Reviewerly/exHarmony huggingface dataset:
Leveraging machine learning and blockchain in E-commerce and beyond: benefits, models, and application

Abstract Blockchain technology (BT) allows market participants to keep track of digital transactions without central recordkeeping. The features of blockchain, including decentralization, persistency, and attack resistance, allow data security and privacy. Machine learning (ML) involves the analytical platform on a massive amount of data to provide precise decisions. Since data reliability, integration, and data security are crucial in machine learning, the emergence of blockchain technology and machine learning has become a unique, most disruptive, and trending research in the last few years, achieving comparable and precise performance. The combination of blockchain and machine learning (BT–ML) has been applied across different applications to assist decision-makers in retrieving valuable data insights while preserving privacy and integration. This paper summarizes the state-of-the-art research in combing BT and ML in e-commerce and other various applications, including healthcare, smart transportation, and the Internet of Things (IoT). The challenges and benefits of integrating machine learning and blockchain technologies are outlined in the paper. We also discuss the advantages and limitations of current algorithms in the BT–ML integration. This paper provides a roadmap for researchers to pave the way for current and future research directions in combing the BT and ML research areas.
Performance of Planar Floquet Codes with Majorana-Based Qubits

Quantum error correction is crucial for any quantum computing platform to achieve truly scalable quantum computation. The surface code and its variants have been considered the most promising quantum error correction scheme due to their high threshold, low overhead, and relatively simple structure that can naturally be implemented in many existing qubit architectures, such as superconducting qubits. The recent development of Floquet codes by Hastings and Haah offers another promising approach. By going beyond the usual paradigm of stabilizer codes, Floquet codes achieve similar performance while being constructed entirely from two-qubit measurements. This makes them particularly suitable for platforms where two-qubit measurements can be implemented directly, such as the measurement-only topological qubits based on Majorana zero modes (MZMs) proposed by Karzig et al. Here, we explain how two variants of Floquet codes can be implemented on MZM-based architectures without any auxiliary qubits for syndrome measurement and with shallow syndrome-extraction sequences. We then numerically demonstrate their favorable performance. In particular, we show that they improve the threshold for scalable quantum computation in MZM-based systems by an order of magnitude and significantly reduce space and time overheads below threshold.3 MoreReceived 5 April 2022Accepted 28 October 2022DOI:https://doi.org/10.1103/PRXQuantum.4.010310Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasMajorana fermionsQuantum error correctionQuantum InformationCondensed Matter, Materials & Applied Physics
Entity and relation extraction from clinical case reports of COVID-19: a natural language processing approach

Extracting relevant information about infectious diseases is an essential task. However, a significant obstacle in supporting public health research is the lack of methods for effectively mining large amounts of health data.This study aims to use natural language processing (NLP) to extract the key information (clinical factors, social determinants of health) from published cases in the literature.The proposed framework integrates a data layer for preparing a data cohort from clinical case reports; an NLP layer to find the clinical and demographic-named entities and relations in the texts; and an evaluation layer for benchmarking performance and analysis. The focus of this study is to extract valuable information from COVID-19 case reports.The named entity recognition implementation in the NLP layer achieves a performance gain of about 1-3% compared to benchmark methods. Furthermore, even without extensive data labeling, the relation extraction method outperforms benchmark methods in terms of accuracy (by 1-8% better). A thorough examination reveals the disease's presence and symptoms prevalence in patients.A similar approach can be generalized to other infectious diseases. It is worthwhile to use prior knowledge acquired through transfer learning when researching other infectious diseases.
Blockchain-Based Secure Localization against Malicious Nodes in IoT-Based Wireless Sensor Networks Using Federated Learning

Wireless sensor networks are the core of the Internet of Things and are used in healthcare, locations, the military, and security. Threats to the security of wireless sensor networks built on the Internet of Things (IoT-WSNs) can come from a variety of sources. This study proposes secure attack localization and detection in IoT-WSNs to improve security and service delivery. The technique used blockchain-based cascade encryption and trust evaluation in a hierarchical design to generate blockchain trust values before beacon nodes broadcast data to the base station. Simulation results reveal that cascading encryption and feature assessment measure the trust value of nodes by rewarding each other for service provisioning and trust by removing malicious nodes that reduce localization accuracy and quality of service in the network. Federated machine learning improves data security and transmission by merging raw device data and placing malicious threats in the blockchain. Malicious nodes are classified through federated learning. Federated learning combines hybrid random forest, gradient boost, ensemble learning, <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>K</mi> </math> -means clustering, and support vector machine approaches to classify harmful nodes via a feature assessment process. Comparing the proposed system to current ones shows an average detection and classification accuracy of 100% for binary and 99.95% for multiclass. This demonstrates that the suggested approach works well for large-scale IoT-WSNs, both in terms of performance and security, when utilizing heterogeneous wireless senor networks for the providing of secure services.
Spatial Dynamics of Land Cover Change in Ternate Tengah District, Ternate City, Indonesia

The phenomenon of urban growth has become an important issue that affects the land use system and land cover in a region for several reasons, such as population growth and the economy. This phenomenon has also become one of the main environmental issues lately because it has devastated urban ecosystems. Ternate Tengah District has the highest population growth rate in Ternate City and has experienced extensive urban development due to several reasons, such as the pace of urbanization, economic growth, and population. Urbanization accelerates the demand to land for living. As a result, there will be gaps or disparities between land needs and available land, a decline in environmental carrying capacity, and potential environmental harm in the future. Spatial modeling of future land covers is needed to provide data on policy-making. GIS and remote sensing methods have been widely introduced, but the most effective one is CA-Markov. This model has been used in various areas worldwide, but its application to predicting land use change in the populous city of a small island under threat of volcanic hazards like Ternate is limited. This study aims to evaluate and forecast the land-use changes brought on by urbanization in Ternate City's Central Ternate District. We used a cellular automata-Markov chain to examine and forecast land cover changes in 2002, 2012, 2022, and 2032. The findings indicate that residential area development will increase along with population expansion and land demand. The results of this study can support the policy-making related to the future arrangement and utilization of space in The Central Ternate District.
Toolformer: Language Models Can Teach Themselves to Use Tools

Language models (LMs) exhibit remarkable abilities to solve new tasks from just a few examples or textual instructions, especially at scale. They also, paradoxically, struggle with basic functionality, such as arithmetic or factual lookup, where much simpler and smaller models excel. In this paper, we show that LMs can teach themselves to use external tools via simple APIs and achieve the best of both worlds. We introduce Toolformer, a model trained to decide which APIs to call, when to call them, what arguments to pass, and how to best incorporate the results into future token prediction. This is done in a self-supervised way, requiring nothing more than a handful of demonstrations for each API. We incorporate a range of tools, including a calculator, a Q\&A system, two different search engines, a translation system, and a calendar. Toolformer achieves substantially improved zero-shot performance across a variety of downstream tasks, often competitive with much larger models, without sacrificing its core language modeling abilities.
Generating Long Sequences with Sparse Transformers

Transformers are powerful sequence models, but require time and memory that grows quadratically with the sequence length. In this paper we introduce sparse factorizations of the attention matrix which reduce this to $O(n \sqrt{n})$. We also introduce a) a variation on architecture and initialization to train deeper networks, b) the recomputation of attention matrices to save memory, and c) fast attention kernels for training. We call networks with these changes Sparse Transformers, and show they can model sequences tens of thousands of timesteps long using hundreds of layers. We use the same architecture to model images, audio, and text from raw bytes, setting a new state of the art for density modeling of Enwik8, CIFAR-10, and ImageNet-64. We generate unconditional samples that demonstrate global coherence and great diversity, and show it is possible in principle to use self-attention to model sequences of length one million or more.
Evaluating generative models in high energy physics

There has been a recent explosion in research into machine-learning-based generative modeling to tackle computational challenges for simulations in high energy physics (HEP). In order to use such alternative simulators in practice, we need well-defined metrics to compare different generative models and evaluate their discrepancy from the true distributions. We present the first systematic review and investigation into evaluation metrics and their sensitivity to failure modes of generative models, using the framework of two-sample goodness-of-fit testing, and their relevance and viability for HEP. Inspired by previous work in both physics and computer vision, we propose two new metrics, the Fr\'echet and kernel physics distances (FPD and KPD, respectively) and perform a variety of experiments measuring their performance on simple Gaussian-distributed and simulated high energy jet datasets. We find FPD, in particular, to be the most sensitive metric to all alternative jet distributions tested and recommend its adoption, along with the KPD and Wasserstein distances between individual feature distributions, for evaluating generative models in HEP. We finally demonstrate the efficacy of these proposed metrics in evaluating and comparing a novel attention-based generative adversarial particle transformer to the state-of-the-art message-passing generative adversarial network jet simulation model. The code for our proposed metrics is provided in the open source jetnet python library.
Gaussian Process Regression for Astronomical Time Series

The past two decades have seen a major expansion in the availability, size, and precision of time-domain data sets in astronomy. Owing to their unique combination of flexibility, mathematical simplicity, and comparative robustness, Gaussian processes (GPs) have emerged recently as the solution of choice to model stochastic signals in such data sets. In this review, we provide a brief introduction to the emergence of GPs in astronomy, present the underlying mathematical theory, and give practical advice considering the key modeling choices involved in GP regression. We then review applications of GPs to time-domain data sets in the astrophysical literature so far, from exoplanets to active galactic nuclei, showcasing the power and flexibility of the method. We provide worked examples using simulated data, with links to the source code; discuss the problem of computational cost and scalability; and give a snapshot of the current ecosystem of open-source GP software packages. In summary: ▪ GP regression is a conceptually simple but statistically principled and powerful tool for the analysis of astronomical time series. ▪ It is already widely used in some subfields, such as exoplanets, and gaining traction in many others, such as optical transients. ▪ Driven by further algorithmic and conceptual advances, we expect that GPs will continue to be an important tool for robust and interpretable time-domain astronomy for many years to come.
Towards Reasoning in Large Language Models: A Survey

Reasoning is a fundamental aspect of human intelligence that plays a crucial role in activities such as problem solving, decision making, and critical thinking.In recent years, large language models (LLMs) have made significant progress in natural language processing, and there is observation that these models may exhibit reasoning abilities when they are sufficiently large.However, it is not yet clear to what extent LLMs are capable of reasoning.This paper provides a comprehensive overview of the current state of knowledge on reasoning in LLMs, including techniques for improving and eliciting reasoning in these models, methods and benchmarks for evaluating reasoning abilities, findings and implications of previous research in this field, and suggestions on future directions.Our aim is to provide a detailed and up-to-date review of this topic and stimulate meaningful discussion and future work. 1
Neural Theory-of-Mind? On the Limits of Social Intelligence in Large LMs

Social intelligence and Theory of Mind (TOM), i.e., the ability to reason about the different mental states, intents, and reactions of all people involved, allow humans to effectively navigate and understand everyday social interactions.As NLP systems are used in increasingly complex social situations, their ability to grasp social dynamics becomes crucial.In this work, we examine the open question of social intelligence and Theory of Mind in modern NLP systems from an empirical and theory-based perspective.We show that one of today's largest language models (GPT-3; Brown et al., 2020) lacks this kind of social intelligence out-of-the box, using two tasks: SOCIALIQA (Sap et al., 2019b), which measures models' ability to understand intents and reactions of participants of social interactions, and TOMI (Le et al., 2019), which measures whether models can infer mental states and realities of participants of situations.Our results show that models struggle substantially at these Theory of Mind tasks, with well-below-human accuracies of 55% and 60% on SOCIALIQA and TOMI, respectively.To conclude, we draw on theories from pragmatics to contextualize this shortcoming of large language models, by examining the limitations stemming from their data, neural architecture, and training paradigms.Challenging the prevalent narrative that only scale is needed, we posit that person-centric NLP approaches might be more effective towards neural Theory of Mind.
Feasibility of Breast Cancer Detection Through a Convolutional Neural Network in Mammographs

In the Iraq female samples, the malignant neoplasm type with the highest mortality rate is breast cancer. When the disease is detected early, the success rate is higher, resulting in improved prognosis and, consequently, cure. The present study aims to analyse the viability of a system capable of detecting breast cancer through convolutional neural networks, classifying a mammogram into five classes: non-cancer; benign calcification; malignant calcification; benign mass; and evil mass. Processing was carried out on the database containing 55,890 images, in which the data was converted from records structure to image format, which is necessary when using the neural network. After this stage, the images were classified into the five categories mentioned to enable tests to be carried out to verify the accuracy of the machine learning algorithm in identifying and classifying breast cancer. Using a small partition of 10% of images from the total database to verify the initial results presented in this work, it was possible to obtain 44% of global accuracy, highlighting the ability to expedite the early and rapid detection of breast cancer using artificial intelligence.
Structural Patterns Beyond Forks: Extending the Complexity Boundaries of Classical Planning

Tractability analysis in terms of the causal graphs of planning problems has emerged as an important area of research in recent years, leading to new methods for the derivation of domain-independent heuristics (Katz and Domshlak 2010). Here we continue this work, extending our knowledge of the frontier between tractable and NP-complete fragments. We close some gaps left in previous work, and introduce novel causal graph fragments that we call the hourglass and semifork, for which under certain additional assumptions optimal planning is in P. We show that relaxing any one of the restrictions required for this tractability leads to NP-complete problems. Our results are of both theoretical and practical interest, as these fragments can be used in existing frameworks to derive new abstraction heuristics. Before they can be used, however, a number of practical issues must be addressed. We discuss these issues and propose some solutions.
Towards Population Scale Activity Recognition: A Framework for Handling Data Diversity

The rising popularity of the sensor-equipped smartphone is changing the possible scale and scope of human activity inference. The diversity in user population seen in large user bases can overwhelm conventional one-size-fits-all classiﬁcation approaches. Although personalized models are better able to handle population diversity, they often require increased effort from the end user during training and are computationally expensive. In this paper, we propose an activity classification framework that is scalable and can tractably handle an increasing number of users. Scalability is achieved by maintaining distinct groups of similar users during the training process, which makes it possible to account for the differences between users without resorting to training individualized classifiers. The proposed framework keeps user burden low by leveraging crowd-sourced data labels, where simple natural language processing techniques in combination with multi-instance learning are used to handle labeling errors introduced by low-commitment everyday users. Experiment results on a large public dataset demonstrate that the framework can cope with population diversity irrespective of population size.
The Pen Is Mightier: Understanding Stylus Behaviour While Inking on Tablets

Although pens and paper are pervasive in the analog world, their digital counterparts, styli and tablets, have yet to achieve the same adoption and frequency of use. To date, little research has identified why inking experiences differ so greatly between analog and digital media or quantified the varied experiences that exist with stylus-enabled tablets. By observing quantitative and behavioural data in addition to querying preferential opinions, the experimentation reaffirmed the significance of accuracy, latency, and unintended touch, whilst uncovering the importance of friction, aesthetics, and stroke beautification to users. The observed participant behaviour and recommended tangible goals should enhance the development and evaluation of future systems.
Combined Static and Motion Features for Deep-Networks-Based Activity Recognition in Videos

Activity recognition in videos in a deep-learning setting-or otherwise-uses both static and pre-computed motion components. The method of combining the two components, while keeping the burden on the deep network less, still remains uninvestigated. Moreover, it is not clear what the level of contribution of individual components is, and how to control the contribution. In this paper, we use a combination of convolutional-neural-network-generated static features and motion features in the form of motion tubes. We propose three schemas for combining static and motion components: based on a variance ratio, principal components, and Cholesky decomposition. The Cholesky-decomposition-based method allows the control of contributions. The ratio given by variance analysis of static and motion features matches well with the experimental optimal ratio used in the Cholesky decomposition-based method. The resulting activity recognition system is better or on par with the existing state-of-the-art when tested with three popular data sets. The findings also enable us to characterize a data set with respect to its richness in motion information.
Weak Disorder Enhancing the Production of Entanglement in Quantum Walks

We find out a few ways to improve the realization of entanglement between the internal (spin) and external (position) degrees of freedom of a quantum particle, through the insertion of disordered time steps in a one-dimensional discrete time quantum walk in different scenarios. The disorder is introduced by a randomly chosen quantum coin obtained from a uniform distribution among infinite quantum coins or only between Hadamard and Fourier coins for all the time steps (strong disorder). We can also decrease the amount of disorder by alternating disordered and ordered time steps throughout a quantum walk or by establishing a probability p < 0.5 to pick a Fourier coin instead of a Hadamard one for each time step (weak disorder). Our results show that both scenarios lead to maximal entanglement outperforming the ordered quantum walks. However, these last scenarios are more efficient to create entanglement, because they achieve high entanglement rates in fewer time steps than the former ones. In order to compare distinct disordered cases, we perform an average entanglement by averaging over a large set of initial qubits over time starting from one site (local state) or spread over many neighbor positions following a Gaussian distribution. Some transient behaviors from order to disorder in quantum walks are also evaluated and discussed. Experimental remarks based on available experimental platforms from the literature are made.