Reviewerly/exHarmony huggingface dataset:
Exploration of user interface mechanisms with affect labeling to enhance online discussion

Heated and uncivil discussions are regrettably common in social media, potentially leading to harmful atmospheres. In the present work, we explore how relatively simple user interface mechanisms might nudge users to choose a more civil tone by promoting an emotion regulation process called affect labeling — i.e., naming one's emotions. In particular, we focus on labeling emotions in response to online news articles, as this is a ubiquitous form of online multimedia. Based on systematic design exploration, we present four concrete mechanisms as alternatives for affect labeling news articles: an emotion wheel, an intensity slider, emotion analysis done through machine learning, and free text labeling. In a qualitative evaluation study, 18 interviewees discussed the designs in focus groups in order to identify design aspects that might lead to high acceptability and usability. The study findings help reflect how emotion regulation could be facilitated by UI mechanisms, which design aspects both encourage affect labeling and are acceptable by users, and how affect labeling can meaningfully influence online interactions.
Methods for In-Sourcing Authority Control with MarcEdit, SQL, and Regular Expressions

This is a report on a developing method to automate authority control in-house (that is, without an outside vendor), especially for batch-loaded bibliographic records for electronic resources. A SQL query of the Innovative Sierra database retrieves entries from the “Headings used for the first time” report. These entries are then processed with some regular expression substitutions to create a list of terms suitable for batch searching in the OCLC Connexion client. Two approaches to this problem are described in detail and results compared. A similar method for using the “Unauthorized headings” report from the SirsiDynix Symphony ILS is also described.
A Text Understandability Approach for Improving Reliability-Centered Maintenance in Manufacturing Enterprises

Textual data majorly reflects objective and subjective human specific knowledge. Focusing on big data in industrial and operation management, the value of textual data is oftentimes undermined. Optimal use of data reinforces the integrative modeling and analysis of RAM (Reliability, Availability, Maintainability). Data-driven reliability engineering and maintenance management, gains benefit from textual data, especially for identifying unknown failure modes and causes, and solving problems. The scientific challenge is how to effectively discover knowledge from text data and convert it into automated processes for inferential reasoning, predicting and prescribing. This paper outlines how the reliability-centered maintenance in production systems can be improved by explicating and discovering human-specific knowledge from maintenance reports and related textual documents. Hence, a theoretical model for text understanding is proposed, which is demonstrated as a proof-of-concept demonstrator using real world manufacturing datasets. The text understanding model is represented by a three-dimensional matrix comprising three indexes, i.e. text readability, word associations within texts as well as sentiment. The implementation of the model as a software prototype involves using text mining techniques and machine learning algorithms. This paper emphasizes on the importance of knowledge extraction from text in the context of industrial maintenance, by demonstrating how an increased value of text understandability of maintenance reports correlates to an early stage detection of failure, the reduction of human failures and leads to an immense improvement of explication of human knowledge.
Evaluating Semantic Similarity Methods to Build Semantic Predictability Norms of Reading Data

Predictability corpora built via Cloze task generally accompany eye-tracking data for the study of processing costs of linguistic structures in tasks of reading for comprehension. Two semantic measures are commonly calculated to evaluate expectations about forthcoming words: (i) the semantic fit of the target word with the previous context of a sentence, and (ii) semantic similarity scores that represent the semantic similarity between the target word and Cloze task responses for it. For Brazilian Portuguese (BP), there was no large eye-tracking corpora with predictability norms. The goal of this paper is to present a method to calculate the two semantic measures used in the first BP corpus of eye movements during silent reading of short paragraphs by undergraduate students. The method was informed by a large evaluation of both static and contextualized word embeddings, trained on large corpora of texts. Here, we make publicly available: (i) a BP corpus for a sentence-completion task to evaluate semantic similarity, (ii) a new methodology to build this corpus based on the scores of Cloze data taken from our project, and (iii) a hybrid method to compute the two semantic measures in order to build predictability corpora in BP.
Smart Contract: Concept, Financial Essence, World Experience and Opportunities of Practical Use

Subject. The article considers smart contracts as digital financial instruments, their financial and economic essence, which is defined as digital instruments for the settlement of financial relations. Objectives. The aim is to introduce into scientific use such a system concept as digital financial instruments and mechanisms based on the consideration of their economic, legal and financial essence. Methods. The study rests on systems approach that enables to present a smart contract from a technological, economic, legal and financial position as a set of elements with their specific functions defining its complex concept. Results. We reveal the nature of financial relations, arising at the time when bilateral or multilateral transactions are concluded and smart contracts are presented as ways to automatically settle them. Completion of a smart contract is a confirmation of the completion of the transaction and the moment of termination of financial relationship. The article shows the fundamental possibility of using smart contracts in the system of budget relations as a tool for the settlement of a multilateral transaction. Conclusions. Currently, smart contracts are an essential element of a new stage in the development of financial technologies. Specialists in the financial and banking sector recognize the emerging opportunities for their use in the system of financial relations. The paper shows an example of possible use of smart contracts in the settlement of budget relations and in improving the utilization efficiency of budget funds.
An ESP-Based Lightweight Model for Joint Object Detection and Affordance Segmentation

Affordance segmentation is one of the most challenging and popular research topics in the field of robotic vision in recent years. Although the existing affordance segmentation methods can achieve accurate segmentation results, the model parameters and network architecture are quite large, and most of these methods cannot achieve real-time performance. In order to reduce the number of model parameters and simplify network architecture, this paper presents a lightweight affordance segmentation model based on ESPNetv2, which can effectively increase the processing speed and reduce the computational requirements required at runtime. The proposed method adopts a one-stage anchor-based object detection model as the backbone network for the integration with a semantic segmentation branch. Because of the advantage of one-stage network architecture, the proposed network model can be implemented by a relatively simpler architecture. In addition, we also use a lightweight ESP module as the basic module in the object detection and affordance segmentation model to reduce the number of model parameters and computational complexity. Experimental results show that in the IIT-AFF data set, the proposed method reaches a high segmentation accuracy of 61 % mIOU and the object detection accuracy of 90% mAP. In the test of processing speed, when the network takes 512×512 RGB image as the input, the proposed method achieves a real-time processing speed of 35 frames per second on a platform with NVIDIA GeForce 1080Ti, which is five times faster than the existing AffordanceNet.
Is Guided Search 6.0 compatible with Reverse Hierarchy Theory

It has been 30 years since the first version of the Guided Search (GS) model of visual search was published. As new data about search accumulated, GS needed modification. The latest version is GS6. GS argues that visual processing is capacity-limited and that attention is needed to “bind” features together into recognizable objects. The core idea of GS is that the deployment of attention is not random but is “guided” from object to object. For example, in a search for your black shoe, search would be guided toward black items. Earlier versions of GS focused on top-down (user-driven) and bottom-up (salience) guidance by basic features like color. Subsequent research adds guidance by history of search (e.g. priming), value of the target, and, most importantly, scene structure and meaning. Your search for the shoe will be guided by your understanding of the scene, including some sophisticated information about scene structure and meaning that is available “preattentively”. In acknowledging the initial, preattentive availability of something more than simple features, GS6 moves closer to ideas that are central to the Reverse Hierarchy Theory of Hochstein and Ahissar. As is so often true in our field, this is another instance where the answer is not Theory A or Theory B, even when they seem diametrically opposed. The next theory tends to borrow and synthesize good ideas from both predecessors.
Real Power Loss Reduction by Extreme Learning Machine Based Leontodon Algorithm

In this paper Extreme Learning Machine based Leontodon Algorithm (ELMLA) has been applied for solving the Real Power loss reduction problem. Key objectives of this paper are Real power loss reduction, Voltage stability enhancement and voltage deviation minimization. Leontodon Algorithm (LA) technique is an innovative swarm based optimization algorithm. In evolutionary procedure of LA, the eminence of the seeds engendered by Leontodon is rutted, and the outstanding seeds will be reserved and appraised, whereas the deprived seeds are rejected. In order to define whether a seed is tremendous or not, an enhancement of Leontodon algorithm with extreme learning machine (ELMLA) is projected in this paper. Based on fitness values the Leontodon population is segregated into outstanding and deprived Leontodons. Subsequently outstanding and deprived Leontodons are apportioned corresponding labels as +1 if outstanding or − 1 if deprived), and it has been considered as a training set, which built on extreme learning machine. Lastly, the design is applied to categorize the Leontodon seeds as excellent or deprived. Only outstanding Leontodon seeds are selected to take part in evolution procedure. Legitimacy of the Extreme Learning Machine based Leontodon Algorithm (ELMLA) is substantiated in IEEE 30 bus system (with and devoid of L-index). Actual Real Power loss reduction is reached. Proportion of actual Real Power loss reduction is augmented.
AI and Data Rights Considerations for U.S. Policy

The inception of artificial intelligence (AI) dates back to the 1950s, with the latest wave of AI featuring unprecedented “machine learning” capabilities, including “deep learning.” With these increased capacities, AI is disrupting society in both beneficial and disempowering ways, exemplified in medical and scientific advances and enabling oppressive surveillance [1]. The rate of adoption and potential of this wave of AI are novel, as are the socio-technical problems developing with emerging technologies powered by AI.
Linkage of sets of cyclic algebras

Let p be a prime integer and F the function field in two algebraically independent variables over a smaller field F0. We prove that if char(F0)=p⩾3 then there exist p2−1 cyclic algebras of degree p over F that have no maximal subfield in common, and if char(F0)=0 then there exist p2 cyclic algebras of degree p over F that have no maximal subfield in common.
28nm asynchronous area-saving AES processor with high Common and Machine learning side-channel attack resistance

An asynchronous Advanced Encryption Standard (AES) cryptographic processor for low-area and side-channel attack (SCA) resistant applications is introduced. To reduce the area and power, two Substituting Byte blocks (S-Boxes) are reused in key expansion and the data encryption module, respectively. To mitigate SCA, we adopt asynchronous dual-rail logic with dual-rail balanced logic and new dual-rail spacer latch. Common and Machine learning (ML) SCA simulations are performed to validate SCA resistance. To the best of our knowledge, we are the first ones to perform the ML SCA evaluations on asynchronous AES. Simulation results with 200K power traces demonstrate that our asynchronous AES is immune to the attacks. Our proposed asynchronous AES occupies an area of 0.016 in TSMC 28nm technology and consumes 1nJ per encryption at a supply voltage of 0.9V.
Pervasive Internet-Wide Low-Latency Authentication

In a world with increasing simplicity to store, transfer, and analyze large volumes of data, it becomes more and more important that data confidentiality and integrity be preserved in transit by default. Unfortunately, a large security gap exists between unprotected or low-security communication, such as opportunistic encryption and trust-on-first-use (TOFU) security, and high-security communication, such as TLS using server certificates or DNSSEC. Our goal is to reduce this gap and achieve a base layer for authentication and secrecy that is strictly better than TOFU security. We achieve this by designing PILA, a novel authentication method with dynamic trust anchors, which leverages irrefutable cryptographic proof of misbehavior to incentivize benign behavior. We implement PILA extensions for SSH, TLS, and DNS and show that the overhead for a typical SSH and TLS connection establishment is negligible, and that PILA only causes a marginal processing overhead of $\sim 100\ \mu \mathrm{s}$ per DNS response at the endpoints.
Joint Spatio-Temporal Bias Estimation and Tracking for GNSS-Denied Sensor Networks

Sensor parameter estimation is a key process that must be considered when performing data fusion in a multi-sensor object tracking scenario. For example, significant relative time delays in sensor data arriving at a fusion centre can result in a reduction of track accuracy, false tracks, or early termination of a true object track. The same issues may arise in the presence of some relative angular bias between sensors. This article presents a technique for simultaneous target tracking and estimation of relative time delays and angular biases in data for a multi-sensor system with no access to a global frame of reference. The proposed technique makes use of a hierarchical Bayesian model and couples a grid-based search method with an array of augmented state Kalman filters to accomplish this. Results are provided comparing the root-mean-squared error in a simulated single object tracking scenario. The performance of a single sensor, two sensors with correct registration, two sensors with incorrect registration, and two sensors with registration correction are compared. The results demonstrate a significant improvement in tracking performance when registration errors are corrected with the proposed method, as well as an increase in accuracy over object tracking with only a single sensor.
Research on Entity Recognition Based on Multi-criteria Fusion Model

In order to fully and comprehensively utilize the Chinese named entity recognition corpus marked according to different labeling criteria, this paper, by applying the word-based BERT bidirectional language model, introducing multi-criteria shared connection layer and conditional random field (CRF) system, and using Microsoft Research Asia MSRA-NER corpus and Peking University’s People’s Daily part-of-speech tagging corpus (RMRB-98-1), firstly formed each Chinese named entity recognition model separately, and then mixed a multi-criteria fusion model with two corpora. Experiments show that the recognition effect of multi-criteria fusion model is better than that of each corpus independent model, reaching 94.46% F1 value and 94.32% F1 value respectively on MSRA-NER and RMRB-98-1 corpus. However, the experiment still has its limit in the scale of the corpus involved in the fusion. Later, integration of more corpora, and combination with entity recognition tasks in specific fields such as biology and military will be further explored to enhance the recognition effect.
Broker-based optimization of SLA negotiations in cloud computing

Cloud computing provisions and allocates resources, in advance or real-time, to dynamic applications planned for execution. This is a challenging task as the Cloud-Service-Providers (CSPs) may not have sufficient resources at all times to satisfy the resource requests of the Cloud-Service-Users (CSUs). Further, the CSPs and CSUs have conflicting interests and may have different utilities. Service-Level-Agreement (SLA) negotiations among CSPs and CSUs can address these limitations. User Agents (UAs) negotiate for resources on behalf of the CSUs and help reduce the overall costs for the CSUs and enhance the resource utilization for the CSPs. This research proposes a broker-based mediation framework to optimize the SLA negotiation strategies between UAs and CSPs in Cloud environment. The impact of the proposed framework on utility, negotiation time, and request satisfaction are evaluated. The empirical results show that these strategies favor cooperative negotiation and achieve significantly higher utilities, higher satisfaction, and faster negotiation speed for all the entities involved in the negotiation.
An Algorithm for Local Transverse Feedback Linearization

Given a multi-input, nonlinear, time-invariant, control-affine system and a controlled-invariant, closed, embedded submanifold , the local transverse feedback linearization (TFL) problem seeks a coordinate and feedback transformation such that, in transformed coordinates, the dynamics governing the system’s transverse evolution with respect to are linear, time-invariant, and controllable. The transformed system is said to be in the TFL normal form. Checkable necessary and sufficient conditions for this problem to be solvable are known, but, unfortunately, the literature does not present a prescription that constructs the required transformation for multi-input systems. In this article we present an algorithm that produces a virtual output of suitable vector relative degree that, using input-output feedback linearization, puts the system into TFL normal form. The procedure is based on dual conditions for TFL and is fundamentally different from existing methods, such as the GS algorithm proposed by Gardner and Shadwick and the Blended Algorithm, because of the “desired” zero dynamics manifold . The proposed algorithm is the first to take into consideration the desired zero dynamics manifold.
Quantization of Generative Adversarial Networks for Efficient Inference: A Methodological Study

Generative adversarial networks (GANs) have an enormous potential impact on digital content creation, e.g., photorealistic digital avatars, semantic content editing, and quality enhancement of speech and images. However, the performance of modern GANs comes together with massive amounts of computations performed during the inference and high energy consumption. That complicates, or even makes impossible, their deployment on edge devices. The problem can be reduced with quantization—a neural network compression technique that facilitates hardware-friendly inference by replacing floating-point computations with low-bit integer ones. While quantization is well established for discriminative models, the performance of modern quantization techniques in application to GANs remains unclear. GANs generate content of a more complex structure than discriminative models, and thus quantization of GANs is significantly more challenging. To tackle this problem, we perform an extensive experimental study of state-of-art quantization techniques on three diverse GAN architectures, namely StyleGAN, Self-Attention GAN, and CycleGAN. As a result, we discovered practical recipes that allowed us to successfully quantize these models for inference with 4/8-bit weights and 8-bit activations while preserving the quality of the original full-precision models.
A Theoretical Framework for a Hybrid View of the N400

In this study, we present a novel theoretical account of the N400 event-related potential (ERP) component. Hybrid views interpret this ERP component in terms of two cognitive operations: (i) access of information, which is related to predictions (predictability component), and (ii) integration of information, which is related to plausibility (plausibility component). Though there is an empirical evidence for this view, what has been left open so far is how these two operations can be defined. In our approach, both components are related to categorization. The critical word and the argument position it is related to are associated with categories that have a graded structure. This graded structure is defined in terms of weights both on attributes and values of features belonging to a category. The weights, in turn, are defined using probability distributions. The predictability component is defined in terms of the information gain with respect to non mismatched features between the two categories. The plausibility component is defined as the difference in the degree of typicality between the two categories. Finally, the N400 amplitude is defined as a function of both components.
Pluggable Micronetwork for Layer Configuration Relay in a Dynamic Deep Neural Surface

The classical convolution neural network architecture adheres to static declaration procedures, which means that the shape of computation is usually predefined and the computation graph is fixed. In this research, the concept of a pluggable micronetwork, which relaxes the static declaration constraint by dynamic layer configuration relay, is proposed. The micronetwork consists of several parallel convolutional layer configurations and relays only the layer settings, incurring a minimum loss. The configuration selection logic is based on the conditional computation method, which is implemented as an output layer of the proposed micronetwork. The proposed micronetwork is implemented as an independent pluggable unit and can be used anywhere on the deep learning decision surface with no or minimal configuration changes. The MNIST, FMNIST, CIFAR-10 and STL-10 datasets have been used to validate the proposed research. The proposed technique is proven to be efficient and achieves appropriate validity of the research by obtaining state-of-the-art performance in fewer iterations with wider and compact convolution models. We also naively attempt to discuss the involved computational complexities in these advanced deep neural structures.