Reviewerly/exHarmony huggingface dataset:
Bitcoin and Turkey: A Good Match or a Perfect Storm?

There was only Bitcoin in January 2009, but now over 3,000 altcoins are traded; as of October 25, the combined market cap of 3,047 cryptocurrencies is $248 billion. Bitcoin (at $9,285.89) with a market cap of $170 billion still dominates 68.4% of the cryptocurrency market. Both market cap figures are significantly below their peaks on December 17, 2017; $830 billion and $321 billion respectively. The adoption of Bitcoin in Turkey has seen its fastest rise since 2016 on account of the failed coup attempt by a fraction of the Turkish military, the conviction and sentencing of the U.S. Pastor Andrew Brunson of terror-related charges, rising geopolitical risks on account of the strained US-Turkey relations, and Turkey’s ongoing military operations along its borders and into Syria. The confluence of domestic and external factors have been a major catalyst in prompting pockets of people in Turkey to flock to Bitcoin as a safe-haven asset. Cryptocurrencies are known for their extreme volatility, but the Turkish lira’s plunge in August 2018 made even most erratic volatilities of cryptocurrencies seem calmer. Bitcoin investors/enthusiasts must keep in mind that the price of Bitcoin has witnessed a total of 16 price corrections (50% or more) in 7 years, and 8 of these have occurred in the last two years. Another substantial price correction, similar to that of December 2017-18 (i.e. price plummeted from almost $20,000 to $3,236), would result in a severe financial catastrophe for Turkish Bitcoin holders.
A Lightweight Object Detection Network for Real-Time Detection of Driver Handheld Call on Embedded Devices

It is necessary to improve the performance of the object detection algorithm in resource-constrained embedded devices by lightweight improvement. In order to further improve the recognition accuracy of the algorithm for small target objects, this paper integrates 5 × 5 deep detachable convolution kernel on the basis of MobileNetV2-SSDLite model, extracts features of two special convolutional layers in addition to detecting the target, and designs a new lightweight object detection network—Lightweight Microscopic Detection Network (LMS-DN). The network can be implemented on embedded devices such as NVIDIA Jetson TX2. The experimental results show that LMS-DN only needs fewer parameters and calculation costs to obtain higher identification accuracy and stronger anti-interference than other popular object detection models.
Precise subtyping for asynchronous multiparty sessions

Session subtyping is a cornerstone of refinement of communicating processes: a process implementing a session type (i.e., a communication protocol) T can be safely used whenever a process implementing one of its supertypes T ′ is expected, in any context, without introducing deadlocks nor other communication errors. As a consequence, whenever T T ′ holds, it is safe to replace an implementation of T ′ with an implementation of the subtype T , which may allow for more optimised communication patterns. We present the first formalisation of the precise subtyping relation for asynchronous multiparty sessions. We show that our subtyping relation is sound (i.e., guarantees safe process replacement, as outlined above) and also complete : any extension of the relation is unsound. To achieve our results, we develop a novel session decomposition technique, from full session types (including internal/external choices) into single input/output session trees (without choices). Previous work studies precise subtyping for binary sessions (with just two participants), or multiparty sessions (with any number of participants) and synchronous interaction. Here, we cover multiparty sessions with asynchronous interaction, where messages are transmitted via FIFO queues (as in the TCP/IP protocol), and prove that our subtyping is both operationally and denotationally precise. In the asynchronous multiparty setting, finding the precise subtyping relation is a highly complex task: this is because, under some conditions, participants can permute the order of their inputs and outputs, by sending some messages earlier or receiving some later, without causing errors; the precise subtyping relation must capture all such valid permutations — and consequently, its formalisation, reasoning and proofs become challenging. Our session decomposition technique overcomes this complexity, expressing the subtyping relation as a composition of refinement relations between single input/output trees, and providing a simple reasoning principle for asynchronous message optimisations.
Addressing Algorithmic Bias in AI-Driven Customer Management

Research on AI has gained momentum in recent years. Many scholars and practitioners increasingly highlight the dark sides of AI, particularly related to algorithm bias. This study elucidates situations in which AI-enabled analytics systems make biased decisions against customers based on gender, race, religion, age, nationality or socioeconomic status. Based on a systematic literature review, this research proposes two approaches (i.e., a priori and post-hoc) to overcome such biases in customer management. As part of a priori approach, the findings suggest scientific, application, stakeholder and assurance consistencies. With regard to the post-hoc approach, the findings recommend six steps: bias identification, review of extant findings, selection of the right variables, responsible and ethical model development, data analysis and action on insights. Overall, this study contributes to the ethical and responsible use of AI applications.
AutoML-Zero: Evolving Machine Learning Algorithms From Scratch

Machine learning research has advanced in multiple aspects, including model structures and learning methods. The effort to automate such research, known as AutoML, has also made significant progress. However, this progress has largely focused on the architecture of neural networks, where it has relied on sophisticated expert-designed layers as building blocks---or similarly restrictive search spaces. Our goal is to show that AutoML can go further: it is possible today to automatically discover complete machine learning algorithms just using basic mathematical operations as building blocks. We demonstrate this by introducing a novel framework that significantly reduces human bias through a generic search space. Despite the vastness of this space, evolutionary search can still discover two-layer neural networks trained by backpropagation. These simple neural networks can then be surpassed by evolving directly on tasks of interest, e.g. CIFAR-10 variants, where modern techniques emerge in the top algorithms, such as bilinear interactions, normalized gradients, and weight averaging. Moreover, evolution adapts algorithms to different task types: e.g., dropout-like techniques appear when little data is available. We believe these preliminary successes in discovering machine learning algorithms from scratch indicate a promising new direction for the field.
Quantum machine learning models are kernel methods

With near-term devices available and the race for fault-tolerant computers in full swing, researchers became interested in the question of what happens if we replace a machine learning model with a circuit. While such quantum are sometimes called quantum neural networks, it has been repeatedly noted that their mathematical structure is actually much more closely related to kernel methods: they analyse data in high-dimensional Hilbert spaces to which we only have access through inner products revealed by measurements. This technical manuscript summarises, formalises and extends the link by systematically rephrasing models as a kernel method. It shows that most near-term and fault-tolerant models can be replaced by a general support vector machine whose kernel computes distances between data-encoding states. In particular, kernel-based training is guaranteed to find better or equally good models than variational circuit training. Overall, the kernel perspective of machine learning tells us that the way that data is encoded into states is the main ingredient that can potentially set models apart from classical machine learning models.
Simple Spectral Graph Convolution

Graph Convolutional Networks (GCNs) have drawn significant attention and become leading methods for learning graph representations. The most GCNs suffer the performance loss when the depth of the model increases. Similarly to CNNs, without specially designed architectures, the performance of a network degrades quickly with increased depth. Some researchers argue that the required neighbourhood size and neural network depth are two completely orthogonal aspects of graph representation. Thus, several methods extend the neighbourhood by aggregating k-hop neighbourhoods of nodes while using shallow neural networks. However, these methods still encounter oversmoothing, high computation and storage costs. In this paper, we use a modified Markov Diffusion Kernel to derive a variant of GCN called Simple Spectral Graph Convolution (S2GC). Our spectral analysis shows that our simple spectral graph convolution used in S2GC is a trade-off of low-pass and high-pass filter which captures the global and local contexts of each node. We provide two theoretical claims which demonstrate that we can aggregate over a sequence of increasingly larger neighborhoods compared to competitors while limiting severe oversmoothing. Our experimental evaluation demonstrates that S2GC with a linear learner is competitive in text, node and graph classification tasks. Moreover, S2GC is comparable to other state-of-the-art methods for node clustering and community prediction tasks.
A Multi-Attribute Fuzzy Evaluation Model for the Teaching Quality of Physical Education in Colleges and Its Implementation Strategies

There are two major problems with teaching quality evaluation of physical education (PE) in colleges: the excessive number of evaluation factors, and the incomplete evaluation system. To solve the problems, this paper puts forward a multi-attribute fuzzy evaluation model of college PE teaching quality, and provides the strategies to implement the model. Firstly, the problems of college PE teaching were analyzed, and a novel multi-dimensional evaluation system was developed for college PE teaching quality. To quantify college PE teaching quality, an evaluation model of college PE teaching quality was established based on the Grey Relational Analysis (GRA). In addition, several strategies were presented to improve college PE teaching quality. The proposed model and strategies provide a good reference for solving similar complex system problems.
Conservation machine learning: a case study of random forests

Abstract Conservation machine learning conserves models across runs, users, and experiments—and puts them to good use. We have previously shown the merit of this idea through a small-scale preliminary experiment, involving a single dataset source, 10 datasets, and a single so-called cultivation method—used to produce the final ensemble. In this paper, focusing on classification tasks, we perform extensive experimentation with conservation random forests, involving 5 cultivation methods (including a novel one introduced herein— lexigarden ), 6 dataset sources, and 31 datasets. We show that significant improvement can be attained by making use of models we are already in possession of anyway, and envisage the possibility of repositories of models (not merely datasets, solutions, or code), which could be made available to everyone, thus having conservation live up to its name, furthering the cause of data and computational science.
Digital storytelling with chatbots: mapping L2 participation and perception patterns

Purpose This study aims to increase language learning (L2) output by incorporating a digital storytelling chatbot system (known as a “storybot”) that focused interactions on a narrative. This study also sought to investigate student perceptions of these storybot interactions and improve on poor perception rates from previous studies. Design/methodology/approach This one-sample exploratory study was of student-storybot participation rates and student perceptions towards a storybot activity designed to increase L2 output. A combination of storybot participation analytics and survey analysis of student perception was carried out. Findings The use of storybots in the L2 class resulted in mixed participation rates. Students read nine times more than they wrote, indicating a high degree of reading comprehension necessary for storybot interaction. Survey results revealed that students believed storybots helped them meet their L2 goals, were relevant to their L2 and were easy to navigate. Research limitations/implications Interactions were through text messaging so no impact on speech or pronunciation could be observed. Further, the context was within a single university class in South Korea, restricting the generalization of findings to outside regions or with younger learners. Finally, while storybots proved to be valuable reading comprehension activities, the next step in this line of chatbot research should incorporate more writing prompts. Practical implications Storybots revealed explicit benefits to reading comprehension, as measured by cohesion between storybot delivered comprehension questions and student responses. Moreover, storybots can be used as examples for students in their own story creation, classroom forms to collect relevant student information regarding learning objectives and platforms for class quizzes. Social implications Storybots scaffold students through conversations, which abide by socio-pragmatic norms, providing models for L2 learners to incorporate in real-world text-based communication. Additionally, a wide range of idiomatic expressions is contextualized in comprehensible interactions that students can learn from the storybot then practice with friends. Originality/value This study contributes to the growing research on the use of chatbots for second L2 and offers specific insight into the use of narrative storybots as a means to increase L2 output and potentially benefit L2 reading comprehension.
Augmented Reality with HoloLens® in Parotid Tumor Surgery: A Prospective Feasibility Study

&lt;b&gt;&lt;i&gt;Introduction:&lt;/i&gt;&lt;/b&gt; Augmented reality can improve planning and execution of surgical procedures. Head-mounted devices such as the HoloLens® (Microsoft, Redmond, WA, USA) are particularly suitable to achieve these aims because they are controlled by hand gestures and enable contactless handling in a sterile environment. &lt;b&gt;&lt;i&gt;Objectives:&lt;/i&gt;&lt;/b&gt; So far, these systems have not yet found their way into the operating room for surgery of the parotid gland. This study explored the feasibility and accuracy of augmented reality-assisted parotid surgery. &lt;b&gt;&lt;i&gt;Methods:&lt;/i&gt;&lt;/b&gt; 2D MRI holographic images were created, and 3D holograms were reconstructed from MRI DICOM files and made visible via the HoloLens. 2D MRI slices were scrolled through, 3D images were rotated, and 3D structures were shown and hidden only using hand gestures. The 3D model and the patient were aligned manually. &lt;b&gt;&lt;i&gt;Results:&lt;/i&gt;&lt;/b&gt; The use of augmented reality with the HoloLens in parotic surgery was feasible. Gestures were recognized correctly. Mean accuracy of superimposition of the holographic model and patient’s anatomy was 1.3 cm. Highly significant differences were seen in position error of registration between central and peripheral structures (&lt;i&gt;p&lt;/i&gt; = 0.0059), with a least deviation of 10.9 mm (centrally) and highest deviation for the peripheral parts (19.6-mm deviation). &lt;b&gt;&lt;i&gt;Conclusion:&lt;/i&gt;&lt;/b&gt; This pilot study offers a first proof of concept of the clinical feasibility of the HoloLens for parotid tumor surgery. Workflow is not affected, but additional information is provided. The surgical performance could become safer through the navigation-like application of reality-fused 3D holograms, and it improves ergonomics without compromising sterility. Superimposition of the 3D holograms with the surgical field was possible, but further invention is necessary to improve the accuracy.
Gaussian Dynamic Convolution for Efficient Single-Image Segmentation

Interactive single-image segmentation is ubiquitous in the scientific and commercial imaging software. In this work, we focus on the single-image segmentation problem only with some seeds such as scribbles. Inspired by the dynamic receptive field in the human being's visual system, we propose the Gaussian dynamic convolution (GDC) to fast and efficiently aggregate the contextual information for neural networks. The core idea is randomly selecting the spatial sampling area according to the Gaussian distribution offsets. Our GDC can be easily used as a module to build lightweight or complex segmentation networks. We adopt the proposed GDC to address the typical single-image segmentation tasks. Furthermore, we also build a Gaussian dynamic pyramid Pooling to show its potential and generality in common semantic segmentation. Experiments demonstrate that the GDC outperforms other existing convolutions on three benchmark segmentation datasets including Pascal-Context, Pascal-VOC 2012, and Cityscapes. Additional experiments are also conducted to illustrate that the GDC can produce richer and more vivid features compared with other convolutions. In general, our GDC is conducive to the convolutional neural networks to form an overall impression of the image.
Understanding and Interpreting the Impact of User Context in Hate Speech Detection

As hate speech spreads on social media and online communities, research continues to work on its automatic detection.Recently, recognition performance has been increasing thanks to advances in deep learning and the integration of user features.This work investigates the effects that such features can have on a detection model.Unlike previous research, we show that simple performance comparison does not expose the full impact of including contextualand user information.By leveraging explainability techniques, we show (1) that user features play a role in the model's decision and (2) how they affect the feature space learned by the model.Besides revealing that-and also illustrating why-user features are the reason for performance gains, we show how such techniques can be combined to better understand the model and to detect unintended bias.
Consistency and Completeness

An increasingly important system requirement for distributed stream processing applications is to provide strong correctness guarantees under unexpected failures and out-of-order data so that its results can be authoritative (not needing complementary batch results). Although existing systems have put a lot of effort into addressing some specific issues, such as consistency and completeness, how to enable users to make flexible and transparent trade-off decisions among correctness, performance, and cost still remains a practical challenge. Specifically, similar mechanisms are usually applied to tackle both consistency and completeness, which can result in unnecessary performance penalties. We present Apache Kafka's core design for stream processing, which relies on its persistent log architecture as the storage and inter-processor communication layers to achieve correctness guarantees. Kafka Streams, a scalable stream processing client library in Apache Kafka, defines the processing logic as read-process-write cycles in which all processing state updates and result outputs are captured as log appends. Idempotent and transactional write protocols are utilized to guarantee exactly-once semantics. Furthermore, revision-based speculative processing is employed to emit results as soon as possible while handling out-of-order data. We also demonstrate how Kafka Streams behaves in practice with large-scale deployments and performance insights exhibiting its flexible and low-overhead trade-offs.
Multilingual Protest News Detection - Shared Task 1, CASE 2021

Benchmarking state-of-the-art text classification and information extraction systems in multilingual, cross-lingual, few-shot, and zeroshot settings for socio-political event information collection is achieved in the scope of the shared task Socio-political and Crisis Events Detection at the workshop CASE @ ACL-IJCNLP 2021.Socio-political event data is utilized for national and international policyand decision-making.Therefore, the reliability and validity of such datasets are of utmost importance.We split the shared task into three parts to address the three aspects of data collection (Task 1), fine-grained semantic classification (Task 2), and evaluation (Task 3).Task 1, which is the focus of this report, is on multilingual protest news detection and comprises four subtasks that are document classification (subtask 1), sentence classification (subtask 2), event sentence coreference identification (subtask 3), and event extraction (subtask 4).All subtasks have English, Portuguese, and Spanish for both training and evaluation data.Data in Hindi language is available only for the evaluation of subtask 1.The majority of the submissions, which are 238 in total, are created using multi-and cross-lingual approaches.Best scores are between 77.27 and 84.55 F1-macro for subtask 1, between 85.32 and 88.61 F1macro for subtask 2, between 84.23 and 93.03 CoNLL 2012 average score for subtask 3, and between 66.20 and 78.11F1-macro for subtask 4 in all evaluation settings.The performance of the best system for subtask 4 is above 66.20 F1 for all available languages.Although there is still a significant room for improvement in cross-lingual and zero-shot settings, the best submissions for each evaluation scenario yield remarkable results.Monolingual models outperformed the multilingual models in a few evaluation scenarios, in which there is relatively much training data.
Privacy-Aware Cloud Auditing for GDPR Compliance Verification in Online Healthcare

Emerging multitenant cloud computing ecosystems allow multiple applications to share virtualized pool of computing and networking resources. As a result, such ecosystems are becoming increasingly prone to data privacy concerns (personal data leakages and unauthorized access). While cloud computing providers support robust security and privacy mechanisms (e.g., public key cryptography, firewalls, and virtual private networks, among many others), they lack mechanisms and frameworks to monitor, audit, and verify these data privacy concerns. The emergence of data protection regulations around the world, such as General Data Protection Regulation in Europe and the Data Protection Act in the U.K., further emphasizes the need to overcome these privacy limitations. In this article, a novel technique for monitoring, auditing, and verifying the operations carried out on a user’s personal data in cloud computing ecosystems is proposed. Our research methodology leverages distributed ledger technologies (e.g., blockchain and smart contracts) for developing an immutable recording technique, which transparently logs, monitors, and verifies the operations carried out on user data. Using a healthcare pharmacy scenario and extensive real-world experiments, we validate the feasibility of the proposed technique. The proposed work handles a large pool of requests ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$&gt;$</tex-math></inline-formula> 13K) ensuring minimal latency ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\approx$</tex-math></inline-formula> 50–60 ms) and overheads for three different service packages varied with respect to the number of actors and operations.