Reviewerly/exHarmony huggingface dataset:
A Two-Step Approach for Improving Sentiment Classification Accuracy

Sentiment analysis is a method for assessing an individual’s thought, opinion, feeling, mentality, and conviction about a specific subject on indicated theme, idea, or product. The point could be a business association, a news article, a research paper, or an online item, etc. Opinions are generally divided into three groups of positive, negative, and unbiased. The way toward investigating different opinions and gathering them in every one of these categories is known as Sentiment Analysis. The enormously growing sentiment data on the web especially social media can be a big source of information. The processing of this unstructured data is of deep interest for researchers in the field of data science. The proposed article is an effort to find a model which can improve the classification accuracy of sentiment data. In this paper, a complete model is introduced to improve the accuracy of binary-class sentiment data. The model is reliable as it has been validated on three distinctive datasets with various sample sizes. In this regard, Amazon reviews, Yelp reviews, and IMDB reviews are taken into account. This model is based on completely referenced datasets and considerable improvement in accuracy is seen as compared to individual classifiers. Various heterogeneous classifiers are trained on the above-mentioned datasets at the base level. These base-level classifiers ought to be chosen from various groups of classifiers. At that point, the output of these base-level classifiers is consolidated to create a new dataset that acts as input for meta-level classifiers. At the meta-level, the base-level classifier with the best outcomes is selected with the new dataset framed. The model accordingly framed can be used to predict any sample query.
Defining behaviorizeable relations to enable inference in semi-automatic program synthesis

Developing and using automatic program synthesis mechanisms within Domain Specific Languages (DSLs) requires transcribing empirical knowledge and specifications to formal models. Since the related expertise is time-consuming to build, software engineers are discouraged from adopting automated synthesis as a development paradigm. New approaches promise a significant reduction of human training effort by synthesizing programs from lax specifications, such as unstructured or semi-structured natural language. However, they do not introduce semantics of logical inference that would help users interact with the synthesis mechanism, for example to treat misinterpreted specifications. To cover this shortcoming, in this work we introduce the concept of behaviorizeable relations, which can be less rigorous than behavioral equivalence –and therefore allow laxer specifications– but still perform inference under implicit logical interpretations of the synthesis domain. Using these relations, we develop a generalized greedy framework that iteratively replaces parts of specifications with equivalent implementations; any behaviorizeable relation can be plugged in this framework to synthesize programs while respecting inference under a corresponding logical system. We finally perform a case study, where we define an example predicate-based behaviorizeable relation that lets our framework linearly combine blocks of Python code based on their comment similarity to natural language specifications. We use the resulting mechanism to demonstrate that implicit inference pertains to understanding how to adjust or state new specifications when lax behaviorizeable relations are employed.
How Many Robots Do You Want? A Cross-Cultural Exploration on User Preference and Perception of an Assistive Multi-Robot System

There has been increased development of assistive robots for the home, along with empirical studies assessing cultural differences on user perception. However, little attention has been paid to cultural differences with respect to non- humanoid, multi-robot interactions in homes or otherwise. In this exploratory paper, we investigate how cultural differences may impact users&#x2019; preferences and perceived usefulness of a multi-robot system by creating an interactive online survey and considering variables often absent in HRI studies. We introduce our multi-robot design and survey construction, and report results evaluated across 191 young adult participants from China, India, and the USA. We find significant effects of culture on both participants&#x2019; preferences and perceived usefulness of the system between India and China or the USA, but not between China and the USA. We also find effects of culture on perceived usefulness to be partially mediated by participant preferences. Our findings reinforce the importance of considering cultural differences in designing domestic multi-robotic assistants.
Dynamic Editors for Well-Typed Expressions

Interactive systems may require complex inputs. Domain experts prefer guidance in the construction of these inputs. An ideal system prevents errors and is flexible in the construction and changes of its input. The iTask system generates web-editors given any first-order algebraic data types. The generated web-editors are useful but have their limitations. It is not possible to combine type safety with overloaded operators and preventing unbounded or ill-typed identifiers is impossible. Using phantom types, generalized algebraic datatypes or functions solves the language problems, but they cannot be handled by the datatype generic system. Moreover, changing expressions can require re-entering large parts of the input. We present dynamic editors that can solve all those problems. The programmer specifies the elements of such an editor by functions. The system shows the applicable edit elements in a drop-down menu to the user. The dynamic editor is used recursively to create the arguments for the selected function. Dynamic editors are seamlessly integrated with the ordinary web-editors of the iTask system. The obtained editors guide the users to make correct and type-safe inputs. These editors can be very flexible as well without making strange abstract syntax trees.
Artificial Steganographic Network Data Generation Concept and Evaluation of Detection Approaches to secure Industrial Control Systems against Steganographic Attacks

Since industrial control systems (ICS) play an important role in our everyday life, their protection is of great importance. At the same time, security researchers observe an increasing usage of steganographic methods in IT networks used by attackers to embed hidden communication in order to stay undetected as long as possible. This leads to a novel digital threat which includes the embedding of steganographic hidden communication in ICS networks. Thus, novel detection approaches specified for steganographic attacks have to be elaborated. Detectors are often based on machine learning approaches and require training and test data. However, the embedding of sophisticated hidden communication in an ICS is a very time consuming and challenging task which currently leads to a lack of suitable training and test data for the evaluation of detection mechanisms. To address this gap, this work presents an artificial steganographic network data (ASND) generation concept for an easy generation of sophisticated steganographic network data which can be provided for the evaluation of detection mechanisms. In this paper, an exemplary data set is created by ASND generation concept and used to evaluate a state-of-the-art detector and a novel detector, also introduced in this work. The accuracy of the detectors is determined and compared. The novel detector reaches a maximum detection accuracy of 92.5%.
An optimization model of electronic medical record query processing on blockchain

research-article An optimization model of electronic medical record query processing on blockchain Share on Authors: Yiming Lv Northeastern University, China Northeastern University, ChinaView Profile , Wenxing Liu Northeastern University, China Northeastern University, ChinaView Profile , Jie Zhong Northeastern University, China Northeastern University, ChinaView Profile , Chuanbo Zhang Northeastern University, China Northeastern University, ChinaView Profile , Kexin Wang Northeastern University, China Northeastern University, ChinaView Profile , Zhiqiong Wang Northeastern University, China Northeastern University, ChinaView Profile Authors Info & Claims ICAIIS 2021: 2021 2nd International Conference on Artificial Intelligence and Information SystemsMay 2021 Article No.: 135Pages 1–5https://doi.org/10.1145/3469213.3470338Online:18 August 2021Publication History 0citation58DownloadsMetricsTotal Citations0Total Downloads58Last 12 Months58Last 6 weeks17 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
The Effect of Software Warranties on Cybersecurity

This workshop focused on bringing software developers and legal professionals together to understand the shared challenges they face in promoting the development of secure software on the one hand, and software at all, on the other hand. This report sum- marizes current scienti c research on the topics and challenges discussed in the workshop breakout sessions. The insights from the workshop highlight a number of interesting directions for fur- ther research on the interplay between software warranties and cybersecurity.
Perspective-Aware Density Regression For Crowd Counting

Scale variation, perspective distortion and severe occlusion are three main problems that affect the accuracy of crowd counting. Existing methods either adopt attention mechanism or perspective values to address these problems, but without considering them as a whole. In this paper, we advocate introducing perspective information across different density distributions to facilitate crowd estimation, and propose a novel model named Perspective-aware Density Regression Network (PDRNet) for crowd counting. Unlike previous works, PDRNet is a bilateral structure with different focuses on the features of perspective and crowd density, and it includes the information interaction module (IIM) and the similarity comparison module (SCM) to enhance the perspective-density interaction in a cross-reference manner. Specifically, IIM achieves the mutual feature guidance by swapping the global information between different branches, and SCM performs feature comparison to refine the prediction of foreground regions. With extensive experiments and competitive performances on four widely used datasets, we demonstrate the effectiveness of the proposed network.
Minutes of action! A contrastive analysis of time expressions in English and Norwegian football match reports

This contrastive study examines the use of two time expressions – on # minutes and etter # minutes (‘after # minutes’) – in football match reports with the aim of shedding light on their conditions of use in English and Norwegian. The cross-linguistic findings suggest that the patterns are typically used to report on the achievements of players. With regard to syntax, the English pattern is clearly preferred in final position, while the Norwegian pattern is found in either initial or final position, with a slight preference for initial position. A more inconclusive difference can be noted regarding the patterns’ distribution across the 90 minutes; the English pattern is more evenly distributed across the two halves of the match than the Norwegian one.
Byzantine Fault-Tolerance in Federated Local SGD Under $2f$-Redundancy

In this article, we study the problem of Byzantine fault-tolerance in a federated optimization setting, where there is a group of agents communicating with a centralized coordinator. We allow up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$f$</tex-math></inline-formula> Byzantine-faulty agents, which may not follow a prescribed algorithm correctly, and may share arbitrarily incorrect information with the coordinator. Associated with each nonfaulty agent is a local cost function. The goal of the nonfaulty agents is to compute a minimizer of their aggregate cost function. For solving this problem, we propose a local gradient-descent algorithm that incorporates a novel <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">comparative elimination</i> filter (aka. aggregation scheme) to provably mitigate the detrimental impact of Byzantine faults. In the deterministic setting, when the agents can compute their local gradients accurately, our algorithm guarantees <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">exact</i> fault-tolerance against a bounded fraction of Byzantine agents, provided the nonfaulty agents satisfy the known necessary condition of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><inline-formula><tex-math notation="LaTeX">$2f$</tex-math></inline-formula>-redundancy</i> . In the stochastic setting, when the agents can only compute stochastic estimates of their gradients, our algorithm guarantees <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">approximate</i> fault-tolerance where the approximation error is proportional to the variance of stochastic gradients and the fraction of Byzantine agents.
QOptCraft: A Python package for the design and study of linear optical quantum systems

The manipulation of the quantum states of light in linear optical systems has multiple applications in quantum optics and quantum computation. The package QOptCraft gives a collection of methods to solve some of the most usual problems when designing quantum experiments with linear interferometers. The methods include functions that compute the quantum evolution matrix for n photons from the classical description of the system and inverse methods that, for any desired quantum evolution, will either give the complete description of the experimental system that realizes that unitary evolution or, when this is impossible, the complete description of the linear system which approximates the desired unitary with a locally minimal error. The functions in the package include implementations of different known decompositions that translate the classical scattering matrix of a linear system into a list of beam splitters and phase shifters and methods to compute the effective Hamiltonian that describes the quantum evolution of states with n photons. The package is completed with routines for useful tasks like generating random linear optical systems and computing matrix logarithms. The routines are chosen to avoid usual numerical problems when dealing with the unitary matrices that appear in the description of linear systems.
Level Set Stereo For Cooperative Grouping With Occlusion

Localizing stereo boundaries is difficult because matching cues are absent in the occluded regions that are adjacent to them. We introduce an energy and level-set optimizer that improves boundaries by encoding the essential geometry of occlusions: The spatial extent of an occlusion must equal the amplitude of the disparity jump that causes it. In a collection of figure-ground scenes from Middlebury and Falling Things stereo datasets, the model provides more accurate boundaries than previous occlusion-handling techniques.
Aircraft Type Recognition in Remote Sensing Images: Bilinear Discriminative Extreme Learning Machine Framework

Small inter-class and massive intra-class changes are important challenges in aircraft model recognition in the field of remote sensing. Although the aircraft model recognition algorithm based on the convolutional neural network (CNN) has excellent recognition performance, it is limited by sample sets and computing resources. To solve the above problems, we propose the bilinear discriminative extreme learning machine (ELM) network (BD-ELMNet), which integrates the advantages of the CNN, autoencoder (AE), and ELM. Specifically, the BD-ELMNet first executes the convolution and pooling operations to form a convolutional ELM (ELMConvNet) to extract shallow features. Furthermore, the manifold regularized ELM-AE (MRELM-AE), which can simultaneously consider the geometrical structure and discriminative information of aircraft data, is developed to extract discriminative features. The bilinear pooling model uses the feature association information for feature fusion to enhance the substantial distinction of features. Compared with the backpropagation (BP) optimization method, BD-ELMNet adopts a layer-by-layer training method without repeated adjustments to effectively learn discriminant features. Experiments involving the application of several methods, including the proposed method, to the MTARSI benchmark demonstrate that the proposed aircraft type recognition method outperforms the state-of-the-art methods.
Application of Computer Multimedia Network Video Teaching in Arrangement of Aerobics

NOTICE OF RETRACTION: While investigating potential publication-related misconduct in connection with the ICIMTech 2021 Conference Proceedings, serious concerns were raised that cast doubt on the integrity of the peer-review process and all papers published in the Proceedings of this Conference. The integrity of the entire Conference has been called into question. As a result, of its investigation, ACM has decided to retract the Entire Conference Proceedings and all related papers from the ACM Digital Library.
PoseRN: A 2D Pose Refinement Network For Bias-Free Multi-View 3D Human Pose Estimation

We propose a new 2D pose refinement network that learns to predict the human bias in the estimated 2D pose. There are biases in 2D pose estimations that are due to differences between annotations of 2D joint locations based on annotators’ perception and those defined by motion capture (MoCap) systems. These biases are crafted into publicly available 2D pose datasets and cannot be removed with existing error reduction approaches. Our proposed pose refinement network allows us to efficiently remove the human bias in the estimated 2D poses and achieve highly accurate multi-view 3D human pose estimation.
A Generalization of the ASR Search Algorithm to 2-Generator Quasi-Twisted Codes

One of the central problems in coding theory is to construct codes with best possible parameters and properties. A special class of codes called quasi-twisted (QT) codes is well-known to produce codes with good parameters. Most of the work on QT codes has been over the 1-generator case. In this work, we focus on 2-generator QT codes and generalize the ASR algorithm that has been very effective to produce new linear codes from 1-generator QT codes. As a result of implementing the generalized algorithm, we have found 103 2-generator QT codes that are new among the class of QT codes. Additionally, most of these codes possess the following additional properties: a) they have the same parameters as best known linear codes, and b) many of them have additional desired properties such as being LCD and dual-containing. Further, we have also found a binary 2-generator QT code that is new (record breaking) among all binary linear codes [I] and its extension yields another record breaking binary linear code.
Research on Long Term Preservation Strategy of University Library’s Books and Materials Based on Internet of Things Data

Abstract With the development of Internet of things (hereinafter referred to as IOT) technology, IBM smart earth strategic plan has gradually become the goal of human development, which has gradually realized the global resource sharing and co construction. Therefore, the IOT system based on computer has become the future development direction, which also promotes the realization of digitization, intelligence and networking in many fields, including university library. In order to improve the service of the library, colleges need to constantly transform and upgrade the service pattern of the library, which provides important intelligent services for readers. Through the global positioning system, radio frequency identification, sensor equipment, etc., the IOT system realizes the interconnection between readers and books, which provides users with a full range of reader services. The IOT can provide intelligent management, reader behavior analysis, intelligent self-borrowing and returning books and materials long-term preservation and other conveniences for the smart library, which provides important help for the intelligent management of the library. Through the IOT technology, the library can improve the service efficiency and collection resource utilization rate, which will improve the state of intelligent management and service. First of all, this paper puts forward the main problems of long-term preservation of digital book resources in library. Then, this paper constructs the hierarchical structure of Smart Library. Finally, some suggestions are put forward.
Spatiotemporal Features and Local Relationship Learning for Facial Action Unit Intensity Regression

The action units (AU) encoded by the Facial Action Coding System (FACS) have been widely used in the representation of facial expressions. Although work on automatic facial AU detection has achieved quite good results in recent years, there remains much research potential for more accurate AU detection and intensity regression. Moreover, most work only considers the spatial information and ignores the temporal information. In practice, changes in facial AUs involve both spatial and temporal variation. In this paper, by extracting multi-scale spatial features and corresponding temporal features from the faces in the video image sequence, and learning the local relationship of the spatiotemporal features we propose a method that can obtain robust and accurate regression for AU intensity. The proposed method outperforms the baseline system on FEAFA dataset and obtains comparable performance on DISFA dataset.
Learn It First: Grounding Language in Compositional Event-Predictive Encodings

While language learning in infants and toddlers progresses somewhat seamlessly, in artificial systems the grounding of language in knowledge structures that are learned from sensorimotor experiences remains a hard challenge. Here we introduce LEARNA, which learns event-characterizing abstractions to resolve natural language ambiguity. LEARNA develops knowledge structures from simulated sensorimotor experiences. Given a possibly ambiguous descriptive utterance, the learned knowledge structures enable LEARNA to infer environmental scenes, and events unfolding within, which essentially constitute plausible imaginations of the utterance’s content. Similar event-predictive structures may help in developing artificial systems that can generate and comprehend descriptions of scenes and events.