Reviewerly/exHarmony huggingface dataset:
The impact of Capitol Hill on Pleroma

The popularity of Decentralised Web (DWeb) platforms (e.g. Pleroma, Mastodon) has grown in recent years. This has presented users with alternatives to the well-known centralised social network platforms like Twitter, Facebook, and YouTube. In the DWeb, infrastructure and data ownership is decentralized and hence not under a single administrative authority. This paper explores the challenge of content moderation in such an environment. Specifically, we seek to motivate the need for better moderation technologies, via a use-case analysis of DWeb activity surrounding the 6th January 2021 events at Capitol Hill. Through empirical measurements, we inspect the activity of instances that have grown in popularity during this period, and explore the policies imposed on them by other instances. To do this, we inspect Pleroma, a major DWeb microblogging platform. We investigate the posts generated before, during and after the storming of Capitol Hill on the 12 largest instances in terms of user base and posts, and measure the policy reaction on them.
Self-orthogonal codes over <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"><mml:msub><mml:mrow><mml:mi mathvariant="double-struck">Z</mml:mi></mml:mrow><mml:mrow><mml:mn>4</mml:mn></mml:mrow></mml:msub></mml:math> arising from the chain ring <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si2.svg"><mml:msub><mml:mrow><mml:mi mathvariant="double-struck">Z</mml:mi></mml:mrow><mml:mrow><mml:mn>4</mml:mn></mml:mrow></mml:msub><mml:mo stretchy="false">[</…

We find a building-up type construction method for self-orthogonal codes over Z4 arising from the chain ring Z4[u]/〈u2+1〉. Our construction produces self-orthogonal codes over Z4 with increased lengths and free ranks from given self-orthogonal codes over Z4 with smaller lengths and free ranks; in the most of the cases their minimum weights are also increased. Furthermore, any self-orthogonal code over Z4 with generator matrix subject to certain conditions can be obtained from our construction. Employing our construction method, we obtain at least 125 new self-orthogonal codes over Z4 up to equivalence; among them, there are 35 self-orthogonal codes which are distance-optimal. Furthermore, we have eight self-orthogonal codes, which are distance-optimal among all linear codes over Z4 with the same type. As a method, we use additive codes over the finite ring Z4[u]/〈u2+1〉 with generator matrices G satisfying GGT=O, and we use a new Gray map from Z4[u]/〈u2+1〉 to Z43 as well.
Hybrid Neural Network for Handwritten Mathematical Expression Recognition system

The mathematical expression is an essential part of every domain they provide the mathematical explanation for one&#x2019;s theory. The technological advancement in the domain of artificial intelligence has aided in various handwritten recognition systems such as handwritten mathematical expression recognition. Symbol recognition and structural analysis are two major obstacles in handwritten mathematical expression recognition. In this paper, we propose a hybrid neural network algorithm called validator, tracker, attention, and parser (VTAP). The hybrid neural network algorithms like CRNN is a blend of recurrent neural network (RNN) and convolutional neural network (CNN). It has shown better and more accurate outputs than the native CNN and RNN algorithms alone. CROHME dataset is used, which is the most widely used dataset. The recognition is divided into 4 parts validator, tracker, attention, and parser (VTAP). A tracker is equipped with a group of Bi-Directional Recurrent Network (BRNN) with the Gated Recurrent Unit (GRU). Succeeded by a tracker, the parser uses a GRU lead by guided hybrid attention. The accuracy and the time complexity of VTAP is compared with existing work Tracker, Attention and Parser (TAP), VTAP shows up to 92.2% of accuracy while TAP shows an accuracy of 89%.
Ex-DoF: Expansion of Action Degree-of-Freedom with Virtual Camera Rotation for Omnidirectional Image

Inter-robot transfer of training data is a little explored topic in learning- and vision-based robot control. Here we propose a transfer method from a robot with a lower Degree-of-Freedom (DoF) to one with a higher DoF utilizing the omnidirectional camera image. The virtual rotation of the robot camera enables data augmentation in this transfer learning process. As an experimental demonstration, a vision-based control policy for a 6- DoF robot is trained using a dataset collected by a wheeled ground robot with only three DoFs. Towards the application of robotic manipulations, we also demonstrate a control system of a 6- DoF arm robot using multiple policies with different fields of view to enable object reaching tasks.
General and Fast Inter-Process Communication via Bypassing Privileged Software

IPC (Inter-Process Communication) is a widely used operating system (OS) technique that allows one process to invoke the services of other processes. The IPC participants may share the same OS ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">internal IPC</i> ) or use a separate OS ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">external IPC</i> ). Even though a long line of researches has optimized the performance of IPC, it is still a major factor of the run-time overhead of IPC-intensive applications. Furthermore, there is no one-size-fits-all solution for both internal and external IPC. This paper presents SkyBridge, a general communication technique designed and optimized for both types of IPC. SkyBridge requires no involvement of the privileged software (the kernel or the hypervisor) and enables a process to directly switch to the virtual address space of the target process, regardless of whether they are running on the same OS or not. We have implemented SkyBridge on two microkernels (seL4 and Google Zircon) as well as an open-source serverless hypervisor (Firecracker). The evaluation results show that SkyBridge improves the latency of internal IPC and external IPC by up to 19.6x and 1265.7x, respectively.
Attention-Guided Image Captioning through Word Information

Image captioning generates written descriptions of an image. In recent image captioning research, attention regions seldom cover all objects, and generated captions may lack the details of objects and may remain far from reality. In this paper, we propose a word guided attention (WGA) method for image captioning. First, WGA extracts word information using the embedded word and memory cell by applying transformation and multiplication. Then, WGA applies word information to the attention results and obtains the attended feature vectors via elementwise multiplication. Finally, we apply WGA with the words from different time steps to obtain previous word guided attention (PW) and current word attention (CW) in the decoder. Experiments on the MSCOCO dataset show that our proposed WGA can achieve competitive performance against state-of-the-art methods, with PW results of a 39.1 Bilingual Evaluation Understudy score (BLEU-4) and a 127.6 Consensus-Based Image Description Evaluation score (CIDEr-D); and CW results of a 39.1 BLEU-4 score and a 127.2 CIDER-D score on a Karpathy test split.
Modeling and Integration for Complex Systems

In this paper, we propose a way forward in modeling complex software-managed systems that relies on two well-known mathematical constructs, the subdirect product and the quotient by a congruence relation. These mathematical techniques allow us to build models that correspond to different aspects of / concerns about the system (which we call viewpoints), and to integrate them afterwards. It does require models of the interactions among these viewpoints, but that kind of consideration should occur no matter what kind of modeling is being carried out. This provides an explicit way for designers to enumerate and analyze all the design considerations, which, of course, they should be considering anyway, but often do not because there are difficulties in studying model interactions, and no particular theoretical base for doing so. This paper is our first attempt to address that problem. We describe some of the relevant viewpoint models, show how some of their interactions might be modeled, and explain the underlying mathematical methods.
Constructing differentially 4-uniform involutions over <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"><mml:msub><mml:mrow><mml:mi mathvariant="double-struck">F</mml:mi></mml:mrow><mml:mrow><mml:msup><mml:mrow><mml:mn>2</mml:mn></mml:mrow><mml:mrow><mml:mn>2</mml:mn><mml:mi>k</mml:mi></mml:mrow></mml:msup></mml:mrow></mml:msub></mml:math> by using Carlitz form

Differentially 4-uniform involutions on F22k play important roles in the design of substitution boxes (S-boxes). Despite the active researches on differentially 4-uniform permutation, there is not so much research on differentially 4-uniform involutions, especially over the field F2n with 4|n. In this paper, we introduce a new approach to construct differentially 4-uniform involutions by using Carlitz form. With this approach, we explicitly construct two new classes of differentially 4-uniform involutions over F2n with 4|n. We also show that our constructions have high nonlinearity and optimal algebraic degree. With the help of computer, we show that our constructions are CCZ-inequivalent to the known differentially 4-uniform involutions over F28.
SPIDE: sybil-proof, incentivized data exchange

Abstract Decentralized, open-access blockchain systems opened up new, exciting possibilities—all without reliance on trusted third parties. Regardless of the employed consensus protocol, the overall security, decentralization and effectiveness of such systems, largely depend upon properly structured incentives. Indeed, as has been previously spotted by Babaiaff et al. Bitcoin-like systems, oftentimes lack some of these. Specifically, current blockchain-systems fail to incentivize one of their crucial aspects–the underlying data exchange. As we rationalize, proper incentivization of that layer could lead to lower transactions’ confirmation-times, improved finalization guarantees and at the same time to discouragement of malicious behaviours such as block-withholding attacks. Indeed, incentivization of the data-exchange layer allows the system to remain operational when all agents, including routing nodes, are assumed to be rational. In this work, while focusing on the problem of sybil-proof data exchange, we revisit previous approaches, showcasing their shortcomings and lay forward the first information exchange framework; with integrated routing and reward-function mechanics, provably secure in thwarting Sybil-nodes in 1-connected or eclipsed networks. The framework neither requires nor assumes any kind of constraints in regard to the network’s topology (i.e. the network is modelled as a random-connected graph) and rewards information propagators through a system-intrinsic virtual asset maintained by the decentralized state-machine. The proposal, while being storage and transmission efficient is suitable for rewarding not only consensus-related datagrams (both data-blocks and transactions) but consensus-extrinsic information as well, thus facilitating an universal sybil-proof data-exchange apparatus, provably valid under the assumption of existence of a data store whose property of non-malleability emerges as time approaches infinity. Our research was conducted under two scenarios—with round leader known and unknown in advance of each transactional round.
AutoML

Machine learning systems both gained significant interest from the academic side and have seen adoption in the industry. However, one aspect that has received insufficient attention so far is the study of the lifecycle of such systems. This aspect is particularly important due to various ML systems' strong dependency on data, which is constantly evolving-and, therefore, changing-over time. The focus of my PhD research is the study of the implications of these dynamics on the ML systems' performance. Concretely, I propose a method of detecting changes caused by drift in the data early. Furthermore, I discuss possibilities for automating large parts of the ML lifecycle management, to ensure a better and more controllable maintenance process.
”A Tale on Abuse and Its Detection over Online Platforms, Especially over Emails”: From the Context of Bangladesh

With the advent of pervasive usage of online platforms, online abusive behavior has become an indispensable part of our life demanding great attention from the research community. Accordingly, the research community is spending its effort on the demanding task, however, perhaps having much less effort on emails, even though emails are identified as a prominent source of exchanging online abusive behaviors. To fill in this gap in the literature, we conduct an in-depth study to investigate online abusive behavior having a special focus on emails. To do so, we perform a mixed-method user study consisting of formative interviews (n=15) and a survey (n=65) over user’s experience, coping strategies, etc., pertinent to online abuse in Bangladesh, especially focusing on abuse over emails. We also dig into users’ perspectives to analyze strengths and challenges associated with different types of abuse detection systems for online platforms, especially for emails. One of the noteworthy findings of our study is that there exists a significant demand for abuse detection systems over emailing platforms even after having a lesser frequency of abuse occurring over emails. Our findings also highlight a certain level of user preference for an automated abuse detection system potentially considering its more control and fewer privacy concerns to users, however, being challenged due to having the limitation of lesser ability to detect implicit abuse. We also identify several limiting factors associated with a human-moderator-based abuse detection system, including less comfort, less trust in different types of moderators, inhumane demands to the moderators, and time delay in detecting abuses. These findings point to opportunities for design interventions for hybrid abuse detection systems, which is the most preferred system to the users, to overcome all the limitations of automated and human-moderator-based systems.
Astrocytoma Brain Tumor Automated Classification Procedure Based on Ki67 Expression

Abstract It is known to be a very challenging task for pathologists to perform astrocytoma biopsy analysis especially with the high demand for an accurate and precise analysis. An inaccurate diagnosis might affect the treatment decisions for the patient. Therefore, this study aims to develop an automated procedure for classification astrocytoma brain tumor. The procedure consists of four stages, namely image pre-processing, image segmentation, feature extraction and selection, and classification of Ki67 expression. Initially, the image’s quality was enhanced by using global stretching and unsharp masking filter. Then, the adaptive color thresholding technique was used to segment the Ki67 expression. The process was followed by extracting the features related to the positive and negative Ki67 cells. ReliefF feature selection algorithm was used to select the best features in defining the positive and negative cells. In the classification stage, two classifiers, namely Support Vector Machine (SVM) and Multilayer Perceptron (MLP) were used to differentiate between the positive and negative cells. From 81 samples images, both classifiers yielded promising results, with an accuracy of 98.5% for SVM and 99.6% for MLP respectively.
Deep Active-Self Learning Applied to Named Entity Recognition

Deep learning models have been the state-of-the-art for a variety of challenging tasks in natural language processing, but to achieve good results they often require big labeled datasets. Deep active learning algorithms were designed to reduce the annotation cost for training such models. Current deep active learning algorithms, however, aim at training a good deep learning model with as little labeled data as possible, and as such are not useful in scenarios where the full dataset must be labeled. As a solution to this problem, this work investigates deep active-self learning algorithms that employ self-labeling using the trained model to help alleviate the cost of annotating full datasets for named entity recognition tasks. The experiments performed indicate that the proposed deep active-self learning algorithm is capable of reducing manual annotation costs for labeling the complete dataset for named entity recognition with less than 2% of the self labeled tokens being mislabeled. We also investigate an early stopping technique that doesn't rely on a validation set, which effectively reduces even further the annotation costs of the proposed active-self learning algorithm in real world scenarios.
Evaluating cloud interactions with costs and SLAs

Abstract In this paper, we investigate how to improve the profits in cloud infrastructures by using price schemes and analyzing the user interactions with the cloud provider. For this purpose, we consider two different types of client behavior, namely regular and high-priority users. Regular users do not require a continuous service, and they can wait to be attended to. In contrast, high-priority users require a continuous service, e.g., a 24/7 service, and usually need an immediate answer to any request. A complete framework has been implemented, which includes a UML profile that allows us to define specific cloud scenarios and the automatic transformations to produce the code for the cloud simulations in the Simcan2Cloud simulator. The engine of Simcan2Cloud has also been modified by adding specific SLAs and price schemes. Finally, we present a thorough experimental study to analyze the performance results obtained from the simulations, thus making it possible to draw conclusions about how to improve the cloud profit for the cloud studied by adjusting the different parameters and resource configuration.
Classification of abnormal behavior in the elderly based on Wi-Fi

Abnormal behavior in daily life is a great threat to the health of the elderly, so it is of great significance to detect the abnormal behavior of the elderly. Using ubiquitous commercial Wi-Fi devices to collect information, behavior detection can be performed in an indoor environment without carrying other devices. The Wi-Fi based abnormal behavior detection for the elderly proposed in this paper is aimed at the fall and wandering behaviors that the elderly are prone to. At the same time, squatting and sitting behaviors those are similar to the fall behaviors are distinguished. Several classical classification models are used to compare the effect and recognition time. The experimental comparison shows that the SVM and KNN machine learning methods after adjusting the model parameters can achieve the same recognition effect as the deep learning model LSTM, shorten the running time and improve the efficiency.
Experimental Demonstration of Nonlinear fibre Distortion Compensation with Integrated Photonic Reservoir Computing

Optical reservoir computing is a machine learning technique in which a photonic chip can be trained on classification tasks of time signals.This paper presents experimental results where linear and nonlinear fibre distortions are mitigated to below the 0.2 × 10 -3 FEC limit using a photonic reservoir.
Behavior Detection in Supermarket Based on Image Caption with Unity

This paper uses image caption to identify human behavior in supermarkets. In addition to observing the human posture to judge the current human behavior, we can reconstruct various human postures and scenes in Unity， and collect the human activity data set in the supermarket through the above methods.
A Fast Small-Sample Modeling Method for Precision Inertial Systems Fault Prediction and Quantitative Anomaly Measurement

Inertial system platforms are a kind of important precision devices, which have the characteristics of difficult acquisition for state data and small sample scale. Focusing on the model optimization for data-driven fault state prediction and quantitative degree measurement, a fast small-sample supersphere one-class SVM modeling method using support vectors pre-selection is systematically studied in this paper. By theorem-proving the irrelevance between the model's learning result and the non-support vectors (NSVs), the distribution characters of the support vectors are analyzed. On this basis, a modeling method with selected samples having specific geometry character from the training sets is also proposed. The method can remarkably eliminate the NSVs and improve the algorithm's efficiency. The experimental results testify that the scale of training samples and the modeling time consumption both give a sharply decrease using the support vectors pre-selection method. The experimental results on inertial devices also show good fault prediction capability and effectiveness of quantitative anomaly measurement.
Resource Management for TensorFlow Inference

TensorFlow, a popular machine learning (ML) platform, allows users to transparently exploit both GPUs and CPUs to run their applications. Since GPUs are optimized for compute-intensive workloads (e.g., matrix calculus), they help boost executions, but introduce resource heterogeneity. TensorFlow neither provides efficient heterogeneous resource management nor allows for the enforcement of user-defined constraints on the execution time. Most of the works address these issues in the context of creating models on existing data sets (training phase), and only focus on scheduling algorithms. This paper focuses on the inference phase, that is, on the application of created models to predict the outcome on new data interactively, and presents a comprehensive resource management solution called ROMA (Resource Constrained ML Applications). ROMA is an extension of TensorFlow that (a) provides means to easily deploy multiple TensorFlow models in containers using Kubernetes b) allows users to set constraints on response times, (c) schedules the execution of requests on GPUs and CPUs using heuristics, and (d) dynamically refines the CPU core allocation by exploiting control theory. The assessment conducted on four real-world benchmark applications compares ROMA against four different systems and demonstrates a significant reduction ( \(75\%\)) in constraint violations and \(24\%\) saved resources on average.
The teacher’s role in the development of soft skills in distance and blended learning

The background for this research includes both theoretical results concerning the development of soft skills in the process of learning and some practical approaches for the development of soft skills in distance learning environments which have become common during the COVID-19 pandemic. The objective of this research is to determine the teacher’s role in the development of soft skills and to identify any particular characteristics of this role in distance and blended learning contexts. This research relied on both general scientific and special methods. General scientific methods, in particular, included analysis, synthesis, inductive and deductive reasoning, comparison, analogy, etc., while special research methods included content analysis, case studies, secondary statistical analysis, historical method, etc. The outcome of the research is the finding that education now should involve the development and building of soft skills in students as a prerequisite for personal and professional growth. As teachers play a critical role in the development of such skills they should promote the creation and operation of a specific environment conducive to the development of communication skills, critical thinking and creative problem-solving abilities, as well as students’ individual and group work. The findings of this research are new for Russian science and also draw the attention of the research community to the particular features of distance learning amid the global COVID-19 pandemic.