Reviewerly/exHarmony huggingface dataset:
Smart contracts from the contract law perspective: outlining new regulative strategies

Abstract Smart contracts nowadays start being widely used in various areas of economic and social life. In most cases smart contracts are somehow related to legal contracts: the former may constitute part of a legal contract, an entire contract, or be used to automate a contract performance. Meanwhile, a question whether modern contract law is applicable to smart contracts is rather debatable, since smart contracts initially were designed to rely only on technical rules embedded in blockchain and considered as self-sufficient instruments capable of addressing various issues which may emerge in practice. However, practice has shown that technical regulation does not often cope with the problems one may face when using smart contracts, which confirms the need for legal regulation. Although smart contracts have many technical peculiarities, they do not make application of contract law provisions totally impossible. Thus, what the modern contract law needs is a set of special rules applicable to the practice of smart contracting.
Guidelines for Conducting Software Engineering Research

This chapter presents a holistic overview of software engineering research strategies. It identifies the two main modes of research within the software engineering research field, namely knowledge-seeking and solution-seeking research—the Design Science model corresponding well with the latter. We present the ABC framework for research strategies as a model to structure knowledge-seeking research. The ABC represents three desirable aspects of research—generalizability over actors (A), precise control of behavior (B), and realism of context (C). Unfortunately, as our framework illustrates, these three aspects cannot be simultaneously maximized. We describe the two dimensions that provide the foundation of the ABC framework—generalizability and control, explain the four different types of settings in which software engineering research is conducted, and position eight archetypal research strategies within the ABC framework. We illustrate each strategy with examples, identify appropriate metaphors, and present an example of how the ABC framework can be used to design a research program.
Persepsi Mahasiswa tentang Penggunaan Media Daring pada Program Studi S1 Ilmu Gizi Sekolah Tinggi Ilmu Kesehatan Surabaya selama Masa Pandemi Corona Virus Disease (COVID-19)

The impact of the covid-19 pandemic is not only felt in the world of health but also has an effect on the world of education This phenomenon makes educators use a variety of online learning media to meet cognitive, affective, and social needs during the learning policy from home. Various institutional and educational creativities use online media with different platforms to facilitate students. This study aims to explore information related to the type of media used by multiple platforms and students' perceptions of media that is liked and disliked. This research method uses a cross-sectional design with a sample of 104 respondents at various levels in the year of the S1 STIKes Surabaya Nutrition Study Program. The research results show that the type of media used includes Google classroom &amp; meeting, Zoom, WhatsApp Group, Kahoot, Youtube, and Quizizz with the highest moodle media usage frequency of 43 and the lowest frequency of media usage of Quizezz by 2%. Students' perceptions of preferred and disliked media with the results of the top 3 most preferred media were WhatsApp Group at 41.7%, Zoom 28.2%, and Google classroom &amp; meeting at 8.7%. The top 3 most disliked media are Zoom at 53.4%, Youtube at 15.6%, and Moodle at 12.6%.
Research on multi-authority CP-ABE access control model in multicloud

In order to solve the problems of data sharing security and policy conflict in multicloud storage systems (MCSS), this work designs an attribute mapping mechanism that extends ciphertext policy attribute-based encryption (CP-ABE), and proposes a multi-authority CP-ABE access control model that satisfies the need for multicloud storage access control. The mapping mechanism mainly involves the tree structure of CP-ABE and provides support for the types of attribute values. The framework and workflow of the model are described in detail. The effectiveness of the model is verified by building a simple prototype system, and the performance of the prototype system is analyzed. The results suggest that the proposed model is of theoretical and practical significance for access control research in MCSS. The CP-ABE has better performance in terms of computation time overhead than other models.
A Cloud-Based UTOPIA Smart Video Surveillance System for Smart Cities

A smart city is a future city that enables citizens to enjoy Information and Communication Technology (ICT) based smart services with any device, anytime, anywhere. It heavily utilizes Internet of Things. It includes many video cameras to provide various kinds of services for smart cities. Video cameras continuously feed big video data to the smart city system, and smart cities need to process the big video data as fast as it can. This is a very challenging task because big computational power is required to shorten processing time. This paper introduces UTOPIA Smart Video Surveillance, which analyzes the big video images using MapReduce, for smart cities. We implemented the smart video surveillance in our middleware platform. This paper explains its mechanism, implementation, and operation and presents performance evaluation results to confirm that the system worked well and is scalable, efficient, reliable, and flexible.
Argumentative Relation Classification with Background Knowledge.

A common conception is that the understanding of relations that hold between argument units requires knowledge beyond the text. But to date, argument analysis systems that leverage knowledge resources are still very rare. In this paper, we propose an unsupervised graph-based ranking method that extracts relevant multi-hop knowledge from a background knowledge resource. This knowledge is integrated into a neural argumentative relation classifier via an attention-based gating mechanism. In contrast to prior work we emphasize the selection of relevant multi-hop knowledge, and apply methods to automatically enrich the knowledge resource with missing knowledge. We assess model performance on two datasets, showing considerable improvement over strong baselines.
A platform to enable self-adaptive cloud applications using trustworthiness properties

Self-Adaptive Systems (SASs) reflect on both their state and on the environment and change their behavior to satisfy the expected objectives. Cloud systems are self-adaptive by nature, especially considering the resources used in a pay-as-you-go manner. Satisfying trustworthiness (worthiness of a service based on evidences of its trust) properties also demands self-adaptation capabilities. Unfortunately, developers lack an easy-to-use platform to support the assessment of such properties and to execute the required adaptions. This paper presents TMA, a platform that implements a MAPE-K control loop for cloud systems, supported by a distributed monitoring system based on probes. Quality Models are used to express trustworthiness properties, resulting in scores, which are used to plan adaptations through evaluation rules. These plans are executed by actuators. A demo shows the scaling up/down of the number of containers in a cloud application of a set of web services from TPC Benchmarks, as a result of changes observed in the environment.
CDA-Net for Automatic Prostate Segmentation in MR Images

Automatic and accurate prostate segmentation is an essential prerequisite for assisting diagnosis and treatment, such as guiding biopsy procedures and radiation therapy. Therefore, this paper proposes a cascaded dual attention network (CDA-Net) for automatic prostate segmentation in MRI scans. The network includes two stages of RAS-FasterRCNN and RAU-Net. Firstly, RAS-FasterRCNN uses improved FasterRCNN and sequence correlation processing to extract regions of interest (ROI) of organs. This ROI extraction serves as a hard attention mechanism to focus the segmentation of the subsequent network on a certain area. Secondly, the addition of residual convolution block and self-attention mechanism in RAU-Net enables the network to gradually focus on the area where the organ exists while making full use of multiscale features. The algorithm was evaluated on the PROMISE12 and ASPS13 datasets and presents the dice similarity coefficient of 92.88% and 92.65%, respectively, surpassing the state-of-the-art algorithms. In a variety of complex slice images, especially for the base and apex of slice sequences, the algorithm also achieved credible segmentation performance.
Learning Optimal Decision Trees using Constraint Programming

Decision trees are among the most popular classification models in machine learning. Traditionally, they are learned using greedy algorithms. However, such algorithms pose several disadvantages: it is difficult to limit the size of the decision trees while maintaining a good classification accuracy, and it is hard to impose additional constraints on the models that are learned. For these reasons, there has been a recent interest in exact and flexible algorithms for learning decision trees. In this paper, we introduce a new approach to learn decision trees using constraint programming. Compared to earlier approaches, we show that our approach obtains better performance, while still being sufficiently flexible to allow for the inclusion of constraints. Our approach builds on three key building blocks: (1) the use of AND/OR search, (2) the use of caching, (3) the use of the CoverSize global constraint proposed recently for the problem of itemset mining. This allows our constraint programming approach to deal in a much more efficient way with the decompositions in the learning problem.
Blockchain Meets DAG: A BlockDAG Consensus Mechanism

With the advent of the blockchain technology, low throughput and scalability have gradually become technical bottlenecks. A DAG (Directed Acyclic Graph)-based blockchain system is deemed to be a potential solution to addressing both issues. However, constructing consensus protocol to meet the requirement of the consistency in a networked environment is an unsolved challenge. In this paper, we propose a novel DAG-oriented consensus mechanism. Specifically, our approach sorts and merges original blocks from a DAG structure and re-construct a single-chain-based blockchain system; hence, consensus in DAG can be achieved on new formed blocks through running the proposed global ordering scheme and block mergence operations. Blockchain-related functions can be retrieved from splitting merged blocks.
Semi-weakly Supervised Learning for Prostate Cancer Image Classification with Teacher-Student Deep Convolutional Networks

Deep Convolutional Neural Networks (CNN) are at the backbone of the state–of–the art methods to automatically analyze Whole Slide Images (WSIs) of digital tissue slides. One challenge to train fully-supervised CNN models with WSIs is providing the required amount of costly, manually annotated data. This paper presents a semi-weakly supervised model for classifying prostate cancer tissue. The approach follows a teacher-student learning paradigm that allows combining a small amount of annotated data (tissue microarrays with regions of interest traced by pathologists) with a large amount of weakly-annotated data (whole slide images with labels extracted from the diagnostic reports). The task of the teacher model is to annotate the weakly-annotated images. The student is trained with the pseudo-labeled images annotated by the teacher and fine-tuned with the small amount of strongly annotated data. The evaluation of the methods is in the task of classification of four Gleason patterns and the Gleason score in prostate cancer images. Results show that the teacher-student approach improves significatively the performance of the fully-supervised CNN, both at the Gleason pattern level in tissue microarrays (respectively \(\kappa = 0.594 \pm 0.022\) and \(\kappa = 0.559 \pm 0.034\)) and at the Gleason score level in WSIs (respectively \(\kappa = 0.403 \pm 0.046\) and \(\kappa = 0.273 \pm 0.12\)). Our approach opens the possibility of transforming large weakly–annotated (and unlabeled) datasets into valuable sources of supervision for training robust CNN models in computational pathology.
Bayesian regression and classification using Gaussian process priors indexed by probability density functions

In this paper, we introduce the notion of Gaussian processes indexed by probability density functions for extending the Matérn family of covariance functions. We use some tools from information geometry to improve the efficiency and the computational aspects of the Bayesian learning model. We particularly show how a Bayesian inference with a Gaussian process prior (covariance parameters estimation and prediction) can be put into action on the space of probability density functions. Our framework has the capacity of classifiying and infering on data observations that lie on nonlinear subspaces. Extensive experiments on multiple synthetic, semi-synthetic and real data demonstrate the effectiveness and the efficiency of the proposed methods in comparison with current state-of-the-art methods.
LSTMs Compose—and Learn—Bottom-Up

Recent work in NLP shows that LSTM language models capture hierarchical structure in language data.In contrast to existing work, we consider the learning process that leads to their compositional behavior.For a closer look at how an LSTM's sequential representations are composed hierarchically, we present a related measure of Decompositional Interdependence (DI) between word meanings in an LSTM, based on their gate interactions.We connect this measure to syntax with experiments on English language data, where DI is higher on pairs of words with lower syntactic distance.To explore the inductive biases that cause these compositional representations to arise during training, we conduct simple experiments on synthetic data.These synthetic experiments support a specific hypothesis about how hierarchical structures are discovered over the course of training: that LSTM constituent representations are learned bottom-up, relying on effective representations of their shorter children, rather than learning the longer-range relations independently from children.
Optimal forgetting: Semantic compression of episodic memories

It has extensively been documented that human memory exhibits a wide range of systematic distortions, which have been associated with resource constraints. Resource constraints on memory can be formalised in the normative framework of lossy compression, however traditional lossy compression algorithms result in qualitatively different distortions to those found in experiments with humans. We argue that the form of distortions is characteristic of relying on a generative model adapted to the environment for compression. We show that this semantic compression framework can provide a unifying explanation of a wide variety of memory phenomena. We harness recent advances in learning deep generative models, that yield powerful tools to approximate generative models of complex data. We use three datasets, chess games, natural text, and hand-drawn sketches, to demonstrate the effects of semantic compression on memory performance. Our model accounts for memory distortions related to domain expertise, gist-based distortions, contextual effects, and delayed recall.
Cyber Resilience Progression Model

Due to the hazardous current cyber environment, cyber resilience is more necessary than ever. Companies are exposed to an often-ignored risk of suffering a cyber incident. This places cyber incidents as one of the main risks for companies in the past few years. On the other hand, the literature meant to aid on the operationalization of cyber resilience is mostly focused on listing the policies required to operationalize it, but is often lacking on how to prioritize these actions and how to strategize their implementation. Therefore, the usage of the current literature in this state is not optimal for companies. Thus, this study proposes a progression model to help companies strategize and prioritize cyber resilience policies by proposing the natural evolution of the policies over time. To develop the model, this study used semi-structured interviews and an analysis of the data obtained from the interviews. Through this methodology, this study found the starting points for each cyber resilience policy and their natural progression over time. These results can help companies in their cyber resilience building process by giving them insights on how to strategize the implementation of the cyber resilience policies.
The Best of Both Worlds: A New Composite Framework Leveraging PoS and PoW for Blockchain Security and Governance

This paper describes a new consensus and governance model for the blockchain, with the objective of protecting the blockchain project against higher-level governance issues by design. As governance issues can lead to human problems, they can impact the proper functioning of the blockchain, leading to loss of cryptocurrency value, hard forks, and even failure of the project. We choose several fail-safe mechanisms and processes used and tested in existing Proof-of-Stake and Proof-of-Work implementations in combination with one another in order to create a framework in line with the philosophy of decentralization of the blockchain and safe against the most common technical and governance pitfalls of the field. Additionally, this composite framework offers ideal circumstances for the creation and success of Decentralized Autonomous Organizations by giving their creators more incentive to start new projects, and more involvement with the governance of the underlying blockchain.
A Parallel Hardware Implementation for 2-D Hierarchical Clustering Based on Fuzzy Logic

In this brief we propose a novel hardware implementation for a bidimensional unconstrained hierarchical clustering method, based on fuzzy logic and membership functions. Unlike classical clustering approaches, this brief is based on an advanced algorithm that shows an intrinsic parallelism. Such parallelism can be exploited to design an efficient hardware implementation suitable for low-resources, low-power and high-computational demanding applications like smart-sensors and IoT devices. We validated our design by an extensive simulation campaign on well known 2D clustering datasets. Our solution shows the same clustering performances of the original algorithm despite the applied mathematical approximations and the small word-lengths used in the fixed point arithmetic.
Mining Infrequent High-Quality Phrases from Domain-Specific Corpora

Phrase mining is a fundamental task for text analysis and has various downstream applications such as named entity recognition, topic modeling, and relation extraction. In this paper, we focus on mining high-quality phrases from domain-specific corpora with special consideration of infrequent ones. Previous methods might miss infrequent high-quality phrases in the candidate selection stage. And these methods rely on explicit features to mine phrases while rarely considering the implicit features. In addition, completeness is rarely explicitly considered in the evaluation of a high-quality phrase. In this paper, we propose a novel approach that exploits a sequence labeling model to capture infrequent phrases. And we employ implicit semantic features and contextual POS tag statistics to measure meaningfulness and completeness, respectively. Experiments over four real-world corpora demonstrate that our method achieves significant improvements over previous state-of-the-art methods across different domains and languages.
A humanitarian approach to the digitization of education

Digitization is a global, all-encompassing process, affecting all spheres of human life and society. In this context education is not an exception, and the changes taking place in it are a natural result of the rapid development, wide dissemination and accessibility of information technology and networks. Understanding the methodological and philosophical foundations and principles of the process is a necessary phase of the transition to the digital education, the digital society and the digital economy. In the narrow sense digital education can be understood as a conversion of learning materials and the learning process itself from analog to digital format (i.e. electronic textbooks, electronic libraries, open online courses and webinars, video lectures, etc.), but this phenomenon can be considered in broader terms as the complex changes of infrastructural, managerial, behavioral, cultural nature. Recent events (quarantine almost all over the world because of the Covid-19 pandemic) have made it clear that the broad approach to digitization is necessary for the translation of all education systems across the world to an online format, since it refers not only to the form of presenting educational and control materials but first and foremost to the aspects of general behavior, psychological, cognitive and axiological issues. Studies have shown that in the process of transferring education from the classroom to a distance form, all participants of the educational process (students, teachers, education managers, parents) underwent a reassessment of views on education and its role in the life of a person and society. Furthermore, the contradictions, implicitly or weakly expressed prior to the forced and urgent transition to the distant form, were sharpened, and the problems related, for the most part, not to the technical aspects (although these problems also exist), but to the components of education such as communication, personal development, socialization and even physiology, became most urgent. All this requires more in-depth study, a comprehensive and systematic understanding of all the elements not only from the standpoint of functionality, but also axiological, epistemological, ontological and anthropological meanings. It has become obvious that the focus should be placed not on the technical tools with which digital education is implemented, but on a humanitarian approach with its humanistic values, in the center of which is a person: teacher - pupil /student - director - parent. As participants of the educational process, they determine the goals, objectives and methods of activity, select the appropriate technical means and evaluate their training, educational and development potential; they are the centrum omnium that underlies education. This article is devoted to the digital transformation of education in view of the humanitarian approach based on the study of culture, values and history of mankind as a whole and of the people in particular, supported by the pedagogical, psychological, sociological and communication theories that focus on the person, the individual.
Expressive yet Tractable Bayesian Deep Learning via Subnetwork Inference

The Bayesian paradigm has the potential to solve some of the core issues in modern deep learning, such as poor calibration, data inefficiency, and catastrophic forgetting. However, scaling Bayesian inference to the high-dimensional parameter spaces of deep neural networks requires restrictive approximations. In this paper, we propose performing inference over only a small subset of the model parameters while keeping all others as point estimates. This enables us to use expressive posterior approximations that would otherwise be intractable for the full model. In particular, we develop a practical and scalable Bayesian deep learning method that first trains a point estimate, and then infers a full covariance Gaussian posterior approximation over a subnetwork. We propose a subnetwork selection procedure which aims to maximally preserve posterior uncertainty. We empirically demonstrate the effectiveness of our approach compared to point-estimated networks and methods that use less expressive posterior approximations over the full network.