latter case of no particle number constraint, the results resolve a recent conjecture on the corresponding variance. Different than the existing methods in computing variances over other generic state models, proving the results of this work relies on a new simplification framework. The framework consists of a set of new tools in simplifying finite summations of what we refer to as dummy summation and re-summation techniques. As a by-product, the proposed framework leads to various new transformation formulas of hypergeometric functions.
Tropical Convolutional Neural Networks (TCNNs) Based Methods for Breast Cancer Diagnosis

One of the leading causes of mortality for women worldwide is breast cancer. The likelihood of breast cancer-related mortality can be decreased by early identification and rapid treatment. Machine learning-based predictive technologies provide ways to detect breast cancer earlier. Several analytical techniques, such as breast MRI, X-ray, thermography, mammography, ultrasound, etc., may be used to find it. Accuracy metrics are the most extensively used approach for performance evaluation, and the Tropical Convolutional Neural Networks (TCNNs) model for breast cancer detection is the most precise and popular model. The proposed approach was examined using the Kaggle Breast Cancer Datasets (KBCD). The data set is partitioned into training and testing. We suggest a new class of CNNs called Tropical Convolutional Neural Networks (TCNNs), which are based on tropical convolutions and replace the multiplications and additions in traditional convolutional layers with additions and min/max operations, respectively, in order to reduce the number of multiplications. The results of the review demonstrated that the Tropical Convolutional Neural Networks (TCNNs) is the most successful and popular model for detecting breast cancer, and that accuracy metrics is the most popular approach for evaluating performance. It is amazing how deep learning is being used to so many different real-world problems. Additionally, because tropical convolution operators are basically nonlinear operators, we anticipate that TCNNs will be better at nonlinear fitting than traditional CNNs. The Kaggle Breast Cancer Datasets (KBCD) findings demonstrate that TCNN can reach more expressive power than regular convolutional layers.
Future Tendencies of Non-fungible Tokens

Abstract Blockchain has been one of the key innovations in information technology in the last 15 years. An important aspect of applying blockchain technology is the creation of so-called non-fungible tokens (NFTs). Although the name resembles cryptocurrencies because of the word token, in practice, NFTs do not represent electronic money but a digital certificate of ownership of an asset. They effectively behave like tokens whose total supply is one, and it is immutable. Considering their technical and conceptual basis, NFTs can be defined as digital certificates of ownership based on blockchain technology, the possession of which proves the indisputable ownership of the purchased digital asset. The subject of this paper is the conceptual basis of NFTs and the scope of their application in digital business. It aims to determine the value factors of NFTs and whether an expansion of their use can be expected in the future. The results of our research show that the essential advantage that NFTs bring to digital business is authentication. NFTs also enable the continuous collection of royalties by the author. The last, but potentially most powerful value generator of NFTs, is the creation of an ecosystem, where an online community is formed based on the initial forms of digital assets. Without standardization and regulation by states, NFTs will remain in the market niche of intensive Internet users.
DMMGAN: Diverse Multi Motion Prediction of 3D Human Joints using Attention-Based Generative Adversarial Network

Human body motion prediction is a fundamental part of many human-robot applications. Despite the recent progress in the area, most studies predict human body motion relative to a fixed joint and only limit their model to predict one possible future motion, or both. However, due to the complex nature of human motion, a single prediction cannot adequately reflect the many possible movements one can make. Also, for any robotics application, prediction of the full human body motion including the absolute 3D trajectory - not just a 3D body pose relative to the hip joint - is needed. In this paper, we try to address these two shortcomings by proposing a transformer-based generative model for forecasting multiple diverse human motions. Our model generates <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$N$</tex> future possible body motions given the human motion history. This is achieved by first predicting the pose of the body relative to the hip joint as was done in prior work. Then, our proposed Hip Prediction Module predicts the trajectory of the hip position relative to a global reference frame for each predicted pose frame, an aspect of human body motion neglected by previous work. To obtain a set of diverse predicted motions, we introduce a similarity loss that penalizes the pairwise sample distance. Our system not only outperforms the state-of-the-art in human motion prediction, but also is able to predict a diverse set of future human body motions, including the hip trajectory.
A Little Bit Attention Is All You Need for Person Re-Identification

Person re-identification plays a key role in applications where a mobile robot needs to track its users over a long period of time, even if they are partially unobserved for some time, in order to follow them or be available on demand. In this context, deep-learning-based real-time feature extraction on a mobile robot is often performed on special-purpose devices whose computational resources are shared for multiple tasks. Therefore, the inference speed has to be taken into account. In contrast, person re-identification is often improved by architectural changes that come at the cost of significantly slowing down inference. Attention blocks are one such example. We will show that some well-performing attention blocks used in the state of the art are subject to inference costs that are far too high to justify their use for mobile robotic applications. As a consequence, we propose an attention block that only slightly affects the inference speed while keeping up with much deeper networks or more complex attention blocks in terms of re-identification accuracy. We perform extensive neural architecture search to derive rules at which locations this attention block should be integrated into the architecture in order to achieve the best trade-off between speed and accuracy. Finally, we confirm that the best performing configuration on a re-identification benchmark also performs well on an indoor robotic dataset.
StereoVoxelNet: Real-Time Obstacle Detection Based on Occupancy Voxels from a Stereo Camera Using Deep Neural Networks

Obstacle detection is a safety-critical problem in robot navigation, where stereo matching is a popular vision-based approach. While deep neural networks have shown impressive results in computer vision, most of the previous obstacle detection works only leverage traditional stereo matching techniques to meet the computational constraints for real-time feedback. This paper proposes a computationally efficient method that employs a deep neural network to detect occupancy from stereo images directly. Instead of learning the point cloud correspondence from the stereo data, our approach extracts the compact obstacle distribution based on volumetric representations. In addition, we prune the computation of safety irrelevant spaces in a coarse-to-fine manner based on octrees generated by the decoder. As a result, we achieve real-time performance on the onboard computer (NVIDIA Jetson TX2). Our approach detects obstacles accurately in the range of 32 meters and achieves better IoU (Intersection over Union) and CD (Chamfer Distance) scores with only 2% of the computation cost of the state-of-the-art stereo model. Furthermore, we validate our method's robustness and real-world feasibility through autonomous navigation experiments with a real robot. Hence, our work contributes toward closing the gap between the stereo-based system in robot perception and state-of-the-art stereo models in computer vision. To counter the scarcity of high-quality real-world indoor stereo datasets, we collect a 1.36 hours stereo dataset with a mobile robot which is used to fine-tune our model. The dataset, the code, and further details including additional visualizations are available at https://lhy.xyz/stereovoxelnet/.
Replay-Driven Continual Learning for the Industrial Internet of Things

The Industrial Internet of Things (IIoT) leverages thousands of interconnected sensors and computing devices to monitor and control large and complex industrial processes. Machine learning (ML) applications in IIoT use data acquired from multiple sensors to perform tasks such as predictive maintenance. While remembering useful learning from the past, these applications need to adapt learning for evolving sensor data stemming from changes in industrial processes and environmental conditions. This paper presents a continual learning pipeline to learn from the evolving data while replaying selected parts of the old data. The pipeline is configured to produce ML experiences (e.g., training a baseline neural network model), improve the baseline model with the new data while replaying part of the old data, and infer/predict using a specific model version given a stream of IIoT sensor data. We have evaluated our approach from an AI Engineering perspective using three industrial case studies, i.e., predicting tool wear, remaining useful lifetime, and anomalies from sensor data acquired from CNC machining and broaching operations. Our results show that configuring experiences for replay-driven continual learning allows dynamic maintenance of ML performance on evolving data while minimizing the excessive accumulation of legacy sensor data.
A break in the cloud: the local sociotechnical affordances underlying global internet infrastructures

How did an enterprising rural community establish Internet infrastructure that later attracted Google's flagship global data center in The Dalles, Oregon? Leveraging a sociotechnical affordance perspective and a descriptive case study approach based on archival research, interviews, site visits, and participant observation, this article examines the local policies and infrastructures leading to the organizational development of a publicly-owned Internet network from 2002 to 2005 that directly precipitated Google's first data center in the world in The Dalles. The conceptualization and realization of QLife Network draw on three central mechanisms that structure and reflect local sociotechnical affordances: cultivating community dialogue, prioritizing public ownership, and harnessing institutional anchors. Broadly, this article contributes to calls for localized sociotechnical agency, especially in underserved rural areas, and creates analytical room for a more multivalent story of local power and community-led stakes in the global distribution of Internet infrastructure.
Corruption, corporate governance, and sustainable development goals in Africa

Purpose The purpose of this study is to investigate the relationship between corruption, corporate governance and sustainable development goals (SDGs) in Africa. Design/methodology/approach The authors use panel data from 42 African countries over the period 2017–2020 and ordinary least square regression to test the research hypotheses. The authors also use alternative estimation techniques, including the fixed effect and random effect regressions and the generalized method of moment, to test the robustness of the results. Findings The results indicate that corruption negatively affects sustainable development (SD), whereas the effect of corporate governance is positive and significant. In addition, the positive influence of corporate governance on SD is stronger for countries with high corruption prevalence. Practical implications Policymakers may rely on the outcome of this study to formulate practical and implementable solutions around corruption and corporate governance that can help toward the achievement of the SDGs. Specifically, corporate governance mechanisms may be relied upon to achieve SD in countries with a high corruption prevalence. Social implications The social implication of this paper is that it demonstrates the adverse impact of corruption, which is rife in most African countries. Understanding corruption and the SDGs relationship will promote discussion with overarching implications for developing countries. Overall, the findings can sensitize society to the harmful effects of corruption and the positive effects of good corporate governance. Originality/value This paper contributes to literature and practice by demonstrating that corporate governance plays a significant role in the realization of national and global objectives such as the SDGs. This paper also provides novel evidence that corporate governance matters more in countries with a higher corruption incidence.
An explainable ensemble of multi-view deep learning model for fake review detection

Online reviews significantly impact consumers who are purchasing or seeking services via the Internet. Businesses and review platforms need to manage these online reviews to avoid misleading customers through fake ones. This necessitates developing intelligent solutions to detect these fake reviews and prevent their negative impact on businesses and customers. Therefore, many fake review detection models have been proposed to help distinguish fake reviews from genuine ones. However, these techniques depend on a limited perspective of features, mainly review content, to detect fake reviews, leading to poor performance in discovering the new patterns of fake review content and the dynamic behaviour of spammers. Therefore, there is still a need to develop new solutions to detect the new patterns of fake reviews. Hence, this paper proposes an explainable multi-view deep learning model to identify fake reviews based on different feature perspectives and classifiers. The proposed model can extract essential features from different perspectives, including review content, reviewer data, and product description. Moreover, we employ an ensemble approach that combines three popular deep learning algorithms: Bi-LSTM, CNN, and DNN, to enhance the performance of the fake review detection model. The results of two real-life datasets presented demonstrated the efficiency of our proposed model, where it outperformed the state-of-the-art methods with improvements ranging from 1% to 7% in terms of the AUC metric. To provide visibility into the outcomes of our proposed model and demonstrate the trust and transparency in the obtained results, we also offer a comprehensive explanation for our model results using Shapely Additive Explanations (SHAP) method and attention techniques. The experimental results prove that our proposed model can provide reasonable explanations that help users understand why specific reviews are classified as fake.
Cryptocurrency return dependency and economic policy uncertainty

This study investigated whether economic policy uncertainty (EPU) affects the degree of Bitcoin return dependency in the cryptocurrency (crypto) market by calculating the Pearson correlation between Bitcoin returns and the average returns of 19 cryptocurrencies to determine the return dependency. First, we found that an increase in global EPU strengthens the dependency effect. Among the country-level EPU indexes, we found that the increased EPU of crypto-friendly countries (United States, United Kingdom, Japan, and South Korea) is the main enhancer of dependency. By contrast, the EPU index of crypto-unfriendly countries (China and Russia) exhibits a statistically nonsignificant effect. Finally, the empirical results remain similar if we replace Bitcoin with Ethereum to measure dependency.
Design and Implementation of EinStein Würfelt Nicht Program Monte_Alpha

The game of EinStein würfelt nicht involves an element of uncertainty due to die rolling, which poses a big challenge in the development of computer game programs. However, the intriguing nature of probabilistic elements has made this game popular in computer game competitions. This study aimed to develop a high-strength EinStein würfelt nicht program that utilizes an efficient bitboard representation for the game board as well as pre-established probability distribution tables and extensively uses bitwise operations to improve the efficiency of game tree expansion. Additionally, this study attempted to replace random simulation with an evaluation function to enhance the accuracy of the Upper Confidence bounds applied to Trees algorithm. Through this design, we improved the strength of our program, and we hope that this program will be able to achieve additional excellent results in future computer game tournaments.
SpecTrHuMS: Spectral transformer for human mesh sequence learning

We present SpecTrHuMS, a Spectral Transformer for 3D triangular Human Mesh Sequence learning which combines known deep learning models with spectral mesh processing to capture characteristics of 3D shapes as well as temporal dependencies between the frames. Unlike previous works in this field, our approach is able to work directly with a compressed representation of the geometry, the spectral coefficients, rather than relying solely on skeleton joints that does not contain surface information. The vertices of each mesh of a sequence are first projected on the eigenvectors of the Graph Laplacian computed from the common triangulation. A convolutional encoder then encodes each frame into lower dimensional latent variables that preserve as much as possible the spectral information. These latent variables are next passed through a transformer architecture so that the model understands the context of the sequence and learns temporal dependencies between the frames. Each frame of the transformer's output is then decoded by a convolutional decoder which aims to reconstruct the input spectral coefficients. Finally, all frames are transformed back into the spatial domain, resulting in a general process able to treat 4D surfaces with a constant connectivity. Our method is evaluated on a prediction task on AMASS, a dataset of human surface sequences, showing the ability of our model to produce realistic movements while preserving the identity of a subject, and showing that this work is a significant step towards efficient and high-quality representation of triangular mesh sequences with constant connectivity. Additional experiments show that our model can be easily extended to other tasks such as long term prediction, completion and that it is generalizable to other datasets with constant connectivity. This work opens up new possibilities for applications in the fields of animation, virtual reality, and computer graphics. Pretrained models, the code to train them and the code to create datasets will be made publicly available.